Scaling up genome annotation using MAKER and work queue

Next generation sequencing technologies have enabled sequencing many genomes. Because of the overall increasing demand and the inherent parallelism available in many required analyses, these bioinformatics applications should ideally run on clusters, clouds and/or grids. We present a modified annota...

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Veröffentlicht in:International journal of bioinformatics research and applications Jg. 10; H. 4-5; S. 447
Hauptverfasser: Thrasher, Andrew, Musgrave, Zachary, Kachmarck, Brian, Thain, Douglas, Emrich, Scott
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland 2014
Schlagworte:
Algorithms
Animals
Anopheles - genetics
Caenorhabditis - genetics
Cluster Analysis
Computational Biology - methods
Computer Systems
Genome
High-Throughput Nucleotide Sequencing - methods
Software
Tsetse Flies - genetics
Caenorhabditis japonica
genome annotation
grid computing
next generation sequencing
bioinformatics
work queue
explicit data transfer
cloud computing
clusters
distributed computing
ISSN:1744-5485
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Zusammenfassung:Next generation sequencing technologies have enabled sequencing many genomes. Because of the overall increasing demand and the inherent parallelism available in many required analyses, these bioinformatics applications should ideally run on clusters, clouds and/or grids. We present a modified annotation framework that achieves a speed-up of 45x using 50 workers using a Caenorhabditis japonica test case. We also evaluate these modifications within the Amazon EC2 cloud framework. The underlying genome annotation (MAKER) is parallelised as an MPI application. Our framework enables it to now run without MPI while utilising a wide variety of distributed computing resources. This parallel framework also allows easy explicit data transfer, which helps overcome a major limitation of bioinformatics tools that often rely on shared file systems. Combined, our proposed framework can be used, even during early stages of development, to easily run sequence analysis tools on clusters, grids and clouds.
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ISSN:1744-5485
DOI:10.1504/IJBRA.2014.062994