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Improved DNA-versus-Protein Homology Search for Protein Fossils

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Algorithms for Computational Biology (AlCoB 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12715))

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Abstract

Protein fossils, i.e. noncoding DNA descended from coding DNA, arise frequently from transposable elements (TEs), decayed genes, and viral integrations. They can reveal, and mislead about, evolutionary history and relationships. They have been detected by comparing DNA to protein sequences, but current methods are not optimized for this task. We describe a powerful DNA-protein homology search method. We use a 64 \(\times \) 21 substitution matrix, which is fitted to sequence data, automatically learning the genetic code. We detect subtly homologous regions by considering alternative possible alignments between them, and calculate significance (probability of occurring by chance between random sequences). Our method detects TE protein fossils much more sensitively than blastx, and \({>}10{\times }\) faster. Of the \(\sim \)7 major categories of eukaryotic TE, three were long thought absent in mammals: we find two of them in the human genome, polinton and DIRS/Ngaro. This method increases our power to find ancient fossils, and perhaps to detect non-standard genetic codes. The alternative-alignments and significance paradigm is not specific to DNA-protein comparison, and could benefit homology search generally.

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Acknowledgments

We thank the Frith and Asai lab members for discussions that clarified our thinking.

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Authors and Affiliations

  1. Graduate School of Frontier Sciences, University of Tokyo, Chiba, Japan

    Yin Yao & Martin C. Frith

  2. Artificial Intelligence Research Center, AIST, Tokyo, Japan

    Yin Yao & Martin C. Frith

  3. Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST, Tokyo, Japan

    Martin C. Frith

Authors
  1. Yin Yao
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  2. Martin C. Frith
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Correspondence to Martin C. Frith .

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Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  2. University of Extremadura, Cáceres, Spain

    Miguel A. Vega-Rodríguez

  3. University of Montana, Missoula, MT, USA

    Travis Wheeler

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Yao, Y., Frith, M.C. (2021). Improved DNA-versus-Protein Homology Search for Protein Fossils. In: Martín-Vide, C., Vega-Rodríguez, M.A., Wheeler, T. (eds) Algorithms for Computational Biology. AlCoB 2021. Lecture Notes in Computer Science(), vol 12715. Springer, Cham. https://doi.org/10.1007/978-3-030-74432-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-74432-8_11

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