Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes
Identifieur interne : 000837 ( Main/Exploration ); précédent : 000836; suivant : 000838Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes
Auteurs : Lisa K. Johnson [États-Unis] ; Harriet Alexander [États-Unis] ; C Titus Brown [États-Unis]Source :
- GigaScience [ 2047-217X ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Eukaryota.
- methods : Computational Biology, Gene Expression Profiling, Genomics.
- Databases, Genetic, Genome, High-Throughput Nucleotide Sequencing, Transcriptome, Workflow.
Abstract
New transcriptome assemblies contained the majority of previous contigs as well as new content. On average, 7.8% of the annotated contigs in the new assemblies were novel gene names not found in the previous assemblies. Taxonomic trends were observed in the assembly metrics. Assemblies from the Dinoflagellata showed a higher number of contigs and unique
Given current bioinformatics approaches, there is no single “best” reference transcriptome for a particular set of raw data. As the optimum transcriptome is a moving target, improving (or not) with new tools and approaches, automated and programmable pipelines are invaluable for managing the computationally intensive tasks required for re-processing large sets of samples with revised pipelines and ensuring a common evaluation workflow is applied to all samples. Thus, re-assembling existing data with new tools using automated and programmable pipelines may yield more accurate identification of taxon-specific trends across samples in addition to novel and useful products for the community.
Url:
DOI: 10.1093/gigascience/giy158
PubMed: 30544207
PubMed Central: 6481552
Affiliations:
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Le document en format XML
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<term>Eukaryota (genetics)</term>
<term>Gene Expression Profiling (methods)</term>
<term>Genome</term>
<term>Genomics (methods)</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Transcriptome</term>
<term>Workflow</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de profil d'expression de gènes ()</term>
<term>Bases de données génétiques</term>
<term>Biologie informatique ()</term>
<term>Eucaryotes (génétique)</term>
<term>Flux de travaux</term>
<term>Génome</term>
<term>Génomique ()</term>
<term>Séquençage nucléotidique à haut débit</term>
<term>Transcriptome</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Eukaryota</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Eucaryotes</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
<term>Gene Expression Profiling</term>
<term>Genomics</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Databases, Genetic</term>
<term>Genome</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Transcriptome</term>
<term>Workflow</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term>
<term>Bases de données génétiques</term>
<term>Biologie informatique</term>
<term>Flux de travaux</term>
<term>Génome</term>
<term>Génomique</term>
<term>Séquençage nucléotidique à haut débit</term>
<term>Transcriptome</term>
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<front><div type="abstract" xml:lang="en"><title>Abstract</title>
<sec id="abs1"><title>Background</title>
<p><italic>De novo</italic>
transcriptome assemblies are required prior to analyzing RNA sequencing data from a species without an existing reference genome or transcriptome. Despite the prevalence of transcriptomic studies, the effects of using different workflows, or “pipelines," on the resulting assemblies are poorly understood. Here, a pipeline was programmatically automated and used to assemble and annotate raw transcriptomic short-read data collected as part of the Marine Microbial Eukaryotic Transcriptome Sequencing Project. The resulting transcriptome assemblies were evaluated and compared against assemblies that were previously generated with a different pipeline developed by the National Center for Genome Research.</p>
</sec>
<sec id="abs2"><title>Results</title>
<p>New transcriptome assemblies contained the majority of previous contigs as well as new content. On average, 7.8% of the annotated contigs in the new assemblies were novel gene names not found in the previous assemblies. Taxonomic trends were observed in the assembly metrics. Assemblies from the Dinoflagellata showed a higher number of contigs and unique <italic>k</italic>
-mers than transcriptomes from other phyla, while assemblies from Ciliophora had a lower percentage of open reading frames compared to other phyla.</p>
</sec>
<sec id="abs3"><title>Conclusions</title>
<p>Given current bioinformatics approaches, there is no single “best” reference transcriptome for a particular set of raw data. As the optimum transcriptome is a moving target, improving (or not) with new tools and approaches, automated and programmable pipelines are invaluable for managing the computationally intensive tasks required for re-processing large sets of samples with revised pipelines and ensuring a common evaluation workflow is applied to all samples. Thus, re-assembling existing data with new tools using automated and programmable pipelines may yield more accurate identification of taxon-specific trends across samples in addition to novel and useful products for the community.</p>
</sec>
</div>
</front>
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</TEI>
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