Towards a More Accurate Error Model for BioNano Optical Maps

Menglu, Li; Mak, Angel C. Y.; Lam, Ernest T.; Kwok, Pui‐Yan; Xiao, Ming; Yip, Kevin Y.; Chan, Ting‐Fung; Yiu, Siu-Ming

doi:10.1007/978-3-319-38782-6_6

Cited by 12 publications

(13 citation statements)

References 19 publications

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“…This error free Rmap data is used for validating the output of our method. Lastly, deletion, insertion and sizing errors were incorporated into the errorfree Rmaps according to the error model discussed in Li et al [12]. The error model was described earlier in the Background section.…”

Section: Datasetsmentioning

confidence: 99%

“…Lastly, the limitations of the imaging component of the optical mapping system and the propensity for the DNA to ball up at the ends introduces more sizing error for smaller fragments. Interested readers will nd more details about the causes of these errors in Valouev et al [26] and Li et al [12]. Because of all these experimental conditions, Rmap data generated through optical mapping experiment has insertion (added cut sites) and deletion (missed cut sites) errors along with fragment sizing errors.…”

Section: Introductionmentioning

confidence: 99%

“…The error rates of optical maps depends on the platform used for generating the maps. A recent paper by Li et al [12] studied the error rates of optical maps produced by the Irys system from BioNano Genomics. According to their study, a missing cut site type of error i.e., error type (1) happens when a restriction site is incompletely digested by the enzyme and causes two anking fragments to merge into one large fragment.…”

Section: Introductionmentioning

confidence: 99%

“…R j = {17,23, 34,12,14,21,14, 5} has overlap with R i ; however, due to noise in the data, this relation is not apparent. By quantizing R j using the same b = 3 we get…”

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confidence: 99%

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Error Correcting Optical Mapping Data

Mukherjee

Washimkar

Muggli

et al. 2018

Preprint

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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 [21]. Recently it has been used for sca olding contigs and assembly validation for large-scale sequencing projects, including the maize [32], goat [6], and amborella [4] 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 is 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 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.

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…R j = {17,23, 34,12,14,21,14, 5} has overlap with R i ; however, due to noise in the data, this relation is not apparent. By quantizing R j using the same b = 3 we get…”

mentioning

confidence: 99%

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Error Correcting Optical Mapping Data

Mukherjee

Washimkar

Muggli

et al. 2018

Preprint

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“…This model was found to be inconsistent with data acquired from latest generation of optical map platforms, namely the Irys System of Bionano Genomics. Li et al (2016) proposed a Laplace distribution function to model the sizing error and showed it to be more consistent with the current technology.…”

Section: Introductionmentioning

confidence: 99%

Aligning optical maps to de Bruijn graphs

et al. 2019

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Motivation Optical maps are high-resolution restriction maps (Rmaps) that give a unique numeric representation to a genome. Used in concert with sequence reads, they provide a useful tool for genome assembly and for discovering structural variations and rearrangements. Although they have been a regular feature of modern genome assembly projects, optical maps have been mainly used in post-processing step and not in the genome assembly process itself. Several methods have been proposed for pairwise alignment of single molecule optical maps—called Rmaps, or for aligning optical maps to assembled reads. However, the problem of aligning an Rmap to a graph representing the sequence data of the same genome has not been studied before. Such an alignment provides a mapping between two sets of data: optical maps and sequence data which will facilitate the usage of optical maps in the sequence assembly step itself. Results We define the problem of aligning an Rmap to a de Bruijn graph and present the first algorithm for solving this problem which is based on a seed-and-extend approach. We demonstrate that our method is capable of aligning 73% of Rmaps generated from the Escherichia coli genome to the de Bruijn graph constructed from short reads generated from the same genome. We validate the alignments and show that our method achieves an accuracy of 99.6%. We also show that our method scales to larger genomes. In particular, we show that 76% of Rmaps can be aligned to the de Bruijn graph in the case of human data. Availability and implementation The software for aligning optical maps to de Bruijn graph, omGraph is written in C++ and is publicly available under GNU General Public License at https://github.com/kingufl/omGraph. Supplementary information Supplementary data are available at Bioinformatics online.

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Advances in optical mapping for genomic research

Yuan

Chung

Chan

2020

Computational and Structural Biotechnology Journal

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Recent advances in optical mapping have allowed the construction of improved genome assemblies with greater contiguity. Optical mapping also enables genome comparison and identification of large-scale structural variations. Association of these large-scale genomic features with biological functions is an important goal in plant and animal breeding and in medical research. Optical mapping has also been used in microbiology and still plays an important role in strain typing and epidemiological studies. Here, we review the development of optical mapping in recent decades to illustrate its importance in genomic research. We detail its applications and algorithms to show its specific advantages. Finally, we discuss the challenges required to facilitate the optimization of optical mapping and improve its future development and application.

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Towards a More Accurate Error Model for BioNano Optical Maps

Cited by 12 publications

References 19 publications

Error Correcting Optical Mapping Data

Error Correcting Optical Mapping Data

Aligning optical maps to de Bruijn graphs

Advances in optical mapping for genomic research

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