Forty-Four and Two

If you like science, you will love Forty-Four and Two. A new blog that delves into genetics and molecular biology for the wider audience. His latest post is an impressive poster describing some data we published together earlier this year in CMLS. Keep up the good work!

Location: St Kilda VIC 3182, Australia

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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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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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My personal thoughts on gene name errors

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Well, our paper "Gene name errors are widespread in the scientific literature" in Genome Biology has stirred up some interest. There are a lot of reasons why this article has taken off beyond what I initially envisioned: Most tech-savvy people hate Excel People over-rely on Excel, when there are better alternatives for analytics Everyone has experienced an auto-correct fail and can relate People love "bloopers" People are interested when scientists get it wrong (especially other scientists)  In this post I want to share a few things: Some responses to journalist questions List of media coverage and whether they are reporting things accurately A look into the scripts used themselves Future directions I'll also be answering your questions, so pop them in the comments section.  Some responses to journalist questions Why did you do this? We saw that the problem was first described in 2004, but these errors were present in files fr...
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