Summary: BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For mapping 100bp sequences, BWA-MEM shows better performance than several state-of-art read aligners to date. 
Availability and implementation: BWA-MEM is implemented as a component of BWA, which is available at this http URL. 
Contact: hengli@broadinstitute.org

Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM

ENDscript 2 is a friendly Web server for extracting and rendering a comprehensive analysis of primary to quaternary protein structure information in an automated way. This major upgrade has been fully re-engineered to enhance speed, accuracy and usability with interactive 3D visualization. It takes advantage of the new version 3 of ESPript, our well-known sequence alignment renderer, improved to handle a large number of data with reduced computation time. From a single PDB entry or file, ENDscript produces high quality figures displaying multiple sequence alignment of proteins homologous to the query, colored according to residue conservation. Furthermore, the experimental secondary structure elements and a detailed set of relevant biophysical and structural data are depicted. All this information and more are now mapped on interactive 3D PyMOL representations. Thanks to its adaptive and rigorous algorithm, beginner to expert users can modify settings to fine-tune ENDscript to their needs. ENDscript has also been upgraded as an open platform for the visualization of multiple biochemical and structural data coming from external biotool Web servers, with both 2D and 3D representations. ENDscript 2 and ESPript 3 are freely available at http://endscript.ibcp.fr and http://espript.ibcp.fr, respectively.

/pdf/deciphering-key-features-in-protein-structures-with-the-new-3kiylhufbn.pdf

Deciphering key features in protein structures with the new ENDscript server

A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core

Metagenomics emerged as an important field of research not only in microbial ecology but also for human health and disease, and metagenomic studies are performed on increasingly larger scales. While recent taxonomic classification programs achieve high speed by comparing genomic k-mers, they often lack sensitivity for overcoming evolutionary divergence, so that large fractions of the metagenomic reads remain unclassified. Here we present the novel metagenome classifier Kaiju, which finds maximum (in-)exact matches on the protein-level using the Burrows–Wheeler transform. We show in a genome exclusion benchmark that Kaiju classifies reads with higher sensitivity and similar precision compared with current k-mer-based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies up to 10 times more reads in real metagenomes. Kaiju can process millions of reads per minute and can run on a standard PC. Source code and web server are available at http://kaiju.binf.ku.dk
 . Here, Anders Krogh and colleagues describe Kaiju, a metagenome taxonomic classification program that uses maximum (in-)exact matches on the protein-level to account for evolutionary divergence. The authors show that Kaiju performs faster and is more sensitive compared with existing algorithms and can be used on a standard computer.

/pdf/fast-and-sensitive-taxonomic-classification-for-metagenomics-2xfdbajgi8.pdf

Fast and sensitive taxonomic classification for metagenomics with Kaiju

https://biblio.ugent.be/publication/8173949/file/8173970.pdf

Domestication and Divergence of Saccharomyces cerevisiae Beer Yeasts

Motivation: The imperfect sequence data produced by nextgeneration sequencing technologies has motivated the development of a number of short-read error correctors in recent years. The majority of methods focus on the correction of substitution errors, which are the dominant error source in data produced by Illumina sequencing technology. Existing tools either score high in terms of recall or precision but not consistently high in terms of both measures. Results: In this paper, we present Musket, an efficient multistage kmer based corrector for Illumina short-read data. We employ the kmer spectrum approach and introduce three correction techniques in a multistage workflow: two-sided conservative correction, one-sided aggressive correction and voting-based refinement. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing correctors. In addition, Musket is multithreaded using a master-slave model and demonstrates superior parallel scalability compared to all other evaluated correctors as well as a highly competitive overall execution time. Availability: Musket is available at http://musket.sourceforge.net. Contact: liuy@uni-mainz.de; bertil.schmidt@uni-mainz.de Supplementary information: available at Bioinformatics online

/pdf/musket-a-multistage-k-mer-spectrum-based-error-corrector-for-4ans8t298h.pdf

Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data.

Background
The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence databases.

/pdf/cudasw-3-0-accelerating-smith-waterman-protein-database-2xbvf30wt2.pdf

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions.

Motivation: The explosive growth of next-generation sequencing datasets poses a challenge to the mapping of reads to reference genomes in terms of alignment quality and execution speed. With the continuing progress of high-throughput sequencing technologies, read length is constantly increasing and many existing aligners are becoming inefficient as generated reads grow larger.

Results: We present CUSHAW2, a parallelized, accurate, and memory-efficient long read aligner. Our aligner is based on the seed-and-extend approach and uses maximal exact matches as seeds to find gapped alignments. We have evaluated and compared CUSHAW2 to the three other long read aligners BWA-SW, Bowtie2 and GASSST, by aligning simulated and real datasets to the human genome. The performance evaluation shows that CUSHAW2 is consistently among the highest-ranked aligners in terms of alignment quality for both single-end and paired-end alignment, while demonstrating highly competitive speed. Furthermore, our aligner shows good parallel scalability with respect to the number of CPU threads.

Availability: CUSHAW2, written in C++, and all simulated datasets are available at http://cushaw2.sourceforge.net

Contact:liuy@uni-mainz.de; bertil.schmidt@uni-mainz.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Long read alignment based on maximal exact match seeds

We present CUSHAW2-GPU to accelerate the CUSHAW2 algorithm using compute unified device architecture (CUDA)-enabled GPUs. Two critical GPU computing techniques, namely intertask hybrid CPU-GPU parallelism and tile-based Smith-Waterman map backtracking using CUDA, are investigated to facilitate fast alignments. By aligning both simulated and real reads to the human genome, our aligner yields comparable or better performance compared to BWA-SW, Bowtie2, and GEM. Furthermore, CUSHAW2-GPU with a Tesla K20c GPU achieves significant speedups over the multithreaded CUSHAW2, BWA-SW, Bowtie2, and GEM on the 12 cores of a high-end CPU for both single-end and paired-end alignment.

CUSHAW2-GPU: Empowering Faster Gapped Short-Read Alignment Using GPU Computing

DNA-based methods like PCR efficiently identify and quantify the taxon composition of complex biological materials, but are limited to detecting species targeted by the choice of the primer assay. We show here how untargeted deep sequencing of foodstuff total genomic DNA, followed by bioinformatic analysis of sequence reads, facilitates highly accurate identification of species from all kingdoms of life, at the same time enabling quantitative measurement of the main ingredients and detection of unanticipated food components. Sequence data simulation and real-case Illumina sequencing of DNA from reference sausages composed of mammalian (pig, cow, horse, sheep) and avian (chicken, turkey) species are able to quantify material correctly at the 1% discrimination level via a read counting approach. An additional metagenomic step facilitates identification of traces from animal, plant and microbial DNA including unexpected species, which is prospectively important for the detection of allergens and pathogens. Our data suggest that deep sequencing of total genomic DNA from samples of heterogeneous taxon composition promises to be a valuable screening tool for reference species identification and quantification in biosurveillance applications like food testing, potentially alleviating some of the problems in taxon representation and quantification associated with targeted PCR-based approaches.

/pdf/all-food-seq-afs-a-quantifiable-screen-for-species-in-541k1re4jz.pdf

Yongchao Liu

Papers

Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data.

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions.

Long read alignment based on maximal exact match seeds

CUSHAW2-GPU: Empowering Faster Gapped Short-Read Alignment Using GPU Computing

All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing