BACKGROUND: Transcripts, which have been subject to Post-transcriptional exon shuffling (PTES), have an exon order inconsistent with the underlying genomic sequence. These have been identified in a wide variety of tissues and cell types from many eukaryotes, and are now known to be mostly circular, cytoplasmic, and non-coding. Although there is no uniformly ascribed function, several have been shown to be involved in gene regulation. Accurate identification of these transcripts can, however, be difficult due to artefacts from a wide variety of sources. RESULTS: Here, we present a computational method, PTESFinder, to identify these transcripts from high throughput RNAseq data. Uniquely, it systematically excludes potential artefacts emanating from pseudogenes, segmental duplications, and template switching, and outputs both PTES and canonical exon junction counts to facilitate comparative analyses. In comparison with four existing methods, PTESFinder achieves highest specificity and comparable sensitivity at a variety of read depths. PTESFinder also identifies between 13 % and 41.6 % more structures, compared to publicly available methods recently used to identify human circular RNAs. CONCLUSIONS: With high sensitivity and specificity, user-adjustable filters that target known sources of false positives, and tailored output to facilitate comparison of transcript levels, PTESFinder will facilitate the discovery and analysis of these poorly understood transcripts.

Upon publication of the original article [1] it has been found that author David J. Elliott has been accidentally misspelt. The correct spelling is David J. Elliott rather than David J. Elliot. This has been herewith corrected with this erratum and also updated in the original article.

MOTIVATION: During the past 4 years, whole-exome sequencing has become a standard tool for finding rare variants causing Mendelian disorders. In that time, there has also been a proliferation of both sequencing platforms and approaches to analyse their output. This requires approaches to assess the performance of different methods. Traditionally, criteria such as comparison with microarray data or a number of known polymorphic sites have been used. Here we expand such approaches, developing a maximum likelihood framework and using it to estimate the sensitivity and specificity of whole-exome sequencing data. RESULTS: Using whole-exome sequencing data for a panel of 19 individuals, we show that estimated sensitivity and specificity are similar to those calculated using microarray data as a reference. We explore the effect of frequency misspecification arising from using an inappropriately selected population and find that, although the estimates are affected, the rankings across procedures remain the same. AVAILABILITY AND IMPLEMENTATION: An implementation using Perl and R can be found at busso.ncl.ac.uk (Username: igm101; Password: Z1z1nts).

Mononucleotide repeats (MNRs) are abundant in eukaryotic genomes and exhibit a high degree of length variability due to insertion and deletion events. However, the relationship between these repeats and mutation rates in surrounding sequences has not been systematically investigated. We have analyzed the frequency of single nucleotide polymorphisms (SNPs) at positions close to and within MNRs in the human genome. Overall, we find a 2- to 4-fold increase in the SNP frequency at positions immediately adjacent to the boundaries of MNRs, relative to that at more distant bases. This relationship exhibits a strong asymmetry between 3' and 5' ends of repeat tracts and is dependent upon the repeat motif, length and orientation of surrounding repeats. Our analysis suggests that the incorporation or exclusion of bases adjacent to the boundary of the repeat through substitutions, in which these nucleotides mutate towards or away from the base present within the repeat, respectively, may be another mechanism by which MNRs expand and contract in the human genome.