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Deciphering the Gene Expression and Alternative Splicing Basis of Muscle Development Through Interpretable Machine

Xiaodong Tan1,2, Minjie Huang1,2, Yuting Jin1,2

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Summary

This study uses machine learning to identify key genes and splicing events linked to chicken breast muscle yield. AI models accurately predict muscle percentage, aiding poultry breeding strategies.

Keywords:
alternative splicingbreast musclechickenmachine learningshapley additive exPlanations

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Area of Science:

  • Genomics
  • Bioinformatics
  • Animal Breeding

Background:

  • Meat yield is critical for poultry breeding programs.
  • Identifying molecular markers for muscle yield is essential for genetic improvement.

Purpose of the Study:

  • To apply transcriptome sequencing and machine learning to identify molecular markers for chicken breast muscle weight percentage (BrP).
  • To develop accurate predictive models for BrP using gene expression and alternative splicing data.

Main Methods:

  • Transcriptome sequencing of broiler and local chicken muscle tissues.
  • Differential gene expression (DEG) and alternative splicing (AS) analysis.
  • Machine learning model development (e.g., XGBoost, Glmnet) and feature importance assessment (SHAP).

Main Results:

  • Identified 50 DEGs and 95 differentially spliced transcripts (DSTs) significantly related to BrP.
  • Achieved >90% accuracy in BrP prediction using DEGs (XGBoost) and 95% using DSTs (Glmnet).
  • Highlighted key genes (e.g., ENSGALG00010012060, HINTW, VIPR2-201) and AS events contributing to BrP.

Conclusions:

  • Developed high-accuracy AI-driven predictive models for poultry breast muscle traits.
  • Identified novel candidate genes and AS targets for molecular breeding.
  • Demonstrated a shift towards AI in identifying genetic markers for economically important traits.