Detecting transcriptomic structural variants in heterogeneous contexts via the Multiple Compatible Arrangements Problem.

ABSTRACT:

Background

Transcriptomic structural variants (TSVs)-large-scale transcriptome sequence change due to structural variation - are common in cancer. TSV detection from high-throughput sequencing data is a computationally challenging problem. Among all the confounding factors, sample heterogeneity, where each sample contains multiple distinct alleles, poses a critical obstacle to accurate TSV prediction.

Results

To improve TSV detection in heterogeneous RNA-seq samples, we introduce the Multiple Compatible Arrangements Problem (MCAP), which seeks k genome arrangements that maximize the number of reads that are concordant with at least one arrangement. This models a heterogeneous or diploid sample. We prove that MCAP is NP-complete and provide a 14 -approximation algorith

SUBMITTER: Qiu Y 

PROVIDER: S-EPMC7227063 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature