Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms
Identifieur interne : 002229 ( Main/Exploration ); précédent : 002228; suivant : 002230Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms
Auteurs : Berat Z. Haznedaroglu [États-Unis] ; Darryl Reeves [États-Unis] ; Hamid Rismani-Yazdi [États-Unis] ; Jordan Peccia [États-Unis]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2012.
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Abstract
The
Analyses of single
This study demonstrated that different
Url:
DOI: 10.1186/1471-2105-13-170
PubMed: 22808927
PubMed Central: 3489510
Affiliations:
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Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Optimization of <italic>de novo</italic>
transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms</title>
<author><name sortKey="Haznedaroglu, Berat Z" sort="Haznedaroglu, Berat Z" uniqKey="Haznedaroglu B" first="Berat Z" last="Haznedaroglu">Berat Z. Haznedaroglu</name>
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<author><name sortKey="Reeves, Darryl" sort="Reeves, Darryl" uniqKey="Reeves D" first="Darryl" last="Reeves">Darryl Reeves</name>
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<series><title level="j">BMC Bioinformatics</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Gene Expression Profiling (methods)</term>
<term>Genome</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Molecular Sequence Annotation</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Analyse de profil d'expression de gènes ()</term>
<term>Analyse de séquence d'ADN</term>
<term>Annotation de séquence moléculaire</term>
<term>Génome</term>
<term>Logiciel</term>
<term>Séquençage nucléotidique à haut débit</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Gene Expression Profiling</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Genome</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Molecular Sequence Annotation</term>
<term>Sequence Analysis, DNA</term>
<term>Software</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term>
<term>Analyse de profil d'expression de gènes</term>
<term>Analyse de séquence d'ADN</term>
<term>Annotation de séquence moléculaire</term>
<term>Génome</term>
<term>Logiciel</term>
<term>Séquençage nucléotidique à haut débit</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>The <italic>k</italic>
-mer hash length is a key factor affecting the output of <italic>de novo</italic>
transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single <italic>k</italic>
-mer choices might result in the loss of unique contiguous sequences (contigs) and relevant biological information. A common solution to this problem is the clustering of single <italic>k</italic>
-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of <italic>k</italic>
-mer selection on the annotation output. This study provides an in-depth <italic>k</italic>
-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual <italic>k</italic>
-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual <italic>k</italic>
-mers and CAs) were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog identifiers (KOIs), and to determine a strategy that maximizes the recovery of biological information in a <italic>de novo</italic>
transcriptome assembly.</p>
</sec>
<sec><title>Results</title>
<p>Analyses of single <italic>k</italic>
-mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of <italic>k</italic>
-mers (<italic>k-</italic>
19 to <italic>k-</italic>
63). For each <italic>k</italic>
-mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other <italic>k</italic>
-mer assemblies. Producing a non-redundant CA of <italic>k</italic>
-mers 19 to 63 resulted in a more complete functional annotation than any single <italic>k</italic>
-mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs) in the assemblies of individual <italic>k</italic>
-mers (<italic>k-</italic>
19 to <italic>k-</italic>
63) that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented.</p>
</sec>
<sec><title>Conclusions</title>
<p>This study demonstrated that different <italic>k</italic>
-mer choices result in various quantities of unique contigs per single <italic>k</italic>
-mer assembly which affects biological information that is retrievable from the transcriptome. This undesirable effect can be minimized, but not eliminated, with clustering of multi-<italic>k</italic>
assemblies with redundancy removal. The complete extraction of biological information in <italic>de novo</italic>
transcriptomics studies requires both the production of a CA and efforts to identify unique contigs that are present in individual <italic>k</italic>
-mer assemblies but not in the CA.</p>
</sec>
</div>
</front>
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<author><name sortKey="Okuda, S" uniqKey="Okuda S">S Okuda</name>
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<author><name sortKey="Yoshizawa, Ac" uniqKey="Yoshizawa A">AC Yoshizawa</name>
</author>
<author><name sortKey="Kanehisa, M" uniqKey="Kanehisa M">M Kanehisa</name>
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<biblStruct><analytic><author><name sortKey="Aoki Kinoshita, Kf" uniqKey="Aoki Kinoshita K">KF Aoki-Kinoshita</name>
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<author><name sortKey="Kanehisa, M" uniqKey="Kanehisa M">M Kanehisa</name>
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<author><name sortKey="Trapnell, C" uniqKey="Trapnell C">C Trapnell</name>
</author>
<author><name sortKey="Pop, M" uniqKey="Pop M">M Pop</name>
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<author><name sortKey="Salzberg, S" uniqKey="Salzberg S">S Salzberg</name>
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<affiliations><list><country><li>États-Unis</li>
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<region><li>Connecticut</li>
<li>Massachusetts</li>
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<tree><country name="États-Unis"><region name="Connecticut"><name sortKey="Haznedaroglu, Berat Z" sort="Haznedaroglu, Berat Z" uniqKey="Haznedaroglu B" first="Berat Z" last="Haznedaroglu">Berat Z. Haznedaroglu</name>
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<name sortKey="Peccia, Jordan" sort="Peccia, Jordan" uniqKey="Peccia J" first="Jordan" last="Peccia">Jordan Peccia</name>
<name sortKey="Reeves, Darryl" sort="Reeves, Darryl" uniqKey="Reeves D" first="Darryl" last="Reeves">Darryl Reeves</name>
<name sortKey="Rismani Yazdi, Hamid" sort="Rismani Yazdi, Hamid" uniqKey="Rismani Yazdi H" first="Hamid" last="Rismani-Yazdi">Hamid Rismani-Yazdi</name>
<name sortKey="Rismani Yazdi, Hamid" sort="Rismani Yazdi, Hamid" uniqKey="Rismani Yazdi H" first="Hamid" last="Rismani-Yazdi">Hamid Rismani-Yazdi</name>
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