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Estimating transcriptome complexities across eukaryotes

BACKGROUND: Genomic complexity is a growing field of evolution, with case studies for comparative evolutionary analyses in model and emerging non-model systems. Understanding complexity and the functional components of the genome is an untapped wealth of knowledge ripe for exploration. With the “rem...

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Autores principales: Titus-McQuillan, James E., Nanni, Adalena V., McIntyre, Lauren M., Rogers, Rebekah L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10173493/
https://www.ncbi.nlm.nih.gov/pubmed/37170194
http://dx.doi.org/10.1186/s12864-023-09326-0
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author Titus-McQuillan, James E.
Nanni, Adalena V.
McIntyre, Lauren M.
Rogers, Rebekah L.
author_facet Titus-McQuillan, James E.
Nanni, Adalena V.
McIntyre, Lauren M.
Rogers, Rebekah L.
author_sort Titus-McQuillan, James E.
collection PubMed
description BACKGROUND: Genomic complexity is a growing field of evolution, with case studies for comparative evolutionary analyses in model and emerging non-model systems. Understanding complexity and the functional components of the genome is an untapped wealth of knowledge ripe for exploration. With the “remarkable lack of correspondence” between genome size and complexity, there needs to be a way to quantify complexity across organisms. In this study, we use a set of complexity metrics that allow for evaluating changes in complexity using TranD. RESULTS: We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity varies. In this study, we define three metrics – TpG, EpT, and EpG- to quantify the transcriptome's complexity that encapsulates the dynamics of alternative splicing. Here we compare complexity metrics across 1) whole genome annotations, 2) a filtered subset of orthologs, and 3) novel genes to elucidate the impacts of orthologs and novel genes in transcript model analysis. Effective Exon Number (EEN) issued to compare the distribution of exon sizes within transcripts against random expectations of uniform exon placement. EEN accounts for differences in exon size, which is important because novel gene differences in complexity for orthologs and whole-transcriptome analyses are biased towards low-complexity genes with few exons and few alternative transcripts. CONCLUSIONS: With our metric analyses, we are able to quantify changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step toward whole-transcriptome analysis in the emerging field of non-model evolutionary genomics, with key insights for evolutionary inference of complexity changes on deep timescales across the tree of life. We suggest a means to quantify biases generated in ortholog calling and correct complexity analysis for lineage-specific effects. With these metrics, we directly assay the quantitative properties of newly formed lineage-specific genes as they lower complexity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09326-0.
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spelling pubmed-101734932023-05-12 Estimating transcriptome complexities across eukaryotes Titus-McQuillan, James E. Nanni, Adalena V. McIntyre, Lauren M. Rogers, Rebekah L. BMC Genomics Research BACKGROUND: Genomic complexity is a growing field of evolution, with case studies for comparative evolutionary analyses in model and emerging non-model systems. Understanding complexity and the functional components of the genome is an untapped wealth of knowledge ripe for exploration. With the “remarkable lack of correspondence” between genome size and complexity, there needs to be a way to quantify complexity across organisms. In this study, we use a set of complexity metrics that allow for evaluating changes in complexity using TranD. RESULTS: We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity varies. In this study, we define three metrics – TpG, EpT, and EpG- to quantify the transcriptome's complexity that encapsulates the dynamics of alternative splicing. Here we compare complexity metrics across 1) whole genome annotations, 2) a filtered subset of orthologs, and 3) novel genes to elucidate the impacts of orthologs and novel genes in transcript model analysis. Effective Exon Number (EEN) issued to compare the distribution of exon sizes within transcripts against random expectations of uniform exon placement. EEN accounts for differences in exon size, which is important because novel gene differences in complexity for orthologs and whole-transcriptome analyses are biased towards low-complexity genes with few exons and few alternative transcripts. CONCLUSIONS: With our metric analyses, we are able to quantify changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step toward whole-transcriptome analysis in the emerging field of non-model evolutionary genomics, with key insights for evolutionary inference of complexity changes on deep timescales across the tree of life. We suggest a means to quantify biases generated in ortholog calling and correct complexity analysis for lineage-specific effects. With these metrics, we directly assay the quantitative properties of newly formed lineage-specific genes as they lower complexity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09326-0. BioMed Central 2023-05-11 /pmc/articles/PMC10173493/ /pubmed/37170194 http://dx.doi.org/10.1186/s12864-023-09326-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Titus-McQuillan, James E.
Nanni, Adalena V.
McIntyre, Lauren M.
Rogers, Rebekah L.
Estimating transcriptome complexities across eukaryotes
title Estimating transcriptome complexities across eukaryotes
title_full Estimating transcriptome complexities across eukaryotes
title_fullStr Estimating transcriptome complexities across eukaryotes
title_full_unstemmed Estimating transcriptome complexities across eukaryotes
title_short Estimating transcriptome complexities across eukaryotes
title_sort estimating transcriptome complexities across eukaryotes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10173493/
https://www.ncbi.nlm.nih.gov/pubmed/37170194
http://dx.doi.org/10.1186/s12864-023-09326-0
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