دورية أكاديمية
أعرض في EDS

Reconstruction of Archaeal Genomes from Short-Read Metagenomes.

التفاصيل البيبلوغرافية
العنوان: Reconstruction of Archaeal Genomes from Short-Read Metagenomes.
المؤلفون: Bornemann TLV; Environmental Microbiology and Biotechnology, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany. till.bornemann@uni-due.de., Adam PS; Environmental Microbiology and Biotechnology, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany., Probst AJ; Environmental Microbiology and Biotechnology, Faculty of Chemistry, University of Duisburg-Essen, Essen, Germany. alexander.probst@uni-due.de.; Centre of Water and Environmental Research (ZWU), University of Duisburg-Essen, Essen, Germany. alexander.probst@uni-due.de.
المصدر: Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2022; Vol. 2522, pp. 487-527.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE
أسماء مطبوعة: Publication: Totowa, NJ : Humana Press
Original Publication: Clifton, N.J. : Humana Press,
مواضيع طبية MeSH: Metagenome* , Nucleic Acids*, Genome, Archaeal ; Lipids ; RNA
مستخلص: As the majority of biological diversity remains unexplored and uncultured, investigating it requires culture-independent approaches. Archaea in particular suffer from a multitude of issues that make their culturing problematic, from them being frequently members of the rare biosphere, to low growth rates, to them thriving under very specific and often extreme environmental and community conditions that are difficult to replicate. OMICs techniques are state of the art approaches that allow direct high-throughput investigations of environmental samples at all levels from nucleic acids to proteins, lipids, and secondary metabolites. Metagenomics, as the foundation for other OMICs techniques, facilitates the identification and functional characterization of the microbial community members and can be combined with other methods to provide insights into the microbial activities, both on the RNA and protein levels. In this chapter, we provide a step-by-step workflow for the recovery of archaeal genomes from metagenomes, starting from raw short-read sequences. This workflow can be applied to recover bacterial genomes as well.
(© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
References: Tyson GW, Chapman J, Hugenholtz P et al (2004) Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428:37–43. https://doi.org/10.1038/nature02340. (PMID: 10.1038/nature0234014961025)
Venter JC, Remington K, Heidelberg JF et al (2004) Environmental genome shotgun sequencing of the Sargasso Sea. Science 304:66–74. https://doi.org/10.1126/science.1093857. (PMID: 10.1126/science.109385715001713)
Eloe-Fadrosh EA, Ivanova NN, Woyke T, Kyrpides NC (2016) Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nat Microbiol 1:1–4. https://doi.org/10.1038/nmicrobiol.2015.32. (PMID: 10.1038/nmicrobiol.2015.32)
Brown CT, Hug LA, Thomas BC et al (2015) Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523:208. https://doi.org/10.1038/nature14486. (PMID: 10.1038/nature1448626083755)
Huber H, Hohn MJ, Rachel R et al (2002) A new phylum of Archaea represented by a nanosized hyperthermophilic symbiont. Nature 417:63–67. https://doi.org/10.1038/417063a. (PMID: 10.1038/417063a11986665)
Baker BJ, Tyson GW, Webb RI et al (2006) Lineages of acidophilic archaea revealed by community genomic analysis. Science 314:1933–1935. https://doi.org/10.1126/science.1132690. (PMID: 10.1126/science.113269017185602)
Eloe-Fadrosh EA, Paez-Espino D, Jarett J et al (2016) Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs. Nat Commun 7. https://doi.org/10.1038/ncomms10476.
Castelle CJ, Hug LA, Wrighton KC et al (2013) Extraordinary phylogenetic diversity and metabolic versatility in aquifer sediment. Nat Commun 4:2120. https://doi.org/10.1038/ncomms3120. (PMID: 10.1038/ncomms312023979677)
Suzek BE, Wang Y, Huang H et al (2015) UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31:926–932. https://doi.org/10.1093/bioinformatics/btu739. (PMID: 10.1093/bioinformatics/btu73925398609)
Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596. https://doi.org/10.1093/nar/gks1219. (PMID: 10.1093/nar/gks121923193283)
Gruber-Vodicka HR, Seah BKB, Pruesse E (2020) phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems 5. https://doi.org/10.1128/mSystems.00920-20.
Pruitt K, Tatusova T, Brown G, Maglott D (2011) NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucl Acids Res 40:D130–D135. https://doi.org/10.1093/nar/gkr1079. (PMID: 10.1093/nar/gkr1079221212123245008)
Teeling H, Glöckner FO (2012) Current opportunities and challenges in microbial metagenome analysis—a bioinformatic perspective. Brief Bioinform 13:728–742. https://doi.org/10.1093/bib/bbs039. (PMID: 10.1093/bib/bbs039229661513504927)
Hug LA, Thomas BC, Brown CT et al (2015) Aquifer environment selects for microbial species cohorts in sediment and groundwater. ISME J 9:1846–1856. https://doi.org/10.1038/ismej.2015.2. (PMID: 10.1038/ismej.2015.2256473494511941)
Breitwieser FP, Lu J, Salzberg SL (2019) A review of methods and databases for metagenomic classification and assembly. Brief Bioinform 20:1125–1136. https://doi.org/10.1093/bib/bbx120. (PMID: 10.1093/bib/bbx12029028872)
Hu X, Friedberg I (2020) Identifying core operons in metagenomic data. bioRxiv 2019.12.20.885269. https://doi.org/10.1101/2019.12.20.885269.
Guo J, Bolduc B, Zayed AA et al (2021) VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9:37. https://doi.org/10.1186/s40168-020-00990-y. (PMID: 10.1186/s40168-020-00990-y335229667852108)
Edgar RC (2007) PILER-CR: fast and accurate identification of CRISPR repeats. BMC Bioinformatics 8:18. https://doi.org/10.1186/1471-2105-8-18. (PMID: 10.1186/1471-2105-8-18172392531790904)
Hyatt D, Chen G-L, LoCascio PF et al (2010) Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119. https://doi.org/10.1186/1471-2105-11-119. (PMID: 10.1186/1471-2105-11-119202110232848648)
West PT, Probst AJ, Grigoriev IV et al (2018) Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res 28:569–580. https://doi.org/10.1101/gr.228429.117. (PMID: 10.1101/gr.228429.117294967305880246)
Sallet E, Gouzy J, Schiex T (2019) EuGene: an automated integrative gene finder for eukaryotes and prokaryotes. Methods Mol Biol 1962:97–120. https://doi.org/10.1007/978-1-4939-9173-0_6. (PMID: 10.1007/978-1-4939-9173-0_631020556)
Rodriguez-R LM, Gunturu S, Tiedje JM et al (2018) Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. mSystems 3. https://doi.org/10.1128/mSystems.00039-18.
Dick GJ, Andersson AF, Baker BJ et al (2009) Community-wide analysis of microbial genome sequence signatures. Genome Biol 10:R85. https://doi.org/10.1186/gb-2009-10-8-r85. (PMID: 10.1186/gb-2009-10-8-r85196981042745766)
Sharon I, Morowitz MJ, Thomas BC et al (2013) Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization. Genome Res 23:111–120. https://doi.org/10.1101/gr.142315.112. (PMID: 10.1101/gr.142315.112229362503530670)
Albertsen M, Hugenholtz P, Skarshewski A et al (2013) Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol 31:533–538. https://doi.org/10.1038/nbt.2579. (PMID: 10.1038/nbt.257923707974)
Wu Y-W, Simmons BA, Singer SW (2016) MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:605–607. https://doi.org/10.1093/bioinformatics/btv638. (PMID: 10.1093/bioinformatics/btv63826515820)
Mallawaarachchi V, Wickramarachchi A, Lin Y (2020) GraphBin: refined binning of metagenomic contigs using assembly graphs. Bioinformatics 36:3307–3313. https://doi.org/10.1093/bioinformatics/btaa180. (PMID: 10.1093/bioinformatics/btaa18032167528)
Miller IJ, Rees ER, Ross J et al (2019) Autometa: automated extraction of microbial genomes from individual shotgun metagenomes. Nucleic Acids Res 47:e57. https://doi.org/10.1093/nar/gkz148. (PMID: 10.1093/nar/gkz148308384166547426)
Laczny CC, Sternal T, Plugaru V et al (2015) VizBin—an application for reference-independent visualization and human-augmented binning of metagenomic data. Microbiome 3:1. https://doi.org/10.1186/s40168-014-0066-1. (PMID: 10.1186/s40168-014-0066-1256211714305225)
Kang DD, Li F, Kirton E et al (2019) MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359. https://doi.org/10.7717/peerj.7359. (PMID: 10.7717/peerj.7359313884746662567)
Graham ED, Heidelberg JF, Tully BJ (2017) BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation. PeerJ 5:e3035. https://doi.org/10.7717/peerj.3035. (PMID: 10.7717/peerj.3035282895645345454)
Uritskiy GV, DiRuggiero J, Taylor J (2018) MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6:158. https://doi.org/10.1186/s40168-018-0541-1. (PMID: 10.1186/s40168-018-0541-1302191036138922)
Sieber CMK, Probst AJ, Sharrar A et al (2018) Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 3:836–843. https://doi.org/10.1038/s41564-018-0171-1. (PMID: 10.1038/s41564-018-0171-1298079886786971)
Probst AJ, Castelle CJ, Singh A et al (2017) Genomic resolution of a cold subsurface aquifer community provides metabolic insights for novel microbes adapted to high CO2 concentrations. Environ Microbiol 19:459–474. https://doi.org/10.1111/1462-2920.13362. (PMID: 10.1111/1462-2920.1336227112493)
Parks DH, Imelfort M, Skennerton CT et al (2015) CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25:1043–1055. https://doi.org/10.1101/gr.186072.114. (PMID: 10.1101/gr.186072.114259774774484387)
Eren AM, Kiefl E, Shaiber A et al (2021) Community-led, integrated, reproducible multi-omics with anvi’o. Nat Microbiol 6:3–6. https://doi.org/10.1038/s41564-020-00834-3. (PMID: 10.1038/s41564-020-00834-3333496788116326)
Wrighton KC, Thomas BC, Sharon I et al (2012) Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337:1661–1665. https://doi.org/10.1126/science.1224041. (PMID: 10.1126/science.122404123019650)
Seah BKB, Gruber-Vodicka HR (2015) gbtools: interactive visualization of metagenome bins in R. Front Microbiol 6. https://doi.org/10.3389/fmicb.2015.01451.
Bornemann TLV, Esser SP, Stach TL et al (2020) uBin—a manual refining tool for metagenomic bins designed for educational purposes. bioRxiv 2020.07.15.204776. https://doi.org/10.1101/2020.07.15.204776.
Sheik CS, Reese BK, Twing KI et al (2018) Identification and removal of contaminant sequences from ribosomal gene databases: lessons from the census of deep life. Front Microbiol 9:840. https://doi.org/10.3389/fmicb.2018.00840. (PMID: 10.3389/fmicb.2018.00840297803695945997)
Joo S, Park P, Park S (2019) Applicability of propidium monoazide (PMA) for discrimination between living and dead phytoplankton cells. PLoS One 14:e0218924. https://doi.org/10.1371/journal.pone.0218924. (PMID: 10.1371/journal.pone.0218924312379316592542)
Ultsch A (2005) ESOM-maps: tools for clustering viszalization and classification with Emergent SOM.
van der Walt AJ, van Goethem MW, Ramond J-B et al (2017) Assembling metagenomes, one community at a time. BMC Genomics 18:521. https://doi.org/10.1186/s12864-017-3918-9. (PMID: 10.1186/s12864-017-3918-9286934745502489)
Buchfink B, Xie C, Huson DH (2015) Fast and sensitive protein alignment using DIAMOND. Nat Methods 12:59–60. https://doi.org/10.1038/nmeth.3176. (PMID: 10.1038/nmeth.317625402007)
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH (2020) GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36:1925–1927. https://doi.org/10.1093/bioinformatics/btz848. (PMID: 10.1093/bioinformatics/btz848)
Mukherjee S, Huntemann M, Ivanova N et al (2015) Large-scale contamination of microbial isolate genomes by Illumina PhiX control. Stand Genom Sci 10:18. https://doi.org/10.1186/1944-3277-10-18. (PMID: 10.1186/1944-3277-10-18)
Bushnell (2021) BBmaps. https://sourceforge.net/projects/bbmap/.
Joshi NA, Fass JN (2011) Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files. https://github.com/najoshi/sickle.
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA (2017) metaSPAdes: a new versatile metagenomic assembler. Genome Res 27:824–834. https://doi.org/10.1101/gr.213959.116. (PMID: 10.1101/gr.213959.116282984305411777)
Li D, Liu C-M, Luo R et al (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31:1674–1676. https://doi.org/10.1093/bioinformatics/btv033. (PMID: 10.1093/bioinformatics/btv03325609793)
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359. https://doi.org/10.1038/nmeth.1923. (PMID: 10.1038/nmeth.1923223882863322381)
Sieber CMK, Probst AJ, Sharrar A et al (2017) Recovery of genomes from metagenomes via a dereplication, aggregation, and scoring strategy. bioRxiv 107789. https://doi.org/10.1101/107789.
Bowers RM, Kyrpides NC, Stepanauskas R et al (2017) Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature Biotechnology 35:725–731. https://doi.org/10.1038/nbt.3893. (PMID: 10.1038/nbt.3893287874246436528)
Chen L-X, Anantharaman K, Shaiber A et al (2020) Accurate and complete genomes from metagenomes. Genome Res 30:315–333. https://doi.org/10.1101/gr.258640.119. (PMID: 10.1101/gr.258640.119321887017111523)
Jain C, Rodriguez-R LM, Phillippy AM et al (2018) High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 9. https://doi.org/10.1038/s41467-018-07641-9.
Letunic I, Bork P (2016) Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res 44:W242–W245. https://doi.org/10.1093/nar/gkw290. (PMID: 10.1093/nar/gkw290270951924987883)
Olm MR, Brown CT, Brooks B, Banfield JF (2017) dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 11:2864. https://doi.org/10.1038/ismej.2017.126. (PMID: 10.1038/ismej.2017.126287420715702732)
Earl D, Bradnam K, St. John J et al (2011) Assemblathon 1: a competitive assessment of de novo short read assembly methods. Genome Res 21:2224–2241. https://doi.org/10.1101/gr.126599.111. (PMID: 10.1101/gr.126599.111219261793227110)
Ondov BD, Treangen TJ, Melsted P et al (2016) Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 17:132. https://doi.org/10.1186/s13059-016-0997-x. (PMID: 10.1186/s13059-016-0997-x273238424915045)
Vieira-Silva S, Rocha EPC (2010) The systemic imprint of growth and its uses in ecological (meta)genomics. PLOS Genet 6:e1000808. https://doi.org/10.1371/journal.pgen.1000808. (PMID: 10.1371/journal.pgen.1000808200908312797632)
R Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
Weissman JL, Hou S, Fuhrman JA (2020) Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. bioRxiv 2020.07.25.221176. doi: https://doi.org/10.1101/2020.07.25.221176.
Brown CT, Olm MR, Thomas BC, Banfield JF (2016) Measurement of bacterial replication rates in microbial communities. Nat Biotechnol 34:1256–1263. https://doi.org/10.1038/nbt.3704. (PMID: 10.1038/nbt.3704278196645538567)
Zatopek KM, Gardner AF, Kelman Z (2018) Archaeal DNA replication and repair: new genetic, biophysical and molecular tools for discovering and characterizing enzymes, pathways and mechanisms. FEMS Microbiol Rev 42:477–488. https://doi.org/10.1093/femsre/fuy017. (PMID: 10.1093/femsre/fuy01729912309)
Shaffer M, Borton MA, McGivern BB et al (2020) DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 48:8883–8900. https://doi.org/10.1093/nar/gkaa621. (PMID: 10.1093/nar/gkaa621327667827498326)
Ruiz-Perez CA, Conrad RE, Konstantinidis KT (2021) MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes. BMC Bioinformatics 22:11. https://doi.org/10.1186/s12859-020-03940-5. (PMID: 10.1186/s12859-020-03940-5334070817789693)
Kanehisa M, Sato Y, Morishima K (2016) BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol 428:726–731. https://doi.org/10.1016/j.jmb.2015.11.006. (PMID: 10.1016/j.jmb.2015.11.00626585406)
Tatusova T, DiCuccio M, Badretdin A et al (2016) NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res 44:6614–6624. https://doi.org/10.1093/nar/gkw569. (PMID: 10.1093/nar/gkw569273422825001611)
Vallenet D, Labarre L, Rouy Z et al (2006) MaGe: a microbial genome annotation system supported by synteny results. Nucleic Acids Res 34:53–65. https://doi.org/10.1093/nar/gkj406. (PMID: 10.1093/nar/gkj406164073241326237)
Pan D, Nolan J, Williams KH et al (2017) Abundance and distribution of microbial cells and viruses in an alluvial aquifer. Front Microbiol 8:1199. https://doi.org/10.3389/fmicb.2017.01199. (PMID: 10.3389/fmicb.2017.01199287442575504356)
Ren J, Ahlgren NA, Lu YY et al (2017) VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome 5:69. https://doi.org/10.1186/s40168-017-0283-5. (PMID: 10.1186/s40168-017-0283-5286838285501583)
Meier-Kolthoff JP, Göker M (2017) VICTOR: genome-based phylogeny and classification of prokaryotic viruses. Bioinformatics 33:3396–3404. https://doi.org/10.1093/bioinformatics/btx440. (PMID: 10.1093/bioinformatics/btx440290362895860169)
Lillestøl R, Redder P, Garrett RA, Brügger K (2006) A putative viral defence mechanism in archaeal cells. Archaea 2:59–72. https://doi.org/10.1155/2006/542818. (PMID: 10.1155/2006/542818168773222685585)
Makarova KS, Grishin NV, Shabalina SA et al (2006) A putative RNA-interference-based immune system in prokaryotes: computational analysis of the predicted enzymatic machinery, functional analogies with eukaryotic RNAi, and hypothetical mechanisms of action. Biol Direct 1:7. https://doi.org/10.1186/1745-6150-1-7. (PMID: 10.1186/1745-6150-1-7165451081462988)
Shmakov SA, Wolf YI, Savitskaya E et al (2020) Mapping CRISPR spaceromes reveals vast host-specific viromes of prokaryotes. Communicat Biol 3:1–9. https://doi.org/10.1038/s42003-020-1014-1. (PMID: 10.1038/s42003-020-1014-1)
Skennerton CT, Imelfort M, Tyson GW (2013) Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res 41:e105. https://doi.org/10.1093/nar/gkt183. (PMID: 10.1093/nar/gkt183235119663664793)
Couvin D, Bernheim A, Toffano-Nioche C et al (2018) CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res 46:W246–W251. https://doi.org/10.1093/nar/gky425. (PMID: 10.1093/nar/gky425297909746030898)
Moller AG, Liang C (2017) MetaCRAST: reference-guided extraction of CRISPR spacers from unassembled metagenomes. PeerJ 5:e3788. https://doi.org/10.7717/peerj.3788. (PMID: 10.7717/peerj.3788288946515592083)
Rahlff J, Turzynski V, Esser SP et al (2021) Lytic archaeal viruses infect abundant primary producers in Earth’s crust. Nat Commun 12:4642. https://doi.org/10.1038/s41467-021-24803-4. (PMID: 10.1038/s41467-021-24803-4343309078324899)
Olm MR, Crits-Christoph A, Bouma-Gregson K et al (2021) inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat Biotechnol 39:1–10. https://doi.org/10.1038/s41587-020-00797-0. (PMID: 10.1038/s41587-020-00797-0)
Quince C, Nurk S, Raguideau S et al (2021) STRONG: metagenomics strain resolution on assembly graphs. Genome Biol 22:214. https://doi.org/10.1186/s13059-021-02419-7. (PMID: 10.1186/s13059-021-02419-7343117618311964)
Bankevich A, Nurk S, Antipov D et al (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477. https://doi.org/10.1089/cmb.2012.0021. (PMID: 10.1089/cmb.2012.0021225065993342519)
Pop M, Phillippy A, Delcher AL, Salzberg SL (2004) Comparative genome assembly. Brief Bioinformatics 5:237–248. https://doi.org/10.1093/bib/5.3.237. (PMID: 10.1093/bib/5.3.23715383210)
Probst AJ, Weinmaier T, Raymann K et al (2014) Biology of a widespread uncultivated archaeon that contributes to carbon fixation in the subsurface. Nat Commun 5:5497. https://doi.org/10.1038/ncomms6497. (PMID: 10.1038/ncomms649725425419)
Page AJ, De Silva N, Hunt M et al (2016) Robust high-throughput prokaryote de novo assembly and improvement pipeline for Illumina data. Microbial Genomics 2:e000083. https://doi.org/10.1099/mgen.0.000083. (PMID: 10.1099/mgen.0.000083283488745320598)
Antipov D, Raiko M, Lapidus A, Pevzner PA (2020) MetaviralSPAdes: assembly of viruses from metagenomic data. Bioinformatics 36:4126–4129. https://doi.org/10.1093/bioinformatics/btaa490. (PMID: 10.1093/bioinformatics/btaa49032413137)
Antipov D, Hartwick N, Shen M et al (2016) plasmidSPAdes: assembling plasmids from whole genome sequencing data. Bioinformatics 32:3380–3387. https://doi.org/10.1093/bioinformatics/btw493. (PMID: 10.1093/bioinformatics/btw49327466620)
Woyke T, Tighe D, Mavromatis K et al (2010) One bacterial cell, one complete genome. PLoS One 5:e10314. https://doi.org/10.1371/journal.pone.0010314. (PMID: 10.1371/journal.pone.0010314204282472859065)
Rinke C, Schwientek P, Sczyrba A et al (2013) Insights into the phylogeny and coding potential of microbial dark matter. Nature 499:431–437. https://doi.org/10.1038/nature12352. (PMID: 10.1038/nature1235223851394)
Moss EL, Maghini DG, Bhatt AS (2020) Complete, closed bacterial genomes from microbiomes using nanopore sequencing. Nat Biotechnol 38:1–7. https://doi.org/10.1038/s41587-020-0422-6. (PMID: 10.1038/s41587-020-0422-6)
Noakes MT, Brinkerhoff H, Laszlo AH et al (2019) Increasing the accuracy of nanopore DNA sequencing using a time-varying cross membrane voltage. Nat Biotechnol 37:651–656. https://doi.org/10.1038/s41587-019-0096-0. (PMID: 10.1038/s41587-019-0096-0310111786658736)
Wick RR, Judd LM, Gorrie CL, Holt KE (2017) Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 13:e1005595. https://doi.org/10.1371/journal.pcbi.1005595. (PMID: 10.1371/journal.pcbi.1005595285948275481147)
Kolmogorov M, Bickhart DM, Behsaz B et al (2020) metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods 17:1103–1110. https://doi.org/10.1038/s41592-020-00971-x. (PMID: 10.1038/s41592-020-00971-x33020656)
Nurk S, Walenz BP, Rhie A et al (2020) HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads. Genome Res 30(9):1291–1305. https://doi.org/10.1101/gr.263566.120. (PMID: 10.1101/gr.263566.120328011477545148)
Speth DR, Orphan VJ (2018) Metabolic marker gene mining provides insight in global mcrA diversity and, coupled with targeted genome reconstruction, sheds further light on metabolic potential of the Methanomassiliicoccales. PeerJ 6:e5614. https://doi.org/10.7717/peerj.5614. (PMID: 10.7717/peerj.5614302459366147122)
Darling AE, Jospin G, Lowe E et al (2014) PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ 2:e243. https://doi.org/10.7717/peerj.243. (PMID: 10.7717/peerj.243244827623897386)
Asnicar F, Thomas AM, Beghini F et al (2020) Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat Commun 11(2500). https://doi.org/10.1038/s41467-020-16366-7.
Parks DH, Chuvochina M, Chaumeil P-A et al (2020) A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol 38:1079–1086. https://doi.org/10.1038/s41587-020-0501-8. (PMID: 10.1038/s41587-020-0501-832341564)
Parks DH, Chuvochina M, Waite DW et al (2018) A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol 36:996–1004. https://doi.org/10.1038/nbt.4229. (PMID: 10.1038/nbt.422930148503)
Price MN, Dehal PS, Arkin AP (2010) FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS One 5:e9490. https://doi.org/10.1371/journal.pone.0009490. (PMID: 10.1371/journal.pone.0009490202248232835736)
Minh BQ, Schmidt HA, Chernomor O et al (2020) IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 37:1530–1534. https://doi.org/10.1093/molbev/msaa015. (PMID: 10.1093/molbev/msaa015320117007182206)
Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313. https://doi.org/10.1093/bioinformatics/btu033. (PMID: 10.1093/bioinformatics/btu033244516233998144)
Lartillot N, Lepage T, Blanquart S (2009) PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating. Bioinformatics 25:2286–2288. https://doi.org/10.1093/bioinformatics/btp368. (PMID: 10.1093/bioinformatics/btp36819535536)
Kapli P, Yang Z, Telford MJ (2020) Phylogenetic tree building in the genomic age. Nat Rev Genet 21:428–444. https://doi.org/10.1038/s41576-020-0233-0. (PMID: 10.1038/s41576-020-0233-032424311)
Raymann K, Brochier-Armanet C, Gribaldo S (2015) The two-domain tree of life is linked to a new root for the Archaea. Proc Natl Acad Sci U S A 112:6670–6675. https://doi.org/10.1073/pnas.1420858112. (PMID: 10.1073/pnas.1420858112259643534450401)
Dombrowski N, Williams TA, Sun J et al (2020) Undinarchaeota illuminate DPANN phylogeny and the impact of gene transfer on archaeal evolution. Nat Commun 11:3939. https://doi.org/10.1038/s41467-020-17408-w. (PMID: 10.1038/s41467-020-17408-w327701057414124)
Martijn J, Schön ME, Lind AE et al (2020) Hikarchaeia demonstrate an intermediate stage in the methanogen-to-halophile transition. Nat Commun 11:5490. https://doi.org/10.1038/s41467-020-19200-2. (PMID: 10.1038/s41467-020-19200-2331279097599335)
فهرسة مساهمة: Keywords: Genome curation; Genome-resolved metagenomics; Prokaryotes; Rare biosphere; Short-read sequencing
المشرفين على المادة: 0 (Lipids)
0 (Nucleic Acids)
63231-63-0 (RNA)
تواريخ الأحداث: Date Created: 20220920 Date Completed: 20220923 Latest Revision: 20220926
رمز التحديث: 20221213
DOI: 10.1007/978-1-0716-2445-6_33
PMID: 36125772
قاعدة البيانات: MEDLINE
الوصف
تدمد:1940-6029
DOI:10.1007/978-1-0716-2445-6_33