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Generative probabilistic biological sequence models that account for mutational variability

View ORCID ProfileEli N. Weinstein, View ORCID ProfileDebora S. Marks
doi: https://doi.org/10.1101/2020.07.31.231381
Eli N. Weinstein
1Program in Biophysics, Harvard University,
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  • For correspondence: eweinstein@g.harvard.edu eweinstein@g.harvard.edu
Debora S. Marks
2Department of Systems Biology, Harvard Medical School,
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  • For correspondence: debbie@hms.harvard.edu
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Abstract

Large-scale sequencing has revealed extraordinary diversity among biological sequences, produced over the course of evolution and within the lifetime of individual organisms. Existing methods for building statistical models of sequences often pre-process the data using multiple sequence alignment, an unreliable approach for many genetic elements (antibodies, disordered proteins, etc.) that is subject to fundamental statistical pathologies. Here we introduce a structured emission distribution (the MuE distribution) that accounts for mutational variability (substitutions and indels) and use it to construct generative and predictive hierarchical Bayesian models (H-MuE models). Our framework enables the application of arbitrary continuous-space vector models (e.g. linear regression, factor models, image neural-networks) to unaligned sequence data. Theoretically, we show that the MuE generalizes classic probabilistic alignment models. Empirically, we show that H-MuE models can infer latent representations and features for immune repertoires, predict functional unobserved members of disordered protein families, and forecast the future evolution of pathogens.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 03, 2020.
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Generative probabilistic biological sequence models that account for mutational variability
Eli N. Weinstein, Debora S. Marks
bioRxiv 2020.07.31.231381; doi: https://doi.org/10.1101/2020.07.31.231381
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Generative probabilistic biological sequence models that account for mutational variability
Eli N. Weinstein, Debora S. Marks
bioRxiv 2020.07.31.231381; doi: https://doi.org/10.1101/2020.07.31.231381

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