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A de novo genome assembly algorithm for repeats and nonrepeats.

ABSTRACT: Next generation sequencing platforms can generate shorter reads, deeper coverage, and higher throughput than those of the Sanger sequencing. These short reads may be assembled de novo before some specific genome analyses. Up to now, the performances of assembling repeats of these current assemblers are very poor.To improve this problem, we proposed a new genome assembly algorithm, named SWA, which has four properties: (1) assembling repeats and nonrepeats; (2) adopting a new overlapping extension strategy to extend each seed; (3) adopting sliding window to filter out the sequencing bias; and (4) proposing a compensational mechanism for low coverage datasets. SWA was evaluated and validated in both simulations and real sequencing datasets. The accuracy of assembling repeats and estimating the copy numbers is up to 99% and 100%, respectively. Finally, the extensive comparisons with other eight leading assemblers show that SWA outperformed others in terms of completeness and correctness of assembling repeats and nonrepeats.This paper proposed a new de novo genome assembly method for resolving complex repeats. SWA not only can detect where repeats or nonrepeats are but also can assemble them completely from NGS data, especially for assembling repeats. This is the advantage over other assemblers.

SUBMITTER: Lian S

PROVIDER: S-EPMC4055594 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A de novo genome assembly algorithm for repeats and nonrepeats.

Lian Shuaibin S Li Qingyan Q Dai Zhiming Z Xiang Qian Q Dai Xianhua X

BioMed research international 20140525

<h4>Background</h4>Next generation sequencing platforms can generate shorter reads, deeper coverage, and higher throughput than those of the Sanger sequencing. These short reads may be assembled de novo before some specific genome analyses. Up to now, the performances of assembling repeats of these current assemblers are very poor.<h4>Results</h4>To improve this problem, we proposed a new genome assembly algorithm, named SWA, which has four properties: (1) assembling repeats and nonrepeats; (2) ...[more]

PMID: 24967398

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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 data sets, 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 data sets 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.AvailabilityALGA is available at http://alga.put.poznan.pl.Supplementary informationSupplementary material is available at Bioinformatics online.

Project description:Background:Metagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enabling de novo assembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes. Methods:Here we evaluate de novo assembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ? 10 kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes. Results:Read mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ?10 kb by 10 to 100-fold for low input metagenomes. Conclusions:PCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improved de novo genome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.

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A de novo genome assembly algorithm for repeats and nonrepeats.

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A de novo genome assembly algorithm for repeats and nonrepeats.

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