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

Francisco J Pérez-Reche1, Ovidiu Rotariu2, Bruno S Lopes3

  • 1Institute of Complex Systems and Mathematical Biology, SUPA, School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, AB24 3UE, Scotland, UK. fperez-reche@abdn.ac.uk.

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|July 24, 2020
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Summary
This summary is machine-generated.

A new minimal multilocus distance (MMD) method efficiently analyzes whole genome sequence (WGS) data for accurate individual and species source attribution. This computationally efficient approach has broad applications in genomics and proteomics.

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Area of Science:

  • Genomics and Bioinformatics
  • Population Genetics
  • Proteomics

Background:

  • Whole genome sequence (WGS) data offers significant potential for individual and population source attribution.
  • Current methods for analyzing large-scale WGS data are computationally intensive and inefficient.
  • There is a need for advanced computational tools to effectively mine WGS data for attribution purposes.

Purpose of the Study:

  • To develop and present a novel computational method for efficient data mining of WGS and proteomic datasets.
  • To demonstrate the application of this method for accurate source attribution in diverse biological contexts.
  • To establish a computationally efficient and broadly applicable tool for analyzing large-scale genomic and proteomic data.

Main Methods:

  • Development of a minimal multilocus distance (MMD) method for rapid analysis of large datasets.
  • Implementation of optimal locus selection strategies for enhanced attribution accuracy.
  • Application of the MMD method to WGS data for human campylobacteriosis source determination, human geographical origin, and proteomic data for breast cancer tumor classification.

Main Results:

  • The MMD method demonstrated high accuracy in attributing individuals to source populations.
  • The method proved computationally efficient, even with extensive genotype data.
  • Successful application across diverse datasets, including pathogen WGS, human population genetics, and cancer proteomics.

Conclusions:

  • The minimal multilocus distance (MMD) method provides a computationally efficient and highly accurate approach for source attribution using large-scale genomic and proteomic data.
  • The developed methods are generic, user-friendly, and suitable for widespread implementation in WGS and proteomic data analysis.
  • This approach has broad applicability in fields ranging from infectious disease epidemiology to cancer research and evolutionary biology.