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Detecting Transcriptomic Structural Variants in Heterogeneous Contexts via the Multiple Compatible Arrangements Problem

Authors Yutong Qiu, Cong Ma, Han Xie, Carl Kingsford

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ACM Subject Classification
  • Applied computing → Computational transcriptomics
Keywords
  • transcriptomic structural variation
  • integer linear programming
  • heterogeneity

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Abstract

Transcriptomic structural variants (TSVs) - large-scale transcriptome sequence change due to structural variation - are common, especially in cancer. Detecting TSVs is a challenging computational problem. Sample heterogeneity (including differences between alleles in diploid organisms) is a critical confounding factor when identifying TSVs. To improve TSV detection in heterogeneous RNA-seq samples, we introduce the Multiple Compatible Arrangement Problem (MCAP), which seeks k genome rearrangements to maximize the number of reads that are concordant with at least one rearrangement. This directly models the situation of a heterogeneous or diploid sample. We prove that MCAP is NP-hard and provide a 1/4-approximation algorithm for k=1 and a 3/4-approximation algorithm for the diploid case (k=2) assuming an oracle for k=1. Combining these, we obtain a 3/16-approximation algorithm for MCAP when k=2 (without an oracle). We also present an integer linear programming formulation for general k. We characterize the graph structures that require k>1 to satisfy all edges and show such structures are prevalent in cancer samples. We evaluate our algorithms on 381 TCGA samples and 2 cancer cell lines and show improved performance compared to the state-of-the-art TSV-calling tool, SQUID.

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Yutong Qiu, Cong Ma, Han Xie, and Carl Kingsford. Detecting Transcriptomic Structural Variants in Heterogeneous Contexts via the Multiple Compatible Arrangements Problem. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 18:1-18:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.WABI.2019.18

Author Details

Yutong Qiu
  • Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
Cong Ma
  • Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
Han Xie
  • Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
Carl Kingsford
  • Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

Funding

This work was supported in part by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative [GBMF4554 to C.K.]; the US National Institutes of Health [R01GM122935]; and The Shurl and Kay Curci Foundation. This project is funded, in part, by a grant (4100070287) from the Pennsylvania Department of Health. The department specifically disclaims responsibility for any analyses, interpretations, or conclusions.

Acknowledgements

The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Specifically, it used the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center (PSC) [Nystrom et al., 2015]. C. K. is co-founder of Ocean Genomics, Inc.

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