On 2013 Nov 01, Stephen Turner commented:

This paper presents a methodology and software implementation that allows users to discover a set of transcription factors or epigenetic modifications that regulate a set of genes of interest. A wealth of data about transcription factor binding exists in the public domain, and this is a good example of a group utilizing those resources to develop tools that are of use to the broader computational biology community.

High-throughput gene expression experiments like microarrays and RNA sequencing (RNA-seq) experiments often result in a list of differentially regulated or co-expressed genes. A common follow-up question asks which transcription factors may regulate those genes of interest. The ENCODE project has completed chromatin immunoprecipitation-sequencing (ChIP-seq) experiments for many transcription factors and epigenetic modifications for a number of different cell lines in both human and model organisms. These researchers crossed this publicly available data on enriched regions from ChIP-seq experiments with genomic coordinates of gene annotations to create a table of gene annotations (rows) by ChIP-peak signals, with a presence/absence peak in each cell. Given a set of genes of interest (e.g. differentially regulated genes from an RNA-seq experiment), the method evaluates the over-/under-representation of target sites for the DNA-binding protein in each ChIP experiment using a Fisher's exact test. Other methods based on motif enrichment (using position weight matrices derived from databases like TRANSFAC or JASPAR) would miss DNA-binding factors like the retinoblastoma protein (RB), which lacks a DNA-binding domain and is recruited to promoters by other transcription factors. In addition to overcoming this limitation, the method presented here also has the advantage of considering tissue-specificity and chromatin accessibility.

On 2013 Nov 01, Stephen Turner commented:

This paper presents a methodology and software implementation that allows users to discover a set of transcription factors or epigenetic modifications that regulate a set of genes of interest. A wealth of data about transcription factor binding exists in the public domain, and this is a good example of a group utilizing those resources to develop tools that are of use to the broader computational biology community.

High-throughput gene expression experiments like microarrays and RNA sequencing (RNA-seq) experiments often result in a list of differentially regulated or co-expressed genes. A common follow-up question asks which transcription factors may regulate those genes of interest. The ENCODE project has completed chromatin immunoprecipitation-sequencing (ChIP-seq) experiments for many transcription factors and epigenetic modifications for a number of different cell lines in both human and model organisms. These researchers crossed this publicly available data on enriched regions from ChIP-seq experiments with genomic coordinates of gene annotations to create a table of gene annotations (rows) by ChIP-peak signals, with a presence/absence peak in each cell. Given a set of genes of interest (e.g. differentially regulated genes from an RNA-seq experiment), the method evaluates the over-/under-representation of target sites for the DNA-binding protein in each ChIP experiment using a Fisher's exact test. Other methods based on motif enrichment (using position weight matrices derived from databases like TRANSFAC or JASPAR) would miss DNA-binding factors like the retinoblastoma protein (RB), which lacks a DNA-binding domain and is recruited to promoters by other transcription factors. In addition to overcoming this limitation, the method presented here also has the advantage of considering tissue-specificity and chromatin accessibility.