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Ancestral transcriptome inference based on RNA-Seq and ChIP-seq data.

Jingwen Yang1, Hang Ruan2, Yangyun Zou2

  • 1MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200433, China; Basic Research Unit, Fudan Human Phenomics Institute, Shanghai, China.

Methods (San Diego, Calif.)
|November 26, 2018
PubMed
Summary
This summary is machine-generated.

We developed AnceTran, a new R package for ancestral transcriptome reconstruction using the Ornstein-Uhlenbeck model. This method enhances understanding of gene regulation evolution in metazoans from RNA-seq and ChIP-seq data.

Keywords:
Ancestral inferenceChIP-seqMolecular phenomesPhylogenyRNA-seqTranscriptome evolution

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Area of Science:

  • Evolutionary biology
  • Genomics
  • Bioinformatics

Background:

  • Ancestral transcriptome reconstruction aids in understanding the evolution of transcriptional regulatory systems in metazoans.
  • Sophisticated functions and morphologies in multicellular organisms are linked to complex gene regulation.

Purpose of the Study:

  • To introduce a novel method for ancestral state inference using a more biologically realistic Ornstein-Uhlenbeck (OU) model.
  • To provide a feasible R package, AnceTran, for analyzing RNA-seq and ChIP-seq data.

Main Methods:

  • Utilized the Ornstein-Uhlenbeck (OU) model, replacing the traditional Brownian motion (BM) model for multi-transcriptome data.
  • Developed the AnceTran R package, specifically designed for RNA-seq and ChIP-seq data analysis.
  • Integrated the method within a statistically sound phylogenetic framework.

Main Results:

  • Demonstrated the method's feasibility through a case study using ChIP-seq and RNA-seq data from four mouse species.
  • Applied the method to liver-specific transcription factor binding and expression data.
  • Discussed technical considerations for ancestral state inference.

Conclusions:

  • The AnceTran package offers a robust tool for ancestral transcriptome reconstruction.
  • The OU model provides a more accurate approach for inferring evolutionary dynamics of gene regulation.
  • Future integration with other molecular phenomes (proteomics, epigenomics, metabolomics) is planned.