Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes
Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes
dc.contributor.author | Johnson, Lisa K. | |
dc.contributor.author | Alexander, Harriet | |
dc.contributor.author | Brown, C. Titus | |
dc.date.accessioned | 2019-09-05T20:55:06Z | |
dc.date.available | 2019-09-05T20:55:06Z | |
dc.date.issued | 2019-04 | |
dc.description | © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Johnson, L. K., Alexander, H., & Brown, C. T. Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. Gigascience, 8(4), (2019): giy158, doi: 10.1093/gigascience/giy158. | en_US |
dc.description.abstract | Background: De novo 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. Results: 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 k-mers than transcriptomes from other phyla, while assemblies from Ciliophora had a lower percentage of open reading frames compared to other phyla. Conclusions: 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. | en_US |
dc.description.sponsorship | Funding was provided by the Gordon and Betty Moore Foundation under award number GBMF4551 to C.T.B. Jetstream cloud platform was used with XSEDE allocation TG-BIO160028 [66, 67]. | en_US |
dc.identifier.citation | Johnson, L. K., Alexander, H., & Brown, C. T. (2019). Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. Gigascience, 8(4), giy158. | en_US |
dc.identifier.doi | 10.1093/gigascience/giy158 | |
dc.identifier.uri | https://hdl.handle.net/1912/24513 | |
dc.publisher | Oxford University Press | en_US |
dc.relation.uri | https://doi.org/10.1093/gigascience/giy158 | |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | marine microbial eukaryote | en_US |
dc.subject | transcriptome assembly | en_US |
dc.subject | automated pipeline | en_US |
dc.subject | re-analysis | en_US |
dc.title | Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | e4b91e8f-672d-407b-83ed-b9fec64cc480 | |
relation.isAuthorOfPublication | 4fe98a87-cf8c-42f3-873c-4d7107297646 | |
relation.isAuthorOfPublication | 6954fbae-53a5-4823-8462-66615c297004 | |
relation.isAuthorOfPublication.latestForDiscovery | e4b91e8f-672d-407b-83ed-b9fec64cc480 |
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