MegaGTA: a sensitive and accurate metagenomic gene-targeted assembler using iterative de Bruijn graphs
Identifieur interne : 000D00 ( Main/Exploration ); précédent : 000C99; suivant : 000D01MegaGTA: a sensitive and accurate metagenomic gene-targeted assembler using iterative de Bruijn graphs
Auteurs : Dinghua Li [Hong Kong] ; Yukun Huang [Hong Kong] ; Chi-Ming Leung [Hong Kong] ; Ruibang Luo [Hong Kong] ; Hing-Fung Ting [Hong Kong] ; Tak-Wah Lam [Hong Kong]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical : Soil.
- methods : Metagenomics.
- Algorithms, Databases, Genetic, Genes, Humans, Pilot Projects, Reference Standards, Rhizosphere, Software, Statistics as Topic.
Abstract
The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of
In this paper, we present a new gene-targeted assembler MegaGTA, which attempts to improve Xander in different aspects. Quality-wise, it utilizes iterative
We have compared MegaGTA and Xander on an HMP-defined mock metagenomic dataset, and showed that MegaGTA excelled in both sensitivity and accuracy. On a large rhizosphere soil metagenomic sample (327Gbp), MegaGTA produced 9.7–19.3% more contigs than Xander, and these contigs were assigned to 10–25% more gene references. In our experiments, MegaGTA, depending on the number of
MegaGTA improves on the algorithm of Xander and achieves higher sensitivity, accuracy and speed. Moreover, it is capable of assembling gene sequences from ultra-large metagenomic datasets. Its source code is freely available at
Url:
DOI: 10.1186/s12859-017-1825-3
PubMed: 29072142
PubMed Central: 5657035
Affiliations:
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Le document en format XML
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<series><title level="j">BMC Bioinformatics</title>
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<term>Pilot Projects</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of <italic>de Bruijn</italic>
graph gives obvious advantage over other assembly methods. Xander, as a pilot study, indeed has a lot of room for improvement. Apart from its slow speed, Xander uses only 1 <italic>k</italic>
-mer size for graph construction and whatever choice of <italic>k</italic>
will compromise either sensitivity or accuracy. Xander uses a Bloom-filter representation of <italic>de Bruijn</italic>
graph to achieve a lower memory footprint. Bloom filters bring in false positives, and it is not clear how this would impact the quality of assembly. Xander does not keep track of the multiplicity of <italic>k</italic>
-mers, which would have been an effective way to differentiate between erroneous <italic>k</italic>
-mers and correct <italic>k</italic>
-mers.</p>
</sec>
<sec><title>Results</title>
<p id="Par2">In this paper, we present a new gene-targeted assembler MegaGTA, which attempts to improve Xander in different aspects. Quality-wise, it utilizes iterative <italic>de Bruijn</italic>
graphs to take full advantage of multiple <italic>k</italic>
-mer sizes to make the best of both sensitivity and accuracy. Computation-wise, it employs succinct <italic>de Bruijn</italic>
graphs (SdBG) to achieve low memory footprint and high speed (the latter is benefited from a highly efficient parallel algorithm for constructing SdBG). Unlike Bloom filters, an SdBG is an exact representation of a <italic>de Bruijn</italic>
graph. It enables MegaGTA to avoid false-positive contigs and to easily incorporate the multiplicity of <italic>k</italic>
-mers for building better HMM model.</p>
<p id="Par3">We have compared MegaGTA and Xander on an HMP-defined mock metagenomic dataset, and showed that MegaGTA excelled in both sensitivity and accuracy. On a large rhizosphere soil metagenomic sample (327Gbp), MegaGTA produced 9.7–19.3% more contigs than Xander, and these contigs were assigned to 10–25% more gene references. In our experiments, MegaGTA, depending on the number of <italic>k</italic>
-mers used, is two to ten times faster than Xander.</p>
</sec>
<sec><title>Conclusion</title>
<p id="Par4">MegaGTA improves on the algorithm of Xander and achieves higher sensitivity, accuracy and speed. Moreover, it is capable of assembling gene sequences from ultra-large metagenomic datasets. Its source code is freely available at <ext-link ext-link-type="uri" xlink:href="https://github.com/HKU-BAL/megagta">https://github.com/HKU-BAL/megagta</ext-link>
.</p>
</sec>
</div>
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<name sortKey="Leung, Chi Ming" sort="Leung, Chi Ming" uniqKey="Leung C" first="Chi-Ming" last="Leung">Chi-Ming Leung</name>
<name sortKey="Leung, Chi Ming" sort="Leung, Chi Ming" uniqKey="Leung C" first="Chi-Ming" last="Leung">Chi-Ming Leung</name>
<name sortKey="Luo, Ruibang" sort="Luo, Ruibang" uniqKey="Luo R" first="Ruibang" last="Luo">Ruibang Luo</name>
<name sortKey="Luo, Ruibang" sort="Luo, Ruibang" uniqKey="Luo R" first="Ruibang" last="Luo">Ruibang Luo</name>
<name sortKey="Ting, Hing Fung" sort="Ting, Hing Fung" uniqKey="Ting H" first="Hing-Fung" last="Ting">Hing-Fung Ting</name>
</country>
</tree>
</affiliations>
</record>
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