SUMMARY: We introduce BRAT-BW, a fast, accurate and memory-efficient tool that maps bisulfite-treated short reads (BS-seq) to a reference genome using the FM-index (Burrows-Wheeler transform). BRAT-BW is significantly more memory efficient and faster on longer reads than current state-of-the-art tools for BS-seq data, without compromising on accuracy. BRAT-BW is a part of a software suite for genome-wide single base-resolution methylation data analysis that supports single and paired-end reads and includes a tool for estimation of methylation level at each cytosine. AVAILABILITY: The software is available in the public domain at http://compbio.cs.ucr.edu/brat/.

MOTIVATION: Nucleosomes are the basic elements of chromatin structure. They control the packaging of DNA and play a critical role in gene regulation by allowing physical access to transcription factors. The advent of second-generation sequencing has enabled landmark genome-wide studies of nucleosome positions for several model organisms. Current methods to determine nucleosome positioning first compute an occupancy coverage profile by mapping nucleosome-enriched sequenced reads to a reference genome; then, nucleosomes are placed according to the peaks of the coverage profile. These methods are quite accurate on placing isolated nucleosomes, but they do not properly handle more complex configurations. Also, they can only provide the positions of nucleosomes and their occupancy level, whereas it is very beneficial to supply molecular biologists additional information about nucleosomes like the probability of placement, the size of DNA fragments enriched for nucleosomes and/or whether nucleosomes are well positioned or 'fuzzy' in the sequenced cell sample. RESULTS: We address these issues by providing a novel method based on a parametric probabilistic model. An expectation maximization algorithm is used to infer the parameters of the mixture of distributions. We compare the performance of our method on two real datasets against Template Filtering, which is considered the current state-of-the-art. On synthetic data, we show that our method can resolve more accurately complex configurations of nucleosomes, and it is more robust to user-defined parameters. On real data, we show that our method detects a significantly higher number of nucleosomes. AVAILABILITY: Visit http://www.cs.ucr.edu/~polishka.

BACKGROUND: As genome-wide experiments and annotations become more prevalent, researchers increasingly require tools to help interpret data at this scale. Many functional genomics experiments involve partitioning the genome into labeled segments, such that segments sharing the same label exhibit one or more biochemical or functional traits. For example, a collection of ChlP-seq experiments yields a compendium of peaks, each labeled with one or more associated DNA-binding proteins. Similarly, manually or automatically generated annotations of functional genomic elements, including cis-regulatory modules and protein-coding or RNA genes, can also be summarized as genomic segmentations. RESULTS: We present a software toolkit called Segtools that simplifies and automates the exploration of genomic segmentations. The software operates as a series of interacting tools, each of which provides one mode of summarization. These various tools can be pipelined and summarized in a single HTML page. We describe the Segtools toolkit and demonstrate its use in interpreting a collection of human histone modification data sets and Plasmodium falciparum local chromatin structure data sets. CONCLUSIONS: Segtools provides a convenient, powerful means of interpreting a genomic segmentation.

Abstract Summary: We present a new, accurate and efficient tool for mapping short reads obtained from the Illumina Genome Analyzer following sodium bisulfite conversion. Our tool, BRAT, supports single and paired-end reads and handles input files containing reads and mates of different lengths. BRAT is faster, maps more unique paired-end reads and has higher accuracy than existing programs. The software package includes tools to end-trim low-quality bases of the reads and to report nucleotide counts for mapped reads on the reference genome. Availability: The source code is freely available for download at http://compbio.cs.ucr.edu/brat/ and is distributed as Open Source software under the GPLv3.0. Contact: [email protected]

Bioinformatics (2010) 26(4), 572–573 The authors would like to apologize for the erroneous statements on the accuracy of the mapping for mrsFAST (Hormozdiari et al., 2009). The parameters for the insert size used for msrFAST to obtain Figure 1 and Table 1 were not correct; using the correct parameters would have yield higher accuracy than the one reported. At the time of the initial simulation there was no documentation available regarding the distance between the reads for paired-end, so we deduced the definition for the distance from the output files of mrsFAST. For 32 bases paired-end reads (Table 1), we used insert size range from 106 to 306 (called min and max, respectively, in mrsFAST), but the correct values should have been [43, 243]. For 24 bases paired-end reads (Fig. 1), we used [136, 324] whereas the correct values should have been [89, 277]. For 64 bases paired-end reads (Fig. 1), we used [136, 364], but the correct parameters should have been [9, 237].