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Identification of the expressome by machine learning on omics data.

Ryan C Sartor1, Jaclyn Noshay2, Nathan M Springer2

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Summary

Machine learning accurately classifies plant genes using epigenetic marks, improving genome annotation. This method distinguishes between expressed and silent genes, crucial for understanding plant genetics.

Keywords:
epigenomicsgenome annotationmachine learningmaizeproteomics

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Area of Science:

  • Plant Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Accurate plant genome annotation is challenging due to pseudogenes and gene fragments.
  • Whole-genome duplication and transposable elements contribute to genomic complexity.
  • Distinguishing functional genes from pseudogenes is critical for genetic studies.

Purpose of the Study:

  • To improve plant genome annotation using machine learning and epigenetic data.
  • To classify putative protein-coding genes as constitutively silent or expressible.
  • To subclassify expressed genes based on mRNA and protein production.

Main Methods:

  • Utilized machine learning algorithms (random forest) on genome-wide epigenetic marks.
  • Incorporated transcriptomic and proteomic data for training.
  • Analyzed histone modifications and DNA methylation patterns within gene bodies.

Main Results:

  • Machine learning classification of genes was accurate, relying on gene body epigenetic marks.
  • CG gene body methylation was linked to genes expressing both mRNA and proteins.
  • The filtered gene set (FGS) in maize inbred B73 is specific, with other lines showing different transcribed gene sets.

Conclusions:

  • Epigenetic marks, particularly gene body methylation and histone modifications, can accurately classify gene expression states.
  • Chromatin information offers a powerful approach to enhance functional gene annotation in plant genomes.
  • This method accurately classifies transcribed genes in different inbred lines based on DNA methylation patterns.