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Accuracy of transcript quantification with RNA-Seq is negatively affected by positional fragment bias. This article introduces Mix 2  (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 Mix 2  are trained by Expectation Maximization resulting in simultaneous transcript abundance and bias estimates. We compare Mix 2  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 Mix 2  overall exceeds the accuracy of the other methods and that its bias estimates converge to the correct solution. We further evaluate Mix 2  on real RNA-Seq data from the Microarray and Sequencing Quality Control (MAQC, SEQC) Consortia. On MAQC data, Mix 2  achieves improved correlation to qPCR measurements with a relative increase in R 2  between 4% and 50%. Mix 2  also yields repeatable concentration estimates across technical replicates with a relative increase in R 2  between 8% and 47% and reduced standard deviation across the full concentration range. We further observe more accurate detection of differential expression with a relative increase in true positives between 74% and 378% for 5% false positives. In addition, Mix 2  reveals 5 dominant biases in MAQC data deviating from the common assumption of a uniform fragment distribution. On SEQC data, Mix 2  yields higher consistency between measured and predicted concentration ratios. A relative error of 20% or less is obtained for 51% of transcripts by Mix 2 , 40% of transcripts by Cufflinks and RSEM and 30% by eXpress. Titration order consistency is correct for 47% of transcripts for Mix 2 , 41% for Cufflinks and RSEM and 34% for eXpress. We, further, observe improved repeatability across laboratory sites with a relative increase in R 2  between 8% and 44% and reduced standard deviation.

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