Cargando…
A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly
BACKGROUND: The lack of genomic resources can present challenges for studies of non-model organisms. Transcriptome sequencing offers an attractive method to gather information about genes and gene expression without the need for a reference genome. However, it is unclear what sequencing depth is ade...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655071/ https://www.ncbi.nlm.nih.gov/pubmed/23496952 http://dx.doi.org/10.1186/1471-2164-14-167 |
_version_ | 1782269826855600128 |
---|---|
author | Francis, Warren R Christianson, Lynne M Kiko, Rainer Powers, Meghan L Shaner, Nathan C D Haddock, Steven H |
author_facet | Francis, Warren R Christianson, Lynne M Kiko, Rainer Powers, Meghan L Shaner, Nathan C D Haddock, Steven H |
author_sort | Francis, Warren R |
collection | PubMed |
description | BACKGROUND: The lack of genomic resources can present challenges for studies of non-model organisms. Transcriptome sequencing offers an attractive method to gather information about genes and gene expression without the need for a reference genome. However, it is unclear what sequencing depth is adequate to assemble the transcriptome de novo for these purposes. RESULTS: We assembled transcriptomes of animals from six different phyla (Annelids, Arthropods, Chordates, Cnidarians, Ctenophores, and Molluscs) at regular increments of reads using Velvet/Oases and Trinity to determine how read count affects the assembly. This included an assembly of mouse heart reads because we could compare those against the reference genome that is available. We found qualitative differences in the assemblies of whole-animals versus tissues. With increasing reads, whole-animal assemblies show rapid increase of transcripts and discovery of conserved genes, while single-tissue assemblies show a slower discovery of conserved genes though the assembled transcripts were often longer. A deeper examination of the mouse assemblies shows that with more reads, assembly errors become more frequent but such errors can be mitigated with more stringent assembly parameters. CONCLUSIONS: These assembly trends suggest that representative assemblies are generated with as few as 20 million reads for tissue samples and 30 million reads for whole-animals for RNA-level coverage. These depths provide a good balance between coverage and noise. Beyond 60 million reads, the discovery of new genes is low and sequencing errors of highly-expressed genes are likely to accumulate. Finally, siphonophores (polymorphic Cnidarians) are an exception and possibly require alternate assembly strategies. |
format | Online Article Text |
id | pubmed-3655071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36550712013-05-16 A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly Francis, Warren R Christianson, Lynne M Kiko, Rainer Powers, Meghan L Shaner, Nathan C D Haddock, Steven H BMC Genomics Research Article BACKGROUND: The lack of genomic resources can present challenges for studies of non-model organisms. Transcriptome sequencing offers an attractive method to gather information about genes and gene expression without the need for a reference genome. However, it is unclear what sequencing depth is adequate to assemble the transcriptome de novo for these purposes. RESULTS: We assembled transcriptomes of animals from six different phyla (Annelids, Arthropods, Chordates, Cnidarians, Ctenophores, and Molluscs) at regular increments of reads using Velvet/Oases and Trinity to determine how read count affects the assembly. This included an assembly of mouse heart reads because we could compare those against the reference genome that is available. We found qualitative differences in the assemblies of whole-animals versus tissues. With increasing reads, whole-animal assemblies show rapid increase of transcripts and discovery of conserved genes, while single-tissue assemblies show a slower discovery of conserved genes though the assembled transcripts were often longer. A deeper examination of the mouse assemblies shows that with more reads, assembly errors become more frequent but such errors can be mitigated with more stringent assembly parameters. CONCLUSIONS: These assembly trends suggest that representative assemblies are generated with as few as 20 million reads for tissue samples and 30 million reads for whole-animals for RNA-level coverage. These depths provide a good balance between coverage and noise. Beyond 60 million reads, the discovery of new genes is low and sequencing errors of highly-expressed genes are likely to accumulate. Finally, siphonophores (polymorphic Cnidarians) are an exception and possibly require alternate assembly strategies. BioMed Central 2013-03-12 /pmc/articles/PMC3655071/ /pubmed/23496952 http://dx.doi.org/10.1186/1471-2164-14-167 Text en Copyright © 2013 Francis et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Francis, Warren R Christianson, Lynne M Kiko, Rainer Powers, Meghan L Shaner, Nathan C D Haddock, Steven H A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly |
title | A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly |
title_full | A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly |
title_fullStr | A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly |
title_full_unstemmed | A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly |
title_short | A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly |
title_sort | comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655071/ https://www.ncbi.nlm.nih.gov/pubmed/23496952 http://dx.doi.org/10.1186/1471-2164-14-167 |
work_keys_str_mv | AT franciswarrenr acomparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT christiansonlynnem acomparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT kikorainer acomparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT powersmeghanl acomparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT shanernathanc acomparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT dhaddockstevenh acomparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT franciswarrenr comparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT christiansonlynnem comparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT kikorainer comparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT powersmeghanl comparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT shanernathanc comparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly AT dhaddockstevenh comparisonacrossnonmodelanimalssuggestsanoptimalsequencingdepthfordenovotranscriptomeassembly |