ArticleBMC evolutionary biology2018
A practical implementation of large transcriptomic data analysis to resolve cryptic species diversity problems in microbial eukaryotes.
Article in BMC evolutionary biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Quantifying genus-level divergence using 18S rDNA and its application to heterolobosea with discovery of a novel genus from Mombasa Kenya.Scientific reports · 2026Article
- Genomic divergence across the tree of life.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Omics of an Enigmatic Marine Amoeba Uncovers Unprecedented Gene Trafficking from Giant Viruses and Provides Insights into Its Complex Life Cycle.Microbiology research · 2023Article
- The draft genome of Cochliopodium minus reveals a complete meiosis toolkit and provides insight into the evolution of sexual mechanisms in Amoebozoa.Scientific reports · 2022Article
- Variation of natural selection in the Amoebozoa reveals heterogeneity across the phylogeny and adaptive evolution in diverse lineages.Frontiers in ecology and evolution · 2022Article
- Comprehensive comparative genomics reveals over 50 phyla of free-living and pathogenic bacteria are associated with diverse members of the amoebozoa.Scientific reports · 2021Article
- Identification of Eukaryotic Microalgal Strains.Journal of applied phycology · 2020Article
- Article
- A New Paramoeba Isolate From Florida Exhibits a Microtubule-Bound Endosymbiont Closely Associated With the Host Nucleus.The Journal of eukaryotic microbiologyArticle
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Authors and funding
2 authors.
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Abstract
backgroundTranscriptome 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.
resultOur 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.
conclusionOur findings demonstrate that transcriptome data is appropriate for understanding cryptic species diversity in microbial eukaryotes.
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