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Population-scale Ancestral Recombination Graphs with tskit 1.0

Ben Jeffery, Yan Wong, Kevin Thornton, Georgia Tsambos, Gertjan Bisschop, Yun Deng, E. Castedo Ellerman, Thomas B. Forest, Halley Fritze, Daniel Goldstein, Gregor Gorjanc, Graham Gower, Simon Gravel, Jeremy Guez, Benjamin C. Haller, Andrew D. Kern, Lloyd Kirk, Ivan Krukov, Hanbin Lee, Brieuc Lehmann, Hossameldin Loay, Matthew M. Osmond, Duncan S. Palmer, Nathaniel S. Pope, Aaron P. Ragsdale, Duncan Robertson, Murillo F. Rodrigues, Hugo van Kemenade, Clemens L. Weiß, Anthony Wilder Wohns, Shing H. Zhan, Brian C. Zhang, Marianne Aspbury, Nikolas A. Baya, Saurabh Belsare, Arjun Biddanda, Francisco Campuzano Jiménez, Ariella Gladstein, Bing Guo, Savita Karthikeyan, Warren W. Kretzschmar, Inés Rebollo, Kumar Saunack, Ruhollah Shemirani, Alexis Simon, Chris Smith, Jeet Sukumaran, Jonathan Terhorst, Per Unneberg, Ao Zhang, Peter Ralph, Jerome Kelleher

TL;DR

Tskit version 1.0 is announced, the underlying rationale is described, its underlying rationale is described, and its stability guarantees are described to provide a foundation for durable computational artefacts and support long-term reproducibility of code and analyses.

Abstract

Ancestral recombination graphs (ARGs) are an increasingly important component of population and statistical genetics. The tskit library has become key infrastructure for the field, providing an expressive and general representation of ARGs together with a suite of efficient fundamental operations. In this note, we announce tskit version 1.0, describe its underlying rationale, and document its stability guarantees. These guarantees provide a foundation for durable computational artefacts and support long-term reproducibility of code and analyses.

Population-scale Ancestral Recombination Graphs with tskit 1.0

TL;DR

Tskit version 1.0 is announced, the underlying rationale is described, its underlying rationale is described, and its stability guarantees are described to provide a foundation for durable computational artefacts and support long-term reproducibility of code and analyses.

Abstract

Ancestral recombination graphs (ARGs) are an increasingly important component of population and statistical genetics. The tskit library has become key infrastructure for the field, providing an expressive and general representation of ARGs together with a suite of efficient fundamental operations. In this note, we announce tskit version 1.0, describe its underlying rationale, and document its stability guarantees. These guarantees provide a foundation for durable computational artefacts and support long-term reproducibility of code and analyses.
Paper Structure (3 sections, 1 figure)

This paper contains 3 sections, 1 figure.

Table of Contents

  1. Acknowledgements
  2. Supplementary Information

Figures (1)

  • Figure 1: Tskit enables an interoperable ARG software ecosystem. ARGs produced by simulation or inference tools can be analysed by diverse downstream applications via tskit’s well-defined tabular data model, C library and Python/Rust/R bindings. Tools shown are representative examples from Table S1 (three per category; ordered by citation count).