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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
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Modeling considerations for using expression data from multiple species.

Elizabeth Siewert1, Katerina J Kechris

  • 1Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA. Stats.Speaking@gmail.com

Statistics in Medicine
|May 25, 2013
PubMed
Summary
This summary is machine-generated.

Integrating correlated genome-wide expression data from multiple species improves predictive models for transcription factor binding sites. A repeated-measures model showed superior performance over a Bayesian approach for this cross-species analysis.

Keywords:
evolutionexpressionmulti-speciesmultivariate modelingtranscription

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide expression data across multiple species is increasingly available.
  • Integrating correlated cross-species data into predictive models remains a challenge.
  • Predicting transcription factor binding sites is crucial for understanding gene regulation.

Purpose of the Study:

  • To investigate methods for integrating correlated, multi-species expression data into predictive models.
  • To compare the performance of different multi-species regression models for predicting transcription factor binding sites.
  • To evaluate the impact of phylogenetic relationships on model accuracy.

Main Methods:

  • Developed a single-species linear regression model as a baseline.
  • Extended the model to multiple species using multivariate regression.
  • Implemented two multi-species approaches: a repeated-measures model and a Bayesian hierarchical model.
  • Accounted for phylogenetic relationships within the multi-species models.

Main Results:

  • Both multi-species models demonstrated improved predictive performance compared to the single-species model.
  • The repeated-measures model significantly outperformed the Bayesian hierarchical model.
  • Phylogenetic relationships were effectively incorporated into the multi-species models.

Conclusions:

  • Integrating correlated, multi-species expression data enhances the accuracy of transcription factor binding site prediction.
  • The repeated-measures model offers a more effective approach for such cross-species genomic analyses.
  • Constraining the error term in repeated-measures models may explain its superior performance.