Efficient algorithms to identify duplicated genotypes in large datasets

Title
Efficient algorithms to identify duplicated genotypes in large datasets
Publication Date
2022
Author(s)
Ferdosi, M H
( author )
OrcID: https://orcid.org/0000-0001-5385-4913
Email: mferdos3@une.edu.au
UNE Id une-id:mferdos3
Masoodi, S
Khansefid, M
Editor
Editor(s): Veerkamp, R F and de Haas, Y
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Wageningen Academic Publishers
Place of publication
Wageningen, The Netherlands
DOI
10.3920/978-90-8686-940-4_346
UNE publication id
une:1959.11/56324
Abstract

In this paper, we introduced two novel algorithms to identify duplicated genotypes. The runtime of these algorithms was compared with the widely adopted Exhaustive Search algorithm using simulated data. We found that both new algorithms could significantly reduce the execution time. Further, the optimised Matrix Algebra Approach algorithm was faster than the Dis-Similarity lookup table and could improve the performance nearly 34 times compared to Exhaustive Search

Link
Citation
Proceedings of the 12th World Congress on Genetics Applied to Livestock Production, v.12, p. 1450-1453
ISBN
978-90-8686-940-4
Start page
1450
End page
1453
Rights
Attribution 4.0 International

Files:

NameSizeformatDescriptionLink