Figure 2.

Prediction of read directions using MLE. (A) Overview of kMC training and MLE of read direction. (Left) S base reads randomly sampled from stranded RNA-seq reads and their matched step-wise k-nearest reads (x_k=1, x_{k = 2}, x_k=3,…) were used for training kMC. Blue arrows are reads in the forward (+) direction, and red arrows are reads in the reverse (−) direction. (Right) Prediction of read direction using MLE. Step-wise k-nearest stranded reads (x_k=1, x_{k = 2}, x_k=3,…) from a query unstranded read (black arrow) were extracted and used to calculate two likelihoods at (+) and (−). A direction with the maximum likelihood is finally assigned to the query read. (B,C) Accuracies of transcriptomes assembled with RPDs (k = 3) and unstranded reads in HeLa (B) and mES cells (C). (D) An example of resulting transfrags reassembled with RPDs. LOC148413 and MRPL20 are convergently overlapped at a locus where unstranded RNA-seq signals (black) are not separated, but blue and red RPD signals are clearly separated in the forward and reverse directions, respectively. (E,F). Comparisons of gene expression values (FPKM, log₂) estimated by stranded (x-axis) and unstranded reads (y-axis, left) or RPDs (y-axis, right) in HeLa (E) and mES cells (F). The correlation coefficients were calculated with Pearson's correlation between the x- and y-axis values. The red dots indicate genes with antisense-overlapped genes.