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Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes
PREMISE: Robust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439824/ https://www.ncbi.nlm.nih.gov/pubmed/37601314 http://dx.doi.org/10.1002/aps3.11533 |
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author | Vuruputoor, Vidya S. Monyak, Daniel Fetter, Karl C. Webster, Cynthia Bhattarai, Akriti Shrestha, Bikash Zaman, Sumaira Bennett, Jeremy McEvoy, Susan L. Caballero, Madison Wegrzyn, Jill L. |
author_facet | Vuruputoor, Vidya S. Monyak, Daniel Fetter, Karl C. Webster, Cynthia Bhattarai, Akriti Shrestha, Bikash Zaman, Sumaira Bennett, Jeremy McEvoy, Susan L. Caballero, Madison Wegrzyn, Jill L. |
author_sort | Vuruputoor, Vidya S. |
collection | PubMed |
description | PREMISE: Robust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The annotation of plant genomes is particularly challenging due to their large sizes, abundant transposable elements, and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and method on protein‐coding gene predictions. METHODS: The impact of repeat masking, long‐read and short‐read inputs, and de novo and genome‐guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. The annotations were benchmarked for structural traits and sequence similarity. RESULTS: Benchmarks that reflect gene structures, reciprocal similarity search alignments, and mono‐exonic/multi‐exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA‐read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence‐based and ab initio approaches are recommended, and a combination of short and long reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome‐guided transcriptome assemblies, or full‐length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post‐processing with functional and structural filters is highly recommended. DISCUSSION: While the annotation of non‐model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation and present a more robust set of metrics to evaluate the resulting predictions. |
format | Online Article Text |
id | pubmed-10439824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104398242023-08-20 Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes Vuruputoor, Vidya S. Monyak, Daniel Fetter, Karl C. Webster, Cynthia Bhattarai, Akriti Shrestha, Bikash Zaman, Sumaira Bennett, Jeremy McEvoy, Susan L. Caballero, Madison Wegrzyn, Jill L. Appl Plant Sci Application Article PREMISE: Robust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The annotation of plant genomes is particularly challenging due to their large sizes, abundant transposable elements, and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and method on protein‐coding gene predictions. METHODS: The impact of repeat masking, long‐read and short‐read inputs, and de novo and genome‐guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. The annotations were benchmarked for structural traits and sequence similarity. RESULTS: Benchmarks that reflect gene structures, reciprocal similarity search alignments, and mono‐exonic/multi‐exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA‐read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence‐based and ab initio approaches are recommended, and a combination of short and long reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome‐guided transcriptome assemblies, or full‐length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post‐processing with functional and structural filters is highly recommended. DISCUSSION: While the annotation of non‐model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation and present a more robust set of metrics to evaluate the resulting predictions. John Wiley and Sons Inc. 2023-08-08 /pmc/articles/PMC10439824/ /pubmed/37601314 http://dx.doi.org/10.1002/aps3.11533 Text en © 2023 The Authors. Applications in Plant Sciences published by Wiley Periodicals LLC on behalf of Botanical Society of America. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Application Article Vuruputoor, Vidya S. Monyak, Daniel Fetter, Karl C. Webster, Cynthia Bhattarai, Akriti Shrestha, Bikash Zaman, Sumaira Bennett, Jeremy McEvoy, Susan L. Caballero, Madison Wegrzyn, Jill L. Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes |
title | Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes |
title_full | Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes |
title_fullStr | Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes |
title_full_unstemmed | Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes |
title_short | Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes |
title_sort | welcome to the big leaves: best practices for improving genome annotation in non‐model plant genomes |
topic | Application Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439824/ https://www.ncbi.nlm.nih.gov/pubmed/37601314 http://dx.doi.org/10.1002/aps3.11533 |
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