Genome Spot

TL;DR We curated a bunch of ENCODE data into gene sets that is super useful in pathway analysis (ie GSEA).
Link to gene sets and data: https://sourceforge.net/projects/encodegenesethub/
Poster presentation: DOI:10.13140/RG.2.2.34302.59208

Now for the longer version. Gene sets are wonderful resources. We use them to do pathway level analyses and identify trends in data that lead us to improved interpretation and new hypotheses. Most pathway analysis tools like GSEA allow us to use custom gene sets, this is really cool as you can start to generate gene sets based on your own profiling work and that of others.

There is huge value in curating experimental data into gene sets, as the MSigDB team have demonstrated. But overall, these data are under-shared. Even our group is guilty of not sharing the gene sets we've used in papers. There have been a few papers where we've used gene sets curated  from ENCODE transcription factor binding site (TFBS) data to understand which TFs were drivi…

So far in this series of posts we've analysed the effect of azacitidine on AML3 cell gene expression using publicly available RNA-seq data. Our pathway analysis showed many interesting trends, but unravelling all the different mechanisms from this point can be really hard. In order to identify the major players at the chromatin level, it can be useful to integrate transcription factor binding data and see whether targets of a particular transcription factor are differentially regulated in a pathway analysis. The problem with this analysis in the past was that ChIP-seq datasets were in varying formats on GEO and processing these into a standardised format would be too time consuming. With the advent of the ENCODE Project, there is now a large body of transcription factor binding data in a uniform format (link), that is being mined in many creative ways. In our group, we used this approach extensively (here, here and here).

In this post, we will mine ENCODE transcription factor bind…