Demo Data from VLDB 2026 / reviewers in the wild / expert
Taotao Tan
dblp:425/2942
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0003-4904-8059ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 2 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
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
population genetics |
1.1 | 2 | 2026 | Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations · Bioinform. 2024 Tractor workflow: a scalable Nextflow framework for local ancestry-aware genome-wide association studies · Bioinform. 2026 |
Bioinformatics and computational biology › genomics
genome-wide association study |
1.0 | 1 | 2026 | Tractor workflow: a scalable Nextflow framework for local ancestry-aware genome-wide association studies · Bioinform. 2026 |
Bioinformatics and computational biology › statistical genetics
genetic association study |
0.8 | 1 | 2024 | Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations · Bioinform. 2024 |
Bioinformatics and computational biology › statistical genetics
genotype-phenotype analysis |
0.8 | 1 | 2024 | Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations · Bioinform. 2024 |
Bioinformatics and computational biology › population genetics › ancestry inference
local ancestry inference |
0.3 | 1 | 2026 | Tractor workflow: a scalable Nextflow framework for local ancestry-aware genome-wide association studies · Bioinform. 2026 |
Methods — techniques the papers use, named apart from their topics
tractor · 1.0nextflow workflow · 1.0polygenic scoring · 0.8genotype simulation · 0.8association testing · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Tractor workflow: a scalable Nextflow framework for local ancestry-aware genome-wide association studiesabstractMOTIVATION: The routine exclusion of admixed individuals from traditional genome-wide association studies (GWAS) due to concerns about spurious associations has limited multi-ancestry genetic discovery. Tractor addresses this issue by incorporating local ancestry into association testing, enabling the identification of ancestry-enriched signals and generating ancestry-specific summary statistics. However, adoption has been constrained by the complexity of prerequisite steps, including phasing and local ancestry inference, which require substantial bioinformatics expertise and introduce key analytical decision points. RESULTS: We developed a scalable, automated Nextflow workflow that integrates phasing, local ancestry inference, and Tractor association testing into a reproducible end-to-end pipeline. To demonstrate its utility, we applied the workflow to 32 blood biomarkers in 6245 two-way African-European admixed individuals from the UK Biobank. This pipeline performed efficiently at scale, replicating known associations and uncovering key ancestry-specific loci. These associations were largely driven by variants present on African ancestral tracts but absent from European tracts, underscoring the value of local ancestry-aware methods in uncovering previously masked genetic signals. AVAILABILITY AND IMPLEMENTATION: The workflow is modular, customizable, and compatible with commonly used phasing and local ancestry tools, minimizing manual intervention while preserving analytical flexibility. By lowering technical barriers to implementation, this framework facilitates broader adoption of local ancestry-aware GWAS, paving the way for expanded genetic discovery. Nirav N. Shah, Taotao Tan, Jessica Honorato-Mauer, Yi-Sian Lin, Adam X. Maihofer, Clement C. Zai, Elizabeth G. Atkinson, Marcos Santoro, Caroline Nievergelt, Adam Maihofer, Clement Zai, M. du Plessis, Jessica Honorato Mauer, Shareefa Dalvie, Estela Bruxel, S. Hemmings, Liriel Almodobar, Caroline M. Nievergelt |
Bioinform. | 2 |
| 2024 | Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populationsabstractSUMMARY: Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic studies of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations. AVAILABILITY AND IMPLEMENTATION: Admix-kit package is open-source and available at https://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available at https://github.com/UW-GAC/admix-kit_workflow. Kangcheng Hou, Stephanie M. Gogarten, Xing Hua, Julie-Alexia Dias, Ying Wang 0074, Taotao Tan, Elizabeth G. Atkinson, Alicia Martin, Jonathan Shortt, Jibril Hirbo, Bogdan Pasaniuc |
Bioinform. | 8 |