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Optimizing de novo genome assembly from PCR-amplified metagenomes

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 micr...

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Autores principales: Roux, Simon, Trubl, Gareth, Goudeau, Danielle, Nath, Nandita, Couradeau, Estelle, Ahlgren, Nathan A., Zhan, Yuanchao, Marsan, David, Chen, Feng, Fuhrman, Jed A., Northen, Trent R., Sullivan, Matthew B., Rich, Virginia I., Malmstrom, Rex R., Eloe-Fadrosh, Emiley A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6511391/
https://www.ncbi.nlm.nih.gov/pubmed/31119088
http://dx.doi.org/10.7717/peerj.6902
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author Roux, Simon
Trubl, Gareth
Goudeau, Danielle
Nath, Nandita
Couradeau, Estelle
Ahlgren, Nathan A.
Zhan, Yuanchao
Marsan, David
Chen, Feng
Fuhrman, Jed A.
Northen, Trent R.
Sullivan, Matthew B.
Rich, Virginia I.
Malmstrom, Rex R.
Eloe-Fadrosh, Emiley A.
author_facet Roux, Simon
Trubl, Gareth
Goudeau, Danielle
Nath, Nandita
Couradeau, Estelle
Ahlgren, Nathan A.
Zhan, Yuanchao
Marsan, David
Chen, Feng
Fuhrman, Jed A.
Northen, Trent R.
Sullivan, Matthew B.
Rich, Virginia I.
Malmstrom, Rex R.
Eloe-Fadrosh, Emiley A.
author_sort Roux, Simon
collection PubMed
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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spelling pubmed-65113912019-05-22 Optimizing de novo genome assembly from PCR-amplified metagenomes Roux, Simon Trubl, Gareth Goudeau, Danielle Nath, Nandita Couradeau, Estelle Ahlgren, Nathan A. Zhan, Yuanchao Marsan, David Chen, Feng Fuhrman, Jed A. Northen, Trent R. Sullivan, Matthew B. Rich, Virginia I. Malmstrom, Rex R. Eloe-Fadrosh, Emiley A. PeerJ Computational Biology 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. PeerJ Inc. 2019-05-09 /pmc/articles/PMC6511391/ /pubmed/31119088 http://dx.doi.org/10.7717/peerj.6902 Text en ©2019 Roux et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Computational Biology
Roux, Simon
Trubl, Gareth
Goudeau, Danielle
Nath, Nandita
Couradeau, Estelle
Ahlgren, Nathan A.
Zhan, Yuanchao
Marsan, David
Chen, Feng
Fuhrman, Jed A.
Northen, Trent R.
Sullivan, Matthew B.
Rich, Virginia I.
Malmstrom, Rex R.
Eloe-Fadrosh, Emiley A.
Optimizing de novo genome assembly from PCR-amplified metagenomes
title Optimizing de novo genome assembly from PCR-amplified metagenomes
title_full Optimizing de novo genome assembly from PCR-amplified metagenomes
title_fullStr Optimizing de novo genome assembly from PCR-amplified metagenomes
title_full_unstemmed Optimizing de novo genome assembly from PCR-amplified metagenomes
title_short Optimizing de novo genome assembly from PCR-amplified metagenomes
title_sort optimizing de novo genome assembly from pcr-amplified metagenomes
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6511391/
https://www.ncbi.nlm.nih.gov/pubmed/31119088
http://dx.doi.org/10.7717/peerj.6902
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