MOTIVATION: In time-critical clinical settings, such as precision medicine, genomic data needs to be processed as fast as possible to arrive at data-informed treatment decisions in a timely fashion. While sequencing throughput has dramatically increased over the past decade, bioinformatics analysis throughput has not been able to keep up with the pace of computer hardware improvement, and consequently has now turned into the primary bottleneck. Modern computer hardware today is capable of much higher performance than current genomic informatics algorithms can typically utilize, therefore presenting opportunities for significant improvement of performance. Accessing the raw sequencing data from BAM files, e.g. is a necessary and time-consuming step in nearly all sequence analysis tools, however existing programming libraries for BAM access do not take full advantage of the parallel input/output capabilities of storage devices. RESULTS: In an effort to stimulate the development of a new generation of faster sequence analysis tools, we developed quickBAM, a software library to accelerate sequencing data access by exploiting the parallelism in commodity storage hardware currently widely available. We demonstrate that analysis software ported to quickBAM consistently outperforms their current versions, in some cases finishing an analysis in under 3 min while the original version took 1.5 h, using the same storage solution. AVAILABILITY AND IMPLEMENTATION: Open source and freely available at https://gitlab.com/yiq/quickbam/, we envision that quickBAM will enable a new generation of high-performance informatics tools, either directly boosting their performance if they are currently data-access bottlenecked, or allow data-access to keep up with further optimizations in algorithms and compute techniques.

Abstract Motivation: A common question arises at the beginning of every experiment where RNA-Seq is used to detect differential gene expression between two conditions: How many reads should we sequence? Results: Scotty is an interactive web-based application that assists biologists to design an experiment with an appropriate sample size and read depth to satisfy the user-defined experimental objectives. This design can be based on data available from either pilot samples or publicly available datasets. Availability: Scotty can be freely accessed on the web at http://euler.bc.edu/marthlab/scotty/scotty.php Contact: [email protected] Supplementary information: Supplementary data are is available at Bioinformatics online.

MOTIVATION: High-throughput biological research requires simultaneous visualization as well as analysis of genomic data, e.g. read alignments, variant calls and genomic annotations. Traditionally, such integrative analysis required desktop applications operating on locally stored data. Many current terabyte-size datasets generated by large public consortia projects, however, are already only feasibly stored at specialist genome analysis centers. As even small laboratories can afford very large datasets, local storage and analysis are becoming increasingly limiting, and it is likely that most such datasets will soon be stored remotely, e.g. in the cloud. These developments will require web-based tools that enable users to access, analyze and view vast remotely stored data with a level of sophistication and interactivity that approximates desktop applications. As rapidly dropping cost enables researchers to collect data intended to answer questions in very specialized contexts, developers must also provide software libraries that empower users to implement customized data analyses and data views for their particular application. Such specialized, yet lightweight, applications would empower scientists to better answer specific biological questions than possible with general-purpose genome browsers currently available. RESULTS: Using recent advances in core web technologies (HTML5), we developed Scribl, a flexible genomic visualization library specifically targeting coordinate-based data such as genomic features, DNA sequence and genetic variants. Scribl simplifies the development of sophisticated web-based graphical tools that approach the dynamism and interactivity of desktop applications. AVAILABILITY AND IMPLEMENTATION: Software is freely available online at http://chmille4.github.com/Scribl/ and is implemented in JavaScript with all modern browsers supported.

UNLABELLED: ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa and Applied Biosystems' SOLiD. ART also allows the flexibility to use customized read error model parameters and quality profiles. AVAILABILITY: Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art.

BACKGROUND: DNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function. RESULTS: As members of the 1000 Genomes Project analysis effort, we investigated 697 samples in which 931 genes were targeted and sampled with 454 or Illumina paired-end sequencing. We developed a rigorous Bayesian method to detect CNVs in the genes, based on read depth within target regions. Despite substantial variability in read coverage across samples and targeted exons, we were able to identify 107 heterozygous deletions in the dataset. The experimentally determined false discovery rate (FDR) of the cleanest dataset from the Wellcome Trust Sanger Institute is 12.5%. We were able to substantially improve the FDR in a subset of gene deletion candidates that were adjacent to another gene deletion call (17 calls). The estimated sensitivity of our call-set was 45%. CONCLUSIONS: This study demonstrates that exonic sequencing datasets, collected both in population based and medical sequencing projects, will be a useful substrate for detecting genic CNV events, particularly deletions. Based on the number of events we found and the sensitivity of the methods in the present dataset, we estimate on average 16 genic heterozygous deletions per individual genome. Our power analysis informs ongoing and future projects about sequencing depth and uniformity of read coverage required for efficient detection.

MOTIVATION: Analysis of genomic sequencing data requires efficient, easy-to-use access to alignment results and flexible data management tools (e.g. filtering, merging, sorting, etc.). However, the enormous amount of data produced by current sequencing technologies is typically stored in compressed, binary formats that are not easily handled by the text-based parsers commonly used in bioinformatics research. RESULTS: We introduce a software suite for programmers and end users that facilitates research analysis and data management using BAM files. BamTools provides both the first C++ API publicly available for BAM file support as well as a command-line toolkit. AVAILABILITY: BamTools was written in C++, and is supported on Linux, Mac OSX and MS Windows. Source code and documentation are freely available at http://github.org/pezmaster31/bamtools.

SUMMARY: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. AVAILABILITY: http://vcftools.sourceforge.net

SUMMARY: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. AVAILABILITY: http://samtools.sourceforge.net.

BACKGROUND: Different classes of haplotype block algorithms exist and the ideal dataset to assess their performance would be to comprehensively re-sequence a large genomic region in a large population. Such data sets are expensive to collect. Alternatively, we performed coalescent simulations to generate haplotypes with a high marker density and compared block partitioning results from diversity based, LD based, and information theoretic algorithms under different values of SNP density and allele frequency. RESULTS: We simulated 1000 haplotypes using the standard coalescent for three world populations--European, African American, and East Asian--and applied three classes of block partitioning algorithms--diversity based, LD based, and information theoretic. We assessed algorithm differences in number, size, and coverage of blocks inferred under different conditions of SNP density, allele frequency, and sample size. Each algorithm inferred blocks differing in number, size, and coverage under different density and allele frequency conditions. Different partitions had few if any matching block boundaries. However they still overlapped and a high percentage of total chromosomal region was common to all methods. This percentage was generally higher with a higher density of SNPs and when rarer markers were included. CONCLUSION: A gold standard definition of a haplotype block is difficult to achieve, but collecting haplotypes covered with a high density of SNPs, partitioning them with a variety of block algorithms, and identifying regions common to all methods may be the best way to identify genomic regions that harbor SNP variants that cause disease.