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A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes
BACKGROUND: Transcriptome sequencing has become a method of choice for evolutionary studies in microbial eukaryotes due to low cost and minimal sample requirements. Transcriptome data has been extensively used in phylogenomic studies to infer ancient evolutionary histories. However, its utility in s...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240226/ https://www.ncbi.nlm.nih.gov/pubmed/30445905 http://dx.doi.org/10.1186/s12862-018-1283-1 |
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author | Tekle, Yonas I. Wood, Fiona C. |
author_facet | Tekle, Yonas I. Wood, Fiona C. |
author_sort | Tekle, Yonas I. |
collection | PubMed |
description | BACKGROUND: Transcriptome sequencing has become a method of choice for evolutionary studies in microbial eukaryotes due to low cost and minimal sample requirements. Transcriptome data has been extensively used in phylogenomic studies to infer ancient evolutionary histories. However, its utility in studying cryptic species diversity is not well explored. An empirical investigation was conducted to test the applicability of transcriptome data in resolving two major types of discordances at lower taxonomic levels. These include cases where species have the same morphology but different genetics (cryptic species) and species of different morphologies but have the same genetics. We built a species comparison bioinformatic pipeline that takes into account the nature of transcriptome data in amoeboid microbes exemplifying such discordances. RESULT: Our analyses of known or suspected cryptic species yielded consistent results regardless of the methods of culturing, RNA collection or sequencing. Over 95% of the single copy genes analyzed in samples of the same species sequenced using different methods and cryptic species had intra- and interspecific divergences below 2%. Only a minority of groups (2.91–4.87%) had high distances exceeding 2% in these taxa, which was likely caused by low data quality. This pattern was also observed in suspected genetically similar species with different morphologies. Transcriptome data consistently delineated all taxa above species level, including cryptically diverse species. Using our approach we were able to resolve cryptic species problems, uncover misidentification and discover new species. We also identified several potential barcode markers with varying evolutionary rates that can be used in lineages with different evolutionary histories. CONCLUSION: Our findings demonstrate that transcriptome data is appropriate for understanding cryptic species diversity in microbial eukaryotes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12862-018-1283-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6240226 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62402262018-11-26 A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes Tekle, Yonas I. Wood, Fiona C. BMC Evol Biol Research Article BACKGROUND: Transcriptome sequencing has become a method of choice for evolutionary studies in microbial eukaryotes due to low cost and minimal sample requirements. Transcriptome data has been extensively used in phylogenomic studies to infer ancient evolutionary histories. However, its utility in studying cryptic species diversity is not well explored. An empirical investigation was conducted to test the applicability of transcriptome data in resolving two major types of discordances at lower taxonomic levels. These include cases where species have the same morphology but different genetics (cryptic species) and species of different morphologies but have the same genetics. We built a species comparison bioinformatic pipeline that takes into account the nature of transcriptome data in amoeboid microbes exemplifying such discordances. RESULT: Our analyses of known or suspected cryptic species yielded consistent results regardless of the methods of culturing, RNA collection or sequencing. Over 95% of the single copy genes analyzed in samples of the same species sequenced using different methods and cryptic species had intra- and interspecific divergences below 2%. Only a minority of groups (2.91–4.87%) had high distances exceeding 2% in these taxa, which was likely caused by low data quality. This pattern was also observed in suspected genetically similar species with different morphologies. Transcriptome data consistently delineated all taxa above species level, including cryptically diverse species. Using our approach we were able to resolve cryptic species problems, uncover misidentification and discover new species. We also identified several potential barcode markers with varying evolutionary rates that can be used in lineages with different evolutionary histories. CONCLUSION: Our findings demonstrate that transcriptome data is appropriate for understanding cryptic species diversity in microbial eukaryotes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12862-018-1283-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-16 /pmc/articles/PMC6240226/ /pubmed/30445905 http://dx.doi.org/10.1186/s12862-018-1283-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Tekle, Yonas I. Wood, Fiona C. A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes |
title | A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes |
title_full | A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes |
title_fullStr | A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes |
title_full_unstemmed | A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes |
title_short | A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes |
title_sort | practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240226/ https://www.ncbi.nlm.nih.gov/pubmed/30445905 http://dx.doi.org/10.1186/s12862-018-1283-1 |
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