Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis

摘要: High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone often not sufficient to accurately identify the read origins from isoforms quantification, we propose explore protein domain-domain interactions as prior knowledge integrative analysis with data. We introduce a Network-based method RNA-Seq-based Transcript Quantification (Net-RSTQ) integrate interaction network short alignments abundance estimation. Based on our observation that abundances neighboring by in are positively correlated, Net-RSTQ models expression transcripts Dirichlet priors likelihood observed against one gene. The all genes then jointly estimated alternating optimization multiple EM problems. In simulation effectively improved isoform quantifications when co-expressions correlate their interactions. qRT-PCR results 25 multi-isoform stem cell line, an ovarian cancer and breast line also showed more consistent proportions experiments Cancer Genome Atlas (TCGA), informative patient sample classification cancer, lung cancer. All experimental collectively support promising approach quantification. toolbox available at http://compbio.cs.umn.edu/Net-RSTQ/.