tag:blogger.com,1999:blog-48854556350963969032024-03-18T14:03:28.736+11:00Genome SpotPractical tips for genome analysisMark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comBlogger148125tag:blogger.com,1999:blog-4885455635096396903.post-19378314970298874042024-02-26T10:29:00.004+11:002024-02-26T10:34:49.824+11:00Yes, mitch can be used for pathway analysis of Methylation array data<p>In 2020, Dr Antony Kaspi and I published a method called "mitch" [1] which is like GSEA, but was specifically designed for multi-contrast analysis, and based on rank-MANOVA statistics inspired by a 2012 paper by Cox and Mann. Mitch worked well for various types of omics data downstream of commonly used differential abundance tools like DESeq2, edgeR, DiffBind, etc, but we didn't consider at the time how mitch could be applied to microarray data. </p><p>You might think that microarrays are outdated, but they are still used extensively for epigenome-wide association studies (EWASs), which are frequently used to understand disease processes and to identify biomarkers of disease. To demonstrate, there are 1536 publicly available methylation array studies on NCBI GEO, and probably many more thousands that are restricted access.</p><p>The tools available for pathway analysis of methylation array data are a bit limited. There's an over-representation method that take into consideration the peculiarities of array data including the variable number of probes per gene and that overlapping genes can share probes [3], but binary classification is known to limit sensitivity, which is why over-representation analysis of epigenomic data is mostly unhelpful. There have been attempts to introduce rank-based methods like ebGSEA [4] and methylGSA [5] but these tools don't retain information about the direction of change in methylation, which is important for interpretation of regulatory data, like gene expression [6].</p><p>So we undertook a study to comprehensively test different methods for rank-based pathway analysis using an exhaustive simulation approach. The eight methods we tested examined different approaches to aggregating methylation data from probes to genes to pathways. In the end the best performing approach involved gene-wise aggregation of limma t-statistics followed by enrichment test with mitch. It was also superior to existing ORA methods over most simulation conditions. With real lung cancer data, it yielded more robust results than ORA and was able to highlight methylation changes that coincided with differential expression of key pathways. The <a href="https://www.biorxiv.org/content/10.1101/2024.02.22.581670v1.full.pdf+html">preprint is available on biorXiv</a>.</p><p>Here is an example of using this method to analyse two independent aging studies:</p><p></p><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhcO_060QBkpv7GGaBaYbZlBZ3_CeaRPS1Eimw3CAlbz-q8mIg8xl-lO_rPBrBy455TQe1AXAC3YepC_svSSntc88DHNwvW6DNPbm9_rSHZk5Jb3LQskkyusNVhlAoNkGC-QYhXGqXFflqJ3HS_PCcp5m3v5NVeSMwojb3gG1aRGW1L5GgJVk8xEg-CRD9m" style="margin-left: 1em; margin-right: 1em;"><img alt="Pathway methylation changes associated with aging identified using mitch." data-original-height="1412" data-original-width="1478" height="612" src="https://blogger.googleusercontent.com/img/a/AVvXsEhcO_060QBkpv7GGaBaYbZlBZ3_CeaRPS1Eimw3CAlbz-q8mIg8xl-lO_rPBrBy455TQe1AXAC3YepC_svSSntc88DHNwvW6DNPbm9_rSHZk5Jb3LQskkyusNVhlAoNkGC-QYhXGqXFflqJ3HS_PCcp5m3v5NVeSMwojb3gG1aRGW1L5GgJVk8xEg-CRD9m=w640-h612" title="Pathway-level DNA methylation alterations with chronological age." width="640" /></a></div><br /><br /></div><br />If you already have differential methylation profiles for limma, then applying mitch is relatively easy. I have added a <a href="https://www.bioconductor.org/packages/devel/bioc/vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.html">new vignette to our Bioconductor package</a> and <a href="https://ziemann-lab.net/public/gmea/example_workflow.html">another guide on our website</a>. Just make sure your mitch version is 1.15.1 or greater.<div><br /></div><div>Feedback welcome!<br /><p><b>References</b></p><p>1. Kaspi, A., Ziemann, M. mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data. BMC Genomics 21, 447 (2020). https://doi.org/10.1186/s12864-020-06856-9</p><p>2. Cox, J., Mann, M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics 13 (Suppl 16), S12 (2012). https://doi.org/10.1186/1471-2105-13-S16-S12</p><p>3. Maksimovic, J., Oshlack, A. & Phipson, B. Gene set enrichment analysis for genome-wide DNA methylation data. Genome Biol 22, 173 (2021). https://doi.org/10.1186/s13059-021-02388-x</p><p>4. Danyue Dong, Yuan Tian, Shijie C Zheng, Andrew E Teschendorff, ebGSEA: an improved Gene Set Enrichment Analysis method for Epigenome-Wide-Association Studies, Bioinformatics, Volume 35, Issue 18, September 2019, Pages 3514–3516, https://doi.org/10.1093/bioinformatics/btz073</p><p>5. Ren X, Kuan PF. methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing. Bioinformatics. 2019 Jun 1;35(11):1958-1959. doi: 10.1093/bioinformatics/bty892. PMID: 30346483.</p><p>6. Hong G, Zhang W, Li H, Shen X, Guo Z. Separate enrichment analysis of pathways for up- and downregulated genes. J R Soc Interface. 2013 Dec 18;11(92):20130950. doi: 10.1098/rsif.2013.0950. PMID: 24352673; PMCID: PMC3899863.</p><div><br /></div></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comMelbourne VIC 3004, Australia-37.8301583 144.9804594-66.140392136178846 109.8242094 -9.5199244638211553 -179.86329060000003tag:blogger.com,1999:blog-4885455635096396903.post-38309741344785718922024-02-15T14:16:00.001+11:002024-02-15T14:20:03.550+11:00Don't use KEGG!!!<p>KEGG is the Kyoto Encyclopedia of Genes and Genomes, a compendium of gene functional annotations which are commonly used for pathway enrichment analysis. KEGG has been around since 1997 (PMID: 9390290) but in 2000, as the draft human genome sequence was released, KEGG became a key database involved in the curation of literature data to catalog the function of all human genes, many of which were newly sequenced and previously uncharacterised. This invigorated interest in KEGG is demonstrated by one of their articles in 2000 (PMID: 10592173) accruing 24,236 citations according to <a href="https://badge.dimensions.ai/details/id/pub.1017305614">Dimensions</a>, 1270 fold higher than other articles from that time. A PubMed search using "KEGG" keyword shows 27,033 results, with matches in abstracts alone and the tragectory is increasing at a rapid rate. Despite the popularity of this tool, I'm urging you not to use it for your research. Here I will lay out the reasons.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgARlniFQPF29VsyZTEqrddQ9kP_NPeGRGxYT9S00Z7Js7cPu3EtqofXXE79_0VoqBBs-Ypw50ZjXgaxGQ2S_5Q8L1BTBQgtRoB-yzvvf2-t6JvpdedJT7o__kVk18x0yh-DKxohXgM0AxhQHQXfvd-1esSuxMpiYD7qy0bKDMRXd_sHE15rcUtphFOw-VF" style="margin-left: 1em; margin-right: 1em;"><img alt="Barchart showing number of mentions of KEGG in PubMed has increased in recent years." data-original-height="403" data-original-width="1046" height="246" src="https://blogger.googleusercontent.com/img/a/AVvXsEgARlniFQPF29VsyZTEqrddQ9kP_NPeGRGxYT9S00Z7Js7cPu3EtqofXXE79_0VoqBBs-Ypw50ZjXgaxGQ2S_5Q8L1BTBQgtRoB-yzvvf2-t6JvpdedJT7o__kVk18x0yh-DKxohXgM0AxhQHQXfvd-1esSuxMpiYD7qy0bKDMRXd_sHE15rcUtphFOw-VF=w640-h246" title="Mentions of KEGG in PubMed" width="640" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div><h3 style="text-align: left;">1. It isn't comprehensive</h3>I did an analysis of KEGG and other pathway sets in <a href="https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp">MSigDB</a> and found some interesting results. Not only does KEGG have fewer gene sets, but the number of unique genes described is very small compared to Reactome or Gene Ontology Biological process. This is likely to bias over-representation based enrichment tests as genes without annotations are routinely excluded from an analysis by some tools. The total number of annotations was seven fold bigger for Reactome compared to KEGG legacy and 49 fold for GO BP. Sure, Reactome and GO BP have a lot of redundancy caused by the hirarchical structure and some sets with different names and similar member genes, but it is clear from this data that KEGG really isn't comprehensive as compared to the alternatives. If you want to see how this was done, you can download my script <a href="https://gist.github.com/markziemann/6126570ace44bee6db5aeff5313324a9">here</a>.<p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjV0expjtkmAA9HFFaweL-6JODPgjzHBOieDxuzVWakE3s8m0pNHTRlL9Y_SOmMKsSl48z6scftbz7IagSTJqLD025KnW5miMIjaIDU0VDolBqrsu1aplVjDOqum9latYu__oWD-_7K-3PSI6gCqS3myn7mbvtnCqJMGJc-ppApn1sChmhI8seYx9pc9BTd" style="margin-left: 1em; margin-right: 1em;"><img alt="Coverage metrics of pathway databases shows that KEGG is a poor choice compared to Reactome or Gene Ontology Biological Process" data-original-height="186" data-original-width="950" height="126" src="https://blogger.googleusercontent.com/img/a/AVvXsEjV0expjtkmAA9HFFaweL-6JODPgjzHBOieDxuzVWakE3s8m0pNHTRlL9Y_SOmMKsSl48z6scftbz7IagSTJqLD025KnW5miMIjaIDU0VDolBqrsu1aplVjDOqum9latYu__oWD-_7K-3PSI6gCqS3myn7mbvtnCqJMGJc-ppApn1sChmhI8seYx9pc9BTd=w640-h126" title="Coverage metrics of pathway sets in MsigDB" width="640" /></a></div><br /><br /><p></p><h3 style="text-align: left;">2. Restrictive licence and cost</h3><div>KEGG may be free to use in some situations, but it is clear that for non-academic users, it is a pay-to-play proposition (<a href="https://www.kegg.jp/kegg/legal.html">link</a>). Moreover academic users may not directly access the FTP data store, presumably to prevent unauthorised data mining. Even academic service providers need to obtain a subscription, which costs USD$5,000 per year if accessed by more than one individual. Moreover, it is unclear from the licence whether subscribers are able to disseminate large scale data derived from the KEGG database. Lastly, if the subscription is terminated, then users must legally delete any derivative data including ML/AI models (<a href="https://www.pathway.jp/doc/subscription_meu.pdf">link</a>). In contrast Gene Ontology provides their data under a relatively permissive Creative Commons Attribution 4.0 Unported License, which provides a high degree of flexibility for users to access, analyse and remix GO data. Reactome has a CC0 licence which essentially puts its data into the public domain.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgL9SmR4YkxvF3O9Ly9ekZfxDpIJSyjXvi6FU76qQCVyY03JQH9HRhbfeVpJKCL_1lzVVbhQSrcjaMDGdqXr_2NplolBG5UUPasHx6ncxpnTOzTOiByp1CCLaIurNwWfRQ5kuggIwN-AEIzev6eKa21zgfBO2FhCw5GSGY4F8NKJfYvyXAQhmC8Qu-Y_zr2" style="margin-left: 1em; margin-right: 1em;"><img alt="KEGG Legal Info https://www.kegg.jp/kegg/legal.html" data-original-height="453" data-original-width="741" height="392" src="https://blogger.googleusercontent.com/img/a/AVvXsEgL9SmR4YkxvF3O9Ly9ekZfxDpIJSyjXvi6FU76qQCVyY03JQH9HRhbfeVpJKCL_1lzVVbhQSrcjaMDGdqXr_2NplolBG5UUPasHx6ncxpnTOzTOiByp1CCLaIurNwWfRQ5kuggIwN-AEIzev6eKa21zgfBO2FhCw5GSGY4F8NKJfYvyXAQhmC8Qu-Y_zr2=w640-h392" title="KEGG Legal Info" width="640" /></a></div><br /><br /></div><h3 style="text-align: left;">3. Lack of archival versions</h3><div>Ms Anusuiya Bora, a PhD candidate in my lab reached out to the KEGG administrators to request some historical KEGG gene sets which were a part of a tool called DAVID version 6.8. Sadly DAVID 6.8 has been retired by the administrators, and so roughly 20,000 articles in PubMed are no longer reproducible with the original tools. A lot of these articles were published from 2018-2021, and so it is disappointing that we can't go back and check them. Even the authors themselves won't be able to reproduce their work, despite the fact that many institutions and funders require researchers retain their data and algorithms for 5 years or more after publication. On the other hand, Gene Ontology has archival data dumps that go back to 2004 (<a href="https://release.geneontology.org/">here</a>). Reactome has earlier versions accessible by MySQL queries (<a href="https://reactome.org/documentation/faq/37-general-website/202-earlier-versions">link</a>). </div><h3 style="text-align: left;">Conclusion</h3><div>KEGG enjoys a big user base due to being an early mover into the pathway annotation space, but the coverage has fallen behind other initiatives like Gene Ontology and Reactome which have large-scale support from the NHGRI and EMBL. KEGG appears to have pivoted towards a commericial direction to raise funds to maintain the curation work, but it is struggling to compete. The lack of archival KEGG versions is a major blunder and reminiscent of the <a href="https://en.wikipedia.org/wiki/Apollo_11_missing_tapes">loss of the original Apollo 11 video recordings</a>. Indeed, it would be nice to look back on the early growth of pathway knowledge in the early 2000's, but this won't be possible for KEGG. This highlights that longer term reproducibility is going to be easier when the tools and data are free and open source. They can be archived permanently in Zenodo or Software Heritage, and future reproducers don't need to agree to unreasonable conditions or fees for access. Finally, when such resources are controlled by a commercial company, it cannot be guaranteed that they will continue to operate, if they go bust, these resources might be gone forever.</div><div><br /></div><div>I would like to thank Ms Anusuiya Bora who collected a lot of the information shown here.</div><div><br /></div><div>Comments and discussion on Mastodon here: https://genomic.social/@mdziemann/111933336579691473</div><div><br /></div><div><br /></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8635199 144.7710013-66.173753736178838 109.6147513 -9.5532860638211545 179.9272513tag:blogger.com,1999:blog-4885455635096396903.post-54014279519802065392024-02-02T14:21:00.000+11:002024-02-02T14:21:00.251+11:00Example blast workflow (nucleotide)<p>BLAST is a stalwart in the bioinformatics space. I've used it in multiple contexts and it is a good way to introduce students to bioinformatics principles and the process of pipeline development. Although there is a web interface, it is still good to use it locally if you need to run a large number of queries. As my group needed to use BLAST again this week, I thought I'd share a small example script which I shared with my Masters research students just getting into bioinformatics.</p><p></p><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgGi39d00oBwzrPZaRQL1xynui3Q6Mlmq_h1jkZtxp3bOBaFSIpw4goOC5sbwRWBI5LiXrLHPkngXMDRVYnP6kYL7mu09ZNcW_qYR_2miWGlSj4GagsbNwu9D_rHQIKskzE-I988ok8vlCglED3ZrurE0KtDw6YBAawGZgYcmn45ZFBR1Mk7-LJ8sMMjtqM" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="188" data-original-width="623" height="97" src="https://blogger.googleusercontent.com/img/a/AVvXsEgGi39d00oBwzrPZaRQL1xynui3Q6Mlmq_h1jkZtxp3bOBaFSIpw4goOC5sbwRWBI5LiXrLHPkngXMDRVYnP6kYL7mu09ZNcW_qYR_2miWGlSj4GagsbNwu9D_rHQIKskzE-I988ok8vlCglED3ZrurE0KtDw6YBAawGZgYcmn45ZFBR1Mk7-LJ8sMMjtqM" width="320" /></a></div></div><p></p><p>The script (shown below) downloads the E. coli gene coding sequences and then extracts by random a few individual sequences. These undergo random mutagenesis and then we can use the mutated sequences as a query to find the original gene with BLAST. The output format is tabular which suits downstream large scale data analysis. The script also includes steps for generating the blast index. The script is uploaded as a gist <a href="https://gist.github.com/markziemann/ed8236ad736e3f186c851364ddd0beee">here</a>. It requires prerequesites:</p><p>sudo apt install ncbi-blast+ emboss</p><p>unwrap_fasta.pl is available <a href="https://chk.ipmb.sinica.edu.tw/wiki/doku.php/tutorials/perl/unwrap_fasta.pl">here</a>. </p><p>---</p><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">#!/bin/bash</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><br aria-hidden="true" /></span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"># Download</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">URL="ftp://ftp.ensemblgenomes.org/pub/bacteria/release-42/fasta/bacteria_0_collection/escherichia_coli_str_k_12_substr_mg1655/cds/Escherichia_coli_str_k_12_substr_mg1655.ASM584v2.cds.all.fa.gz"</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><br aria-hidden="true" /></span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"># unzip</span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">if [[ ! -r $FA ]] ; then</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"> wget -N $URL</span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"> gunzip -kf $FA.gz</span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">fi</span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><br aria-hidden="true" /></span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"># extract a few sequences</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"># requires unwrap_fasta.pl</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">cut -d ' ' -f1 $FA \</span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">| perl unwrap_fasta.pl - - \</span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">| paste - - | shuf | head -100 | tr '\t' '\n' | tee sample_named.fa \</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">| grep -v '>' | nl -n ln | sed 's/^/>/' | tr '\t' '\n' > sample.fa</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><br aria-hidden="true" /></span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"># in case we need to reindex the db</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">if [[ ! -r $FA.ndb ]] ; then</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"> formatdb -p F -o T -i $FA</span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">fi</span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><br aria-hidden="true" /></span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"># incorporate some mismatches</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"># it may generate some error output but actually works (check the output)</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">msbar -sequence sample.fa -count 100 -point 4 -block 0 -codon 0 -outseq sample_mutated.fa</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><br aria-hidden="true" /></span></div><div style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"># run the blastn</span></div><div class="x_elementToProof" style="background-color: white; border: 0px; color: #242424; font-family: "Segoe UI", "Segoe UI Web (West European)", "Segoe UI", -apple-system, BlinkMacSystemFont, Roboto, "Helvetica Neue", sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 15px; font-stretch: inherit; font-variant-alternates: inherit; font-variant-east-asian: inherit; font-variant-numeric: inherit; font-variant-position: inherit; font-variation-settings: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;"><span style="border: 0px; color: black; font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-feature-settings: inherit; font-kerning: inherit; font-optical-sizing: inherit; font-size: 11pt; font-stretch: inherit; font-style: inherit; font-variant: inherit; font-variation-settings: inherit; font-weight: inherit; line-height: inherit; margin: 0px; padding: 0px; vertical-align: baseline;">blastn -outfmt 6 -evalue 0.001 -db $FA -query sample_mutated.fa > blast_results.tsv</span></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-54871782980300387802023-12-21T14:36:00.003+11:002023-12-21T14:37:35.930+11:00Update your gene names when doing pathway analysis of array data!<p>If you are doing analysis of microarray data such as Infinium methylation arrays, then those genomic annotations you're using might be several years old. </p><p>The EPIC methylation chip was released in 2016 and the R bioconductor annotation set hasn't been updated much since.</p><p>So we expect that some gene names have changed, which will reduce the performance of the downstream pathway analysis.</p><p>The gene symbols you're using can be updated using the HGNChelper R package on CRAN.</p><p>Let's say we want to make a table that maps probe IDs to gene names, the following code can be used.</p><p><br /></p><p>library("IlluminaHumanMethylationEPICanno.ilm10b4.hg19")</p><p>anno <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)</p><p>myann <- data.frame(anno[,c("UCSC_RefGene_Name","UCSC_RefGene_Group","Islands_Name","Relation_to_Island")])</p><p>gp <- myann[,"UCSC_RefGene_Name",drop=FALSE]</p><p>gp2 <- strsplit(gp$UCSC_RefGene_Name,";")</p><p>names(gp2) <- rownames(gp)</p><p>gp2 <- lapply(gp2,unique)</p><p>gt <- stack(gp2)</p><p>colnames(gt) <- c("gene","probe")</p><p>dim(gt)</p><p>str(gt)</p><p>head(gt)</p><div><br /></div><div>Now the gene symbols can be updated. Note that the built-in gene symbol data is from 2019, but you can get current gene symbols using the following script.</div><p>library("HGNChelper")</p><p>new.hgnc.table <- getCurrentHumanMap()</p><p>fix <- checkGeneSymbols(gt$gene,map=new.hgnc.table)</p><p>fix2 <- fix[which(fix$x != fix$Suggested.Symbol),]</p><p>length(unique(fix2$x))</p><p>gt$gene <- fix$Suggested.Symbol</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjd0C7_MhUc1HrVyltm-ZyULL27mSlq0tk9CK4zWjsgaBNalNn-3upiAcNVgJzUimeGwI09HbxfaxGHIt9SLyecl5uqZAemH-TuxHvRTshxk2LlE-eORlTjOFvr-XQZVdTxXAWE6PeH8AtwZTI8Ry2lez1IxurFYcPGPPrtAjtdrfbXweBd0OfJfVneOZrT" style="margin-left: 1em; margin-right: 1em;"><img data-original-height="342" data-original-width="341" height="400" src="https://blogger.googleusercontent.com/img/a/AVvXsEjd0C7_MhUc1HrVyltm-ZyULL27mSlq0tk9CK4zWjsgaBNalNn-3upiAcNVgJzUimeGwI09HbxfaxGHIt9SLyecl5uqZAemH-TuxHvRTshxk2LlE-eORlTjOFvr-XQZVdTxXAWE6PeH8AtwZTI8Ry2lez1IxurFYcPGPPrtAjtdrfbXweBd0OfJfVneOZrT=w398-h400" width="398" /></a></div><br /><br /><p></p><p>If you run this code you will see that 3253 genes in our table have new names. That is about 14% of all genes on the chip!</p><p>So please remember to update your gene symbols to get the best results out of your pathway analysis!</p><p><br /></p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-28808065795935786442023-12-13T15:04:00.000+11:002023-12-13T15:04:02.731+11:00Reflections on 2023 and outlook<p>It has been an amazing year of research. I've been at Burnet Institute since August 2023 as Head of Bioinformatics and I've really enjoyed the challenge of serving the many and varied 'omics projects at the Institute and loved discussing new project ideas with everyone here. I'm still active at Deakin in student supervision and project collaborations and this is ongoing.</p><p>In terms of research directions, my group has been focused on our three themes: </p><p>1. Bioinformatics collaborative analysis</p><p>2. Building better software tools for omics analysis</p><p>3. Reproducibility and research rigour</p><p>Some of the long-running projects have been completed including methylation analysis of type-1 diabetes complications, which has been about 10 years in the making [1]. The number of collaborative projects has dipped, which is normal when changing institutes and I hope this will lift in the coming years as Burnet work gets completed.</p><p>In terms of research directions for 2024, there are many. One thing I hope to share with you soon is our approach to applying functional class scoring (like GSEA) to Infinium array data. This has been tricky since each gene can have many probes, and sometimes probes can be annotated to more than one gene. But I think we have a solution. It will be interesting to apply this to some of the larger EWAS datasets and prospective studies to see if it has any potential for methylation signatures to preceed disease onset. In doing this work we have learned a bit more about the differences between over-representation and functional class scoring tests which I hope to bring back to the transcriptome world.</p><p>Reproducibility has become an ever bigger part of my portfolio of projects since last year's paper "Urgent need for consistent standards for functional enrichment analysis" [2], which was very successful and probably my most important work of my career. Since then we have been conducting reproducibility surveys with my new PhD student Anusuiya Bora and we have learned a great deal about what it takes to make bioinformatics research reproducible and reliable. We also put together a review called "The five pillars of computational reproducibility: Bioinformatics and beyond" [3] which outlines the principles of reproducibility that we should be adopting. Along these lines, Anusuiya and I have written a protocol that outlines how to do enrichment analysis using these five pillar principles [4]. Next year we will be going even harder on reproducibility and enrichment analysis. We will have pieces on the utility of web-servers for bioinformatics and how we can make them more robust. We will also be examining some tools that are used by the microRNA community and in particular whether functional enrichment analysis of microRNA targets is something that is useful or not. </p><p>This work on reproducibility is very satisfying as it directly educates emerging researchers on best practices, but the challenging part with this reproducibility agenda is that focused funding in this area is difficult to obtain as our work doesn't seem to fit in any existing grant scheme. We could do a lot more and better work in this direction if the funding issue were resolved.</p><p>1. Khurana I, Kaipananickal H, Maxwell S, Birkelund S, Syreeni A, Forsblom C, Okabe J, Ziemann M, Kaspi A, Rafehi H, Jørgensen A, Al-Hasani K, Thomas MC, Jiang G, Luk AO, Lee HM, Huang Y, Thewjitcharoen Y, Nakasatien S, Himathongkam T, Fogarty C, Njeim R, Eid A, Hansen TW, Tofte N, Ottesen EC, Ma RC, Chan JC, Cooper ME, Rossing P, Groop PH, El-Osta A. Reduced methylation correlates with diabetic nephropathy risk in type 1 diabetes. J Clin Invest. 2023 Feb 15;133(4):e160959. doi: 10.1172/JCI160959. PMID: 36633903; PMCID: PMC9927943.</p><p>2. Wijesooriya K, Jadaan SA, Perera KL, Kaur T, Ziemann M. Urgent need for consistent standards in functional enrichment analysis. PLoS Comput Biol. 2022 Mar 9;18(3):e1009935. doi: 10.1371/journal.pcbi.1009935. PMID: 35263338; PMCID: PMC8936487.</p><p>3. Ziemann M, Poulain P, Bora A. The five pillars of computational reproducibility: bioinformatics and beyond. Brief Bioinform. 2023 Sep 22;24(6):bbad375. doi: 10.1093/bib/bbad375. PMID: 37870287; PMCID: PMC10591307.</p><p>4. Ziemann M, Bora A. A recipe for extremely reproducible enrichment analysis. DOI: dx.doi.org/10.17504/protocols.io.j8nlkwpdxl5r/v2</p><p><br /></p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-7087174102696509322023-07-26T11:37:00.005+10:002023-07-26T12:32:10.382+10:00Energy expenditure of computational research in contextThere have been a few papers recently on “green computing”, and changes we can make to ensure our work is more sustainable. The most important aspect to this is the overuse of energy in conducting our analysis, especially if the energy is derived from burning fossil fuels. What I want to do here is to put in context the energy expenditure of research computing systems by comparing it to other energy expenditures such as travel and transport.<br /><br /><h2 style="text-align: left;">Energy consumption of a workstation or small server</h2><br />In order to quantify the energy expenditure of a compute system, we need to make some assumptions.<br /><br />We will assume that for bioinformatics, the CPU is the main consumer of power, is working at 50% of capacity.<br /><br /><ul style="text-align: left;"><li>AMD Ryzen 9 5950X (16 cores / 32 threads) Maximum power consumption = 142 W /2 = 71 W <a href="https://www.eatyourbytes.com/cpu-maximum-power/">source</a></li><li>High end motherboard: 75 W.</li><li>RAM stick: 3 W * 8 = 24 W</li><li>Mid range graphics card idle = 12 W</li><li>HDD Storage: 9 W * 2 = 18 W</li></ul>Total: 71 + 75 + 24 + 12 + 18 = 200 W<br /><br />So a workstation, or small server with 32 threads and 64-128 GB RAM will consume about 200 W at 50% CPU utilisation.<br /><br />Multiply this by the number of hours in the year (8766 hrs) and you get 1753.2 kWh (or 54.8 kWh per thread) consumed per year.<br /><br /><h2 style="text-align: left;">Commuting by car</h2><br />It is stated that 100 km traveled by mid size car (Volkswagen Passat) costs 83.5 kWh/100km. So a 30 km commute will consume 25 kWh each way. Attending work 150 days per year will cost 25 * 150 * 2 = 7,500 kWh per year.<br /><br />So 1 person commuting by car 150 days per year is the same as running 137 CPU threads, or 4 small servers for a year.<br /><br /><h2 style="text-align: left;">Overseas travel</h2><br />Conferences are a key part of science these days, so it is fair to calculate the energy cost of airline travel.<br /><br />We will calculate a travel from New York to Berlin, which is 7,934 km return.<br /><br />Fuel consumption per passenger is approx 3.5 L/100 km.<br /><br />So a return flight NY-Berlin will consume: 7934 km * 3.5 L / 100 km = 277.7 L fuel.<br /><br />Energy content of aviation fuel is 690 MJ/L, so the energy expenditure is 690 MJ/L * 277.7 L = 191.6 GJ<br /><br />This comes to 53,200 kWh.<br /><br />This does not include the energy expenditure to extract, refine and transport the fuel itself.<br /><br />So the international conference trip is the same as running 971 CPU threads, or ~30 small servers for a whole year.<br /><br /><h2 style="text-align: left;">Conclusion</h2><br />While power consumption from computational research could be an environmental concern, it is important to put it in context.<br /><br />If researcher A uses one server at 50% CPU load, and works remotely, they will expends 1,753 kWh/yr.<br /><br />If reseracher B uses one server and commutes 150 days er year by car, that is 9,253 kWh/yr.<br /><br />If researcher C uses one server and commutes by car 150 days per year and makes two intercontinental return flights annually, they will use 115,653 kWh.<br /><br /><img height="397" src="data:image/png;base64,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" width="397" /><br /><br />Therefore, for typical workloads involving five servers or less, an individual’s travel behaviour is a much greater consideration than the energy cost of computing.<br /><br />The energy consumption of air travel is substantial and we should strongly discourage intercontinental conference travel.<br /><br />Lastly, the environmental impact of air travel is likely much worse than stated here due to the environmental damages caused by fuel extraction, refining and transport, and the fact that fixed-grid energy is more likely to originate from renewable sources like solar and wind, whereas transport fuel combustion results directly in emissions.<br /><br />Source: https://github.com/markziemann/energy/Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8635199 144.7710013-66.173753736178838 109.6147513 -9.5532860638211545 179.9272513tag:blogger.com,1999:blog-4885455635096396903.post-72907869880803454562023-07-21T22:49:00.005+10:002023-07-21T22:53:45.133+10:00 "Dev" and "Prod" for bioinformaticsI’ve been thinking a lot about best practices lately. I even co-wrote a <a href="https://osf.io/4pd9n/">best practices article</a> just last month. As I have been working with students and colleagues and reflecting on my own practices I have come to the conclusion that us researchers need to align our work more closely towards software developers rather than other researchers.<br /><br />In software development there is a strong differentiation between “development” and “production” work environments. Production is the live app after release to consumers, it is critical that the app functions as expaected, is reliable, useful and with a good user experience. This is after all, where these tech companies make their money. Any downtime is going to be embarrassing and will cost the company money and customers. <br /><br />The development environment on the other hand is the place where software developers can experiment with creating new features, prototype, and refine ideas. As things are built, the software code contains lots of bugs, and this nascent codebase undergoes progressive improvement. <br /><br />In order to minimise issues in production, newly developed code is extensively tested in a “staging” environment, which is identical to the production environment, except that it is not released to consumers.<br /><br /><a href="https://miamioh.edu/_files/images/it-services/news-articles/2017/08/dev-graphic.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="343" data-original-width="800" height="271" src="https://miamioh.edu/_files/images/it-services/news-articles/2017/08/dev-graphic.png" width="632" /></a><div><br />As bioinformaticians, I think we should have a stronger mindset around “dev”, “staging” and “prod”. So what is “prod” for a bioinformatician? I say it is any analysis work that we present for publication (preprint, peer-reviewed article, conference, etc). Just like the software developers, our reputation depends on the reliability of the workflow we use, so it better be thoroughly tested, documented and use environment management so that it yields consistent and reliable/reproducible results.<br /><br />As for the “dev” environment, such considerations are less important. In the dev mindset we are focused on creating new features and data visualisations that we don’t worry too much about whether it is reproducible or not. I sometimes see practitioners using Rstudio interactively executing commands out of sequence in the Rmd scripts to generate charts for presentation. Such work could be problematic due to the effect of code order execution, as well as the presence of objects in the environment that maybe don’t belong. My advice in such cases is for practitioners to not rely on interactive execution much at all. Sure, if you’re actively developing a chart or other analysis task, feel free to use interactive execution, but when it comes to presenting this work to other people, only show results that have come from a successfully completed R Markdown script (Jupyter for Python folks). The reason for this is two-fold. First, when knitting an Rmd file, it begins with a clean environment - no loaded packages and no objects in the environment. Secondly, knitting forces code to be executed in a set sequence so it should be reproducible.<br /><br />So how do we know our work is ready for “prod” (publication)? It needs to be proven to be reproducible. That is, it should be able to be re-run on another computer system, yielding the same result. This could be considered “staging”. But do you go to the effort of doing this for your research projects? Have you asked your colleagues to reproduce your entire workflow to ensure that documentation is up to scratch? Maybe you should. Remember your reputation as a good analyst rests on how reliable your work is.<br /><br />As most research computer systems have different setups, from processor architecture, to operating systems, and low level dependencies this process is made a lot easier using containers such as Docker. Documentation is also important so that folks reading through your work can understand the thought process (including your future self 6 months from now). Code testing is also extremely important. In its simplest form, this could be ensuring that the R Markdown script completes without errors. Code testing probably needs its own blog post. <br /><br />As I reflect on my own work and those around me I see the tendency to rely too much on preliminary “dev” results. As computational researchers, we need to treat the work we present to others more like the mission critical “prod” like our software developer cousins.<br /><br /><b>Related reading<br /></b><a href="https://www.itprotoday.com/devops-and-software-development/development-staging-and-production-model#close-modal">Development, Staging and Production - IT PRo<br /></a><a href="https://osf.io/4pd9n/">The Five Pillars of Computational Reprducibility: Bioinformatics and Beyond</a><div class="separator" style="clear: both; text-align: center;"><br /></div><br /></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-88383246634124766842023-06-19T23:51:00.000+10:002023-06-19T23:51:58.987+10:00The five pillars of computational reproducibility: Bioinformatics and beyond<p>I've been working on a new project to follow-up our <a href="https://doi.org/10.1371/journal.pcbi.1009935">paper last year on the problems with pathway enrichment analysis.</a> That article turned out to be a bleak and depressing look into how frequently used tools in genomics are misused. It is not an exaggeration to say that most articles showing some type of enrichment analysis are doing it wrong and no doubt this is severely impacting the literature.</p><p>However I think it isn't helpful to only focus on the negative aspects of bioinformatics and computational research. We also need to lead the way towards resolving these issues. The best way to do this is in my view is to provide step-by-step guides and tutorials for common routines. So this is what we are in the process of doing, making a protocol for pathway enrichment analysis that is "extremely reproducible". By this, I mean that the analysis could be reproduced independently in future with the minimum of fuss and time. As we were writing this we also recognised that the overall philosophy we devised had not really been generalised yet, and would probably be of interest for researchers doing things outside of genomics.</p><p>We drafted the infographic with the five pillars and sought feedback and contributions from our contacts. Then with the coauthors Pierre Poulain (Université Paris Cité) and Anusuiya Bora (my student at Deakin Uni) we elaborated the infographic into a review that gives the full picture of current best practices in computational research. <a href="https://osf.io/4pd9n/">The preprint is online at OSF here</a>. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhvjc2dBVYOaryROssKew3Pb9TxcmUR1oXGuJ1PD1icRgChYwR8R76R_YS9qKMzZF6V-E4frLQBCaVGJ9Y7X1vGoQybFICudIFnwoxLYOaHVlgrWODw_kS8zjnjkqehRRaf8RoeqY26l4sr_S7deN7h0AFZ5irRGUXPKOu-mEXoFhIpJyQgjfQRnbsEuLDh" style="margin-left: 1em; margin-right: 1em;"><img data-original-height="1152" data-original-width="1536" height="480" src="https://blogger.googleusercontent.com/img/a/AVvXsEhvjc2dBVYOaryROssKew3Pb9TxcmUR1oXGuJ1PD1icRgChYwR8R76R_YS9qKMzZF6V-E4frLQBCaVGJ9Y7X1vGoQybFICudIFnwoxLYOaHVlgrWODw_kS8zjnjkqehRRaf8RoeqY26l4sr_S7deN7h0AFZ5irRGUXPKOu-mEXoFhIpJyQgjfQRnbsEuLDh=w640-h480" width="640" /></a></div>In the spirit of the content of the review, the document itself is a reproducible R Markdown document, able to be executed using a R docker image, with the code living on github version control <a href="https://github.com/markziemann/5pillars/">here</a> along with helpful documentation courtesy of a detailed README.<p></p><p>If you have feedback on the preprint, feel free to raise an issue on GitHub. It is appreciated. </p><p>Thanks to Dr Aziz Khan (Stanford University), Dr Sriganesh Srihari (QIMR Berghofer Medical Research Institute), Dr Barbara Zdrazil (EMBL-EBI) and Dr Irene López Santiago (EMBL-EBI) for comment on the five pillars idea. Special thanks to Dr Altuna Akalin (Max Delbrück Center, Germany), Dr Simon Tournier (Paris Cité University, France), Dr Samuel S. Minot (Fred Hutchinson Cancer Center) and Dr Martin O’Hely (Deakin University) for feedback, comments and advice.</p><p><br /></p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8635199 144.7710013-66.173753736178838 109.6147513 -9.5532860638211545 179.9272513tag:blogger.com,1999:blog-4885455635096396903.post-85754089908536629282023-05-05T16:13:00.003+10:002023-05-05T16:13:31.572+10:00Two dimensional filled contour plots in RAlso called kernel density plots, these are two-dimensional contour heatmaps which are useful to replace scatterplots when the number of datapoints is so large that it risks overplotting. I've used these sort of plots for many years, most notably in the mitch bioconductor package where it is used to map the relationship of differential expression between two contrasts. There are solutions in ggplot, but I thought I'd begin with a base R approach.<div><br /></div><div>In the example below, some random data is generated and plotted. Just subsitute your own data and give it a try.<br /><div> <hr />
<code><div style="font-family: "Times New Roman";"><br /></div><div style="font-family: "Times New Roman";"><div>xvals <- rnorm(100,10,100)</div><div>yvals <- rnorm(100,10,200)</div><div><br /></div><div>mx <- cbind(xvals,yvals)</div><div><br /></div><div>palette <- colorRampPalette(c("white", "yellow", "orange", "red",</div><div> "darkred", "black"))</div><div><br /></div><div>k <- MASS::kde2d(mx[,1], mx[,2])</div><div><br /></div><div>X_AXIS = "x axis label" </div><div>Y_AXIS = "y axis label" </div><div><br /></div><div>filled.contour(k, color.palette = palette, plot.title = {</div><div> abline(v = 0, h = 0, lty = 2, lwd = 2, col = "blue")</div><div> title(main = "Filled contour plot", xlab = X_AXIS,</div><div> ylab = Y_AXIS)</div><div> })</div><div><br /></div><div>grid()</div></div><div style="font-family: "Times New Roman";"><br /></div><- abline="" black="" c="" cbind="" code="" col="blue" color.palette="palette," colorramppalette="" darkred="" filled.contour="" grid="" h="0," k="" lty="2," lwd="2," main="Filled contour plot" mass::kde2d="" mx="" orange="" palette="" plot.title="{" red="" rnorm="" title="" v="0," white="" x_axis="x axis label" xlab="X_AXIS," xvals="" y_axis="y axis label" yellow="" ylab="Y_AXIS)" yvals=""><hr />
</-><div><br /></div><div><br /></div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjllXc71uLT0D27HPRX2QKwe_-eR80IzWZVaEXPI-2JP7PQdsqnVQDf5LeN8HYiZAsT9gsZ2QPWJbLeRsoLSup4diAcYqYw905yFriT_AqSCavaHsAAifw9tYtanNrtMlNhCiPJ6sc4RV9of9GnjWFYsiYdSSEwXrlntkejHYJ90ZWoF5EqkExcBK7g8A" style="margin-left: 1em; margin-right: 1em;"><img data-original-height="726" data-original-width="691" height="640" src="https://blogger.googleusercontent.com/img/a/AVvXsEjllXc71uLT0D27HPRX2QKwe_-eR80IzWZVaEXPI-2JP7PQdsqnVQDf5LeN8HYiZAsT9gsZ2QPWJbLeRsoLSup4diAcYqYw905yFriT_AqSCavaHsAAifw9tYtanNrtMlNhCiPJ6sc4RV9of9GnjWFYsiYdSSEwXrlntkejHYJ90ZWoF5EqkExcBK7g8A=w608-h640" width="608" /></a></div><br /><br /></div></code></div></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-20090502088826126412023-03-27T14:18:00.004+11:002023-03-27T14:23:51.314+11:00A docker image for infinum methylation analysisPerforming a differential methylation analysis of infinium array data requires an impressively large number of R packages, such as `minfi`, `missmethyl`, `limma`, `genomicRanges`, `DMRcate`, `bunpHunter` and many others. Each of these in turn are considered heavy packages as they each require many dependancies. This means it can take up to an hour to go from a vanilla R installation to one with all the needed packages installed. If you are using multiple computers you might find that these have slightly different versions of R, bioconductor and this large stack of dependancies, which could lead to different results. You may also find that it is difficult to install this large set of dependancies on shared systems, as some dependenncies might require installation of system libraries that need admin permissions to install.<div><br /></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEitfkKuc_VvMyNZsKvhPoQsXWWVyDKlpvJW3ZGwxSYGdXpZXKv5YdGFKZRLqVVO4j-NU2dnQNpBrHvKxqtqEWBBhs3CGeLxD-_4I4im1VwJyqvydiiSMsvkyidXIQXummeGpDXiOYmjEFboavCuyGnY50AoDXf7klpLo7gireFKLwYkeEnBz6COzRCl8A/s1728/infiniumdocker.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="491" data-original-width="1728" height="91" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEitfkKuc_VvMyNZsKvhPoQsXWWVyDKlpvJW3ZGwxSYGdXpZXKv5YdGFKZRLqVVO4j-NU2dnQNpBrHvKxqtqEWBBhs3CGeLxD-_4I4im1VwJyqvydiiSMsvkyidXIQXummeGpDXiOYmjEFboavCuyGnY50AoDXf7klpLo7gireFKLwYkeEnBz6COzRCl8A/w320-h91/infiniumdocker.png" width="320" /></a></div><br /><div><div><br /></div><div>The way I've tried to alleviate this problem is to install all my needed packages into a Docker image which can then be downloaded and run in a few minutes on a new system, rather than an hour. The image is based on bioconductor release version 3:14*, which uses R version 4.1.3 Visit the DockerHub image <a href="https://hub.docker.com/r/mziemann/meth_analysis">here</a> to learn more.</div><div><br /></div><div><div>On a system where docker is installed, you can run the following to get the image and then start an interactive shell:</div></div><div><br /></div><div><div></div></div></div><div><code>docker pull mziemann/meth_analysis<br />docker run -it --entrypoint /bin/bash mziemann/meth_analysis</code></div><div><br /></div><div><div>Once inside the image, you can access all those dependancies, and perform those analyses. You can use the wormhole tool to copy results files out of the container, or just exit the container and then use docker cp command to fetch what you need.</div><div><br /></div><div>For those of you on a shared system without docker or sudo access, you can still use this image, but you'll need to pick a tool like singularity or udocker which doesn't require root permissions. Personally I prefer <a href="https://github.com/indigo-dc/udocker">udocker</a> as it can be installed without sudo permissions, and the process is relatively simple. Once it is installed, you type the commands </div><div><br /></div><div><code>udocker pull mziemann/meth_analysis</div>udocker run mziemann/meth_analysis bash</code><div><br /></div><div>which will pull the image and start a new container. If you need to load some data into the container, this command can "mount" a folder into a location in the container so you can work with it.</div><div><br /></div><div><code>udocker run --volume=/home/user/data:/analysis mziemann/meth_analysis /bin/bash</code></div><div><br /></div><div>So feel free to remix the Dockerfile to your needs, create your own image for infinium methylation analysis with your favourite packages and rest easy that not only will your reproducibility be better, but installing stuff on a new computer will be a lot less painful.</div><div><br /></div><div>*You may be wondering why I'm using a relatively old bioC version. The answer is that this is the newest one that can be supported on the server I'm using at my uni, which runs Ubuntu 20.04 LTS. I tried newer ones but it complained about missing system libraries that I could not fix.</div></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comGeelong VIC 3220, Australia-38.1492994 144.3598426-66.459533236178842 109.20359260000001 -9.8390655638211513 179.5160926tag:blogger.com,1999:blog-4885455635096396903.post-37446846803080068092022-05-31T23:42:00.001+10:002022-05-31T23:42:20.665+10:00Beeswarm chart for categorical data<p> Biomedical journal articles are full of categorical data, showing data for control and case groups using barplots with whiskers. While these are popular, that type of chart can hide the underlying data patterns and are discouraged by statisticians. There are other alternatives such as boxplots, violin charts and strip charts, but I've become a big fan of beeswarm charts lately. The reason is that beeswarms show the distribution just like violin plots but have the benefit of showing the individual points, which is helpful if sample size varies between categories.</p><p>The way I like to emply beeswarm charts is to first create a boxplot and then overlay the beeswarm. With that approach, the mean and interquartile ranges are shown, along with the actual datapoints. Here's the result.</p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiLSmYXl-j-HCSeJBAacMriyuVPE79aw8UIPsPrZ_kENzYKDNoujl3sBVaemmhat5RyHQ5o_wg7tc9NyrvKcQiK-x3rJeZTELhWKHkwmN_aS_8b40JAJMRPlYp7kC3zZDrnp61uPVkilQv-7cpyHM0w3w_sirMxuYqRVZRu5dOsI3YEN53i2Bw7Oe67xw/s728/Screenshot%20from%202022-05-31%2023-36-17.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="728" data-original-width="691" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiLSmYXl-j-HCSeJBAacMriyuVPE79aw8UIPsPrZ_kENzYKDNoujl3sBVaemmhat5RyHQ5o_wg7tc9NyrvKcQiK-x3rJeZTELhWKHkwmN_aS_8b40JAJMRPlYp7kC3zZDrnp61uPVkilQv-7cpyHM0w3w_sirMxuYqRVZRu5dOsI3YEN53i2Bw7Oe67xw/s320/Screenshot%20from%202022-05-31%2023-36-17.png" width="304" /></a></div><p>Some notes on how to make this chart: First step is to collect the data into a list of vectors, see the example code below for the iris dataset. Then make the boxplot. And finally the beeswarm plot with add=TRUE to overlay the points. Notice that the beeswarm plot uses pch=19 to create filled circles. </p><p>library("beeswarm") # use install.packages("beeswarm") if the package is not yet instaled</p><p><br /></p><p>set <- subset(iris,Species=="setosa")</p><p>ver <- subset(iris,Species=="versicolor")</p><p>vir <- subset(iris,Species=="virginica")</p><p><br /></p><p>l <- list("setosa"=set$Petal.Length,</p><p> "versicolor"=ver$Petal.Length,</p><p> "virginica"=vir$Petal.Length)</p><p><br /></p><p>boxplot(l,col="white",main="petal length", ylab="cm" )</p><p>beeswarm(l,add=TRUE,pch=19)</p><p><br /></p><p>See also: <a href="https://r-charts.com/distribution/ggbeeswarm/">Beeswarm charts in ggplot2</a></p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-55992328400535784232022-04-29T15:20:00.001+10:002022-04-29T15:20:38.375+10:00Mass download from google drive using R<p>Google drive is great for sharing documents and other small files but it's definitely not suited to moving many large files around. For example I just received 170 fastq files that are about 200 MB in size. If you use the browser to download the whole folder, the web app will zip the contents for you which will take a LOOONG time. Alternatively you can download each and every one of those files one by one, which is annoying and prone to human error.</p><p>You can insist to your collaborators to transfer in a different way, but there are not that many user-friendly and economic approaches. Your biologist collaborators probably won't be able to use <a href="https://en.wikipedia.org/wiki/Rsync">rsync</a> to get the data to you safely. And fast convenient tools for moving around large files like <a href="https://www.hightail.com/">Hightail</a> cost a lot of money.</p><p>A good solution to this problem is to use the R package <a href="https://googledrive.tidyverse.org/">googledrive</a> which enables command line automation of tasks that might take a long time manually. The package vignette has a good overview of the main commands but lacks the most important application for me: bulk downloads.</p><p>Let's say you have a folder of data files and you want to download them all. Try the following:</p><p><span style="font-family: courier;">library(googledrive)</span></p><p><span style="font-family: courier;">myfiles <- drive_ls(path="~/myfoldername")</span></p><p><span style="font-family: courier;">sapply(myfiles$id , drive_download)</span></p><p>It will download all those files into the current working directory. The speeds are also significantly faster than in the browser.</p><p>Simple!</p><p><br /></p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-70017364715249730792021-09-09T16:35:00.018+10:002023-07-26T17:31:24.043+10:00Uploading data to GEO - which method is faster?<div>If you have had to upload omics data to GEO before, you'll know it's a bit of a hassle and takes a long time. There are a few methods suggested by the GEO team if you are using the Unix command line:<br /><br /><h3 style="text-align: left;"> Using 'ncftp'</h3><br /></div><code style="background-color: #eff9f4; border-bottom-color: initial; border-bottom-style: initial; border-image: initial; border-left-color: rgb(153, 204, 153); border-left-style: solid; border-right-color: initial; border-right-style: initial; border-top-color: initial; border-top-style: initial; border-width: 0px 0px 0px 3px; color: #226622; display: block; font-family: "Courier New", Courier, monospace; font-size: 13px; font-weight: bold; line-height: 1.5em; margin: 10px 0px 10px 1px; outline: 0px; padding: 5px 5px 5px 10px; vertical-align: baseline;">ncftp<br />set passive on<br />set so-bufsize 33554432<br />open <span class="ftp-url" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov</span><br />cd uploads/your<span class="ftp-folder-name" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">@mail.com_</span>yourfolder<br />put -R Folder_with_submission_files<br /></code><h3 style="text-align: left;">Using 'lftp'</h3><div><br /></div><code style="background-color: #eff9f4; border-bottom-color: initial; border-bottom-style: initial; border-image: initial; border-left-color: rgb(153, 204, 153); border-left-style: solid; border-right-color: initial; border-right-style: initial; border-top-color: initial; border-top-style: initial; border-width: 0px 0px 0px 3px; color: #226622; display: block; font-family: "Courier New", Courier, monospace; font-size: 13px; font-weight: bold; line-height: 1.5em; margin: 10px 0px 10px 1px; outline: 0px; padding: 5px 5px 5px 10px; vertical-align: baseline;">lftp <span class="ftp-url" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov</span><br />cd uploads/<span class="ftp-folder-name" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">your<span class="ftp-folder-name" style="border: 0px; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">@mail.com</span>_</span>yourfolder<br />mirror -R Folder_with_submission_files</code><br /><h3 style="text-align: left;">Using 'sftp' </h3><br />(expect slower transfer speeds since this method encrypts on-the-fly)<br /><p style="border: 0px; color: #222222; font-family: arial, sans-serif; font-size: 13px; line-height: 1.3em; margin: 0px 0px 15px; outline: 0px; padding: 0px; vertical-align: baseline;"><br /></p><code style="background-color: #eff9f4; border-bottom-color: initial; border-bottom-style: initial; border-image: initial; border-left-color: rgb(153, 204, 153); border-left-style: solid; border-right-color: initial; border-right-style: initial; border-top-color: initial; border-top-style: initial; border-width: 0px 0px 0px 3px; color: #226622; display: block; font-family: "Courier New", Courier, monospace; font-size: 13px; font-weight: bold; line-height: 1.5em; margin: 10px 0px 10px 1px; outline: 0px; padding: 5px 5px 5px 10px; vertical-align: baseline;">sftp <span class="ftp-name" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">geoftp</span>@s<span class="ftp-host" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">ftp-private.ncbi.nlm.nih.gov</span><br />password: yourpasscode<br />cd uploads/<span class="ftp-folder-name" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">your<span class="ftp-folder-name" style="border: 0px; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">@mail.com</span>_</span>yourfolder<br />mkdir new_geo_submission<br />cd new_geo_submission<br />put file_name<br /></code><br /><h3 style="text-align: left;">Using 'ncftpput' </h3><br />(transfers from the command-line without entering an interactive shell)<br />Usage example:<br /><br /><code style="background-color: #eff9f4; border-bottom-color: initial; border-bottom-style: initial; border-image: initial; border-left-color: rgb(153, 204, 153); border-left-style: solid; border-right-color: initial; border-right-style: initial; border-top-color: initial; border-top-style: initial; border-width: 0px 0px 0px 3px; color: #226622; display: block; font-family: "Courier New", Courier, monospace; font-size: 13px; font-weight: bold; line-height: 1.5em; margin: 10px 0px 10px 1px; outline: 0px; padding: 5px 5px 5px 10px; vertical-align: baseline;">ncftpput -F -R -z -u <span class="ftp-name" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">geoftp</span> -p "yourpasscode" <span class="ftp-host" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">ftp-private.ncbi.nlm.nih.gov</span> ./uploads/<span class="ftp-folder-name" style="border: 0px; font-family: inherit; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">your<span class="ftp-folder-name" style="border: 0px; font-style: inherit; font-weight: inherit; margin: 0px; outline: 0px; padding: 0px; vertical-align: baseline;">@mail.com</span>_</span>yourfolder ./local_dir_path</code><br /><br />local_dir_path: path to the local submission directory you are transferring to your personalized upload space<br /><br /><div>-F to use passive (PASV) data connection<br />-z is for resuming upload if a file upload gets interrupted<br />-R to recursively upload an entire directory/tree<br /><br /></div><div><br /></div><div><h3 style="text-align: left;">The speed test</h3><br />For those of you uploding seq data to NCBI, you would have noticed that GEO has 3 recommended upload tools from unix servers ncftp, lftp and sftp. There is a big speed difference, at least for us folks based in Australia: <div><ul style="text-align: left;"><li>ncftp 364.7 kB/s</li><li>lftp 11.83 MB/s</li><li>sftp 8.6 MB/s</li></ul></div><br />lftp is the winner - hopefully this will save you a few minutes!</div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-49130770322528048482021-09-08T12:51:00.005+10:002021-09-08T13:01:15.950+10:00Urgent need for minimum standards for reproducible functional enrichment analysis<p><span style="font-family: Arial; font-size: 11pt; white-space: pre-wrap;">So our preprint is online called “<a href="https://www.biorxiv.org/content/10.1101/2021.09.06.459114v1">Guidelines for reliable and reproducible functional enrichment analysis” so I thought I’d give you an overview</a>.</span></p><span id="docs-internal-guid-260ca507-7fff-9dfd-b594-a8ae963f9b84"><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Enrichment analysis is widely used for exploration and interpretation of omics data, but I’ve noticed sloppy work is becoming more common. Over the past few years I’ve been asked to review several manuscripts where the enrichment analysis was poorly conducted and reported. Examples of this include lack of FDR control, incorrect background gene list specification and lack of essential methodological details. In the article we show how common these problems are and what the impact could be on the results.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">In this article we did two screens of articles from PubMed Central. Firstly selected 200 articles with terms in the abstract related to enrichment analysis. These were examined with regards to a checklist around reporting and methodological issues, and were cross-checked for accuracy. As some articles describe >1 analysis, the total number of analyses was 235.</span></p><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgsmAbZ75ROTDtL8YM6F9VXsb2QJS5h8z8j6xpBY4dhcxhkBER6sa7kRZqPVtJhWydr5WcybKRuP4EsLt7yqxXApTIvYE18XBWBbSRSk0jZ7A40ErzGiFXVGnQelZ0uq2SzdPW0lCoYFGii/s1344/journals.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgsmAbZ75ROTDtL8YM6F9VXsb2QJS5h8z8j6xpBY4dhcxhkBER6sa7kRZqPVtJhWydr5WcybKRuP4EsLt7yqxXApTIvYE18XBWBbSRSk0jZ7A40ErzGiFXVGnQelZ0uq2SzdPW0lCoYFGii/s320/journals.png" width="320" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">They did a variety of different types of omics analysis, but the most common were gene expression analysis</span></p><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbURVsB4eyCnQwIZTwkANwvmYGSmese4yBxkWBYzEOvBN1g01_ZraK8qU5RICEhDLWS6wZ_9WlVUzbM1W9pkZc9EWTzsqLSUmPg2jpxJHU6VCAPp11SMsaPbQYcywFwD1BKIxPZXbPmvjD/s1344/omics.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbURVsB4eyCnQwIZTwkANwvmYGSmese4yBxkWBYzEOvBN1g01_ZraK8qU5RICEhDLWS6wZ_9WlVUzbM1W9pkZc9EWTzsqLSUmPg2jpxJHU6VCAPp11SMsaPbQYcywFwD1BKIxPZXbPmvjD/s320/omics.png" width="320" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">we show that these methodological problems are very common. For example out of 235 analyses in our sample, only 119 (53%) specified conducting p-value correction for multiple testing! </span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhPLfIMHWniehGJdMVTfowrn4xZe9Ot1dIR9XmQpkbwHfTwVuF8Oi6k4P-YBzaWEw4OXfZ1HagaM5frEi-8L_Kqcao6sUX-1zLfEovtNLta0EtCL3EFYqlPSqIOVoAKOYXK37HCGtP8zcnR/s1344/fdr.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhPLfIMHWniehGJdMVTfowrn4xZe9Ot1dIR9XmQpkbwHfTwVuF8Oi6k4P-YBzaWEw4OXfZ1HagaM5frEi-8L_Kqcao6sUX-1zLfEovtNLta0EtCL3EFYqlPSqIOVoAKOYXK37HCGtP8zcnR/s320/fdr.png" width="320" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">In terms of tools, over-representation analysis including DAVID, Ingenuity and PANTHER were the most popular, followed by GSEA</span></p><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWTQsy1PhUvaVWErfSnHl5wvNr3ofWhOF8tl9_CE1u-1j_nQKgTksmFAyeSsfyYlHCfd9BAVcR9-DY8wP3DDhwt3Y3BYxvstHoplxVjBItl4td6e74QzzbTLw-9CiyHZYJf-d-Izajtxgy/s1344/tools.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWTQsy1PhUvaVWErfSnHl5wvNr3ofWhOF8tl9_CE1u-1j_nQKgTksmFAyeSsfyYlHCfd9BAVcR9-DY8wP3DDhwt3Y3BYxvstHoplxVjBItl4td6e74QzzbTLw-9CiyHZYJf-d-Izajtxgy/s320/tools.png" width="320" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Over-representation relies on an accurate background gene list, but we found this was correctly described in only 8/197 cases (4.1%)!!</span></p><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg03Z7JS6RgBi0q5hMANAU8nri2yzJstGhcA7JOgHntbn0iRJZw6_nrIcYK8U77t2NIAcjHNzogtvc2gNWymqKQm1hQ_ZJxG_g7pyRjnH9GVONAfh8l-blsSq3IRDggOb125HxL07JZN4Xo/s1344/background.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg03Z7JS6RgBi0q5hMANAU8nri2yzJstGhcA7JOgHntbn0iRJZw6_nrIcYK8U77t2NIAcjHNzogtvc2gNWymqKQm1hQ_ZJxG_g7pyRjnH9GVONAfh8l-blsSq3IRDggOb125HxL07JZN4Xo/s320/background.png" width="320" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">There were 15 analyses that lacked even the most basic methodological detail, like which tool was used to calculate enrichment. The excerpt below is an example basically saying that KEGG pathway analysis was conducted but not stating how it was done. The article linked is the original KEGG paper from 2000 (PMID: 10592173) which doesn't describe any statistical enrichment analysis. </span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><br /></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJhhuRYiXBr9DySSCvy5s2x5ajbh04bIVZyWgu-l93kePE7fAg4-H4Zz5xFy-5_M7WLJkLoMEK4Qvuuvjc0PU677Q3jVqCiGTBgCaolqlnQxLaayO_BWVW9ajmRyWCrdNHJ5Xj2Wc82Uth/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="106" data-original-width="689" height="49" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJhhuRYiXBr9DySSCvy5s2x5ajbh04bIVZyWgu-l93kePE7fAg4-H4Zz5xFy-5_M7WLJkLoMEK4Qvuuvjc0PU677Q3jVqCiGTBgCaolqlnQxLaayO_BWVW9ajmRyWCrdNHJ5Xj2Wc82Uth/" width="320" /></a></div><br /><br /><p></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">After examining another 1395 articles, we looked for trends between rigour/reporting and publication metrics. There wasn’t any association between out analysis scores and metrics like Scimago Journal Rank and number of citations accrued. This s</span><span style="font-family: Arial; font-size: 11pt; white-space: pre-wrap;">hows this isn’t a problem only with lower ranked journals. </span></p><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3k-fflkGo4aBmK5uE7WbWZecpULXAROP_LrCEFqhlkCofh2MT5XoqUPZfA0yCBJJDuT5UhlV7A4fVasfGk4p8HFB13wNd-MWhoOv4F-xn1oSjmy3NhK0_wJwVZL4otsWcZL4KwhAXrOoh/s1152/metrics.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="960" data-original-width="1152" height="267" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj3k-fflkGo4aBmK5uE7WbWZecpULXAROP_LrCEFqhlkCofh2MT5XoqUPZfA0yCBJJDuT5UhlV7A4fVasfGk4p8HFB13wNd-MWhoOv4F-xn1oSjmy3NhK0_wJwVZL4otsWcZL4KwhAXrOoh/s320/metrics.png" width="320" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">This is a massive wake-up call that serious methodological issues are widespread with enrichment analysis.</span></p><div><span><br /></span></div><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">We also conducted some benchmarking analysis to understand how much these errors can impact results. We began by u</span><span style="font-family: Arial; font-size: 11pt; white-space: pre-wrap;">sing differential RNA-seq expression data, we show that when used properly, over-representation (ORA) and GSEA like methods (functional class scoring; FCS) give similar results. In this example we used the clusterProfiler package for ORA and mitch package for FCS; more details in the article.</span></p><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhjuDaLsa0389yHIfieHBpgI9l5_8xNmYzMBtbXSBp4RwEXkVNz8GZTeDoKcQzU3RJ7vZ3_GgHjBF9UA4dYvkhUqRnIPcH2dgdHLdZ-4eAx_99OVPkv0f-q-bjuM1YpgRjYErsU6bKtmT2u/s693/venn1.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="446" data-original-width="693" height="206" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhjuDaLsa0389yHIfieHBpgI9l5_8xNmYzMBtbXSBp4RwEXkVNz8GZTeDoKcQzU3RJ7vZ3_GgHjBF9UA4dYvkhUqRnIPcH2dgdHLdZ-4eAx_99OVPkv0f-q-bjuM1YpgRjYErsU6bKtmT2u/s320/venn1.png" width="320" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">When an inappropriate background gene list is used in this case the whole genome (instead of only the genes detected in the assay),</span><span style="font-family: Arial; font-size: 11pt; white-space: pre-wrap;"> the results are vastly different! Likely these extra sets are false positives.</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQP6xzw2bSsJqnHAbvOZJOB-jKzFde-d-VsRWQFFmvFphVdWanyne0uzY19BtBOLwAQB6IJKtWAcg5Tjg43wKa6cwY9urT1X_o0eXhWjsww3zp7TAxWG99S9o2Y5WRpL6GBsrwVXuVyuf5/s647/venn2.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="500" data-original-width="647" height="247" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQP6xzw2bSsJqnHAbvOZJOB-jKzFde-d-VsRWQFFmvFphVdWanyne0uzY19BtBOLwAQB6IJKtWAcg5Tjg43wKa6cwY9urT1X_o0eXhWjsww3zp7TAxWG99S9o2Y5WRpL6GBsrwVXuVyuf5/s320/venn2.png" width="320" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; white-space: pre-wrap;">We also observed that most over-representation analyses performed enrichment of up and down genes combined into one list. Our own testing shows that this approach has much lower power to discover differentially regulated gene sets as compared to analysing up and down separately. In fact the number of differentially regulated gene sets was down by ~90% using the combined approach.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuWQLm876_eB1I5c51oOjnbA5y07xdzEdPVt0zbv-ET4712bTx9ezj_ZOhHeCyVgaE8OytvE71aNa3xjbJAuf9LhCABlKi5VD1VXyOG0AdJCbEEpCpS48M4HF_pf9gm4vqpbtUZIMAQlhz/s677/venn3.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="473" data-original-width="677" height="224" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhuWQLm876_eB1I5c51oOjnbA5y07xdzEdPVt0zbv-ET4712bTx9ezj_ZOhHeCyVgaE8OytvE71aNa3xjbJAuf9LhCABlKi5VD1VXyOG0AdJCbEEpCpS48M4HF_pf9gm4vqpbtUZIMAQlhz/s320/venn3.png" width="320" /></a></div><br /></span><p></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">To combat these problems we provide a checklist of minimum standards for enrichment analysis.</span></p><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMHjXoknjGIH2ueVHjT0bQzAmJDK8TgJFeXZAlVZLut6HAGVOPC2nORNp5qwyOfRQFvx-TrLbcA8uIqzOkSAH6vkPGqBudSw04UBtZPKnlt8lxAjAVgF67Tab0iWvzgqa2YoCV2Ok3Q53W/s905/guidelines.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="905" data-original-width="773" height="640" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMHjXoknjGIH2ueVHjT0bQzAmJDK8TgJFeXZAlVZLut6HAGVOPC2nORNp5qwyOfRQFvx-TrLbcA8uIqzOkSAH6vkPGqBudSw04UBtZPKnlt8lxAjAVgF67Tab0iWvzgqa2YoCV2Ok3Q53W/w546-h640/guidelines.png" width="546" /></a></div><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; white-space: pre-wrap;">The manuscript also contains some recommendations if you want to take your analysis from these minimum standards up to gold standard.</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; white-space: pre-wrap;"><br /></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; white-space: pre-wrap;">Hopefully this is of use to you. Happy to receive your feedback</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span face="Roboto, sans-serif" style="color: #0f1419; font-size: 11.5pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Big shout out to Sameer Jadaan, Kaushalya Perera and Tanuveer Kaur for massive help in screening these articles. Also big thanks to Nick Wong, Antony Kasi and Anup Shah </span><span face="Roboto, sans-serif" style="color: #0f1419; font-size: 11.5pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">for comments on the draft.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span face="Roboto, sans-serif" style="color: #0f1419; font-size: 11.5pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">If you are interested in exploring the data collected and code used, our R scripts are deposited here: </span><a href="https://github.com/markziemann/SurveyEnrichmentMethods" style="text-decoration-line: none;"><span face="Roboto, sans-serif" style="color: #1155cc; font-size: 11.5pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">https://github.com/markziemann/SurveyEnrichmentMethods</span></a><span face="Roboto, sans-serif" style="color: #0f1419; font-size: 11.5pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> </span></p><div><span face="Roboto, sans-serif" style="color: #0f1419; font-size: 11.5pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><br /></span></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-74194719787834073472021-08-03T13:40:00.006+10:002021-08-03T13:42:40.891+10:00Gene name errors: Redux<p>Our latest article "Gene name errors: Lessons not learned" previously @biorxiv_genomic has just been published in PLoS Computational Biology (<a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008984">link here</a>). In this post I'll walk you through why it is so important to how computational biology is done. If you are a regular GenomeSpot reader you are probably well aware about how Excel mangles gene names. So why the need for a 2021 update? </p><p>Well we thought that the broader genomics community would know about it by now. It has been nearly 20 years since <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-5-80">Zeeberg et al's paper</a> on the issue, and five years since <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1044-7">our article in Genome Biology</a> which got picked up in a <a href="https://www.bbc.com/news/technology-37176926">few media outlets</a> and led to SEPT, MARCH and DEC genes <a href="https://www.nature.com/articles/s41588-020-0669-3">being renamed</a>.</p><p>So with <a href="https://orcid.org/0000-0003-2163-6203">Mandhri Abeysooriya</a> an outstanding Masters student at Deakin University, we set out to see whether gene name errors in supplementary data files was still an issue. We also had massive assistance from <a href="https://orcid.org/0000-0002-8715-6854">Megan Soria</a> and <a href="https://orcid.org/0000-0002-7891-836X">Mary Kasu</a>.</p><p>When designing this new work, we wanted to make it bigger and better than the 2016 study, so we decided to analyse ALL genomics related articles we could get our hands on from NCBI <a href="https://www.ncbi.nlm.nih.gov/pmc/">Pubmed Central</a>. We decided to screen articles in the period 2014 to 2020 because that would highlight whether researcher behaviour and excel use mighthave changed in the period after 2016</p><p>All up we looked for supplementary Excel files in 166,139 PMC articles and 11,117 of these had Excel files with gene lists we could examine. The script we used to perform the error detection was slightly improved so that it also picked up 5-digit numbers (internal date format) as errors, something we overlooked previously. The new code is uploaded to GitHub <a href="https://github.com/markziemann/GeneNameErrors2020">here</a>.</p><p>Of this set of 11117 articles, 3436 were affected by gene name errors which was actually higher than we previously estimated, at approximately 31% of articles with excel gene lists. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh3PQkpQFobmMa5z_C_zxtm_BLydyJlMbs9h505bjcpxMXSAzztzOMub1IH7ndDmrwZVhXyE1vpZT4ZZ34zZsRfyC14cFrwCpo4ewlL8-J7dvbGJ0sURzZ5Edwb7tedf1tsgsDmrbvlI2q3/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="632" data-original-width="2250" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh3PQkpQFobmMa5z_C_zxtm_BLydyJlMbs9h505bjcpxMXSAzztzOMub1IH7ndDmrwZVhXyE1vpZT4ZZ34zZsRfyC14cFrwCpo4ewlL8-J7dvbGJ0sURzZ5Edwb7tedf1tsgsDmrbvlI2q3/w640-h180/image.png" width="640" /></a></div><br />This figure is quite a bit higher than the 2016 article and not what we were expecting at all. We attribute this discrepancy to the inclusion of internal date format errors as well as the differnt sampling approach used. Instead of just 18 major journals last time, here we have articles from 741 journals.<p></p><p>The team opened up each of the spreadsheets to confirm the errors, which took a team of 4 people a few weeks to do. There were a few false positives, but overall the script was working as expected.</p><p>What shocked us was that when looking at how frequently these errors occurred, the high impact journals like <i>Nature</i>, <i>Cell</i> and <i>PNAS</i> were at the top of the list! Clearly these journals didn't get the 2016 memo!</p><p>While we were opening all these spreadsheets, we found a number of edge cases where excel was doing really weird things to some gene names like AGO2, MEI1 and TAMM41. These turned out to be related to the locale settings of the software, recognising these as dates!</p><p>There were other cases where protein names like jun-1 were converted to May 31st, so we're presuming that Excel is interpreting jun-1 as the month of June minus 1. Truly bizzare.</p><p>What these edge cases demonstrate is that simply changing SEPT, MARCH and DEC gene names isn't going to solve the issue because there are problematic gene names in other locates. Also those protein names like jun-1 will need to be fixed too. </p><p>Also, this isn't a problem limited to human and mouse. All eukaryotic kingdoms represented in Ensembl has excel genes, which you can explore in the supplement.</p><p>Yes it is ironic that our article has XLS supplementary files, but understand this is the best was to demonstrate how the errors look. We still recommend researchers use TXT, CSV and TSV formats to share their genomics data. We also share a set of recommendations that researchers can use to avoid gene name errors. (Thanks to the reviewers who recommended this)</p><p>From the evidence we see, it is likely that gene name errors will continue frustrating bioinformaticians well in to the future. Excel is just so engrained in the way science is taught and done that it will take generational change for it to be eradicated. I joke that Excel will keep me employed forever as there's always new error modes being discovered. Just in the few hours after tweeting this Larry Parnell @larry_parnell replied that he saw the Apple spreadsheet "Numbers" converting gene names into currencies! Lord give me strength.</p><div>But this isn't just about gene name errors, it's also about potential errors in formulas and analysis that can occur when scientists use spreadsheets. Research on how spreadsheets indicate that ~1% of formula cells contain errors and that complex spreadsheets are almost certain to contain critical errors; See the <a href="https://sci-hub.se/10.4018/joeuc.2009070102">article by Powell et al (2009)</a>. As far as I know there hasn't been any assessment of the rate of analytical errors in scientific spreadsheets except for the case of gene name errors. We are considering this as a follow up study. </div><p>Until journals start mandating that analytical code be provided for analysis of high throughput data, it's unlikely that Excel will be unseated as the analytical workhorse it is now. </p><p>That's not to say that workflows like Rmarkdown and Jupyter notebooks are perfect; they're not. But at least shared code can be reproduced, and more readily audited.</p><p>Before I go I need to thank Neil Saunders @neilfws - his blog post was the motivation behind the 2016 study and the title of this update. **THANKS!!**</p><p>I would also like to thank the reviewers they really did provide criticism that was constructive and helped the quality of the end product.</p><p>I also set up a bot which scrapes PMC each month for errors, so you can see which journals are the worst offenders, see the reports <a href="http://ziemann-lab.net/public/gene_name_errors/">here</a>.</p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-45725740178930139392021-02-19T22:44:00.001+11:002021-02-19T22:44:38.723+11:00Understanding pathway-level regulation of chromatin marks with the "mitch" Bioconductor package (Epigenetics 2021 Conference Presentation)<p>Presented 18th February 2021</p><div class="separator" style="clear: both; text-align: center;"><iframe allowfullscreen="" class="BLOG_video_class" height="266" src="https://www.youtube.com/embed/pXukrB0PyR8" width="320" youtube-src-id="pXukrB0PyR8"></iframe></div><br /><p><br /></p><h3 style="text-align: left;">Abstract</h3><p>Gene expression is governed by numerous chromatin modifications. Understanding these dynamics is critical to understanding human health and disease, but there are few software options for researchers looking to integrate multi-omics data at the level of pathways. To address this, we developed mitch, an R package for multi-contrast gene set enrichment analysis. It uses a rank-MANOVA statistical approach to identify sets of genes that exhibit joint enrichment across multiple contrasts. In this talk I will demonstrate using mitch and showcase its advanced visualisation features to explore the regulation of signaling and biochemical pathways at the chromatin level.</p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8592043 144.7704046-66.169440184802426 109.61415191779099 -9.5489684151975815 179.92665728220902tag:blogger.com,1999:blog-4885455635096396903.post-6127530490771352902021-02-10T13:44:00.005+11:002021-02-10T15:06:56.088+11:0010 quick tips for genomics data management<p>I get asked a lot about the best ways to store sequence data because the files are massive and researchers have various levels of knowledge of the hardware and software. Here I'll run through some best practices for genomics research data management based on my 10 years of experience in the space.</p><h3 style="text-align: left;">1. Always work on servers, not remote machines or laptops</h3><div>On-prem machines and cloud servers are preferred because you can log into the from anywhere using ssh or other protocol. These machines are better suited to heavy loads and are less likely to breakdown because of the institutional tech support and maintenance. Institutional data transfer speeds will be far superior to your home network. Never do computational work on a laptop. Avoid storing data on your own portable hard drives or flash drives. If you don't have a server, ask for access at your institution or research cloud provider (we use <a href="https://nectar.org.au/research-cloud/">Nectar</a> in Australia).</div><h3 style="text-align: left;">2. Download the data to the place where you will be working on it the most. </h3><p>The raw sequencing data should be downloaded in a project subfolder called "fastq" or similar. </p><p>I recommend using a command line tool because these are better suited to really large files. Your genomics service provider will probably give you an ftp or rsync command to use. Download to the server where you will be working the most.</p><h3 style="text-align: left;">3. Write a project README</h3><div>It should contain an overview of the purpose of the experiment and the sample groups. Include metadata for each file which includes sample descriptions and any comparisons that need to be made.</div><h3 style="text-align: left;">4. Check that the files are complete and not corrupted </h3><p>The service provider will also give you checksums for each file which is like a digital fingerprint. The md5 method is the most used. You should check the checksums on the files you've downloaded. If its one file it can be done like this:</p><p>md5sum mysample.fastq.gz</p><p>If you have lots of checksums in a file called checksums.md5 you can check them on mass with parallel processing (link for more unix 1 liners <a href="http://genomespot.blogspot.com/2013/08/a-selection-of-useful-bash-one-liners.html">here</a>)</p><p>cat checksums.md5 | parallel --pipe -N1 md5sum -c</p><h3 style="text-align: left;">5. Immediately copy data to institutional research data store (RDS)</h3><div>Nearly every university and research institute will have a data store where you can dump the raw data and metadata (sample descriptions). It is really important to do this in the case of your server disks failing. It also protects you against things like natural disasters because RDS themselves have geographically independent redundancy built in. For example RDS data is snapshot regularly and stored at other locations. You will need to repeat the md5sum checks for this RDS copy. From time to time you should also check that the data in the RDS is recoverable, so put an annual reminder into your diary.</div><div><h3>6. Do not rely on the service provider to keep a copy for you</h3></div><div>This includes people who are keeping their data on Illumina basespace. You still need to follow #5 above. Genomics service providers will normally delete the data after a few months.</div><div><h3>7. Do your preliminary analysis carefully</h3></div><div>Once downloaded and checked, it is a good idea to set the files to read only mode using the immutable flag so they won't accidentally be modified/destroyed by you or someone else.</div><div><br /></div><div>chattr +i foo/bar.fastq.gz</div><div>More info <a href="https://unix.stackexchange.com/questions/67508/how-do-i-make-a-file-not-modifiable">here</a>.</div><div><br /></div><div>Run a fastq file validator (eg: https://genome.sph.umich.edu/wiki/FastQValidator) to check the integrity of the file including whether each file is complete and pairs have same number of reads.</div><div>Run fastqc and multiqc to assess the overall quality of the data.</div><div><div><h3>8. Consider uploading to SRA/GEO early</h3></div></div><div>One you've done an analysis of the quality of the dataset and it represents a complete experiment, it is a good idea to upload it to an archiving repository. My work is mostly epigenomics and RNA-seq so I submit to NCBI GEO. If you are doing other applications such as genome sequencing, you could submit to NCBI SRA. You can keep it private for 12 months or however long you expect that it will need to be embargoed. The good thing about this is that in case there is some future disaster with the lab group, institution or yourself, the data will at least be available in future.</div><div><br /></div><div>The added benefit of SRA/GEO upload is that you can then delete the raw files from your working server to make space for new projects. </div><h3 style="text-align: left;">9. Formalise your data management plan in writing</h3><div>Draft an <a href="https://en.wikipedia.org/wiki/Standard_operating_procedure">SOP</a> and discuss it with your lab group. Share it with your project collaborators so they are aware of how the data is managed. Show your lab head and other members how to recover the data. </div><h3 style="text-align: left;">10. Consider file formats carefully</h3><div>The standard for genomics is compressed fastq, which will have a .fastq.gz or .fq.gz suffix. If your files are not compressed, use gzip or pigz to compress them, especally before long term storage. While there are alernative compression tools available, the benefit is not much. An alternative storage format is BAM or CRAM format which is most useful for large scale genome resequencing data, like 1000 Genomes. Whatever you do, make sure that the format is something that will exist in the future.</div><div><br /></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8592043 144.7704046-66.169438136178854 109.6141546 -9.5489704638211563 179.9266546tag:blogger.com,1999:blog-4885455635096396903.post-73001899977168290462021-01-16T21:52:00.002+11:002021-01-18T12:02:39.789+11:00DEE2 projects on demand<p>We have noted that the time between new datasets appearing on SRA and being processed by DEE2 has been about 3 to 6 months. Our dream is to shrink this down to two weeks, but we simply do not have access to that much compute power at the moment. To address this we have devised an "on-demand" feature so that you can request certain datasets to be processed rapidly. We think this is a great feature because it serves the main mission of the DEE2 project which is to make all RNA-seq data freely available to everyone. </p><h4 style="text-align: left;">Here's how to use it: </h4><p>1. Visit <a href="http://dee2.io/request.html">http://dee2.io/request.html</a> and you will be greeted with a webform. Select the organism of interest.</p><p>2. Provide the SRA project accession number of the dataset. These numbers begin in SRP/ERP/DRP. If you have a different type of accession such as GEO Series (GSE) or Bioproject (PRJNA) then you will need to navigate <a href="https://www.ncbi.nlm.nih.gov/">NCBI</a> to find the SRP number. </p><p>3. Check that the SRP number is in the standard DEE2 queue. To do that, follow the link provided above the request web form, click on the queue that corresponds to the organism of interest and use ctrl+f to search the text file.</p><p>4. If it's there, return to the webform and paste or type the SRP accession into the first text box. Give your email address as well if you want to be notified that processing has completed.</p><p>5. You will receive an email when processing is complete. The email will indicate whether any errors were encountered. There will be a link to a folder of recently requested datasets <a href="http://dee2.io/requests/">here</a>. </p><p>6. Download and enjoy the dataset as you would any DEE2 data.</p><p></p><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEidpvF-rCkv65HDGtTtYwRkDlmiJdryEjD8wAcnbe7M2nGeTIHkvX9Hy5m-Na_-9wXY8-0P_SGGeu-TJ9bKwIKrr5_JHOc1Pub3GFwrGMnnqFiu7j0zQE8UjFLNU6A46bksU49v5AV3g-5s/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="952" data-original-width="844" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEidpvF-rCkv65HDGtTtYwRkDlmiJdryEjD8wAcnbe7M2nGeTIHkvX9Hy5m-Na_-9wXY8-0P_SGGeu-TJ9bKwIKrr5_JHOc1Pub3GFwrGMnnqFiu7j0zQE8UjFLNU6A46bksU49v5AV3g-5s/" width="213" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div></div><h4 style="text-align: left;">Video walkthrough</h4><div>Check out this video guide to using the new feature!</div><div><br /></div><div class="separator" style="clear: both; text-align: center;"><iframe allowfullscreen="" class="BLOG_video_class" height="266" src="https://www.youtube.com/embed/O4B54LhWqNU" width="320" youtube-src-id="O4B54LhWqNU"></iframe></div><br /><div><br /></div><h4 style="text-align: left;">Boring technical details</h4><p></p><p>Here how it works:</p><p>* There is a html webform which executes a cgi script and writes to a file on the webserver that stores the request. Each request consists of a species, SRP number and optional email address.</p><p>* Every 10 minutes, a remote computer runs a script which downloads the request file and checks to see if there are any new requests. If there are, it processes all runs in the SRA project with the docker pipeline. </p><p>* After processing, the data files are copied out of the docker container, tabulated into matrix format for downstream analysis, zipped and uploaded to the webserver.</p><p>* Every 5 minutes, the webserver runs a separate script which checks whether requested datasets have been completed and present in the <a href="http://dee2.io/requests/">folder</a>. If it is, then an automated email is sent to the user to notify the data is ready.</p><p>This efficient approach ensures that users can rapidly access new and interesting datasets, and that these data are incorporated into the monthly updates. If this becomes popular then there is definitely scope to increase the available compute power.</p><p>I would love to hear your feedback and suggestions.</p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-13976825891992833722021-01-07T10:12:00.004+11:002021-01-07T10:14:21.891+11:00Effect of COVID-19 on genomics publications in 2020<p>COVID-19 was and remains a major crisis in many countries, disrupting general life as well as scientific research. But how has it impacted scientific output in genomics?</p><p>To evaluate this I investigated the number of papers published in <a href="https://www.ncbi.nlm.nih.gov/pmc/">PubMed Central</a> (PMC) in the period from 2016 through 2020. I used total number of papers as well as those matching the genomics search term with the approach below:</p><p>(genom*[Abstract]) AND ("2020"[Publication Date] : "2020"[Publication Date]) </p><p><Note: the number of papers is only a lagging proxy measure of aggregate activity in a field, it does not relate to scientific quality></p><p>Here are the number of papers and genomics papers published annually over this period.</p><p style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi9IWVqvddwy-FAsAnjY0uqw2UrY9qBLONSqMSxI8DJK5IBGdMl4V4qc4rDnlz1wC9Lds1_oL9C4kxQaQEHVtiMo2jT81BoQoUMBtkI6o5xvuz13UdIdtly9ywT7fqbRO2cwcHMnKhnMnOG/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="102" data-original-width="256" height="127" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi9IWVqvddwy-FAsAnjY0uqw2UrY9qBLONSqMSxI8DJK5IBGdMl4V4qc4rDnlz1wC9Lds1_oL9C4kxQaQEHVtiMo2jT81BoQoUMBtkI6o5xvuz13UdIdtly9ywT7fqbRO2cwcHMnKhnMnOG/" width="320" /></a></p><p style="text-align: center;">What you can see is that genomics experienced a major fall in number of papers appearing in PMC in 2020 while total papers did not.</p><p style="-webkit-text-stroke-width: 0px; color: black; font-family: "Times New Roman"; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhdKvje4St-_nssb1D9vHORVL0-vCthIfJx7mPNEUOd5wM2K__BpSV7ZdXxaDW7l8UirbNoQqmGKdJHZgUROpAFPel67GcYWjAN4T3K9CVOJNCOWFoyEVAxNYzTwtqAVU1RmyCXTcSRacE/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="340" data-original-width="605" height="225" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhdKvje4St-_nssb1D9vHORVL0-vCthIfJx7mPNEUOd5wM2K__BpSV7ZdXxaDW7l8UirbNoQqmGKdJHZgUROpAFPel67GcYWjAN4T3K9CVOJNCOWFoyEVAxNYzTwtqAVU1RmyCXTcSRacE/w400-h225/pmc2.png" width="400" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_vXBaflj-T-tM7iGTPEAjcpgDH38ILQP1V15JfAxAsvdfu-BNsAcpqBvgEy9rqofvA2krNMGYQ4o_Ayd0Fu5QShz_kIKBvLGm4Gg979bhluBUoGRE9watbI9zSvU42FXY4TtCgVKKO6Ah/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="340" data-original-width="605" height="225" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_vXBaflj-T-tM7iGTPEAjcpgDH38ILQP1V15JfAxAsvdfu-BNsAcpqBvgEy9rqofvA2krNMGYQ4o_Ayd0Fu5QShz_kIKBvLGm4Gg979bhluBUoGRE9watbI9zSvU42FXY4TtCgVKKO6Ah/w400-h225/pmc1.png" width="400" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><br /></div></div>Indeed 2020 was the only year since 2000 that the number of published genomics papers has actually gone down compared to the previous year<p></p><p style="-webkit-text-stroke-width: 0px; color: black; font-family: "Times New Roman"; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; orphans: 2; text-align: left; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><span style="font-weight: 400;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTSe1Mb4THxcInN1t0dljTJpGOzx8-fTKI6m1u_er8TVaxAK8eRL3OKCrCV9yTrC-epibl6I7xcpU2BjBvForNlqRCbbM7ivit31RVWpHnyNIWl-Z3OrscDpQWm5fHX3X2J4tx7OAF0FJF/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="340" data-original-width="605" height="180" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjTSe1Mb4THxcInN1t0dljTJpGOzx8-fTKI6m1u_er8TVaxAK8eRL3OKCrCV9yTrC-epibl6I7xcpU2BjBvForNlqRCbbM7ivit31RVWpHnyNIWl-Z3OrscDpQWm5fHX3X2J4tx7OAF0FJF/" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div>There are a lot of ways to predict what amount of output was lost in the genomics field in 2020. We might have expected growth something like the mean year-on-year growth over the past 5 years (7.5%) meaning we should have expected the number of papers to be ~30550. But instead it was <b>-3.6% lower than the year before</b>. This would suggest that the pandemic (and maybe other factors too like funding) have resulted in drop of 11% of scientific output in the field of genomics. <div><br /></div><div>On the other hand, growth in PMC in 2020 was on par with previous years.<p style="-webkit-text-stroke-width: 0px;">Yes, there are still a few 2020 papers yet to be included in the PMC database but I expect it's only a few hundred and won't impact these findings.</p><p style="-webkit-text-stroke-width: 0px;">Clearly the pandemic has impacted different fields of study with varying severity, and genomics has taken a big hit. It would be interesting to contrast genomics to other fields like immunology. Looking at smaller timescles would also be interesting.</p><p style="-webkit-text-stroke-width: 0px;">Let me know your thoughts in the comments below.</p></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8592043 144.7704046-66.169440184802426 109.61415191779099 -9.5489684151975815 179.92665728220902tag:blogger.com,1999:blog-4885455635096396903.post-63039598015540781382020-10-28T12:12:00.002+11:002021-01-05T16:24:16.750+11:00DEE2 project update October 2020<p>Bit of a thread for some updates to the DEE2 data set. It's a resource of uniformly processed RNA-seq data free to use under a permissive GPL3 licence. Find it at http://dee2.io</p><p style="text-align: left;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj4R1fgxqRIm-2soCxkKqcik3JFLxxCUPPn-cGqHC1hUsXusMlvZdxXT4ksShPdDE9Uxa_ROetibRansDVzwktv7zLMNkLa9KzTheGQHL8Z6Cil_QFY2skTQFNPk0NwKMxpjwGubcpMvZcc/s600/dee_datasets+%25283%2529.png" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="600" data-original-width="600" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj4R1fgxqRIm-2soCxkKqcik3JFLxxCUPPn-cGqHC1hUsXusMlvZdxXT4ksShPdDE9Uxa_ROetibRansDVzwktv7zLMNkLa9KzTheGQHL8Z6Cil_QFY2skTQFNPk0NwKMxpjwGubcpMvZcc/s320/dee_datasets+%25283%2529.png" /></a><br /></p><p>Yesterday a batch of 117k human runs were uploaded. This brings the total number of runs to 1,298,581. To my knowledge this is the largest such data set in the world. This is 10x larger than our first release in 2015! (125k runs)</p><p>The number of SRA projects with completed data analysis bundles is 32692 </p><p>Accesible here: http://dee2.io/huge/</p><p><br /></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwZu1lOyLjFWAfwFLQkhpFXdP4ZeWsn6LREvam-yjcZeSfkSOW7ZY8YPVrfEhvXOEvgpe3wvPQFcHKnSV2QnDI0E2kMZvNzv_yjn91TFLKijE0JlmcBRYwUVzuWXbpZ8LWhiXjSJpq8Y3j/s660/Screenshot+from+2020-10-28+11-55-15.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="487" data-original-width="660" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwZu1lOyLjFWAfwFLQkhpFXdP4ZeWsn6LREvam-yjcZeSfkSOW7ZY8YPVrfEhvXOEvgpe3wvPQFcHKnSV2QnDI0E2kMZvNzv_yjn91TFLKijE0JlmcBRYwUVzuWXbpZ8LWhiXjSJpq8Y3j/s320/Screenshot+from+2020-10-28+11-55-15.png" width="320" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><p>The getDEE2 package is the recommended way to access this data if you are familiar with R. You can access individual runs or enrire data bundles with the various functions. The pkg is part of the latest BioC release out today https://www.bioconductor.org/packages/devel/bioc/html/getDEE2.html</p><p>The button for redirecting DEE2 data directly to Degust is broken and we are looking for a fix. For now you will need to download the data to the disk and upload to the Degust webpage: https://degust.erc.monash.edu/</p><p>Search capability has always been a bit underwhelming with the DEE2 web interface so we are working on a more modern approach to this in the next couple of months.</p><p>We always hoped that DEE2 data could be used to generate canonical gene signatures. We have completed a pilot study with budding three undergrad biocurators who focused on generating gene sets related to our research focus: cardiovascular disease, diabetes, epilepsy and COVID-19. http://dee2.io/genesets.html</p><p>The time between SRA deposition and DEE2 has been something like 3-6 months. Too long! We will be focusing on bringing this down to about 1 month. If we receive an additional HPC grant we will be able to shrink this to a few weeks. If that grant is funded we will also be in a good place to expand DEE2 to encompass a number of other species including some crops like wheat, maize and tomato, as well as animals such as cattle, chicken and macaques.</p><p>Further ahead we are aware that the new mouse genome assembly GRCm39 will be annotated and part of the Ensembl release soon so we will be looking for additional compute to reproces all the mouse data on the new assembly in the next 12 months.</p><p>Although the dataset is GPL3, if you use the dataset for financial gain, you should consider sponsoring the project. It will help the long term viability, expansion and new features and improvements.</p><p>I'd like to thank a few people:</p><p></p><ul style="text-align: left;"><li>Antony Kaspi at WEHI for his work on the project</li><li>Undergrad biocurators Aaron Kovacs, Chelsia Sritharan and Haris Lekovic</li><li>Computational partners at MASSIVE, Deakin eResearch and Nectar/ARDC</li><li>Users for your suggestions and encouragement</li></ul><p></p>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-65395657015462251332020-10-06T17:06:00.003+11:002020-10-06T21:29:07.025+11:00EdgeR or DESeq2? Comparing the performance of differential expression tools<p>It seems like this discussion comes up a lot. Choosing a differential expression (DE) tool could change the results and conclusions of studies. How much depends on the strength of the data. Indeed this has been coveres by others already (<a href="https://liorpachter.wordpress.com/2017/08/02/how-not-to-perform-a-differential-expression-analysis-or-science/">here</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/27022035/">here</a>).</p><p>In this post I will compare these tools for a particular dataset that highlights the different ways these algorithms perform. So you can try it out at home, I've uploaded the code for this to GitHub <a href="https://github.com/markziemann/de_compare">here</a>. To get it to work, clone the repository and open the Rmarkdown (.Rmd) file in Rstudio. You will need to install Bioconductor packages <a href="http://www.bioconductor.org/packages/release/bioc/html/edgeR.html">edgeR</a>, <a href="http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html">DESeq2</a> and the CRAN package <a href="https://cran.r-project.org/web/packages/eulerr/index.html">eulerr</a> (for the nifty venn diagrams). Once that's done you can start working with the Rmd file. For an introduction to Rmd read on <a href="https://rmarkdown.rstudio.com/articles_intro.html">here</a>. Suffice to say that it's the most convenient way to generate a data report that includes code, results and descriptive text for background information, interpretation, references and the rest. To create the report in Rstudio, click the "knit" button and it will execute the code and you'll get a HTML report. If you're working in the R console, for example if you are connecting to a server via SSH, then you can generate the report using the command rmarkdown::render("myreport.Rmd") and you will get the output HTML file.</p><p>In case you're impatient and just want to see the result for yourself, I've hosted my output report at this <a href="http://118.138.234.73/public/blog/deg_compare.html">link</a>. For everyone else, here's the walkthrough. </p><h3 style="text-align: left;">Multidimensional scaling analysis</h3><p>Always do an MDS because it will give you an idea of the intra and inter group differences. As you can see here, there is a good measure of both that distinguishes this n=3 experiment. (The counts were derived from an ATAC-seq experiment).</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh6zeised6FbRjAuEiFypZU8XrQ8z6v7EY8BnlECFKNRnAJ0_7qVMqpzJW-HtTF_MHB8Hwyj1vM_WwwM2efV-F0WKy_wUpzJhZ9Q0DgokTg2L8SCShnrL1r6l09_B5VuL7gicBvxReYdIXL/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1344" data-original-width="1344" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh6zeised6FbRjAuEiFypZU8XrQ8z6v7EY8BnlECFKNRnAJ0_7qVMqpzJW-HtTF_MHB8Hwyj1vM_WwwM2efV-F0WKy_wUpzJhZ9Q0DgokTg2L8SCShnrL1r6l09_B5VuL7gicBvxReYdIXL/" width="240" /></a></div><div><h3>DE analysis</h3></div><div>Here is the summary of the results with the different tools:</div><div><br /></div><div>edgeR (LRT) Up: 297 Down:589</div><div>edgeR (QL) Up:15 Down:204</div><div>DESeq2 Up:3 Down:113</div><div>Voom Up:100 Down:128</div><div><br />You can see the massive difference between them. In particular between edgeR LRT and DESeq. Let's take a look. The Euler package venn diagrams are quite easy to make and the areas of the shaded parts are proportional which is a nice touch.<br /><p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgSA1ycD73dbrmZdoFIcwydG0MjcRxcJpZ3k6N_CFSHhs63a_OvoFSlicZTjmnS5x3qq5FR_gFtDBwnOrj-1bm0dekjf82Yyt2PKUTdbuymdivJv9PeD34RxcX6qAd9wTgZXWCMrzxN3y9W/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgSA1ycD73dbrmZdoFIcwydG0MjcRxcJpZ3k6N_CFSHhs63a_OvoFSlicZTjmnS5x3qq5FR_gFtDBwnOrj-1bm0dekjf82Yyt2PKUTdbuymdivJv9PeD34RxcX6qAd9wTgZXWCMrzxN3y9W/" width="320" /></a></div><p>I selected 9 genes that were significant with edgeR that were discordant with DESeq2 and created barcharts. Gene9565 in demonstrates that the algorithms deviate in how they treat outliers. </p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhNxBO2RP7Qg45lOzy8CZNERFJAo_cBdMCxPZ8RYtkiIIVZI6TTvDaMQHzKYZpSn7x8RJYB-9CMt_QwDOKpHBcsOX07tTLrcnBev9vuBqROue_wE-_d3DMcjrz-sQeZX8KJN3XDNcSItsI6/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="746" data-original-width="757" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhNxBO2RP7Qg45lOzy8CZNERFJAo_cBdMCxPZ8RYtkiIIVZI6TTvDaMQHzKYZpSn7x8RJYB-9CMt_QwDOKpHBcsOX07tTLrcnBev9vuBqROue_wE-_d3DMcjrz-sQeZX8KJN3XDNcSItsI6/" width="244" /></a></div><p>Here are the stats:</p><p><b>EdgeR</b></p><p> logFC logCPM LR PValue FDR dispersion</p><p>Gene9565 -0.7916072 2.291451 11.07955 0.0008728513 0.04036433 0.064547</p><p> C1 C2 C3 T1 T2 T3</p><p>Gene9565 21.96188 14.17148 40.87624 24.08976 24.94944 20.96064</p><p><br /></p><p><b>DESeq2</b></p><p> baseMean log2FoldChange lfcSE stat pvalue padj C1</p><p>Gene9565 24.7313 -0.8373232 0.3698349 -2.264046 0.0235713 NA 7.203133</p><p> C2 C3 T1 T2 T3</p><p>Gene9565 7.148011 7.06639 6.875975 6.935422 6.907739</p><div><br /></div><p>The T2 sample is clearly a bit of an outlier here but it is being marked as statistically significant by edgeR and NA by DESeq2, because of the <a href="https://support.bioconductor.org/p/75103/">outlier detection</a>. </p><p>The next gene I want to highlight is Gene28045. </p><p><b>EdgeR</b></p><p> logFC logCPM LR PValue FDR dispersion</p><p>Gene28045 -0.3876685 5.166385 13.8639 0.0001965379 0.01514142 0.00657676</p><p> C1 C2 C3 T1 T2 T3</p><p>Gene28045 148.1977 95.62844 275.8308 215.6399 223.3354 187.6296</p><p><br /></p><p><b>DESeq2</b></p><p> baseMean log2FoldChange lfcSE stat pvalue padj</p><p>Gene28045 183.5495 -0.4058583 0.1610077 -2.520739 0.01171087 0.4080693</p><p> C1 C2 C3 T1 T2 T3</p><p>Gene28045 8.468181 8.443831 8.337265 8.230507 8.135978 8.113126</p><p><br /></p><p>Clearly this is a problem because there's no way that these genes could be validated with qPCR and t-test and get a p-value anywhere near 0.05. Therefore I'm happy to recommend people not use edgeR LRT anymore and go with the more conservative DESeq2.</p><h3 style="text-align: left;">What about the other tools?</h3><div>Based on twitter discussions I also assessed edger with the QL test, which gave results more similar to DESeq.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUFKBGLOM9JTL9Vg7mOBqACW5ZCVZqQ2-eJLPi-Q4VY5jZrmjAredTfvOLniJTlQh-NAxBGq6MomobjmEO21UWkZcEiroqPeKaQmO1YQSh6ys_js3UYRYvILxg7WgARpEao9Pf341F4vAY/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUFKBGLOM9JTL9Vg7mOBqACW5ZCVZqQ2-eJLPi-Q4VY5jZrmjAredTfvOLniJTlQh-NAxBGq6MomobjmEO21UWkZcEiroqPeKaQmO1YQSh6ys_js3UYRYvILxg7WgARpEao9Pf341F4vAY/" width="320" /></a></div>Voom-limma as well</div><div><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhXgkN32T60j3VyV9ezO7OcZiiEDyhVq2VLj_rKIcue-XSbTQgJWN2iKQffG52qJDXv23Jl7C8kdblQVHj1actRLGwCt5Hnz9PR9B8uJJyuCihik45sMbhZIqlRJYW6Wl7dJqPU7LD9nO2r/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhXgkN32T60j3VyV9ezO7OcZiiEDyhVq2VLj_rKIcue-XSbTQgJWN2iKQffG52qJDXv23Jl7C8kdblQVHj1actRLGwCt5Hnz9PR9B8uJJyuCihik45sMbhZIqlRJYW6Wl7dJqPU7LD9nO2r/" width="320" /></a></div>One thing I'm curious about is how the results from DESeq2 are biased with very few upregulated genes, which makes sense because this is a knock-down experiment. However with Voom-Limma, there is roughly equal number of up and downregulated genes.<br /><br /></div><div>UpSet plots can be useful to visualise commonlalities between three or more lists. Let's see how it looks for the 4 methods above. Firstly for the upregulated genes:</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguG7dy7v-m1kzvYREa6LMEiSfAnPbJQJPRHoeTZOUA59ZOR6HLSdees92Yki8aUF11UTaFtnzRhPaFr2fTWUBHoiMQuIY4JwPGJbgx1sSYCjIL9_CiZjKb_H6HzO_D3GqevSEVAT6zMDVu/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEguG7dy7v-m1kzvYREa6LMEiSfAnPbJQJPRHoeTZOUA59ZOR6HLSdees92Yki8aUF11UTaFtnzRhPaFr2fTWUBHoiMQuIY4JwPGJbgx1sSYCjIL9_CiZjKb_H6HzO_D3GqevSEVAT6zMDVu/" width="320" /></a></div><div class="separator" style="clear: both; text-align: center;"><br /></div>Then for the downregulated genes:</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRXtvfl4E6fpm9fs3RQ2mCsDmtUmSgFBARAk4W348KuBV6n_g40pvHjU4iWf0MeKkNp4WseclgSjhU7C40cS3fZ5pcfoCPk4KBDgCXHp6rSTJCkS2i4BEnkiHcgLXP_2Q_x4B8rWC1OBrW/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="960" data-original-width="1344" height="229" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhRXtvfl4E6fpm9fs3RQ2mCsDmtUmSgFBARAk4W348KuBV6n_g40pvHjU4iWf0MeKkNp4WseclgSjhU7C40cS3fZ5pcfoCPk4KBDgCXHp6rSTJCkS2i4BEnkiHcgLXP_2Q_x4B8rWC1OBrW/" width="320" /></a></div><br /><br /></div><div><br /><br /></div><p></p></div>Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comtag:blogger.com,1999:blog-4885455635096396903.post-72309415216612430102020-06-24T00:48:00.002+10:002020-06-24T00:48:17.332+10:00Installing R-4.0 on Ubuntu 18.04 painlesslyThere are some great instructions <a href="https://www.digitalocean.com/community/tutorials/how-to-install-r-on-ubuntu-18-04-quickstart">here</a> but there are a few gotchas to be aware of. Follow these instructions first, and it will make the process a LOT easier:<br />
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1. Make sure you have these dependancies installed. If you don't, you're going to have trouble installing any R packages like R curl, tidyverse, devtools, etc<br />
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sudo apt install libssl-dev libcurl4-openssl-dev libxml2-dev<br />
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2. Remove any existing R install.<br />
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sudo apt remove r-base* --purge<br />
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3. Remove any remaining packages. It's better to install them "fresh". They will be in locations like /home/user/R/R-3.6.6 and /usr/lib/R/library/<br />
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4. Remove any existing entry for R in the etc/apt/sources.list to install an older R version.<br />
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sudo nano /etc/apt/sources.list<br />
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For example you might have something like this:<br />
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deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/<br />
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5. Then follow the DigitalOcean instructions <a href="https://www.digitalocean.com/community/tutorials/how-to-install-r-on-ubuntu-18-04-quickstart">here</a> and you should be set up in just a few minutes.</div>
Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8592043 144.7704046-37.8842783 144.7300641 -37.834130300000005 144.81074510000002tag:blogger.com,1999:blog-4885455635096396903.post-91527712909700364632020-05-22T17:16:00.001+10:002020-06-30T15:15:23.648+10:00Mitch: An R package for Multi-Contrast Gene Set Enrichment AnalysisGene set enrichment is one of the key methods in the understanding of gene expression patterns. As omics are becoming more widely used, large experiments are common and include not just one contrast (control vs case), but perhaps many contrasts (eg: healthy, disease, treatment 1, treatment 2, etc). Previously, as part of an epigenetics/epigenomics lab we were looking at ways to integrate different types of omics profiling such as RNA expression, histone acetylation and DNA methylation.<br />
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We stumbled across a paper that proposed the use of multivariate ANOVA on ranks (<a href="https://pubmed.ncbi.nlm.nih.gov/23176165/">Cox & Mann 2012</a>) to identify gene sets that exhibited enrichment in one or more contrasts. We thought this could be a simple way to identify gene set enrichments when looking at multiple contrasts without the need to run GSEA multiple times and summarise the results. The tool described in Cox & Mann (2012) is written for the Perseus suite which works for Windows. We saw there was no similar implementation in R so Antony Kaspi (now based at WEHI) wrote the first draft of the R implementation. We saw the general applicability of the tools, packaged it up and gave it a new name, <b>mitch</b>; a mashup of <u>m</u>ulti enr<u>ich</u>ment.<br />
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This tools was influential in several studies. In one of those, we were interested in whether the drug valproic acid (VPA) could be used to supress the negative effects of high glucose exposure on liver cells (<a href="https://www.biorxiv.org/content/10.1101/253591v1.abstract">Felisbino et al, 2018</a>). In that study we grew cells in low and high glucose condition and with and without VPA in triplicate. We ran edgeR to identify (i) the genes altered by high glucose and (ii) the genes altered by VPA in the high glucose condition; then we supplied the edgeR tables along with some Reactome gene sets and found something very interesting. Firstly clotting and complement cascade genes were dramatically upregulated in cells exposed to high glucose, a finding that is important in the cardiovascular disease risk of diabetics, and secondly that VPA attenuated the expression of these pathways. Sure, we could have used GSEA to identify this trend as well, but the power of mitch is the ability to also generate these landscape charts which beautifully show the enrichment in two dimensions (Fig 1E below) as compared to all expressed genes (background shown in Fig 1D).<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img alt="" height="235" 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" 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<tr><td class="tr-caption" style="font-size: 12.8px;">Fig 1. Application of mitch to understand effects of hyperglycemia (HG) and valproic acid (VPA) on gene expression.<br />
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</td></tr>
</tbody></table>
This multi-contrast gene set enrichment approach is well suited to a few different applications:<br />
<br />
<ul>
<li>Multi-contrast omics experiments, like the 2D RNA-seq example above</li>
<li>Multi-omics case-control experiments like RNA, DNA methylation, histone, proteomics, etc</li>
<li>Multi-sample case-control single-cell RNA sequencing experiments after pseudobulk summarisation with Muscat (<a href="https://www.biorxiv.org/content/10.1101/713412v2">Crowell et al, 2019</a>)</li>
<li>Comparison of different upstream bioinformatics tools - for example, testing whether edgeR and DESeq2 give different pathway-level results</li>
<li>Exploring the effect of covariates in a clinical study - for example, visualising the pathway-level effects of age, smoking, diet in a case-control study looking at gene expression in cardiovascular disease</li>
</ul>
To make the package more useful generally, we wrote additional functions that assist in incorporating mitch into existing R-based differential expression workflows (Fig 2).<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5AYdTy7kaP6j9rk4tGfIE1Xqa89QHxcj-U1ADUlawYv7R7mSieskXD_DC7WVLMKzGNQx1VAGIkwkJ6D2QQX8U4Yo3QZei9i-YczaCXddoYCydf_3Nyo-smkrm0nZrgvc3fWAskHww2vBW/s1600/Fig1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="785" data-original-width="1600" height="312" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg5AYdTy7kaP6j9rk4tGfIE1Xqa89QHxcj-U1ADUlawYv7R7mSieskXD_DC7WVLMKzGNQx1VAGIkwkJ6D2QQX8U4Yo3QZei9i-YczaCXddoYCydf_3Nyo-smkrm0nZrgvc3fWAskHww2vBW/s640/Fig1.png" width="640" /></a></div>
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<tr><td class="tr-caption" style="font-size: 12.8px;">Fig 2. Overview of the mitch workflow.The mitch package consists of five functions (left). Example minimal workflow (right)</td></tr>
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<ul>
<li>A function to import and generate ranked profiles from limma, edgeR, DESeq2, Muscat and several other tools. </li>
<li>A function to import gene sets in the GMT format (for example from MSigDB). </li>
<li>A function to calculate the enrichment</li>
<li>A function to create a HTML report using rmarkdown which contains these pretty charts and a couple of interactive ones too</li>
<li>A function to create charts in PDF. High-res for publications.</li>
</ul>
<br />
Mitch was accepted and <a href="https://bioconductor.org/packages/mitch">available</a> in Bioconductor 3.11 release (<a href="https://github.com/markziemann/mitch">Ziemann & Kaspi 2020</a>). We have a journal paper at BMC Genomics demonstrating the features and accuracy of mitch (<a href="https://doi.org/10.1186/s12864-020-06856-9">link</a>). Any bugs or suggestions can be reported at the GitHub repo (<a href="https://github.com/markziemann/mitch">https://github.com/markziemann/mitch</a>).<br />
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<tr><td class="tr-caption" style="text-align: center;">Obligatory hex-logo. </td></tr>
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<br />
<b>References</b><br />
<br />
Cox J, Mann M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics. 2012;13 Suppl 16(Suppl 16):S12. doi:10.1186/1471-2105-13-S16-S12<br />
<br />
Felisbino MB, Ziemann M, Khurana I, de Oliveira CBM, Mello MLS, El-Osta A. Valproic acid attenuates hyperglycemia-induced complement and coagulation cascade gene expression. bioRxiv 253591; doi: https://doi.org/10.1101/253591<br />
<br />
Crowell HL, Soneson C, Germain PL, Calini D, Collin L, Raposo C, Malhotra D, Robinson MD. On the discovery of subpopulation-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data. bioRxiv 713412; doi: https://doi.org/10.1101/713412<br />
<br />
Ziemann M, Kaspi A (2020). mitch: Multi-Contrast Gene Set Enrichment Analysis. R package version 1.0.2, https://github.com/markziemann/mitch.Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8592043 144.7704046-37.8842783 144.7300641 -37.834130300000005 144.81074510000002tag:blogger.com,1999:blog-4885455635096396903.post-17523942846980997962020-05-13T11:18:00.000+10:002020-05-13T11:18:17.628+10:00Managing linux shell sessions with tmuxIf you run shell scripts for data analysis which have a long execution time on a server, you probably feel the frustration when your network connection drops out momentarily and terminates the job. Sure, we can use the 'nohup' command to force the command to proceed despite loss of connection, but this is not ideal when for example we are doing an interactive analysis or trying to fix issues in a large job. In those cases, I like to use tmux to save my session. It enables me to rejoin that analysis if I get interrupted with other work and continue working interactively later.<br />
<br />
Here I will give you a guide to use tmux to save server sessions and rejoin them later.<br />
<br />
First check whether tmux is already installed on your server:<br />
<blockquote class="tr_bq">
tmux ls</blockquote>
<div>
If tmux is installed it will list the current sessions. If it is not installed, then it will say something along the lines of "tmux cannot be found".</div>
<div>
<br /></div>
<div>
If you need to install it, you can ask your sysadmin to run:</div>
<blockquote class="tr_bq">
sudo apt update && sudo apt install tmux</blockquote>
To start a new session, use the following syntax:<br />
<blockquote class="tr_bq">
tmux new -s <new_session_name> </blockquote>
For example:<br />
<blockquote class="tr_bq">
tmux new -s pipeline1</blockquote>
You will now find yourself inside the session, and all the jobs you run will persist even if your ssh connection drops out. You can detach from the active session by using the command:<br />
<blockquote class="tr_bq">
tmux detach</blockquote>
When detached, the session is still active, it's just running in the background. The alternative to detach is to close the terminal window.<br />
<br />
On the server, you can then run "tmux ls" to list the current sessions.<br />
To connect to a session run the following:<br />
<blockquote class="tr_bq">
tmux attach -t <existing_session_name></blockquote>
For example:<br />
<blockquote class="tr_bq">
tmux attach -t pipeline1</blockquote>
That's all there is to it. You can develop more advanced used of tmux as covered elsewhere in a multitude of blogs and youtube videos.Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.comLaverton VIC 3028, Australia-37.8592043 144.7704046-37.8842783 144.7300641 -37.834130300000005 144.81074510000002tag:blogger.com,1999:blog-4885455635096396903.post-42783330809255340042020-01-09T21:59:00.000+11:002020-01-09T21:59:55.741+11:00DEE2 project update Jan 2020Happy New Year! Here I'll go through a few updates on the DEE2 project. In case you don't know about it you can read the paper <a href="https://academic.oup.com/gigascience/article/8/4/giz022/5426567">here</a>.<br />
<h4>
1) Data processing</h4>
One of the most obvious updates since the paper came out was that the number of datasets hosted by DEE2 has increased substantially from 581,094 to 805,385 runs. This was achieved by using a combination of minicluster at Deakin Uni, several Nectar Coud servers and the Massive HPC located at Monash Uni. The queue is still growing, so I'll need to further expand compute resources in future.<br />
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<br />
<h4>
2) Getting a new source of metadata</h4>
DEE2 was reliant on the <a href="https://github.com/seandavi/SRAdbV2">SRADBV2</a> package for SRA metadata. This was really critical to provide a list of accession numbers for the database to process as well as sample information so that end users can search for data sets of interest. Unfortunately, SRADBV2 became deprecated only a few months after the paper was published. This was possibly due to the expanding size of the SRA metadata making it infeasible to be distributed as a single file. <a href="https://github.com/seandavi/OmicIDXR/">OmicIDXR</a> is slated to be a replacement source of metadata, however I have doubts to its sustainability over the next few years. Therefore I have taken the approach to obtain metadata directly from the SRA website through the Run Selector app. This has the drawback that it needs to be done "manually" and has a batch limit of about 40,000 runs but as it is based on the SRA website, it is likely to be a better long term solution. It is also relatively straightforward to download rmetadata for recently added studies, for example every month. After incorporating new metadata from SRA last week, the queue has grown substantially.<br />
<h4>
3) The problem of studies missing runs</h4>
In the next few weeks I will be working to improve some of the backend scripts. One of the drawbacks of DEE2 at the moment is the fact that for some of the projects, some datasets/runs are missing. I will be working toward ensuring that only wholly completed studies are added to the dataset.<br />
<h4>
4) Increasing queue efficiency</h4>
Related to that is the proposed changes to data processing queue management. The queue should prioritise these missing datasets. As the webserver relays the data sets from nodes to the central data server, it can remove those datasets from the queue which have already passed basic checks.<br />
<h4>
5) Making the R package more efficient</h4>
There are also some improvements that can be made to the R interface, the getDEE2 package. In particular, the handling of the metadata can be improved by only downloading one time. Perhaps searching metadata can be made better with a search engine type function. I'm also considering adding the package to Bioconductor.<br />
<h4>
6) Website improvements</h4>
Other improvements on the horizon include facilitating quicker metadata searches with the DEE2 webpage with elastic search or similar serch engine. I have tried this before but could not pull it off. If you have any good resources for how to implement search engine for a tsv file please share in the comments.<br />
<h4>
7) Call for compute resources</h4>
Finally, if you are a frequent DEE2 user or have used the bulk datasets already or would like to contribute to the project, we are calling for contributors to volunteer their server or HPC compute resources. There is a docker based pipeline for servers/cloud and singularity for HPC. Docker nodes can be initiated using the command:<br />
<span style="font-family: Courier New, Courier, monospace;">docker run mziemann/tallyup hsapiens</span><br />
Substituting you favourite organism. If you would like to process a specific dataset you can provide the SRA run accessions like this<br />
<span style="font-family: Courier New, Courier, monospace;">docker run mziemann/tallyup hsapiens SRR123,SRR124,SRR125</span><br />
You will be able to access the data immediately from the docker container, as well as contributing them to the public database.<br />
Although I will be applying for grants to expand DEE2, your contribution will help a lot.Mark Ziemannhttp://www.blogger.com/profile/00623549232702735102noreply@blogger.com