link-ancestors: fast simulation of local ancestry with tree sequence software-Reference-Cited by-同舟云学术

link-ancestors: fast simulation of local ancestry with tree sequence software

Published:2023-01-01 Issue:1 Volume:3 Page:
ISSN:2635-0041
Container-title:Bioinformatics Advances
language:en
Short-container-title:

Author:

Tsambos Georgia¹²³^ORCID,Kelleher Jerome⁴,Ralph Peter⁵,Leslie Stephen¹²⁶,Vukcevic Damjan¹²⁷

Affiliation:

1. School of Mathematics and Statistics, University of Melbourne , Melbourne, Victoria, 3052, Australia

2. Melbourne Integrative Genomics, University of Melbourne , Melbourne, Victoria, 3052, Australia

3. Department of Genome Sciences, University of Washington , Seattle, Washington, 98195, United States

4. Big Data Institute, University of Oxford , Oxford, Oxfordshire, OX3 7LF, United Kingdom

5. Institute of Ecology and Evolution, University of Oregon , Eugene, Oregon, 97403, United States

6. School of BioSciences, University of Melbourne , Melbourne, Victoria, 3052, Australia

7. Department of Econometrics and Business Statistics, Monash University , Melbourne, Victoria, 3168, Australia

Abstract

Abstract Summary It is challenging to simulate realistic tracts of genetic ancestry on a scale suitable for simulation-based inference. We present an algorithm that enables this information to be extracted efficiently from tree sequences produced by simulations run with msprime and SLiM. Availability and implementation A C-based implementation of the link-ancestors algorithm is in tskit (https://tskit.dev/tskit/docs/stable/). We also provide a user-friendly wrapper for link-ancestors in tspop, a Python-based utility package.

Funder

Australian Government’s Research Training Scheme

Helen Freeman

Robertson Foundation

University of Melbourne’s Research Computing Services and Petascale Campus Initiative

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Genetics,Molecular Biology,Structural Biology

Link

https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbad163/53587622/vbad163.pdf