Recent papers

Some work that I've been involved with over the past few months. 


Title
Authors
Mark Ziemann, Atul Kamboj, Runyararo M Hove, Shanon Loveridge, Assam El-Osta, Mrinal Bhave
Journal name
Acta Physiologiae Plantarum
Pages
1-10
Publisher
Springer-Verlag
Description
Abstract: Salinity is a threat to crops in many parts of the world, and together with drought, it is predicted to be a serious constraint to food security. However, understanding the impact of this stressor on plants is a major challenge due to the involvement of numerous genes and regulatory pathways. While transcriptomic analyses of barley (Hordeum vulgare L.) under salt stress have been reported with microarrays, there are no reports as yet of the use of mRNA-Seq. We demonstrate the utility of mRNA-Seq by analysing cDNA libraries derived from acutely salt-stressed and unstressed leaf material of H. vulgare cv. Hindmarsh. The data yielded >50 million sequence tags which aligned to 26,944 sequences in the Unigene reference database. To gain maximum information, we performed de novo assembly of unaligned reads and discovered >3,800 contigs, termed novel tentative consensus sequences, which are either new, or significant improvements on current databases. Differential gene expression screening found 48 significantly up-regulated and 62 significantly down-regulated transcripts. The work provides comprehensive insights into genome-wide effects of salinity and is a new resource for the study of gene regulation in barley and wheat. Further, the bioinformatics workflow may be applicable to other non-model plants to establish their transcriptomes and identify unique sequences. 


Title
Authors
Mark Ziemann, Antony Kaspi, Ross Lazarus, Assam El-Osta
Bioinformation
Description
Abstract: Reliable identification of cis regulatory elements influencing transcription remains a challenging problem in molecular bioinformatics. This is especially true for enhancer elements which are often located hundreds of kilobases from the gene promoter. High resolution DNase hypersensitivity and connectivity profiling by the ENCODE consortium provides evidence of millions of interacting cis-acting elements in the human genome. This prior knowledge can be incorporated into genome-wide expression analyses, in the form of gene sets sharing regulatory sequence motifs in known DNase hypersensitivity peak regions. High proportions of enrichment among the most extreme differentially transcribed genes from controlled biological experiments may suggest novel hypotheses about signalling pathways. The utility of this approach is demonstrated with the reanalysis of a microarray-derived gene expression data set through the Gene Set Enrichment Analysis pipeline, uncovering new putative distal cis elements in the context of innate immunity. The DNase Hypersensitivity Connectivity informed Motif Enrichment in Gene Expression (DHC-MEGE) method described here has the advantage of identifying distal elements such as enhancers, which are often overlooked with standard promoter motif analysis


Title
Authors
Mark Ziemann, Atul Kamboj, Mrinal Bhave
Bioinformation
Description
Abstract: Plant and animal genomes are replete with large gene families, making the task of ortholog identification difficult and labor intensive. OrthoRBH is an automated reciprocal blast pipeline tool enabling the rapid identification of specific gene families of  interest in related species, streamlining the collection of homologs prior to downstream molecular evolutionary analysis. The efficacy of OrthoRBH is demonstrated with the identification of the 13-member PYR/PYL/RCAR gene family in Hordeum vulgare using Oryza sativa query sequences. OrthoRBH runs on the Linux command line and is freely available at SourceForge.


Popular posts from this blog

Data analysis step 8: Pathway analysis with GSEA

Image
In our RNA-seq series so far we've performed differential analysis and generated some pretty graphs, showing thousands of differentially expressed genes after azacitidine treatment. In order to understand the biology underlying the differential gene expression profile, we need to perform pathway analysis. We use Gene Set Enrichment Analysis ( GSEA ) because it can detect pathway changes more sensitively and robustly than some methods. A 2013 paper compared a bunch of gene set analyses software with microarrays and is worth a look. Generate a rank file The rank file is a list of detected genes and a rank metric score. At the top of the list are genes with the "strongest" up-regulation, at the bottom of the list are the genes with the "strongest" down-regulation and the genes not changing are in the middle. The metric score I like to use is the sign of the fold change multiplied by the inverse of the p-value, although there may be better methods out there
Read more

Two subtle problems with over-representation analysis

Image
Over-representation analysis is a helpful and really frequently used technique for understanding trends from omics data and gene lists more generally. Just to demonstrate this, I tabulated the number of citations of some of the most popular web tools and packages, which reaches a massive 191k citations! But if you have used some of these tools, you will notice that they yield subtly different results. We were curiuous about that and ran a whole bunch of investigations into the internal workings of these tools, in particular clusterProfiler. We identified two subtle problems with clusterProfiler, which we unpack in detail in our new publication , but here I will give you a quick overview. Problem #1: The background problem The first one we call the “background problem,” because it involves the software eliminating large numbers of genes from the background list if they are not annotated as belonging to any category. This results in removing a large number of unannotated genes from the b
Read more

Uploading data to GEO - which method is faster?

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: Using 'ncftp' ncftp set passive on set so-bufsize 33554432 open  ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov cd uploads/your @mail.com_ yourfolder put -R Folder_with_submission_files Using 'lftp' lftp  ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov cd uploads/ your @mail.com _ yourfolder mirror -R Folder_with_submission_files Using 'sftp' (expect slower transfer speeds since this method encrypts on-the-fly) sftp  geoftp @s ftp-private.ncbi.nlm.nih.gov password: yourpasscode cd uploads/ your @mail.com _ yourfolder mkdir new_geo_submission cd new_geo_submission put file_name Using 'ncftpput' (transfers from the command-line without entering an interactive shell) Usage example: ncftpput -F -R -z -u  geoftp  -p "yourpasscode"
Read more