Dataset Information

Plasmid detection and assembly in genomic and metagenomic data sets.

ABSTRACT: Although plasmids are important for bacterial survival and adaptation, plasmid detection and assembly from genomic, let alone metagenomic, samples remain challenging. The recently developed plasmidSPAdes assembler addressed some of these challenges in the case of isolate genomes but stopped short of detecting plasmids in metagenomic assemblies, an untapped source of yet to be discovered plasmids. We present the metaplasmidSPAdes tool for plasmid assembly in metagenomic data sets that reduced the false positive rate of plasmid detection compared with the state-of-the-art approaches. We assembled plasmids in diverse data sets and have shown that thousands of plasmids remained below the radar in already completed genomic and metagenomic studies. Our analysis revealed the extreme variability of plasmids and has led to the discovery of many novel plasmids (including many plasmids carrying antibiotic-resistance genes) without significant similarities to currently known ones.

SUBMITTER: Antipov D

PROVIDER: S-EPMC6581055 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Plasmid detection and assembly in genomic and metagenomic data sets.

Antipov Dmitry D Raiko Mikhail M Lapidus Alla A Pevzner Pavel A PA

Genome research 20190502 6

Although plasmids are important for bacterial survival and adaptation, plasmid detection and assembly from genomic, let alone metagenomic, samples remain challenging. The recently developed plasmidSPAdes assembler addressed some of these challenges in the case of isolate genomes but stopped short of detecting plasmids in metagenomic assemblies, an untapped source of yet to be discovered plasmids. We present the metaplasmidSPAdes tool for plasmid assembly in metagenomic data sets that reduced the ...[more]

PMID: 31048319

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Project description:BackgroundMulti-dimensional scaling (MDS) is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2), so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data.ResultsWe developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS) is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data.ConclusionOur new method reduces the computational complexity from O(N3) to O(N) when the dimension of the feature space is far less than the number of genes N, and it successfully reconstructs the low dimensional representation as does the classical MDS. Its performance depends on the grouping method and the minimal number of the intersection points between groups. Feasible methods for grouping methods are suggested; each group must contain both neighboring and far apart data points. Our method can represent high dimensional large data set in a low dimensional space not only efficiently but also effectively.

Dataset Information

Plasmid detection and assembly in genomic and metagenomic data sets.

Publications

Plasmid detection and assembly in genomic and metagenomic data sets.

Similar Datasets

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