Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

PLoS Computational Biology
Andreas TuerkSerhat Güler

Abstract

Accuracy of transcript quantification with RNA-Seq is negatively affected by positional fragment bias. This article introduces Mix2 (rd. "mixquare"), a transcript quantification method which uses a mixture of probability distributions to model and thereby neutralize the effects of positional fragment bias. The parameters of Mix2 are trained by Expectation Maximization resulting in simultaneous transcript abundance and bias estimates. We compare Mix2 to Cufflinks, RSEM, eXpress and PennSeq; state-of-the-art quantification methods implementing some form of bias correction. On four synthetic biases we show that the accuracy of Mix2 overall exceeds the accuracy of the other methods and that its bias estimates converge to the correct solution. We further evaluate Mix2 on real RNA-Seq data from the Microarray and Sequencing Quality Control (MAQC, SEQC) Consortia. On MAQC data, Mix2 achieves improved correlation to qPCR measurements with a relative increase in R2 between 4% and 50%. Mix2 also yields repeatable concentration estimates across technical replicates with a relative increase in R2 between 8% and 47% and reduced standard deviation across the full concentration range. We further observe more accurate detection of differential...Continue Reading

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Datasets Mentioned

BETA
GSE5350
GSE47792

Methods Mentioned

BETA
RNA-Seq
3

Software Mentioned

PennSeq
baySeq
Mix
UPGMA
SEQC
NOISeq
eXpress
Cufflinks
DSS
Tophat2

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