Evidence map›Paper›PMID 34583643›Full record

ArticleBMC genomics2021

A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences.

Stephen Among James, Hui San Ong, Ranjeev Hari, Asif M Khan

Abstract read
In one paragraph

Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stephen Among JamesCentre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia.
Hui San OngCentre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia.
Ranjeev HariCentre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia.
Asif M KhanCentre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia. asif@perdanauniversity.edu.my.ORCID http://orcid.org/0000-0001-9202-279X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBiology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications.

resultsThis study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions.

conclusionMapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.

Indexed as

FlaviviridaeZika VirusZika Virus InfectionComputational BiologyHepacivirusHumansPhylogenyand Molecular mimicry.BioinformaticsCross-reactivityCrossreactomeDengue virusFlaviviridaeFlavivirusHepacivirusHepatitis C virusHost-pathogenLarge-scaleMethodologyPegivirusPeptide overlapPeptide sharingPestivirusShared sequencesShare-omeWest Nile virus

Identifiers

PMID34583643
PMCPMC8477458

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.