A multi-sample approach increases the accuracy of transcript assembly

Li Song, Sarven Sabunciyan, Guangyu Yang and Liliana Florea ()
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Li Song: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
Sarven Sabunciyan: Johns Hopkins School of Medicine
Guangyu Yang: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
Liliana Florea: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-12990-0

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