Count-based transcriptome analysis to identify differentially expressed genes for breast cancer

Abstract

Sequencing the coding regions or the whole cancer transcriptome can provide valuable information about the differential expression patterns of the genes. Previous researches centered on ~2% of coding human genome, assuming that the non-coding sequences were “junk” lacking significant functional information. Recent medical research show that a major percentage of the human genome (~70-90%) are non-coding, stored in the cell in the form of non-coding RNA (ncRNA) which overshadows the coding information limited only to a small percentage. These ncRNAs are composed of mostly ultraconserved elements, lacking protein-coding potential and regulating gene expression acting as enhancers whose aberrant expression may be involved in pathological process such as cancer. Here, we have described RNA-seq data analysis for the profiling of transcriptome of Breast cells and provided a generic outline of the whole pipeline from next-generation sequencing (NGS) output for quantification of differential gene expression across different conditions (e.g., control vs test). We have used tool Cufflinks-Cuffdiff to estimate transcript-level expression for gene discovery extracted from high-throughput RNA-seq data across distinct conditions that represent candidate biomarkers for future research. This study provides the survey of coding transcripts associated genes expression within a cancer system.