Mining whole genome sequence data to efficiently attribute individuals to source populations

Francisco J. Pérez-Reche; Ovidiu Rotariu; Bruno S. Lopes; Ken J. Forbes; Norval J.C. Strachan

doi:10.1038/s41598-020-68740-6

Mining whole genome sequence data to efficiently attribute individuals to source populations

Francisco J. Pérez-Reche, Ovidiu Rotariu, Bruno S. Lopes, Ken J. Forbes, Norval J.C. Strachan

SHLS Life Sciences

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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Abstract

Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.

Original language	English
Article number	12124
Journal	Scientific Reports
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41598-020-68740-6
Publication status	Published - 22 Jul 2020

Bibliographical note

Funding Information:
The Campylobacter work in this project was supported by Food Standards Scotland project FSS00017 and the Scottish Government (Rural and Environment Science and Analytical Services Division) project A13559368.

Publisher Copyright:
© 2020, The Author(s).

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Cite this

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Mining whole genome sequence data to efficiently attribute individuals to source populations

Abstract

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