Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation.
Identifieur interne : 000B21 ( Main/Exploration ); précédent : 000B20; suivant : 000B22Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation.
Auteurs : Albi Celaj [Canada] ; Janet Markle [États-Unis] ; Jayne Danska [Canada] ; John Parkinson [Canada]Source :
- Microbiome ; 2014.
Abstract
Microbiome-wide gene expression profiling through high-throughput RNA sequencing ('metatranscriptomics') offers a powerful means to functionally interrogate complex microbial communities. Key to successful exploitation of these datasets is the ability to confidently match relatively short sequence reads to known bacterial transcripts. In the absence of reference genomes, such annotation efforts may be enhanced by assembling reads into longer contiguous sequences ('contigs'), prior to database search strategies. Since reads from homologous transcripts may derive from several species, represented at different abundance levels, it is not clear how well current assembly pipelines perform for metatranscriptomic datasets. Here we evaluate the performance of four currently employed assemblers including de novo transcriptome assemblers - Trinity and Oases; the metagenomic assembler - Metavelvet; and the recently developed metatranscriptomic assembler IDBA-MT.
DOI: 10.1186/2049-2618-2-39
PubMed: 25411636
Affiliations:
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Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Molecular Structure and Function, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, Ontario M5G 0A4, Canada ; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1</wicri:regionArea><orgName type="university">Université de Toronto</orgName><placeName><settlement type="city">Toronto</settlement><region type="state">Ontario</region></placeName></affiliation></author><author><name sortKey="Markle, Janet" sort="Markle, Janet" uniqKey="Markle J" first="Janet" last="Markle">Janet Markle</name><affiliation wicri:level="4"><nlm:affiliation>Department of Immunology, University of Toronto, Medical Sciences Building, 1 King's College Circle, Room 5207, Toronto, Ontario M5S 1A8, Canada ; Genetics and Genomic Biology, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, Ontario M5G 0A4, Canada ; 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Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada ; Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Molecular Structure and Function, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, Ontario M5G 0A4, Canada ; Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada ; Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8</wicri:regionArea><orgName type="university">Université de Toronto</orgName><placeName><settlement type="city">Toronto</settlement><region type="state">Ontario</region></placeName></affiliation></author></analytic><series><title level="j">Microbiome</title><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Microbiome-wide gene expression profiling through high-throughput RNA sequencing ('metatranscriptomics') offers a powerful means to functionally interrogate complex microbial communities. Key to successful exploitation of these datasets is the ability to confidently match relatively short sequence reads to known bacterial transcripts. In the absence of reference genomes, such annotation efforts may be enhanced by assembling reads into longer contiguous sequences ('contigs'), prior to database search strategies. Since reads from homologous transcripts may derive from several species, represented at different abundance levels, it is not clear how well current assembly pipelines perform for metatranscriptomic datasets. Here we evaluate the performance of four currently employed assemblers including de novo transcriptome assemblers - Trinity and Oases; the metagenomic assembler - Metavelvet; and the recently developed metatranscriptomic assembler IDBA-MT.</div></front></TEI><affiliations><list><country><li>Canada</li><li>États-Unis</li></country><region><li>Ontario</li><li>État de New York</li></region><settlement><li>Toronto</li></settlement><orgName><li>Université de Toronto</li></orgName></list><tree><country name="Canada"><region name="Ontario"><name sortKey="Celaj, Albi" sort="Celaj, Albi" uniqKey="Celaj A" first="Albi" last="Celaj">Albi Celaj</name></region><name sortKey="Danska, Jayne" sort="Danska, Jayne" uniqKey="Danska J" first="Jayne" last="Danska">Jayne Danska</name><name sortKey="Parkinson, John" sort="Parkinson, John" uniqKey="Parkinson J" first="John" last="Parkinson">John Parkinson</name></country><country name="États-Unis"><region name="État de New York"><name sortKey="Markle, Janet" sort="Markle, Janet" uniqKey="Markle J" first="Janet" last="Markle">Janet Markle</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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