ArticleGigaScience2018
Error correcting optical mapping data.
Article in GigaScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A chromosome-level genome assembly of the Korean crossbred pig Nanchukmacdon (Sus scrofa).Scientific data · 2023Article
- Fast and efficient Rmap assembly using the Bi-labelled de Bruijn graph.Algorithms for molecular biology : AMB · 2021Article
- Chromonomer: A Tool Set for Repairing and Enhancing Assembled Genomes Through Integration of Genetic Maps and Conserved Synteny.G3 (Bethesda, Md.) · 2020Article
- Fast and accurate correction of optical mapping data via spaced seeds.Bioinformatics (Oxford, England) · 2020Article
- Advances in optical mapping for genomic research.Computational and structural biotechnology journal · 2020Review
- Error correcting optical mapping data.GigaScience · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical mapping is a unique system that is capable of producing high-resolution, high-throughput genomic map data that gives information about the structure of a genome . Recently it has been used for scaffolding contigs and for assembly validation for large-scale sequencing projects, including the maize, goat, and Amborella genomes. However, a major impediment in the use of this data is the variety and quantity of errors in the raw optical mapping data, which are called Rmaps. The challenges associated with using Rmap data are analogous to dealing with insertions and deletions in the alignment of long reads. Moreover, they are arguably harder to tackle since the data are numerical and susceptible to inaccuracy. We develop cOMet to error correct Rmap data, which to the best of our knowledge is the only optical mapping error correction method. Our experimental results demonstrate that cOMet has high prevision and corrects 82.49% of insertion errors and 77.38% of deletion errors in Rmap data generated from the Escherichia coli K-12 reference genome. Out of the deletion errors corrected, 98.26% are true errors. Similarly, out of the insertion errors corrected, 82.19% are true errors. It also successfully scales to large genomes, improving the quality of 78% and 99% of the Rmaps in the plum and goat genomes, respectively. Last, we show the utility of error correction by demonstrating how it improves the assembly of Rmap data. Error corrected Rmap data results in an assembly that is more contiguous and covers a larger fraction of the genome.
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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.