MOTIVATION: Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. RESULTS: Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, screening for enrichment of RBP binding sites, differential exon RNase R resistance and circRNA-specific primer design. circtools supports researchers with visualization options and data export into commonly used formats. AVAILABILITY AND IMPLEMENTATION: circtools is available via https://github.com/dieterich-lab/circtools and http://circ.tools under GPLv3.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The Burrows-Wheeler transform (BWT) is the foundation of many algorithms for compression and indexing of text data, but the cost of computing the BWT of very large string collections has prevented these techniques from being widely applied to the large sets of sequences often encountered as the outcome of DNA sequencing experiments. In previous work, we presented a novel algorithm that allows the BWT of human genome scale data to be computed on very moderate hardware, thus enabling us to investigate the BWT as a tool for the compression of such datasets. RESULTS: We first used simulated reads to explore the relationship between the level of compression and the error rate, the length of the reads and the level of sampling of the underlying genome and compare choices of second-stage compression algorithm. We demonstrate that compression may be greatly improved by a particular reordering of the sequences in the collection and give a novel 'implicit sorting' strategy that enables these benefits to be realized without the overhead of sorting the reads. With these techniques, a 45× coverage of real human genome sequence data compresses losslessly to under 0.5 bits per base, allowing the 135.3 Gb of sequence to fit into only 8.2 GB of space (trimming a small proportion of low-quality bases from the reads improves the compression still further). This is >4 times smaller than the size achieved by a standard BWT-based compressor (bzip2) on the untrimmed reads, but an important further advantage of our approach is that it facilitates the building of compressed full text indexes such as the FM-index on large-scale DNA sequence collections. AVAILABILITY: Code to construct the BWT and SAP-array on large genomic datasets is part of the BEETL library, available as a github repository at https://github.com/BEETL/BEETL.

MOTIVATION: The introduction of next-generation sequencing techniques and especially the high-throughput systems Solexa (Illumina Inc.) and SOLiD (ABI) made the mapping of short reads to reference sequences a standard application in modern bioinformatics. Short-read alignment is needed for reference based re-sequencing of complete genomes as well as for gene expression analysis based on transcriptome sequencing. Several approaches were developed during the last years allowing for a fast alignment of short sequences to a given template. Methods available to date use heuristic techniques to gain a speedup of the alignments, thereby missing possible alignment positions. Furthermore, most approaches return only one best hit for every query sequence, thus losing the potentially valuable information of alternative alignment positions with identical scores. RESULTS: We developed SARUMAN (Semiglobal Alignment of short Reads Using CUDA and NeedleMAN-Wunsch), a mapping approach that returns all possible alignment positions of a read in a reference sequence under a given error threshold, together with one optimal alignment for each of these positions. Alignments are computed in parallel on graphics hardware, facilitating an considerable speedup of this normally time-consuming step. Combining our filter algorithm with CUDA-accelerated alignments, we were able to align reads to microbial genomes in time comparable or even faster than all published approaches, while still providing an exact, complete and optimal result. At the same time, SARUMAN runs on every standard Linux PC with a CUDA-compatible graphics accelerator. AVAILABILITY: http://www.cebitec.uni-bielefeld.de/brf/saruman/saruman.html.