Supporting data for "Error Correcting Optical Mapping Data"

Dataset type: Genomic, Software, Imaging
Data released on May 01, 2018

Mukherjee K; Washimkar D; Muggli M; Salmela L; Boucher C (2018): Supporting data for "Error Correcting Optical Mapping Data" GigaScience Database.


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 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—and thus, optical mapping data—is analogous to dealing with insertions and deletions in the alignment of long reads. Moreover, they are arguably harder to tackle since the data is integral 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 corrects 82.49% of insertion errors and 77.38% of deletion errors in Rmap data generated from the E. 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. Lastly, 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.

Additional details

Read the peer-reviewed publication(s):

Mukherjee, K., Washimkar, D., Muggli, M. D., Salmela, L., & Boucher, C. (2018). Error correcting optical mapping data. GigaScience, 7(6). doi:10.1093/gigascience/giy061

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Additional information:

File NameSample IDData TypeFile FormatSizeRelease Date 
GitHub archivearchive12.06 MB2018-03-28
ReadmeTEXT2.03 KB2018-03-28
Displaying 1-2 of 2 File(s).
Funding body Awardee Award ID Comments
Academy of Finland L Salmela 284598 CoECGR
National Science Foundation C Boucher 1618814 Div Of Information & Intelligent Systems (IIS)
Academy of Finland L Salmela 308030 Academy Research Fellow LT
Academy of Finland L Salmela 314170 Academy Research Fellows: initial funding for research costs LT
Date Action
May 1, 2018 Dataset publish
July 9, 2018 Manuscript Link added : 10.1093/gigascience/giy061