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

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.