Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Daiki Tagami

dblp:354/7522 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0002-0923-1070ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › population genetics › coalescent theory
ancestral recombination graph
0.812024
<tt>tstrait</tt> : a quantitative trait simulator for ancestral recombination graphs · Bioinform. 2024
Bioinformatics and computational biology
population genetics
0.812024
<tt>tstrait</tt> : a quantitative trait simulator for ancestral recombination graphs · Bioinform. 2024
Bioinformatics and computational biology › statistical genetics › genetic data simulation
quantitative trait simulation
0.812024
<tt>tstrait</tt> : a quantitative trait simulator for ancestral recombination graphs · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

ancestral recombination graph simulation · 0.8
YearPublicationVenuePosition
2024 <tt>tstrait</tt> : a quantitative trait simulator for ancestral recombination graphs
abstract
SUMMARY: Ancestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer. AVAILABILITY AND IMPLEMENTATION: tstrait is available for download on the Python Package Index. Full documentation with examples and workflow templates is available on https://tskit.dev/tstrait/docs/, and the development version is maintained on GitHub (https://github.com/tskit-dev/tstrait).
Daiki Tagami, Gertjan Bisschop, Jerome Kelleher
Bioinform.1