Genome Spot

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…

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 (link).

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MSigDB contains thousands of gene sets which have been mined from a range of genome wide studies and these are a valuable resource for gene ontology and pathway analysis. You probably know that many gene sets are curated by KEGG, REACTOME and BIOCARTA - as well as dry lab scientists who specialise in analysing these data sets and curating gene sets. What you may not know is that if you follow a few basic guidelines, you can start generating your own custom gene sets and these can become a valuable resource for running gene ontology and Gene Set Enrichment Analysis (as per the graphic)


For instance within our lab, we have extensively used ENCODE ChIP-Seq data to help us to analyse our mRNA-seq data and this has provided a huge leg-up in generating hypothesis and designing follow-up experiments. For this example, I want to show an overview of how I made biomarker gene sets for a bunch of cell types analysed by ENCODE. Biomarker gene sets can useful in array or mRNA-Seq analysis that you…