Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data

Sara Saheb Kashaf, Alexandre Almeida, Julia A. Segre, Robert D. Finn

Published: 2021-04-15 DOI: 10.1038/s41596-021-00508-2

metagenomic sequencing

prokaryotic genomes

MAGs recovery

draft genomes

high-performance computing

AI 解读

暂无数据

Efficient and safe single-cell cloning of human pluripotent stem cells using the CEPT cocktail

Profiling native pulmonary basement membrane stiffness using atomic force microscopy

Genetic circuit design automation with Cello 2.0

Synthesis of siRNA nanoparticles to silence plaque-destabilizing gene in atherosclerotic lesional macrophages

Scalable dual-omics profiling with single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-seq2)

A practical guide to scanning light-field microscopy with digital adaptive optics

Effective high-throughput isolation of fully human antibodies targeting infectious pathogens

Improved sampling and DNA extraction procedures for microbiome analysis in food-processing environments

MOF-derived nanoporous carbons with diverse tunable nanoarchitectures

Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease

查看全部

Sections

Figures

References

Andrews S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics. http://www.bioinformatics.babraham.ac.uk/projects/fastqc
Baker, B. J. & Dick, G. J. Omic approaches in microbial ecology: charting the unknown. Microbe. Wash DC 8, 353–359 (2013).
Oh, J. Temporal stability of the human skin microbiome. Cell 165, 854–866 (2016).
McKain, N., Genc, B., Snelling, T. J. & Wallace, R. J. Differential recovery of bacterial and archaeal 16S rRNA genes from ruminal digesta in response to glycerol as cryoprotectant. J. Microbiol. Methods 95, 381–383 (2013).
Watson, E.-J., Giles, J., Scherer, B. L. & Blatchford, P. Human faecal collection methods demonstrate a bias in microbiome composition by cell wall structure. Sci. Rep. 9, 16831 (2019).
Sui, H.-Y. et al. Impact of DNA extraction method on variation in human and built environment microbial community and functional profiles assessed by shotgun metagenomics sequencing. Front. Microbiol. 11, 953 (2020).
Jones, M. B. et al. Library preparation methodology can influence genomic and functional predictions in human microbiome research. Proc. Natl Acad. Sci. USA 112, 14024–14029 (2015).
Lukjancenko, O., Wassenaar, T. M. & Ussery, D. W. Comparison of 61 sequenced Escherichia coli genomes. Microb. Ecol. 60, 708–720 (2010).
Fitzsimons, M. S. et al. Nearly finished genomes produced using gel microdroplet culturing reveal substantial intraspecies genomic diversity within the human microbiome. Genome Res. 23, 878–888 (2013).
Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol. 39, 105–114 (2021).
Chen, L.-X., Anantharaman, K., Shaiber, A., Eren, A. M. & Banfield, J. F. Accurate and complete genomes from metagenomes. Genome Res. 30, 315–333 (2020).
Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).
Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662.e20 (2019).
Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509 (2015).
van Kessel, M. A. H. J. et al. Complete nitrification by a single microorganism. Nature 528, 555–559 (2015).
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
Kong, H. H. et al. Performing skin microbiome research: a method to the madness. J. Invest. Dermatol. 137, 561–568 (2017).
Amarasinghe, S. L. et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 21, 30 (2020).
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).
Yuan, C., Lei, J., Cole, J. & Sun, Y. Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics. 31, i35–i43 (2015).
McCarthy, A. Third generation DNA sequencing: Pacific Biosciences’ single molecule real time technology. Chem. Biol. 17, 675–676 (2010).
Mikheyev, A. S. & Tin, M. M. Y. A first look at the Oxford Nanopore MinION sequencer. Mol. Ecol. Resour. 14, 1097–1102 (2014).
Overholt, W. A. et al. Inclusion of Oxford Nanopore long reads improves all microbial and phage metagenome-assembled genomes from a complex aquifer system. bioRxiv. 2019; 2019.12.18.880807.
Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat. Commun. 9, 870 (2018).
Forster, S. C. et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat. Biotechnol. 37, 186–192 (2019).
Royalty, T. M., Steen, A. D. Theoretical and simulation-based investigation of the relationship between sequencing effort, microbial community richness, and diversity in binning metagenome-assembled genomes. mSystems http://doi.org/10.1128/mSystems.00384-19 (2019).
Sczyrba, A. et al. Critical assessment of metagenome interpretation—a benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017).
Maguire, F. et al. Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic islands. Microb. Genomics 6, 1–12 (2020).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 541 (2012).
van der Walt, A. J. et al. Assembling metagenomes, one community at a time. BMC Genomics 18, 521 (2017).
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. metaSPAdes: a new versatile de novo metagenomics assembler. Genome Res. 27, 824–834 (2017).
Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
Coates, R. C. et al. Characterization of cyanobacterial hydrocarbon composition and distribution of biosynthetic pathways. PLoS ONE 9, e85140 (2014).
Vollmers, J., Wiegand, S. & Kaster, A.-K. Comparing and evaluating metagenome assembly tools from a microbiologist’s perspective—not only size matters! PLoS ONE 12, e0169662 (2017).
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Sedlar, K., Kupkova, K. & Provaznik, I. Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics. Comput. Struct. Biotechnol. J. 15, 48–55 (2017).
Teeling, H., Waldmann, J., Lombardot, T., Bauer, M. & Glöckner, F. O. TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics 5, 163 (2004).
Kislyuk, A., Bhatnagar, S., Dushoff, J. & Weitz, J. S. Unsupervised statistical clustering of environmental shotgun sequences. BMC Bioinformatics 10, 316 (2009).
Wu, Y.-W. & Ye, Y. A novel abundance-based algorithm for binning metagenomic sequences using l-tuples. in Research in Computational Molecular Biology 535–549 (Springer, 2010).
Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).
Kang, D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. Preprint at PeerJ http://doi.org/10.7287/peerj.preprints.27522 (2019).
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).
Lin, H.-H., Liao, Y.-C. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci. Rep. http://doi.org/10.1038/srep24175 (2016).
Sieber, C. M. K., et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).
Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).
Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ’omics data. PeerJ 3, e1319 (2015).
von Meijenfeldt, F. A. B., Arkhipova, K., Cambuy, D. D., Coutinho, F. H. & Dutilh, B. E. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol. 20, 217 (2019).
Evans, J. T. & Denef, V. J. To dereplicate or not to dereplicate? mSphere http://doi.org/10.1128/mSphere.00971-19 (2020).
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).
Ondov, B. D. et al. Mash Screen: high-throughput sequence containment estimation for genome discovery. Genome Biol. 20, 232 (2019).
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).
Varghese, N. J. et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 43, 6761–6771 (2015).
Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).
Parks. D. H. et al. A complete domain-to-species taxonomy for acteria and Archaea. Nat. Biotechnol. http://doi.org/10.1038/s41587-020-0501-8 (2020).
Chaumeil. P.-A., Mussig. A. J., Hugenholtz, P., Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics http://doi.org/10.1093/bioinformatics/btz848 (2019).
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).
Fritz, A,. et al. CAMISIM: simulating metagenomes and microbial communities. Microbiome http://doi.org/10.1186/s40168-019-0633-6 (2019).
Perkel, J. M. Workflow systems turn raw data into scientific knowledge. Nature 573, 149–150 (2019).
Kitts, P. A. et al. Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res. 44, D73–D80 (2016).
Stinson, L. F., Keelan, J. A. & Payne, M. S. Identification and removal of contaminating microbial DNA from PCR reagents: impact on low‐biomass microbiome analyses. Lett. Appl. Microbiol. 68, 2–8 (2019).
Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).
Anantharaman K, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. http://doi.org/10.1038/ncomms13219 (2016).
Solden, L. M. et al. Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nat. Microbiol. 3, 1274–1284 (2018).
Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).
Saary, P., Mitchell, A. L. & Finn, R. D. Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC. Genome Biol. 21, 244 (2020).
Olm, M. R. et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat. Biotechnol. http://doi.org/10.1038/s41587-020-00797-0 (2021).

Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data

推荐阅读