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A multi-sample approach increases the accuracy of transcript assembly

Author

Listed:
  • Li Song

    (McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
    Johns Hopkins University
    Dana Farber Cancer Institute)

  • Sarven Sabunciyan

    (Johns Hopkins School of Medicine)

  • Guangyu Yang

    (McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
    Johns Hopkins University)

  • Liliana Florea

    (McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
    Johns Hopkins University
    Johns Hopkins School of Medicine)

Abstract

Transcript assembly from RNA-seq reads is a critical step in gene expression and subsequent functional analyses. Here we present PsiCLASS, an accurate and efficient transcript assembler based on an approach that simultaneously analyzes multiple RNA-seq samples. PsiCLASS combines mixture statistical models for exonic feature selection across multiple samples with splice graph based dynamic programming algorithms and a weighted voting scheme for transcript selection. PsiCLASS achieves significantly better sensitivity-precision tradeoff, and renders precision up to 2-3 fold higher than the StringTie system and Scallop plus TACO, the two best current approaches. PsiCLASS is efficient and scalable, assembling 667 GEUVADIS samples in 9 h, and has robust accuracy with large numbers of samples.

Suggested Citation

  • Li Song & Sarven Sabunciyan & Guangyu Yang & Liliana Florea, 2019. "A multi-sample approach increases the accuracy of transcript assembly," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12990-0
    DOI: 10.1038/s41467-019-12990-0
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