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Beyond the E-Value: Stratified Statistics for Protein Domain Prediction.

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Stratified analysis using local False Discovery Rates (lFDR) and q-values improves protein domain prediction accuracy. This approach enhances multiple hypothesis testing in bioinformatics, outperforming traditional E-values.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • E-values are standard for protein sequence analysis, including local alignments and domain family identification.
  • Multiple hypothesis testing is crucial in bioinformatics, but traditional methods like E-values have limitations.

Purpose of the Study:

  • To investigate the effectiveness of stratified analysis and local False Discovery Rates (lFDR) for protein domain prediction.
  • To compare the performance of q-values, E-values, and lFDRs in stratified multiple hypothesis testing.

Main Methods:

  • Developed novel FDR-estimating algorithms for protein domain prediction.
  • Stratified statistical tests by protein domain family.
  • Evaluated performance using five complementary empirical FDR estimation approaches.

Main Results:

  • Stratified q-value thresholds significantly outperformed E-values for protein domain prediction.
  • lFDR thresholds showed better performance than q-values for specific domain families with inaccurate random sequence models.
  • Identified a subset of domain families where current random sequence models yield inaccurate significance measures.

Conclusions:

  • Stratified q-values and lFDRs offer improved accuracy in protein domain prediction compared to E-values.
  • Findings suggest potential improvements for other bioinformatics applications like GWAS and orthology prediction.
  • Further advancements in random sequence modeling could enhance domain prediction accuracy.