Motivation: The rapid development of next-generation sequencing technology provides an opportunity to study genome-wide DNA methylation at single-base resolution. However, depletion of unmethylated cytosines brings challenges for aligning bisulfite-converted sequencing reads to a large reference. Software tools for aligning methylation reads have not yet been comprehensively evaluated, especially for the widely used reduced representation bisulfite sequencing (RRBS) that involves enrichment for CpG islands (CGIs). Results: We specially developed a simulator, RRBSsim, for benchmarking analysis of RRBS data. We performed extensive comparison of seven mapping algorithms for methylation analysis in both real and simulated RRBS data. Eighteen lung tumors and matched adjacent tissues were sequenced by the RRBS protocols. Our empirical evaluation found that methylation results were less consistent between software tools for CpG sites with low sequencing depth, medium methylation level, on CGI shores or gene body. These observations were further confirmed by simulations that indicated software tools generally had lower recall of detecting these vulnerable CpG sites and lower precision of estimating methylation levels in these CpG sites. Among the software tools tested, bwa-meth and BS-Seeker2 (bowtie2) are currently our preferred aligners for RRBS data in terms of recall, precision and speed. Existing aligners cannot efficiently handle moderately methylated CpG sites and those CpG sites on CGI shores or gene body. Interpretation of methylation results from these vulnerable CpG sites should be treated with caution. Our study reveals several important features inherent in methylation data, and RRBSsim provides guidance to advance sequence-based methylation data analysis and methodological development. Availability and implementation: RRBSsim is a simulator for benchmarking analysis of RRBS data and its source code is available at https://github.com/xwBio/RRBSsim or https://github.com/xwBio/Docker-RRBSsim. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: RNA-Seq (also called whole-transcriptome sequencing) is an emerging technology that uses the capabilities of next-generation sequencing to detect and quantify entire transcripts. One of its important applications is the improvement of existing genome annotations. RNA-Seq provides rapid, comprehensive and cost-effective tools for the discovery of novel genes and transcripts compared with expressed sequence tag (EST), which is instrumental in gene discovery and gene sequence determination. The rat is widely used as a laboratory disease model, but has a less well-annotated genome as compared with humans and mice. In this study, we incorporated deep RNA-Seq data from three rat tissues-bone marrow, brain and kidney-with EST data to improve the annotation of the rat genome. RESULTS: Our analysis identified 32 197 transcripts, including 13 461 known transcripts, 13 934 novel isoforms and 4802 new genes, which almost doubled the numbers of transcripts in the current public rat genome database (rn5). Comparisons of our predicted protein-coding gene sets with those in public datasets suggest that RNA-Seq significantly improves genome annotation and identifies novel genes and isoforms in the rat. Importantly, the large majority of novel genes and isoforms are supported by direct evidence of RNA-Seq experiments. These predicted genes were integrated into the Rat Genome Database (RGD) and can serve as an important resource for functional studies in the research community. AVAILABILITY AND IMPLEMENTATION: The predicted genes are available at http://rgd.mcw.edu.