ArticleGenetics2019
Fast Estimation of Recombination Rates Using Topological Data Analysis.
Article in Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- Estimating recombination fraction via Pearson correlation.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Simplicial complex entropy for time series analysis.Scientific reports · 2023Article
- Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- The topology of data: opportunities for cancer research.Bioinformatics (Oxford, England) · 2021Article
- Global Patterns of Recombination across Human Viruses.Molecular biology and evolution · 2021Article
- From molecules to populations: appreciating and estimating recombination rate variation.Nature reviews. Genetics · 2020Review
- The shape of things to come: Topological data analysis and biology, from molecules to organisms.Developmental dynamics : an official publication of the American Association of Anatomists · 2020Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Accurate estimation of recombination rates is critical for studying the origins and maintenance of genetic diversity. Because the inference of recombination rates under a full evolutionary model is computationally expensive, we developed an alternative approach using topological data analysis (TDA) on genome sequences. We find that this method can analyze datasets larger than what can be handled by any existing recombination inference software, and has accuracy comparable to commonly used model-based methods with significantly less processing time. Previous TDA methods used information contained solely in the first Betti number ([Formula: see text]) of a set of genomes, which aims to capture the number of loops that can be detected within a genealogy. These explorations have proven difficult to connect to the theory of the underlying biological process of recombination, and, consequently, have unpredictable behavior under perturbations of the data. We introduce a new topological feature, which we call
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.