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Characterizing uncertainty in predictions of genomic sequence-to-activity models
Ayesha Bajwa1, Ruchir Rastogi1, Pooja Kathail2
1Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA, USA.
Biorxiv : the Preprint Server for Biology
|January 8, 2024
Summary
Genomic sequence-to-activity models show high confidence but low accuracy on reference genomes. These models struggle with genetic variants, yielding inconsistent predictions for eQTLs and personal genomes.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Genomic sequence-to-activity models predict gene regulatory function and the impact of genetic variations.
- Current models accurately predict activity on the human reference genome but are less effective for individual genetic variations and gene expression.
- Understanding the limitations of these models is crucial for advancing genetic research and personalized medicine.
Conclusions:
- Current genomic sequence-to-activity models require improvement to accurately predict the functional impact of genetic variants.
- Model confidence does not always correlate with prediction accuracy, especially on non-reference sequences.
- Addressing prediction uncertainty in models is essential for reliable interpretation of regulatory variation and personalized genomics.
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