ArticleScientific data2022
Optical mapping compendium of structural variants across global cattle breeds.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
The trial behind it
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Who cites it
11 citing papers in PubMed, 28 citations in OpenAlex.
- Deciphering the Evolution Pattern of Structural Variations Overlapped With Repetitive Sequence During Cattle Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- High-quality phased genome assemblies of line-bred Korean Hanwoo cattle.Scientific data · 2025Article
- Review
- Genomic Analysis of Indel and SV Reveals Functional and Adaptive Signatures in Hubei Indigenous Cattle Breeds.Animals : an open access journal from MDPI · 2025Article
- Taurine pangenome uncovers a segmental duplication upstream ofGenome research · 2025Article
- A comprehensive water buffalo pangenome reveals extensive structural variation linked to population-specific signatures of selection.GigaScience · 2025Article
- Article
- Structural variations in livestock genomes and their associations with phenotypic traits: a review.Frontiers in veterinary science · 2024Review
- Graph construction method impacts variation representation and analyses in a bovine super-pangenome.Genome biology · 2023Article
- AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap.Scientific data · 2023Article
- Optical mapping compendium of structural variants across global cattle breeds.Scientific data · 2022Article
Corrections and comments
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Authors and funding
19 authors at 9 institutions in 7 countries.
Funding
Abstract
Structural variants (SV) have been linked to important bovine disease phenotypes, but due to the difficulty of their accurate detection with standard sequencing approaches, their role in shaping important traits across cattle breeds is largely unexplored. Optical mapping is an alternative approach for mapping SVs that has been shown to have higher sensitivity than DNA sequencing approaches. The aim of this project was to use optical mapping to develop a high-quality database of structural variation across cattle breeds from different geographical regions, to enable further study of SVs in cattle. To do this we generated 100X Bionano optical mapping data for 18 cattle of nine different ancestries, three continents and both cattle sub-species. In total we identified 13,457 SVs, of which 1,200 putatively overlap coding regions. This resource provides a high-quality set of optical mapping-based SV calls that can be used across studies, from validating DNA sequencing-based SV calls to prioritising candidate functional variants in genetic association studies and expanding our understanding of the role of SVs in cattle evolution.
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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.