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What went wrong with variant effect predictor performance for the PCM1 challenge.

Maximilian Miller1, Yanran Wang1, Yana Bromberg1

  • 1Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey.

Human Mutation
|July 4, 2019
PubMed
Summary
This summary is machine-generated.

Computational tools predicting genetic variant effects performed inconsistently in the Critical Assessment of Genome Interpretation-5 (CAGI-5) challenge for the Pericentriolar material 1 protein (PCM1). Analysis revealed residue conservation and protein structure influence predictor accuracy.

Keywords:
CAGIPCM1missense mutationprotein sequence position typesvariant effect prediction

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic sequence data has rapidly expanded, necessitating robust tools for assessing genetic variation impact.
  • The Critical Assessment of Genome Interpretation (CAGI) initiative evaluates computational variant effect prediction tools.
  • The CAGI-5 challenge focused on missense variants in the human Pericentriolar material 1 protein (PCM1).

Purpose of the Study:

  • To analyze the performance of variant effect predictors in the CAGI-5 challenge.
  • To identify factors influencing the accuracy of these computational tools for PCM1 variants.
  • To understand why a top-performing SNAP-based submission underperformed expectations.

Main Methods:

  • Comparison of CAGI-5 challenge submissions.
  • Evaluation of 24 additional common variant effect predictors.
  • Analysis of per-residue conservation, structural, and functional characteristics of PCM1.

Main Results:

  • Predictors struggled with variants in non-conserved positions.
  • Accuracy was higher in structurally rich regions compared to less-structured regions.
  • Tools were more accurate in identifying benign variants than deleterious ones in less-structured regions.
  • Most PCM1 was predicted to be functionally robust to mutation.

Conclusions:

  • Per-residue conservation and protein structure are key factors affecting variant effect predictor performance.
  • The functional robustness of PCM1 presents a challenge for generalized prediction tools.
  • Further development is needed to improve predictor accuracy, especially in less-structured protein regions.