57,746 research outputs found
Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing
<p>Abstract</p> <p>Background</p> <p>Massively parallel sequencing readouts of epigenomic assays are enabling integrative genome-wide analyses of genomic and epigenomic variation. Pash 3.0 performs sequence comparison and read mapping and can be employed as a module within diverse configurable analysis pipelines, including ChIP-Seq and methylome mapping by whole-genome bisulfite sequencing.</p> <p>Results</p> <p>Pash 3.0 generally matches the accuracy and speed of niche programs for fast mapping of short reads, and exceeds their performance on longer reads generated by a new generation of massively parallel sequencing technologies. By exploiting longer read lengths, Pash 3.0 maps reads onto the large fraction of genomic DNA that contains repetitive elements and polymorphic sites, including indel polymorphisms.</p> <p>Conclusions</p> <p>We demonstrate the versatility of Pash 3.0 by analyzing the interaction between CpG methylation, CpG SNPs, and imprinting based on publicly available whole-genome shotgun bisulfite sequencing data. Pash 3.0 makes use of gapped k-mer alignment, a non-seed based comparison method, which is implemented using multi-positional hash tables. This allows Pash 3.0 to run on diverse hardware platforms, including individual computers with standard RAM capacity, multi-core hardware architectures and large clusters.</p
High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP
This paper explores the pros and cons of reconfigurable computing in the form of FPGAs for high performance efficient computing. In particular, the paper presents the results of a comparative study between three different acceleration technologies, namely, Field Programmable Gate Arrays (FPGAs), Graphics Processor Units (GPUs), and IBMâs Cell Broadband Engine (Cell BE), in the design and implementation of the widely-used Smith-Waterman pairwise sequence alignment algorithm, with general purpose processors as a base reference implementation. Comparison criteria include speed, energy consumption, and purchase and development costs. The study shows that FPGAs largely outperform all other implementation platforms on performance per watt criterion and perform better than all other platforms on performance per dollar criterion, although by a much smaller margin. Cell BE and GPU come second and third, respectively, on both performance per watt and performance per dollar criteria. In general, in order to outperform other technologies on performance per dollar criterion (using currently available hardware and development tools), FPGAs need to achieve at least two orders of magnitude speed-up compared to general-purpose processors and one order of magnitude speed-up compared to domain-specific technologies such as GPUs
Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools
This paper introduces a high-throughput software tool framework called {\it
sam2bam} that enables users to significantly speedup pre-processing for
next-generation sequencing data. The sam2bam is especially efficient on
single-node multi-core large-memory systems. It can reduce the runtime of data
pre-processing in marking duplicate reads on a single node system by 156-186x
compared with de facto standard tools. The sam2bam consists of parallel
software components that can fully utilize the multiple processors, available
memory, high-bandwidth of storage, and hardware compression accelerators if
available.
The sam2bam provides file format conversion between well-known genome file
formats, from SAM to BAM, as a basic feature. Additional features such as
analyzing, filtering, and converting the input data are provided by {\it
plug-in} tools, e.g., duplicate marking, which can be attached to sam2bam at
runtime.
We demonstrated that sam2bam could significantly reduce the runtime of NGS
data pre-processing from about two hours to about one minute for a whole-exome
data set on a 16-core single-node system using up to 130 GB of memory. The
sam2bam could reduce the runtime for whole-genome sequencing data from about 20
hours to about nine minutes on the same system using up to 711 GB of memory
SWAPHI: Smith-Waterman Protein Database Search on Xeon Phi Coprocessors
The maximal sensitivity of the Smith-Waterman (SW) algorithm has enabled its
wide use in biological sequence database search. Unfortunately, the high
sensitivity comes at the expense of quadratic time complexity, which makes the
algorithm computationally demanding for big databases. In this paper, we
present SWAPHI, the first parallelized algorithm employing Xeon Phi
coprocessors to accelerate SW protein database search. SWAPHI is designed based
on the scale-and-vectorize approach, i.e. it boosts alignment speed by
effectively utilizing both the coarse-grained parallelism from the many
co-processing cores (scale) and the fine-grained parallelism from the 512-bit
wide single instruction, multiple data (SIMD) vectors within each core
(vectorize). By searching against the large UniProtKB/TrEMBL protein database,
SWAPHI achieves a performance of up to 58.8 billion cell updates per second
(GCUPS) on one coprocessor and up to 228.4 GCUPS on four coprocessors.
Furthermore, it demonstrates good parallel scalability on varying number of
coprocessors, and is also superior to both SWIPE on 16 high-end CPU cores and
BLAST+ on 8 cores when using four coprocessors, with the maximum speedup of
1.52 and 1.86, respectively. SWAPHI is written in C++ language (with a set of
SIMD intrinsics), and is freely available at http://swaphi.sourceforge.net.Comment: A short version of this paper has been accepted by the IEEE ASAP 2014
conferenc
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing
continues to decline and small sequencing devices become available. Enormous
community databases store and share this data with the research community, but
some of these genomic data analysis problems require large scale computational
platforms to meet both the memory and computational requirements. These
applications differ from scientific simulations that dominate the workload on
high end parallel systems today and place different requirements on programming
support, software libraries, and parallel architectural design. For example,
they involve irregular communication patterns such as asynchronous updates to
shared data structures. We consider several problems in high performance
genomics analysis, including alignment, profiling, clustering, and assembly for
both single genomes and metagenomes. We identify some of the common
computational patterns or motifs that help inform parallelization strategies
and compare our motifs to some of the established lists, arguing that at least
two key patterns, sorting and hashing, are missing
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