ArticleAlgorithms for molecular biology : AMB2020
Detecting transcriptomic structural variants in heterogeneous contexts via the Multiple Compatible Arrangements Problem.
Article in Algorithms for molecular biology : AMB, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- A guide for the diagnosis of rare and undiagnosed disease: beyond the exome.Genome medicine · 2022Review
- MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data.Genome biology · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTranscriptomic 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.
resultsTo improve TSV detection in heterogeneous RNA-seq samples, we introduce the Multiple Compatible Arrangements Problem (MCAP), which seeks
conclusionsWe show that the solution to MCAP accurately addresses sample heterogeneity during TSV detection. Our algorithms have improved performance on TCGA cancer samples and cancer cell line samples compared to a TSV calling tool, SQUID. The software is available at https://github.com/Kingsford-Group/diploidsquid.
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