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PreprintCited by 9Genomics and Phylogenetic StudiesChromosomal and Genetic VariationsBioinformatics and Genomic Networks

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Fishing for a reelGene: evaluating gene models with evolution and machine learning

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Schulz, Aimee J, Zhai, Jingjing, AuBuchon-Elder, Taylor, El-Walid, Mohamed, Ferebee, Taylor H, Gilmore, Elizabeth H, Hufford, Matthew B, Johnson, Lynn C, Kellogg, Elizabeth A, La, Thuy, Long, Evan, Miller, Zachary R, Romay, M Cinta, Seetharam, Arun S., Stitzer, Michelle C, Wrightsman, Travis, Buckler, Edward S, Monier, Brandon, Hsu, Sheng-Kai·2023·The Plant Journal
DOI 10.1101/2023.09.19.558246

Preprint — published as 10.1111/tpj.70483 · Paper page

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Abstract

Assembled genomes and their associated annotations have transformed our study of gene function. However, each new assembly generates new gene models. Inconsistencies between annotations likely arise from biological and technical causes, including pseudogene misclassification, transposon activity, and intron retention from sequencing of unspliced transcripts. To evaluate gene model predictions, we developed reelGene, a pipeline of machine learning models focused on (1) transcription boundaries, (2) mRNA integrity, and (3) protein structure. The first two models leverage sequence characteristics and evolutionary conservation across related taxa to learn the grammar of conserved transcription boundaries and mRNA sequences, while the third uses conserved evolutionary grammar of protein sequences to predict whether a gene can produce a protein. Evaluating 1.8 million gene models in maize, reelGene found that 28% were incorrectly annotated or nonfunctional. By leveraging a large cohort of related species and through learning the conserved grammar of proteins, reelGene provides a tool for both evaluating gene model accuracy and genome biology.

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Metadata

Authors
Schulz, A. J., Zhai, J., AuBuchon-Elder, T., El-Walid, M., Ferebee, T. H., Gilmore, E. H., Hufford, M. B., Johnson, L. C., Kellogg, E. A., La, T., Long, E., Miller, Z. R., Romay, M. C., Seetharam, A. S., Stitzer, M. C., Wrightsman, T., Buckler, E. S., Monier, B., & Hsu, S.-K.
Affiliations
  • Cornell University (US)
  • Iowa State University (US)
  • Donald Danforth Plant Science Center (US)
  • Agricultural Research Service (US)
Corresponding author
Schulz, Aimee J
ORCID
Venue
The Plant Journal
Publisher
openRxiv
Published
2023-09-21
Volume
123
Issue
6
Pages
e70483-e70483
ISSN
0960-7412 (ISSN-L)
DOI
10.1101/2023.09.19.558246
OpenAlex
W4414397231
Cited by
9
References
1
Citations by year
2026: 2 · 2025: 4 · 2024: 3
FWCI
1.81
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • The Plant Journal (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (1822330, 2139899, 1907343)
  • U.S. Department of Agriculture
  • National Institute of Food and Agriculture (2022‐67011‐36564)
  • Agricultural Research Service (8062‐21000‐043‐00‐D, 6066-21310-005-00-D, 0201- 88888-002-000D, 5030–21000-072–00-D, 0201-88888-003-000D)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Chromosomal and Genetic Variations
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
genome annotation, mRNA integrity, transposon activity, intron retention, maize, dispensable genes, duplicate gene retention, Andropogoneae, MaizeGDB, machine learning, evolutionary conservation
MeSH terms
Machine Learning, Zea mays, Models, Genetic, Plant Proteins, RNA, Messenger, Genes, Plant, Genome, Plant, Evolution, Molecular, Molecular Sequence Annotation

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