Dataset Information

OMACC: an Optical-Map-Assisted Contig Connector for improving de novo genome assembly.

ABSTRACT: BACKGROUND: Genome sequencing and assembly are essential for revealing the secrets of life hidden in genomes. Because of repeats in most genomes, current programs collate sequencing data into a set of assembled sequences, called contigs, instead of a complete genome. Toward completing a genome, optical mapping is powerful in rendering the relative order of contigs on the genome, which is called scaffolding. However, connecting the neighboring contigs with nucleotide sequences requires further efforts. Nagarajian et al. have recently proposed a software module, FINISH, to close the gaps between contigs with other contig sequences after scaffolding contigs using an optical map. The results, however, are not yet satisfying. RESULTS: To increase the accuracy of contig connections, we develop OMACC, which carefully takes into account length information in optical maps. Specifically, it rescales optical map and applies length constraint for selecting the correct contig sequences for gap closure. In addition, it uses an advanced graph search algorithm to facilitate estimating the number of repeat copies within gaps between contigs. On both simulated and real datasets, OMACC achieves a <10% false gap-closing rate, three times lower than the ~27% false rate by FINISH, while maintaining a similar sensitivity. CONCLUSION: As optical mapping is becoming popular and repeats are the bottleneck of assembly, OMACC should benefit various downstream biological studies via accurately connecting contigs into a more complete genome. AVAILABILITY: http://140.116.235.124/~tliu/omacc.

SUBMITTER: Chen YM

PROVIDER: S-EPMC4029551 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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OMACC: an Optical-Map-Assisted Contig Connector for improving de novo genome assembly.

Chen Yi-Min YM Yu Chun-Hui CH Hwang Chi-Chuan CC Liu Tsunglin T

BMC systems biology 20131213

<h4>Background</h4>Genome sequencing and assembly are essential for revealing the secrets of life hidden in genomes. Because of repeats in most genomes, current programs collate sequencing data into a set of assembled sequences, called contigs, instead of a complete genome. Toward completing a genome, optical mapping is powerful in rendering the relative order of contigs on the genome, which is called scaffolding. However, connecting the neighboring contigs with nucleotide sequences requires fur ...[more]

PMID: 24564959

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Project description:MotivationThere are very few methods for de novo genome assembly based on the overlap graph approach. It is considered as giving more exact results than the so-called de Bruijn graph approach but in much greater time and of much higher memory usage. It is not uncommon that assembly methods involving the overlap graph model are not able to successfully compute greater datasets, mainly due to memory limitation of a computer. This was the reason for developing in last decades mainly de Bruijn-based assembly methods, fast and fairly accurate. However, the latter methods can fail for longer or more repetitive genomes, as they decompose reads to shorter fragments and lose a part of information. An efficient assembler for processing big datasets and using the overlap graph model is still looked out.ResultsWe propose a new genome-scale de novo assembler based on the overlap graph approach, designed for short-read sequencing data. The method, ALGA, incorporates several new ideas resulting in more exact contigs produced in short time. Among these ideas, we have creation of a sparse but quite informative graph, reduction of the graph including a procedure referring to the problem of minimum spanning tree of a local subgraph, and graph traversal connected with simultaneous analysis of contigs stored so far. What is rare in genome assembly, the algorithm is almost parameter-free, with only one optional parameter to be set by a user. ALGA was compared with nine state-of-the-art assemblers in tests on genome-scale sequencing data obtained from real experiments on six organisms, differing in size, coverage, GC content and repetition rate. ALGA produced best results in the sense of overall quality of genome reconstruction, understood as a good balance between genome coverage, accuracy and length of resulting sequences. The algorithm is one of tools involved in processing data in currently realized national project Genomic Map of Poland.Availability and implementationALGA is available at http://alga.put.poznan.pl.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

OMACC: an Optical-Map-Assisted Contig Connector for improving de novo genome assembly.

Publications

OMACC: an Optical-Map-Assisted Contig Connector for improving de novo genome assembly.

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