We're not alone: the genomics bioinformatics blogosphere

Go forth and explore!

http://seqanswers.com/

http://plindenbaum.blogspot.com.au/

http://gettinggeneticsdone.blogspot.com.au/

http://kevin-gattaca.blogspot.com.au/

http://core-genomics.blogspot.com.au/

http://massgenomics.org/

http://www.michaeleisen.org/blog/

http://nextgenseq.blogspot.com.au/

http://thepersonalgenome.com/

http://omicsomics.blogspot.com.au/

http://bioinformaticsblog.org/

http://bioinformatics.whatheblog.com/

http://blog.openhelix.eu/

http://www.rna-seqblog.com/

http://thegenomefactory.blogspot.com.au/

http://bcbio.wordpress.com/

http://flxlexblog.wordpress.com/

http://jermdemo.blogspot.com.au/

http://nsaunders.wordpress.com/

http://manuelcorpas.com/

http://thegenomefactory.blogspot.com.au/2012/11/sorting-fastq-files-by-sequence-length.html
Location: St Kilda VIC 3182, Australia

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Two subtle problems with over-representation analysis

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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...
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Data analysis step 8: Pathway analysis with GSEA

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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...
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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...
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