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Extension of the viral ecology in humans using viral profile hidden Markov models

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  • Zurab Bzhalava
  • Emilie Hultin
  • Joakim Dillner

Abstract

When human samples are sequenced, many assembled contigs are “unknown”, as conventional alignments find no similarity to known sequences. Hidden Markov models (HMM) exploit the positions of specific nucleotides in protein-encoding codons in various microbes. The algorithm HMMER3 implements HMM using a reference set of sequences encoding viral proteins, “vFam”. We used HMMER3 analysis of “unknown” human sample-derived sequences and identified 510 contigs distantly related to viruses (Anelloviridae (n = 1), Baculoviridae (n = 34), Circoviridae (n = 35), Caulimoviridae (n = 3), Closteroviridae (n = 5), Geminiviridae (n = 21), Herpesviridae (n = 10), Iridoviridae (n = 12), Marseillevirus (n = 26), Mimiviridae (n = 80), Phycodnaviridae (n = 165), Poxviridae (n = 23), Retroviridae (n = 6) and 89 contigs related to described viruses not yet assigned to any taxonomic family). In summary, we find that analysis using the HMMER3 algorithm and the “vFam” database greatly extended the detection of viruses in biospecimens from humans.

Suggested Citation

  • Zurab Bzhalava & Emilie Hultin & Joakim Dillner, 2018. "Extension of the viral ecology in humans using viral profile hidden Markov models," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-12, January.
    • Handle: RePEc:plo:pone00:0190938
    DOI: 10.1371/journal.pone.0190938
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    Cited by:

    1. Ardi Tampuu & Zurab Bzhalava & Joakim Dillner & Raul Vicente, 2019. "ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-17, September.

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