Nature Methods, volume 17, issue 11, pages 1103-1110
metaFlye: scalable long-read metagenome assembly using repeat graphs.
Kolmogorov Mikhail
1
,
Bickhart Derek M
2
,
Behsaz Bahar
3
,
Gurevich Alexey
4
,
Rayko Mike
4
,
Shin Sung Bong
5
,
Kuhn Kristen
5
,
Yuan Jeffrey
3
,
Polevikov Evgeny
4, 6
,
Smith Timothy P. L.
5
,
Pevzner Pavel A.
1, 7
1
Department of Computer Science and Engineering, University of California, San Diego, USA
|
2
Cell Wall Biology and Utilization Laboratory, Dairy Forage Research Center, USDA, Madison, USA
|
3
Graduate Program in Bioinformatics and System Biology, University of California, San Diego, USA
|
5
USDA-ARS US Meat Animal Research Center, Clay Center, USA
|
6
Bioinformatics Institute, St. Petersburg, Russia
|
7
Center for Microbiome Innovation, University of California, San Diego, USA
|
Publication type: Journal Article
Publication date: 2020-10-05
Journal:
Nature Methods
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor: 48
ISSN: 15487091, 15487105
Biochemistry
Molecular Biology
Cell Biology
Biotechnology
Abstract
Long-read sequencing technologies have substantially improved the assemblies of many isolate bacterial genomes as compared to fragmented short-read assemblies. However, assembling complex metagenomic datasets remains difficult even for state-of-the-art long-read assemblers. Here we present metaFlye, which addresses important long-read metagenomic assembly challenges, such as uneven bacterial composition and intra-species heterogeneity. First, we benchmarked metaFlye using simulated and mock bacterial communities and show that it consistently produces assemblies with better completeness and contiguity than state-of-the-art long-read assemblers. Second, we performed long-read sequencing of the sheep microbiome and applied metaFlye to reconstruct 63 complete or nearly complete bacterial genomes within single contigs. Finally, we show that long-read assembly of human microbiomes enables the discovery of full-length biosynthetic gene clusters that encode biomedically important natural products. Long-read metagenomics offers a valuable approach for profiling bacterial communities. This work presents a long-read assembler, metaFlye, that specifically addresses the challenges of assembling metagenomes.
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- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
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GOST
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Kolmogorov M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. // Nature Methods. 2020. Vol. 17. No. 11. pp. 1103-1110.
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Kolmogorov M., Bickhart D. M., Behsaz B., Gurevich A., Rayko M., Shin S. B., Kuhn K., Yuan J., Polevikov E., Smith T. P. L., Pevzner P. A. metaFlye: scalable long-read metagenome assembly using repeat graphs. // Nature Methods. 2020. Vol. 17. No. 11. pp. 1103-1110.
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TY - JOUR
DO - 10.1038/s41592-020-00971-x
UR - https://doi.org/10.1038%2Fs41592-020-00971-x
TI - metaFlye: scalable long-read metagenome assembly using repeat graphs.
T2 - Nature Methods
AU - Kolmogorov, Mikhail
AU - Bickhart, Derek M
AU - Behsaz, Bahar
AU - Gurevich, Alexey
AU - Rayko, Mike
AU - Shin, Sung Bong
AU - Kuhn, Kristen
AU - Yuan, Jeffrey
AU - Polevikov, Evgeny
AU - Smith, Timothy P. L.
AU - Pevzner, Pavel A.
PY - 2020
DA - 2020/10/05 00:00:00
PB - Springer Nature
SP - 1103-1110
IS - 11
VL - 17
SN - 1548-7091
SN - 1548-7105
ER -
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@article{2020_Kolmogorov,
author = {Mikhail Kolmogorov and Derek M Bickhart and Bahar Behsaz and Alexey Gurevich and Mike Rayko and Sung Bong Shin and Kristen Kuhn and Jeffrey Yuan and Evgeny Polevikov and Timothy P. L. Smith and Pavel A. Pevzner},
title = {metaFlye: scalable long-read metagenome assembly using repeat graphs.},
journal = {Nature Methods},
year = {2020},
volume = {17},
publisher = {Springer Nature},
month = {oct},
url = {https://doi.org/10.1038%2Fs41592-020-00971-x},
number = {11},
pages = {1103--1110},
doi = {10.1038/s41592-020-00971-x}
}
Cite this
MLA
Copy
Kolmogorov, Mikhail, et al. “metaFlye: scalable long-read metagenome assembly using repeat graphs..” Nature Methods, vol. 17, no. 11, Oct. 2020, pp. 1103-1110. https://doi.org/10.1038%2Fs41592-020-00971-x.