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PanEffect: a pan-genome visualization tool for variant effects in maize.
Andorf, Carson M; Haley, Olivia C; Hayford, Rita K; Portwood, John L; Harding, Stephen; Sen, Shatabdi; Cannon, Ethalinda K; Gardiner, Jack M; Kim, Hye-Seon; Woodhouse, Margaret R.
Afiliação
  • Andorf CM; USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States.
  • Haley OC; Department of Computer Science, Iowa State University, Ames, IA 50011, United States.
  • Hayford RK; USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States.
  • Portwood JL; USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States.
  • Harding S; USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States.
  • Sen S; USDA-ARS, Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Peoria, IL 61604, United States.
  • Cannon EK; Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, United States.
  • Gardiner JM; USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, United States.
  • Kim HS; Division of Animal Sciences, University of Missouri, Columbia, MO 65211, United States.
  • Woodhouse MR; USDA-ARS, Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Peoria, IL 61604, United States.
Bioinformatics ; 40(2)2024 02 01.
Article em En | MEDLINE | ID: mdl-38337024
ABSTRACT

SUMMARY:

Understanding the effects of genetic variants is crucial for accurately predicting traits and functional outcomes. Recent approaches have utilized artificial intelligence and protein language models to score all possible missense variant effects at the proteome level for a single genome, but a reliable tool is needed to explore these effects at the pan-genome level. To address this gap, we introduce a new tool called PanEffect. We implemented PanEffect at MaizeGDB to enable a comprehensive examination of the potential effects of coding variants across 50 maize genomes. The tool allows users to visualize over 550 million possible amino acid substitutions in the B73 maize reference genome and to observe the effects of the 2.3 million natural variations in the maize pan-genome. Each variant effect score, calculated from the Evolutionary Scale Modeling (ESM) protein language model, shows the log-likelihood ratio difference between B73 and all variants in the pan-genome. These scores are shown using heatmaps spanning benign outcomes to potential functional consequences. In addition, PanEffect displays secondary structures and functional domains along with the variant effects, offering additional functional and structural context. Using PanEffect, researchers now have a platform to explore protein variants and identify genetic targets for crop enhancement. AVAILABILITY AND IMPLEMENTATION The PanEffect code is freely available on GitHub (https//github.com/Maize-Genetics-and-Genomics-Database/PanEffect). A maize implementation of PanEffect and underlying datasets are available at MaizeGDB (https//www.maizegdb.org/effect/maize/).
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Zea mays / Bases de Dados Genéticas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Zea mays / Bases de Dados Genéticas Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article