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

Yutong Qiu1, Cong Ma1, Han Xie1

  • 1Computational Biology Department, Carnegie Mellon University, 5000 Forbes Ave, 15213 Pittsburgh, PA USA.

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|May 30, 2020
PubMed
Summary
This summary is machine-generated.

Accurate detection of transcriptomic structural variants (TSVs) in heterogeneous cancer samples is improved by the Multiple Compatible Arrangements Problem (MCAP). MCAP algorithms enhance TSV prediction accuracy by modeling sample heterogeneity.

Keywords:
HeterogeneityInteger linear programmingTranscriptomic structural variation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcriptomic structural variants (TSVs) are common in cancer and challenging to detect.
  • Sample heterogeneity, with multiple distinct alleles, significantly hinders accurate TSV prediction from RNA-seq data.

Purpose of the Study:

  • To develop a computational method for improving transcriptomic structural variant detection in heterogeneous samples.
  • To model heterogeneous or diploid samples for more accurate TSV prediction.

Main Methods:

  • Introduced the Multiple Compatible Arrangements Problem (MCAP) to find k genome arrangements maximizing read concordance.
  • Developed approximation algorithms for MCAP, including a polynomial-time algorithm for the diploid case.
  • Formulated MCAP as an integer linear program for general k and analyzed conflict structures.

Main Results:

  • MCAP is proven NP-complete, with developed approximation algorithms providing theoretical guarantees.
  • The approach effectively models sample heterogeneity for improved TSV detection.
  • Algorithms demonstrated superior performance on TCGA and cancer cell line samples compared to the SQUID tool.

Conclusions:

  • The MCAP solution accurately addresses sample heterogeneity in TSV detection.
  • The developed algorithms offer improved performance for TSV calling in cancer genomics.
  • Associated software is publicly available for broader research application.