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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Vydané v:International journal of bioinformatics research and applications Ročník 10; číslo 4-5; s. 447
Hlavní autori: Thrasher, Andrew, Musgrave, Zachary, Kachmarck, Brian, Thain, Douglas, Emrich, Scott
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland 2014
Predmet:
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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Abstract 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.
AbstractList 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.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.
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.
Author Kachmarck, Brian
Musgrave, Zachary
Thrasher, Andrew
Thain, Douglas
Emrich, Scott
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  organization: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
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  surname: Emrich
  fullname: Emrich, Scott
  organization: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
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CitedBy_id crossref_primary_10_1002_cpe_4683
crossref_primary_10_3389_fgene_2020_00876
crossref_primary_10_1016_j_dib_2020_106531
crossref_primary_10_1016_j_procs_2015_10_116
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genome annotation
grid computing
next generation sequencing
bioinformatics
work queue
explicit data transfer
cloud computing
clusters
distributed computing
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SubjectTerms Algorithms
Animals
Anopheles - genetics
Caenorhabditis - genetics
Cluster Analysis
Computational Biology - methods
Computer Systems
Genome
High-Throughput Nucleotide Sequencing - methods
Software
Tsetse Flies - genetics
Title Scaling up genome annotation using MAKER and work queue
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