High-throughput mRNA sequencing (RNA-Seq) provides valuable information for accurate transcript quantification. In this paper, we introduce a Networkbased method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction information with short read alignment for transcript abundance estimation. Based on the observation that the abundances of the neighboring transcripts by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with a heuristic alternating optimization algorithm. We demonstrate in the experiments that (1) qRT-PCR confirmed that Net-RSTQ achieves better transcript quantification accuracy with RNA-Seq data from a stem cell line and an ovarian cancer cell line compared with the models without using transcript network; and (2) the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification tested on the RNA-Seq data of ovarian cancer, breast cancer and lung cancer in The Cancer Genome Atlas (TCGA).
['Wei Zhang', 'Kay Minn', 'Lilong Lin', 'Baolin Wu', 'Jeremy Chien', 'Hui Zheng', 'Rui Kuang']
Network-based Transcript Quantification with RNA-Seq Data