K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of Trinity
Identifieur interne : 000D26 ( Main/Exploration ); précédent : 000D25; suivant : 000D27K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of Trinity
Auteurs : Chang Sik Kim [Royaume-Uni] ; Martyn D. Winn ; Vipin Sachdeva [États-Unis] ; Kirk E. Jordan [États-Unis]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : RNA.
- chemical , genetics : RNA.
- Algorithms, Cluster Analysis, High-Throughput Nucleotide Sequencing, Sequence Analysis, RNA, Transcriptome.
Abstract
De novo transcriptome assembly is an important technique for understanding gene expression in non-model organisms. Many de novo assemblers using the de Bruijn graph of a set of the RNA sequences rely on in-memory representation of this graph. However, current methods analyse the complete set of read-derived k-mer sequence at once, resulting in the need for computer hardware with large shared memory.
We introduce a novel approach that clusters k-mers as the first step. The clusters correspond to small sets of gene products, which can be processed quickly to give candidate transcripts. We implement the clustering step using the MapReduce approach for parallelising the analysis of large datasets, which enables the use of compute clusters. The computational task is distributed across the compute system using the industry-standard MPI protocol, and no specialised hardware is required. Using this approach, we have re-implemented the Inchworm module from the widely used Trinity pipeline, and tested the method in the context of the full Trinity pipeline. Validation tests on a range of real datasets show large reductions in the runtime and per-node memory requirements, when making use of a compute cluster.
Our study shows that MapReduce-based clustering has great potential for distributing challenging sequencing problems, without loss of accuracy. Although we have focussed on the Trinity package, we propose that such clustering is a useful initial step for other assembly pipelines.
The online version of this article (10.1186/s12859-017-1881-8) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12859-017-1881-8
PubMed: 29100493
PubMed Central: 5670514
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">De novo transcriptome assembly is an important technique for understanding gene expression in non-model organisms. Many de novo assemblers using the de Bruijn graph of a set of the RNA sequences rely on in-memory representation of this graph. However, current methods analyse the complete set of read-derived k-mer sequence at once, resulting in the need for computer hardware with large shared memory.</p>
</sec>
<sec><title>Results</title>
<p id="Par2">We introduce a novel approach that clusters k-mers as the first step. The clusters correspond to small sets of gene products, which can be processed quickly to give candidate transcripts. We implement the clustering step using the MapReduce approach for parallelising the analysis of large datasets, which enables the use of compute clusters. The computational task is distributed across the compute system using the industry-standard MPI protocol, and no specialised hardware is required. Using this approach, we have re-implemented the Inchworm module from the widely used Trinity pipeline, and tested the method in the context of the full Trinity pipeline. Validation tests on a range of real datasets show large reductions in the runtime and per-node memory requirements, when making use of a compute cluster.</p>
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<sec><title>Conclusions</title>
<p id="Par3">Our study shows that MapReduce-based clustering has great potential for distributing challenging sequencing problems, without loss of accuracy. Although we have focussed on the Trinity package, we propose that such clustering is a useful initial step for other assembly pipelines.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (10.1186/s12859-017-1881-8) contains supplementary material, which is available to authorized users.</p>
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