BACKGROUND: Analysis of somatic mutations from tumor whole exomes has fueled discovery of novel cancer driver genes. However, ~ 98% of the genome is non-coding and includes regulatory elements whose normal cellular functions can be disrupted by mutation. Whole genome sequencing (WGS), on the other hand, allows for identification of non-coding somatic variation and expanded estimation of background mutation rates, yet fewer computational tools exist for specific interrogation of this space. RESULTS: We present MutEnricher, a flexible toolset for investigating somatic mutation enrichment in both coding and non-coding genomic regions from WGS data. MutEnricher contains two distinct modules for these purposes that provide customizable options for calculating sample- and feature-specific background mutation rates. Additionally, both MutEnricher modules calculate feature-level and local, or "hotspot," somatic mutation enrichment statistics. CONCLUSIONS: MutEnricher is a flexible software package for investigating somatic mutation enrichment that is implemented in Python, is freely available, can be efficiently parallelized, and is highly configurable to researcher's specific needs. MutEnricher is available online at https://github.com/asoltis/MutEnricher .

MOTIVATION: Variant detection from next-generation sequencing (NGS) data is an increasingly vital aspect of disease diagnosis, treatment and research. Commonly used NGS-variant analysis tools generally rely on accurately mapped short reads to identify somatic variants and germ-line genotypes. Existing NGS read mappers have difficulty accurately mapping short reads containing complex variation (i.e. more than a single base change), thus making identification of such variants difficult or impossible. Insertions and deletions (indels) in particular have been an area of great difficulty. Indels are frequent and can have substantial impact on function, which makes their detection all the more imperative. RESULTS: We present ABRA, an assembly-based realigner, which uses an efficient and flexible localized de novo assembly followed by global realignment to more accurately remap reads. This results in enhanced performance for indel detection as well as improved accuracy in variant allele frequency estimation. AVAILABILITY AND IMPLEMENTATION: ABRA is implemented in a combination of Java and C/C++ and is freely available for download at https://github.com/mozack/abra.

BACKGROUND: Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results. RESULTS: Here we present ReQON, a tool that recalibrates the base quality scores from an input BAM file of aligned sequencing data using logistic regression. ReQON also generates diagnostic plots showing the effectiveness of the recalibration. We show that ReQON produces quality scores that are both more accurate, in the sense that they more closely correspond to the probability of a sequencing error, and do a better job of discriminating between sequencing errors and non-errors than the original quality scores. We also compare ReQON to other available recalibration tools and show that ReQON is less biased and performs favorably in terms of quality score accuracy. CONCLUSION: ReQON is an open source software package, written in R and available through Bioconductor, for recalibrating base quality scores for next-generation sequencing data. ReQON produces a new BAM file with more accurate quality scores, which can improve the results of downstream analysis, and produces several diagnostic plots showing the effectiveness of the recalibration.

UNLABELLED: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. AVAILABILITY: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The residence of spliceosomal introns within protein-coding genes can fluctuate over time, with genes gaining, losing or conserving introns in a complex process that is not entirely understood. One approach for studying intron evolution is to compare introns with respect to position and type within closely related genes. Here, we describe new, freely available software called Common Introns Within Orthologous Genes (CIWOG), available at http://ciwog.gdcb.iastate.edu/, which detects common introns in protein-coding genes based on position and sequence conservation in the corresponding protein alignments. CIWOG provides dynamic web displays that facilitate detailed intron studies within orthologous genes. User-supplied options control how introns are clustered into sets of common introns. CIWOG also identifies special classes of introns, in particular those with GC- or U12-type donor sites, which enables analyses of these introns in relation to their counterparts in the other genes in orthologous groups. The software is demonstrated with application to a comprehensive study of eight plant transcriptomes. Three specific examples are discussed: intron class conversion from GT- to GC-donor-type introns in monocots, plant U12-type intron conservation and a global analysis of intron evolution across the eight plant species.

BACKGROUND: Whole genome shotgun sequencing produces increasingly higher coverage of a genome with random sequence reads. Progressive whole genome assembly and eventual finishing sequencing is a process that typically takes several years for large eukaryotic genomes. In the interim, all sequence reads of public sequencing projects are made available in repositories such as the NCBI Trace Archive. For a particular locus, sequencing coverage may be high enough early on to produce a reliable local genome assembly. We have developed software, Tracembler, that facilitates in silico chromosome walking by recursively assembling reads of a selected species from the NCBI Trace Archive starting with reads that significantly match sequence seeds supplied by the user. RESULTS: Tracembler takes one or multiple DNA or protein sequence(s) as input to the NCBI Trace Archive BLAST engine to identify matching sequence reads from a species of interest. The BLAST searches are carried out recursively such that BLAST matching sequences identified in previous rounds of searches are used as new queries in subsequent rounds of BLAST searches. The recursive BLAST search stops when either no more new matching sequences are found, a given maximal number of queries is exhausted, or a specified maximum number of rounds of recursion is reached. All the BLAST matching sequences are then assembled into contigs based on significant sequence overlaps using the CAP3 program. We demonstrate the validity of the concept and software implementation with an example of successfully recovering a full-length Chrm2 gene as well as its upstream and downstream genomic regions from Rattus norvegicus reads. In a second example, a query with two adjacent Medicago truncatula genes as seeds resulted in a contig that likely identifies the microsyntenic homologous soybean locus. CONCLUSION: Tracembler streamlines the process of recursive database searches, sequence assembly, and gene identification in resulting contigs in attempts to identify homologous loci of genes of interest in species with emerging whole genome shotgun reads. A web server hosting Tracembler is provided at http://www.plantgdb.org/tool/tracembler/, and the software is also freely available from the authors for local installations.