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A statistical reference-free genomic algorithm subsumes common workflows and enables novel discovery

View ORCID ProfileKaitlin Chaung, View ORCID ProfileTavor Z. Baharav, View ORCID ProfileIvan N. Zheludev, View ORCID ProfileJulia Salzman
doi: https://doi.org/10.1101/2022.06.24.497555
Kaitlin Chaung
1Department of Biomedical Data Science, Stanford University; Stanford, 94305, USA
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Tavor Z. Baharav
2Department of Electrical Engineering, Stanford University; Stanford, 94305, USA
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Ivan N. Zheludev
3Department of Biochemistry, Stanford University; Stanford, 94305, USA
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Julia Salzman
1Department of Biomedical Data Science, Stanford University; Stanford, 94305, USA
3Department of Biochemistry, Stanford University; Stanford, 94305, USA
4Department of Statistics (by courtesy), Stanford University; Stanford, 94305, USA
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  • For correspondence: julia.salzman@stanford.edu
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Abstract

We introduce a probabilistic model that enables study of myriad, disparate and fundamental problems in genome science and expands the scope of inference currently possible. Our model formulates an unrecognized unifying goal of many biological studies – to discover sample-specific sequence diversification – and subsumes many application-specific models. With it, we develop a novel algorithm, NOMAD, that performs valid statistical inference on raw reads, completely bypassing references and sample metadata. NOMAD’s reference-free approach enables data-scientifically driven discovery with previously unattainable generality, illustrated with de novo prediction of adaptation in SARS-CoV-2, novel single-cell resolved, cell-type-specific isoform expression, including in the major histocompatibility complex, and de novo identification of V(D)J recombination. NOMAD is a unifying, provably valid and highly efficient algorithmic solution that enables expansive discovery.

One-Sentence Summary We present a unifying formulation of disparate genomic problems and design an efficient, reference-free solution.

Competing Interest Statement

K.C., T.Z.B., and J.S. are inventors on provisional patents related to this work. The authors declare no other competing interests.

Footnotes

  • ↵† Co-first authors

  • Paper updated and reformatted, figures updated, supplement and methods separated and updated, supplemental figures added.

  • https://github.com/kaitlinchaung/nomad

Copyright 
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 4.0 International license.
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Posted July 22, 2022.
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A statistical reference-free genomic algorithm subsumes common workflows and enables novel discovery
Kaitlin Chaung, Tavor Z. Baharav, Ivan N. Zheludev, Julia Salzman
bioRxiv 2022.06.24.497555; doi: https://doi.org/10.1101/2022.06.24.497555
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A statistical reference-free genomic algorithm subsumes common workflows and enables novel discovery
Kaitlin Chaung, Tavor Z. Baharav, Ivan N. Zheludev, Julia Salzman
bioRxiv 2022.06.24.497555; doi: https://doi.org/10.1101/2022.06.24.497555

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