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Which genes are differentially expressed between my two phenotypes, based on my RNA-seq data?

This recipe provides one method to identify and visualize gene expression in different diseases and during cell differentiation and development. In collecting ChIP-seq data, we can obtain genome-wide maps of transcription factor occupancies or histone modifications between a treatment and control. In locating these regions, we can integrate ChIP-seq and RNA-seq data to better understand how these binding events regulate associated gene expression of nearby genes. An example use case of this recipe is when Laurent et al. observed how the binding of the Prep1 transcription factor influences gene regulation in mouse embryonic stem cells. The integration of both RNA-seq and Chip-seq data allows a user to identify target genes that are directly regulated by transcription factor binding or any other epigenetic occupancy in the genome.

What is Model-based Analysis of ChiP-seq (MACS)?

Model-based Analysis of ChIP-seq (MACS) is a computational algorithm that identifies genome-wide locations of transcription/chromatin factor binding or histone modifications. It is often preferred over other peak calling algorithms due to its consistency in reporting fewer false positives and its finer spatial resolution. First, it removes redundant reads to account for possible over-amplification of ChIP-DNA, which may affect peak-calling downstream. Then it shifts read positions based on the fragment size distribution to better represent the original ChIP-DNA fragment positions. Once read positions are adjusted, peak enrichment is calculated by identifying regions that are significantly enriched relative to the genomic background. MACS empirically estimates the FDR for experiments with controls for each peak, which can be used as a cutoff to filter enriched peaks. The treatment and control samples are swapped and any enriched peaks found in the control sample are regarded as false positives.

Why differential expression analysis?

We assume that most genes are not expressed all the time, but rather are expressed in specific tissues, stages of development, or under certain conditions. Genes which are expressed in one condition, such as cancerous tissue, are said to be differentially expressed when compared to normal conditions.

Use Case: ChIP-Seq and RNA-Seq Analyses Identify Components of the Wnt and Fgf Signaling Pathways as Prep1 Target Genes in Mouse Embryonic Stem Cells (Laurent et al., PLoS ONE, 2015)

The sample datatset, Series GSE6328, used for this recipe are from NCBI's GEO. We identify the interplay between epigentics and transcriptomics mouse embryonic stems cells by observing how the binding of the transcription factor, Prep1, influences gene expression. Prep1 is predominantly known for its contribution in embryonic development. In comparing genome-wide maps of mouse embryonic cells experiencing Prep1 binding to those that do not, we can identify potential target genes that are being differentially regulated by these binding events.

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