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A Multi-Species Functional Embedding Integrating Sequence and Network Structure

Jason Fan, Anthony Cannistra, Inbar Fried, Tim Lim, Thomas Schaffner, Mark Crovella, Benjamin Hescott, Mark DM Leiserson
doi: https://doi.org/10.1101/229211
Jason Fan
Univ of Maryland, College Park, Ctr for Bioinformatics and Computational Biology;
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Anthony Cannistra
University of Washington;
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Inbar Fried
University of North Carolina Medical School;
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Tim Lim
Boston University;
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Thomas Schaffner
Princeton University;
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Mark Crovella
Boston University;
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Benjamin Hescott
Northeastern University
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Mark DM Leiserson
Univ of Maryland, College Park, Ctr for Bioinformatics and Computational Biology;
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  • For correspondence: mdml@cs.umd.edu
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Abstract

A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment. In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HANDL (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space capture functional similarity across species, and the vectors themselves are useful for a variety of cross species tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HANDL-embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are co-located in HANDL-space both within and across species. Software for the HANDL algorithm is available at http://github.com/lrgr/HANDL.

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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 March 30, 2018.
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A Multi-Species Functional Embedding Integrating Sequence and Network Structure
Jason Fan, Anthony Cannistra, Inbar Fried, Tim Lim, Thomas Schaffner, Mark Crovella, Benjamin Hescott, Mark DM Leiserson
bioRxiv 229211; doi: https://doi.org/10.1101/229211
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A Multi-Species Functional Embedding Integrating Sequence and Network Structure
Jason Fan, Anthony Cannistra, Inbar Fried, Tim Lim, Thomas Schaffner, Mark Crovella, Benjamin Hescott, Mark DM Leiserson
bioRxiv 229211; doi: https://doi.org/10.1101/229211

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