RNA-seq Data Analysis Mathematics & Economics Statistics Project

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RNA-seq Data Analysis
The publication is intended to validate the need for new methods of analyzing Gene Ontology for RNA-seq data, and mainly focuses on the GOseq method. GO analysis generally helps in minimizing complication and highlighting biological processes in the studies of genome-wide expression. It provides exceptional detail regarding the transcriptional background of organisms. However, standard analysis methods tend to give biased results. As such, there is need for developing techniques that incorporate the technical characteristics of the RNA-seq output. The published data sets comprise of over 17M short cDNA reads acquired for the untreated and treated cell-line and run on Illumina's 1G analyzer. The reads were drawn again back to the original genome and the number of reads per gene recorded.
Key findings
The regular techniques for examining over-representation of a GO grouping presume that in the null proposition, every gene has equal prospect of being perceived to be DE. As such, the total number of genes linked to a group that overlie with DE genes set tends to follow a hypergeometric distribution. The presence of selection bias makes genes with additional reads to be perceived as DE, and, this violates the hypothesi...