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3. Disentangling top-down drivers of mortality underlying diel population dynamics of Prochlorococcus in the North Pacific Subtropical Gyre

Author

Beckett, Stephen J., Demory, David, Coenen, Ashley R., Casey, John R., Dugenne, Mathilde, Follett, Christopher L., Connell, Paige, Carlson, Michael C. G., Hu, Sarah K., Wilson, Samuel T., Muratore, Daniel, Rodriguez-Gonzalez, Rogelio A., Peng, Shengyun, Becker, Kevin W., Mende, Daniel R., Armbrust, E. Virginia, Caron, David A., Lindell, Debbie, White, Angelicque E., Ribalet, François, and Weitz, Joshua S.

Published
2024
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4. Critical Assessment of Metagenome Interpretation: the second round of challenges

Author

Meyer, Fernando, Fritz, Adrian, Deng, Zhi-Luo, Koslicki, David, Lesker, Till Robin, Gurevich, Alexey, Robertson, Gary, Alser, Mohammed, Antipov, Dmitry, Beghini, Francesco, Bertrand, Denis, Brito, Jaqueline J, Brown, C Titus, Buchmann, Jan, Buluç, Aydin, Chen, Bo, Chikhi, Rayan, Clausen, Philip TLC, Cristian, Alexandru, Dabrowski, Piotr Wojciech, Darling, Aaron E, Egan, Rob, Eskin, Eleazar, Georganas, Evangelos, Goltsman, Eugene, Gray, Melissa A, Hansen, Lars Hestbjerg, Hofmeyr, Steven, Huang, Pingqin, Irber, Luiz, Jia, Huijue, Jørgensen, Tue Sparholt, Kieser, Silas D, Klemetsen, Terje, Kola, Axel, Kolmogorov, Mikhail, Korobeynikov, Anton, Kwan, Jason, LaPierre, Nathan, Lemaitre, Claire, Li, Chenhao, Limasset, Antoine, Malcher-Miranda, Fabio, Mangul, Serghei, Marcelino, Vanessa R, Marchet, Camille, Marijon, Pierre, Meleshko, Dmitry, Mende, Daniel R, Milanese, Alessio, Nagarajan, Niranjan, Nissen, Jakob, Nurk, Sergey, Oliker, Leonid, Paoli, Lucas, Peterlongo, Pierre, Piro, Vitor C, Porter, Jacob S, Rasmussen, Simon, Rees, Evan R, Reinert, Knut, Renard, Bernhard, Robertsen, Espen Mikal, Rosen, Gail L, Ruscheweyh, Hans-Joachim, Sarwal, Varuni, Segata, Nicola, Seiler, Enrico, Shi, Lizhen, Sun, Fengzhu, Sunagawa, Shinichi, Sørensen, Søren Johannes, Thomas, Ashleigh, Tong, Chengxuan, Trajkovski, Mirko, Tremblay, Julien, Uritskiy, Gherman, Vicedomini, Riccardo, Wang, Zhengyang, Wang, Ziye, Wang, Zhong, Warren, Andrew, Willassen, Nils Peder, Yelick, Katherine, You, Ronghui, Zeller, Georg, Zhao, Zhengqiao, Zhu, Shanfeng, Zhu, Jie, Garrido-Oter, Ruben, Gastmeier, Petra, Hacquard, Stephane, Häußler, Susanne, Khaledi, Ariane, Maechler, Friederike, Mesny, Fantin, Radutoiu, Simona, Schulze-Lefert, Paul, Smit, Nathiana, and Strowig, Till

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Networking and Information Technology R&D (NITRD), Archaea, Metagenome, Metagenomics, Reproducibility of Results, Sequence Analysis, DNA, Software, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Published
2022

5. Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage

Author

Boeuf, Dominique, Eppley, John M, Mende, Daniel R, Malmstrom, Rex R, Woyke, Tanja, and DeLong, Edward F

Subjects
Microbiology, Biological Sciences, Ecology, Human Genome, Biotechnology, Genetics, Bacteria, Microbiota, Oceans and Seas, Phylogeny, Seawater, Marine microbiome, Microbial ecology, Ecotype, Pangenomic, Metagenomic, Metatransciptomic, Plankton, Deep ocean, Photoheterotrophy, Chemoautotrophy, Medical Microbiology, Evolutionary biology
Abstract

BackgroundOceanic microbiomes play a pivotal role in the global carbon cycle and are central to the transformation and recycling of carbon and energy in the ocean's interior. SAR324 is a ubiquitous but poorly understood uncultivated clade of Deltaproteobacteria that inhabits the entire water column, from ocean surface waters to its deep interior. Although some progress has been made in elucidating potential metabolic traits of SAR324 in the dark ocean, very little is known about the ecology and the metabolic capabilities of this group in the euphotic and twilight zones. To investigate the comparative genomics, ecology, and physiological potential of the SAR324 clade, we examined the distribution and variability of key genomic features and metabolic pathways in this group from surface waters to the abyss in the North Pacific Subtropical Gyre, one of the largest biomes on Earth.ResultsWe leveraged a pangenomic ecological approach, combining spatio-temporally resolved single-amplified genome, metagenomic, and metatranscriptomic datasets. The data revealed substantial genomic diversity throughout the SAR324 clade, with distinct depth and temporal distributions that clearly differentiated ecotypes. Phylogenomic subclade delineation, environmental distributions, genomic feature similarities, and metabolic capacities revealed strong congruence. The four SAR324 ecotypes delineated in this study revealed striking divergence from one another with respect to their habitat-specific metabolic potentials. The ecotypes living in the dark or twilight oceans shared genomic features and metabolic capabilities consistent with a sulfur-based chemolithoautotrophic lifestyle. In contrast, those inhabiting the sunlit ocean displayed higher plasticity energy-related metabolic pathways, supporting a presumptive photoheterotrophic lifestyle. In epipelagic SAR324 ecotypes, we observed the presence of two types of proton-pumping rhodopsins, as well as genomic, transcriptomic, and ecological evidence for active photoheterotrophy, based on xanthorhodopsin-like light-harvesting proteins.ConclusionsCombining pangenomic and both metagenomic and metatranscriptomic profiling revealed a striking divergence in the vertical distribution, genomic composition, metabolic potential, and predicted lifestyle strategies of geographically co-located members of the SAR324 bacterial clade. The results highlight the utility of metapangenomic approaches employed across environmental gradients, to decipher the properties and variation in function and ecological traits of specific phylogenetic clades within complex microbiomes. Video abstract.

Published
2021

6. Alternative strategies of nutrient acquisition and energy conservation map to the biogeography of marine ammonia-oxidizing archaea

Author

Qin, Wei, Zheng, Yue, Zhao, Feng, Wang, Yulin, Urakawa, Hidetoshi, Martens-Habbena, Willm, Liu, Haodong, Huang, Xiaowu, Zhang, Xinxu, Nakagawa, Tatsunori, Mende, Daniel R, Bollmann, Annette, Wang, Baozhan, Zhang, Yao, Amin, Shady A, Nielsen, Jeppe L, Mori, Koji, Takahashi, Reiji, Virginia Armbrust, E, Winkler, Mari-K H, DeLong, Edward F, Li, Meng, Lee, Po-Heng, Zhou, Jizhong, Zhang, Chuanlun, Zhang, Tong, Stahl, David A, and Ingalls, Anitra E

Subjects
Life Below Water, Ammonia, Archaea, Nitrification, Nutrients, Oxidation-Reduction, Phylogeny, Environmental Sciences, Biological Sciences, Technology, Microbiology
Abstract

Ammonia-oxidizing archaea (AOA) are among the most abundant and ubiquitous microorganisms in the ocean, exerting primary control on nitrification and nitrogen oxides emission. Although united by a common physiology of chemoautotrophic growth on ammonia, a corresponding high genomic and habitat variability suggests tremendous adaptive capacity. Here, we compared 44 diverse AOA genomes, 37 from species cultivated from samples collected across diverse geographic locations and seven assembled from metagenomic sequences from the mesopelagic to hadopelagic zones of the deep ocean. Comparative analysis identified seven major marine AOA genotypic groups having gene content correlated with their distinctive biogeographies. Phosphorus and ammonia availabilities as well as hydrostatic pressure were identified as selective forces driving marine AOA genotypic and gene content variability in different oceanic regions. Notably, AOA methylphosphonate biosynthetic genes span diverse oceanic provinces, reinforcing their importance for methane production in the ocean. Together, our combined comparative physiological, genomic, and metagenomic analyses provide a comprehensive view of the biogeography of globally abundant AOA and their adaptive radiation into a vast range of marine and terrestrial habitats.

Published
2020

7. A distinct lineage of giant viruses brings a rhodopsin photosystem to unicellular marine predators

Author

Needham, David M, Yoshizawa, Susumu, Hosaka, Toshiaki, Poirier, Camille, Choi, Chang Jae, Hehenberger, Elisabeth, Irwin, Nicholas AT, Wilken, Susanne, Yung, Cheuk-Man, Bachy, Charles, Kurihara, Rika, Nakajima, Yu, Kojima, Keiichi, Kimura-Someya, Tomomi, Leonard, Guy, Malmstrom, Rex R, Mende, Daniel R, Olson, Daniel K, Sudo, Yuki, Sudek, Sebastian, Richards, Thomas A, DeLong, Edward F, Keeling, Patrick J, Santoro, Alyson E, Shirouzu, Mikako, Iwasaki, Wataru, and Worden, Alexandra Z

Subjects
Microbiology, Biological Sciences, Ecology, Infectious Diseases, Eye Disease and Disorders of Vision, Genetics, Life Below Water, Biological Evolution, Ecosystem, Eukaryota, Genome, Viral, Giant Viruses, Metagenomics, Oceans and Seas, Phycodnaviridae, Phylogeny, Protons, Rhodopsin, Seawater, Viral Proteins, giant viruses, viral evolution, marine carbon cycle, single-cell genomics, host-virus interactions, host–virus interactions
Abstract

Giant viruses are remarkable for their large genomes, often rivaling those of small bacteria, and for having genes thought exclusive to cellular life. Most isolated to date infect nonmarine protists, leaving their strategies and prevalence in marine environments largely unknown. Using eukaryotic single-cell metagenomics in the Pacific, we discovered a Mimiviridae lineage of giant viruses, which infects choanoflagellates, widespread protistan predators related to metazoans. The ChoanoVirus genomes are the largest yet from pelagic ecosystems, with 442 of 862 predicted proteins lacking known homologs. They are enriched in enzymes for modifying organic compounds, including degradation of chitin, an abundant polysaccharide in oceans, and they encode 3 divergent type-1 rhodopsins (VirR) with distinct evolutionary histories from those that capture sunlight in cellular organisms. One (VirRDTS) is similar to the only other putative rhodopsin from a virus (PgV) with a known host (a marine alga). Unlike the algal virus, ChoanoViruses encode the entire pigment biosynthesis pathway and cleavage enzyme for producing the required chromophore, retinal. We demonstrate that the rhodopsin shared by ChoanoViruses and PgV binds retinal and pumps protons. Moreover, our 1.65-Å resolved VirRDTS crystal structure and mutational analyses exposed differences from previously characterized type-1 rhodopsins, all of which come from cellular organisms. Multiple VirR types are present in metagenomes from across surface oceans, where they are correlated with and nearly as abundant as a canonical marker gene from Mimiviridae Our findings indicate that light-dependent energy transfer systems are likely common components of giant viruses of photosynthetic and phagotrophic unicellular marine eukaryotes.

Published
2019

11. Towards the biogeography of prokaryotic genes

Author

Coelho, Luis Pedro, Alves, Renato, del Río, Álvaro Rodríguez, Myers, Pernille Neve, Cantalapiedra, Carlos P., Giner-Lamia, Joaquín, Schmidt, Thomas Sebastian, Mende, Daniel R., Orakov, Askarbek, Letunic, Ivica, Hildebrand, Falk, Van Rossum, Thea, Forslund, Sofia K., Khedkar, Supriya, Maistrenko, Oleksandr M., Pan, Shaojun, Jia, Longhao, Ferretti, Pamela, Sunagawa, Shinichi, Zhao, Xing-Ming, Nielsen, Henrik Bjørn, Huerta-Cepas, Jaime, and Bork, Peer

Published
2022
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12. Impact of antibiotics on gut microbiome composition and resistome in the first years of life in low- to middle-income countries: A systematic review

Author

Luchen, Charlie C., Chibuye, Mwelwa, Spijker, Rene, Simuyandi, Michelo, Chisenga, Caroline, Bosomprah, Samuel, Chilengi, Roma, Schultsz, Constance, Mende, Daniel R., and Harris, Vanessa C.

Subjects
Microbiota (Symbiotic organisms) -- Composition, Drug resistance in microorganisms -- Prevention, Children -- Health aspects, Disease susceptibility -- Analysis, Antibiotics -- Dosage and administration, Biological sciences
Abstract

Background Inappropriate antimicrobial usage is a key driver of antimicrobial resistance (AMR). Low- and middle-income countries (LMICs) are disproportionately burdened by AMR and young children are especially vulnerable to infections with AMR-bearing pathogens. The impact of antibiotics on the microbiome, selection, persistence, and horizontal spread of AMR genes is insufficiently characterized and understood in children in LMICs. This systematic review aims to collate and evaluate the available literature describing the impact of antibiotics on the infant gut microbiome and resistome in LMICs. Methods and findings In this systematic review, we searched the online databases MEDLINE (1946 to 28 January 2023), EMBASE (1947 to 28 January 2023), SCOPUS (1945 to 29 January 2023), WHO Global Index Medicus (searched up to 29 January 2023), and SciELO (searched up to 29 January 2023). A total of 4,369 articles were retrieved across the databases. Duplicates were removed resulting in 2,748 unique articles. Screening by title and abstract excluded 2,666 articles, 92 articles were assessed based on the full text, and 10 studies met the eligibility criteria that included human studies conducted in LMICs among children below the age of 2 that reported gut microbiome composition and/or resistome composition (AMR genes) following antibiotic usage. The included studies were all randomized control trials (RCTs) and were assessed for risk of bias using the Cochrane risk-of-bias for randomized studies tool. Overall, antibiotics reduced gut microbiome diversity and increased antibiotic-specific resistance gene abundance in antibiotic treatment groups as compared to the placebo. The most widely tested antibiotic was azithromycin that decreased the diversity of the gut microbiome and significantly increased macrolide resistance as early as 5 days posttreatment. A major limitation of this study was paucity of available studies that cover this subject area. Specifically, the range of antibiotics assessed did not include the most commonly used antibiotics in LMIC populations. Conclusion In this study, we observed that antibiotics significantly reduce the diversity and alter the composition of the infant gut microbiome in LMICs, while concomitantly selecting for resistance genes whose persistence can last for months following treatment. Considerable heterogeneity in study methodology, timing and duration of sampling, and sequencing methodology in currently available research limit insights into antibiotic impacts on the microbiome and resistome in children in LMICs. More research is urgently needed to fill this gap in order to better understand whether antibiotic-driven reductions in microbiome diversity and selection of AMR genes place LMIC children at risk for adverse health outcomes, including infections with AMR-bearing pathogens., Author(s): Charlie C. Luchen 1,2,3, Mwelwa Chibuye 1,2,3, Rene Spijker 1, Michelo Simuyandi 2, Caroline Chisenga 2, Samuel Bosomprah 2,4, Roma Chilengi 2,5,6, Constance Schultsz 1,3,7, Daniel R. Mende 3,7, [...]

Published
2023
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17. proGenomes3: approaching one million accurately and consistently annotated high-quality prokaryotic genomes

Author

European Molecular Biology Laboratory, Swiss National Science Foundation, German Research Foundation, European Commission, Agencia Estatal de Investigación (España), Ministerio de Universidades (España), Fullam, Anthony [0000-0002-0884-8124], Letunic, Ivica [0000-0003-3560-4288], Schmidt, Thomas Sebastian [B0000-0001-8587-4177], Ducarmon, Quinten R. [0000-0001-7077-2127], Karcher, Nicolai [0000-0001-7894-8182], Khedkar, Supriya [0000-0001-6606-2202], Kuhn, Michael [0000-0002-2841-872X], Larralde, Martin [0000-0002-3947-4444], Maistrenko, Oleksandr M. [0000-0003-1961-7548], Malfertheiner, Lukas [0000-0002-5697-2007], Milanese, Alessio [0000-0002-7050-2239], Rodrigues, Joao Frederico Matias [0000-0001-8413-9920], Sanchis-López, Claudia [0000-0002-8206-1565], Schudoma, Christian [0000-0003-1157-1354], Szklarczyk, Damian [0000-0002-4052-5069], Sunagawa, Shinichi [0000-0003-3065-0314], Zeller, Georg [0000-0003-1429-7485], Huerta-Cepas, Jaime [0000-0003-4195-5025], von Mering, Christian [0000-0001-7734-9102], Bork, Peer [0000-0002-2627-833X], Mende, Daniel R. [0000-0001-6831-4557], Fullam, Anthony, Letunic, Ivica, Schmidt, Thomas Sebastian, Ducarmon, Quinten R., Karcher, Nicolai, Khedkar, Supriya, Kuhn, Michael, Larralde, Martin, Maistrenko, Oleksandr M., Malfertheiner, Lukas, Milanese, Alessio, Rodrigues, Joao Frederico Matias, Sanchis-López, Claudia, Schudoma, Christian, Szklarczyk, Damian, Sunagawa, Shinichi, Zeller, Georg, Huerta-Cepas, Jaime, von Mering, Christian, Bork, Peer, Mende, Daniel R., European Molecular Biology Laboratory, Swiss National Science Foundation, German Research Foundation, European Commission, Agencia Estatal de Investigación (España), Ministerio de Universidades (España), Fullam, Anthony [0000-0002-0884-8124], Letunic, Ivica [0000-0003-3560-4288], Schmidt, Thomas Sebastian [B0000-0001-8587-4177], Ducarmon, Quinten R. [0000-0001-7077-2127], Karcher, Nicolai [0000-0001-7894-8182], Khedkar, Supriya [0000-0001-6606-2202], Kuhn, Michael [0000-0002-2841-872X], Larralde, Martin [0000-0002-3947-4444], Maistrenko, Oleksandr M. [0000-0003-1961-7548], Malfertheiner, Lukas [0000-0002-5697-2007], Milanese, Alessio [0000-0002-7050-2239], Rodrigues, Joao Frederico Matias [0000-0001-8413-9920], Sanchis-López, Claudia [0000-0002-8206-1565], Schudoma, Christian [0000-0003-1157-1354], Szklarczyk, Damian [0000-0002-4052-5069], Sunagawa, Shinichi [0000-0003-3065-0314], Zeller, Georg [0000-0003-1429-7485], Huerta-Cepas, Jaime [0000-0003-4195-5025], von Mering, Christian [0000-0001-7734-9102], Bork, Peer [0000-0002-2627-833X], Mende, Daniel R. [0000-0001-6831-4557], Fullam, Anthony, Letunic, Ivica, Schmidt, Thomas Sebastian, Ducarmon, Quinten R., Karcher, Nicolai, Khedkar, Supriya, Kuhn, Michael, Larralde, Martin, Maistrenko, Oleksandr M., Malfertheiner, Lukas, Milanese, Alessio, Rodrigues, Joao Frederico Matias, Sanchis-López, Claudia, Schudoma, Christian, Szklarczyk, Damian, Sunagawa, Shinichi, Zeller, Georg, Huerta-Cepas, Jaime, von Mering, Christian, Bork, Peer, and Mende, Daniel R.

Abstract

The interpretation of genomic, transcriptomic and other microbial 'omics data is highly dependent on the availability of well-annotated genomes. As the number of publicly available microbial genomes continues to increase exponentially, the need for quality control and consistent annotation is becoming critical. We present proGenomes3, a database of 907 388 high-quality genomes containing 4 billion genes that passed stringent criteria and have been consistently annotated using multiple functional and taxonomic databases including mobile genetic elements and biosynthetic gene clusters. proGenomes3 encompasses 41 171 species-level clusters, defined based on universal single copy marker genes, for which pan-genomes and contextual habitat annotations are provided. The database is available at http://progenomes.embl.de/.

Published
2023

18. eggNOG 6.0: enabling comparative genomics across 12 535 organisms

Author

Agencia Estatal de Investigación (España), European Commission, Silicon Valley Community Foundation, Novo Nordisk Foundation, Swiss Institute of Bioinformatics, Hernández-Plaza, Ana [0000-0002-9844-7999], Szklarczyk, Damian [0000-0002-4052-5069], Botas, Jorge [0000-0001-7292-8981], Cantalapiedra, Carlos P [0000-0001-5263-533X], Giner-Lamia, Joaquín [0000-0003-1553-8295], Mende, Daniel R. [0000-0001-6831-4557], Kirsch, Rebecca [0000-0003-1126-0089], Rattei, Thomas [0000-0002-0592-7791], Letunic, Ivica [0000-0003-3560-4288], Jensen, Lars J. [0000-0001-7885-715X], Bork, Peer [0000-0002-2627-833X], von Mering, Christian [0000-0001-7734-9102], Huerta-Cepas, Jaime [0000-0003-4195-5025], Hernández-Plaza, Ana, Szklarczyk, Damian, Botas, Jorge, Cantalapiedra, Carlos P, Giner-Lamia, Joaquín, Mende, Daniel R., Kirsch, Rebecca, Rattei, Thomas, Letunic, Ivica, Jensen, Lars J., Bork, Peer, von Mering, Christian, Huerta-Cepas, Jaime, Agencia Estatal de Investigación (España), European Commission, Silicon Valley Community Foundation, Novo Nordisk Foundation, Swiss Institute of Bioinformatics, Hernández-Plaza, Ana [0000-0002-9844-7999], Szklarczyk, Damian [0000-0002-4052-5069], Botas, Jorge [0000-0001-7292-8981], Cantalapiedra, Carlos P [0000-0001-5263-533X], Giner-Lamia, Joaquín [0000-0003-1553-8295], Mende, Daniel R. [0000-0001-6831-4557], Kirsch, Rebecca [0000-0003-1126-0089], Rattei, Thomas [0000-0002-0592-7791], Letunic, Ivica [0000-0003-3560-4288], Jensen, Lars J. [0000-0001-7885-715X], Bork, Peer [0000-0002-2627-833X], von Mering, Christian [0000-0001-7734-9102], Huerta-Cepas, Jaime [0000-0003-4195-5025], Hernández-Plaza, Ana, Szklarczyk, Damian, Botas, Jorge, Cantalapiedra, Carlos P, Giner-Lamia, Joaquín, Mende, Daniel R., Kirsch, Rebecca, Rattei, Thomas, Letunic, Ivica, Jensen, Lars J., Bork, Peer, von Mering, Christian, and Huerta-Cepas, Jaime

Abstract

The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de.

Published
2023

20. SPIRE: a Searchable, Planetary-scale mIcrobiome REsource

Author

Schmidt, Thomas S B, primary, Fullam, Anthony, additional, Ferretti, Pamela, additional, Orakov, Askarbek, additional, Maistrenko, Oleksandr M, additional, Ruscheweyh, Hans-Joachim, additional, Letunic, Ivica, additional, Duan, Yiqian, additional, Van Rossum, Thea, additional, Sunagawa, Shinichi, additional, Mende, Daniel R, additional, Finn, Robert D, additional, Kuhn, Michael, additional, Pedro Coelho, Luis, additional, and Bork, Peer, additional

Published
2023
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21. eggNOG 6.0: enabling comparative genomics across 12 535 organisms

Author

Hernández-Plaza, Ana, Szklarczyk, Damian, Botas, Jorge, Cantalapiedra, Carlos P, Giner-Lamia, Joaquín, Mende, Daniel R, Kirsch, Rebecca, Rattei, Thomas, Letunic, Ivica, Jensen, Lars J, Bork, Peer, von Mering, Christian, Huerta-Cepas, Jaime, Medical Microbiology and Infection Prevention, AII - Infectious diseases, Agencia Estatal de Investigación (España), European Commission, Silicon Valley Community Foundation, Novo Nordisk Foundation, Swiss Institute of Bioinformatics, Hernández-Plaza, Ana, Szklarczyk, Damian, Botas, Jorge, Cantalapiedra, Carlos P, Giner-Lamia, Joaquín, Mende, Daniel R., Kirsch, Rebecca, Rattei, Thomas, Letunic, Ivica, Jensen, Lars J., Bork, Peer, von Mering, Christian, Huerta-Cepas, Jaime, and University of Zurich

Subjects
UFSP13-7 Evolution in Action: From Genomes to Ecosystems, Genetics, 570 Life sciences, biology, 10124 Institute of Molecular Life Sciences
Abstract

6 Pág., The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de., National Programme for Fostering Excellence in Scientific and Technical Research [PGC2018-098073-A-I00 MCIU/AEI/FEDER, UE to J.H.-C., J.G.-L.]; Chan Zuckerberg Initiative DAF [2020-218584]; Silicon Valley Community Foundation (to J.B. and J.H.C.); Severo Ochoa Centres of Excellence Programme from the State Research Agency (AEI) of Spain [SEV-2016–0672 (2017–2021) to C.P.C.]; Research Technical Support Staff Aid [PTA2019-017593-I/AEI/10.13039/501100011033 to A.H.P.]; Novo Nordisk Foundation [NNF14CC0001 to R.K., L.J.J.]; SIB Swiss Institute of Bioinformatics (to D.S. and C.vM.). Funding for open access charge: Institutional CSIC and EMBL agreements.

Published
2023
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23. Microbial abundance, activity and population genomic profiling with mOTUs2

Author

Milanese, Alessio, Mende, Daniel R, Paoli, Lucas, Salazar, Guillem, Ruscheweyh, Hans-Joachim, Cuenca, Miguelangel, Hingamp, Pascal, Alves, Renato, Costea, Paul I, Coelho, Luis Pedro, Schmidt, Thomas S. B., Almeida, Alexandre, Mitchell, Alex L, Finn, Robert D., Huerta-Cepas, Jaime, Bork, Peer, Zeller, Georg, and Sunagawa, Shinichi

Published
2019
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24. eggNOG 6.0:enabling comparative genomics across 12 535 organisms

Author

Hernández-Plaza, Ana, Szklarczyk, Damian, Botas, Jorge, Cantalapiedra, Carlos P, Giner-Lamia, Joaquín, Mende, Daniel R, Kirsch, Rebecca, Rattei, Thomas, Letunic, Ivica, Jensen, Lars J, Bork, Peer, von Mering, Christian, Huerta-Cepas, Jaime, Hernández-Plaza, Ana, Szklarczyk, Damian, Botas, Jorge, Cantalapiedra, Carlos P, Giner-Lamia, Joaquín, Mende, Daniel R, Kirsch, Rebecca, Rattei, Thomas, Letunic, Ivica, Jensen, Lars J, Bork, Peer, von Mering, Christian, and Huerta-Cepas, Jaime

Abstract

The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de.

Published
2023

25. proGenomes3: approaching one million accurately and consistently annotated high-quality prokaryotic genomes

Author

Fullam, Anthony; https://orcid.org/0000-0002-0884-8124, Letunic, Ivica; https://orcid.org/0000-0003-3560-4288, Schmidt, Thomas S B; https://orcid.org/0000-0001-8587-4177, Ducarmon, Quinten R; https://orcid.org/0000-0001-7077-2127, Karcher, Nicolai; https://orcid.org/0000-0001-7894-8182, Khedkar, Supriya; https://orcid.org/0000-0001-6606-2202, Kuhn, Michael; https://orcid.org/0000-0002-2841-872X, Larralde, Martin; https://orcid.org/0000-0002-3947-4444, Maistrenko, Oleksandr M; https://orcid.org/0000-0003-1961-7548, Malfertheiner, Lukas; https://orcid.org/0000-0002-5697-2007, Milanese, Alessio; https://orcid.org/0000-0002-7050-2239, Rodrigues, Joao Frederico Matias; https://orcid.org/0000-0001-8413-9920, Sanchis-López, Claudia; https://orcid.org/0000-0002-8206-1565, Schudoma, Christian; https://orcid.org/0000-0003-1157-1354, Szklarczyk, Damian; https://orcid.org/0000-0002-4052-5069, Sunagawa, Shinichi; https://orcid.org/0000-0003-3065-0314, Zeller, Georg; https://orcid.org/0000-0003-1429-7485, Huerta-Cepas, Jaime; https://orcid.org/0000-0003-4195-5025, von Mering, Christian; https://orcid.org/0000-0001-7734-9102, Bork, Peer; https://orcid.org/0000-0002-2627-833X, Mende, Daniel R; https://orcid.org/0000-0001-6831-4557, Fullam, Anthony; https://orcid.org/0000-0002-0884-8124, Letunic, Ivica; https://orcid.org/0000-0003-3560-4288, Schmidt, Thomas S B; https://orcid.org/0000-0001-8587-4177, Ducarmon, Quinten R; https://orcid.org/0000-0001-7077-2127, Karcher, Nicolai; https://orcid.org/0000-0001-7894-8182, Khedkar, Supriya; https://orcid.org/0000-0001-6606-2202, Kuhn, Michael; https://orcid.org/0000-0002-2841-872X, Larralde, Martin; https://orcid.org/0000-0002-3947-4444, Maistrenko, Oleksandr M; https://orcid.org/0000-0003-1961-7548, Malfertheiner, Lukas; https://orcid.org/0000-0002-5697-2007, Milanese, Alessio; https://orcid.org/0000-0002-7050-2239, Rodrigues, Joao Frederico Matias; https://orcid.org/0000-0001-8413-9920, Sanchis-López, Claudia; https://orcid.org/0000-0002-8206-1565, Schudoma, Christian; https://orcid.org/0000-0003-1157-1354, Szklarczyk, Damian; https://orcid.org/0000-0002-4052-5069, Sunagawa, Shinichi; https://orcid.org/0000-0003-3065-0314, Zeller, Georg; https://orcid.org/0000-0003-1429-7485, Huerta-Cepas, Jaime; https://orcid.org/0000-0003-4195-5025, von Mering, Christian; https://orcid.org/0000-0001-7734-9102, Bork, Peer; https://orcid.org/0000-0002-2627-833X, and Mende, Daniel R; https://orcid.org/0000-0001-6831-4557

Abstract

The interpretation of genomic, transcriptomic and other microbial 'omics data is highly dependent on the availability of well-annotated genomes. As the number of publicly available microbial genomes continues to increase exponentially, the need for quality control and consistent annotation is becoming critical. We present proGenomes3, a database of 907 388 high-quality genomes containing 4 billion genes that passed stringent criteria and have been consistently annotated using multiple functional and taxonomic databases including mobile genetic elements and biosynthetic gene clusters. proGenomes3 encompasses 41 171 species-level clusters, defined based on universal single copy marker genes, for which pan-genomes and contextual habitat annotations are provided. The database is available at http://progenomes.embl.de/.

Published
2023

26. eggNOG 6.0: enabling comparative genomics across 12 535 organisms

Author

Hernández-Plaza, Ana; https://orcid.org/0000-0002-9844-7999, Szklarczyk, Damian; https://orcid.org/0000-0002-4052-5069, Botas, Jorge; https://orcid.org/0000-0001-7292-8981, Cantalapiedra, Carlos P; https://orcid.org/0000-0001-5263-533X, Giner-Lamia, Joaquín; https://orcid.org/0000-0003-1553-8295, Mende, Daniel R; https://orcid.org/0000-0001-6831-4557, Kirsch, Rebecca, Rattei, Thomas; https://orcid.org/0000-0002-0592-7791, Letunic, Ivica; https://orcid.org/0000-0003-3560-4288, Jensen, Lars J; https://orcid.org/0000-0001-7885-715X, Bork, Peer; https://orcid.org/0000-0002-2627-833X, von Mering, Christian; https://orcid.org/0000-0001-7734-9102, Huerta-Cepas, Jaime; https://orcid.org/0000-0003-4195-5025, Hernández-Plaza, Ana; https://orcid.org/0000-0002-9844-7999, Szklarczyk, Damian; https://orcid.org/0000-0002-4052-5069, Botas, Jorge; https://orcid.org/0000-0001-7292-8981, Cantalapiedra, Carlos P; https://orcid.org/0000-0001-5263-533X, Giner-Lamia, Joaquín; https://orcid.org/0000-0003-1553-8295, Mende, Daniel R; https://orcid.org/0000-0001-6831-4557, Kirsch, Rebecca, Rattei, Thomas; https://orcid.org/0000-0002-0592-7791, Letunic, Ivica; https://orcid.org/0000-0003-3560-4288, Jensen, Lars J; https://orcid.org/0000-0001-7885-715X, Bork, Peer; https://orcid.org/0000-0002-2627-833X, von Mering, Christian; https://orcid.org/0000-0001-7734-9102, and Huerta-Cepas, Jaime; https://orcid.org/0000-0003-4195-5025

Abstract

The eggNOG (evolutionary gene genealogy Non-supervised Orthologous Groups) database is a bioinformatics resource providing orthology data and comprehensive functional information for organisms from all domains of life. Here, we present a major update of the database and website (version 6.0), which increases the number of covered organisms to 12 535 reference species, expands functional annotations, and implements new functionality. In total, eggNOG 6.0 provides a hierarchy of over 17M orthologous groups (OGs) computed at 1601 taxonomic levels, spanning 10 756 bacterial, 457 archaeal and 1322 eukaryotic organisms. OGs have been thoroughly annotated using recent knowledge from functional databases, including KEGG, Gene Ontology, UniProtKB, BiGG, CAZy, CARD, PFAM and SMART. eggNOG also offers phylogenetic trees for all OGs, maximising utility and versatility for end users while allowing researchers to investigate the evolutionary history of speciation and duplication events as well as the phylogenetic distribution of functional terms within each OG. Furthermore, the eggNOG 6.0 website contains new functionality to mine orthology and functional data with ease, including the possibility of generating phylogenetic profiles for multiple OGs across species or identifying single-copy OGs at custom taxonomic levels. eggNOG 6.0 is available at http://eggnog6.embl.de.

Published
2023

27. SPIRE: a Searchable, Planetary-scale mIcrobiome REsource.

Author

Schmidt, Thomas S B, Fullam, Anthony, Ferretti, Pamela, Orakov, Askarbek, Maistrenko, Oleksandr M, Ruscheweyh, Hans-Joachim, Letunic, Ivica, Duan, Yiqian, Van Rossum, Thea, Sunagawa, Shinichi, Mende, Daniel R, Finn, Robert D, Kuhn, Michael, Pedro Coelho, Luis, and Bork, Peer

Published
2024
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29. proGenomes3: approaching one million accurately and consistently annotated high-quality prokaryotic genomes

Author

Fullam, Anthony, primary, Letunic, Ivica, additional, Schmidt, Thomas S B, additional, Ducarmon, Quinten R, additional, Karcher, Nicolai, additional, Khedkar, Supriya, additional, Kuhn, Michael, additional, Larralde, Martin, additional, Maistrenko, Oleksandr M, additional, Malfertheiner, Lukas, additional, Milanese, Alessio, additional, Rodrigues, Joao Frederico Matias, additional, Sanchis-López, Claudia, additional, Schudoma, Christian, additional, Szklarczyk, Damian, additional, Sunagawa, Shinichi, additional, Zeller, Georg, additional, Huerta-Cepas, Jaime, additional, von Mering, Christian, additional, Bork, Peer, additional, and Mende, Daniel R, additional

Published
2022
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30. eggNOG 6.0: enabling comparative genomics across 12 535 organisms

Author

Hernández-Plaza, Ana, primary, Szklarczyk, Damian, additional, Botas, Jorge, additional, Cantalapiedra, Carlos P, additional, Giner-Lamia, Joaquín, additional, Mende, Daniel R, additional, Kirsch, Rebecca, additional, Rattei, Thomas, additional, Letunic, Ivica, additional, Jensen, Lars J, additional, Bork, Peer, additional, von Mering, Christian, additional, and Huerta-Cepas, Jaime, additional

Published
2022
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31. Towards the biogeography of prokaryotic genes

Author

European Commission, European Research Council, Helmut Horten Foundation, National Key Research and Development Program (China), Shanghai Municipal Education Commission, International Development Research Centre (Canada), Fundación la Caixa, Agencia Estatal de Investigación (España), Innovation Fund Denmark, Coelho, Luis Pedro [0000-0002-9280-7885], Alves, Renato [0000-0002-7212-0234], Del Río, Álvaro Rodríguez [0000-0003-3907-3904], Myers, Pernille Neve [0000-0002-1824-3305], Cantalapiedra, Carlos P [0000-0001-5263-533X], Giner-Lamia, Joaquín [0000-0003-1553-8295], Mende, Daniel R. [0000-0001-6831-4557], Orakov, Askarbek [0000-0001-6823-5269], Letunic, Ivica [0000-0003-3560-4288], Hildebrand, Falk [0000-0002-0078-8948], Van Rossum, Thea [0000-0002-3598-5001], Forslund, Sofia K [0000-0003-4285-6993], Khedkar, Supriya [0000-0001-6606-2202], Maistrenko, Oleksandr M [0000-0003-1961-7548], Pan, Shaojun [0000-0002-5270-5614], Jia, Longhao [0000-0002-3490-840X], Ferretti, Pamela [0000-0002-1707-9013], Sunagawa, Shinichi [0000-0003-3065-0314], Nielsen, Henrik Bjørn [0000-0003-2281-5713], Huerta-Cepas, Jaime [0000-0003-4195-5025], Bork, Peer [0000-0002-2627-833X], Coelho, Luis Pedro, Alves, Renato, Del Río, Álvaro Rodríguez, Myers, Pernille Neve, Cantalapiedra, Carlos P, Giner-Lamia, Joaquín, Schmidt, Thomas Sebastian, Mende, Daniel R., Orakov, Askarbek, Letunic, Ivica, Hildebrand, Falk, Van Rossum, Thea, Forslund, Sofía K., Khedkar, Supriya, Maistrenko, Oleksandr M., Pan, Shaojun, Jia, Longhao, Ferretti, Pamela, Sunagawa, Shinichi, Zhao, Xing-Ming, Nielsen, Henrik Bjørn, Huerta-Cepas, Jaime, Bork, Peer, European Commission, European Research Council, Helmut Horten Foundation, National Key Research and Development Program (China), Shanghai Municipal Education Commission, International Development Research Centre (Canada), Fundación la Caixa, Agencia Estatal de Investigación (España), Innovation Fund Denmark, Coelho, Luis Pedro [0000-0002-9280-7885], Alves, Renato [0000-0002-7212-0234], Del Río, Álvaro Rodríguez [0000-0003-3907-3904], Myers, Pernille Neve [0000-0002-1824-3305], Cantalapiedra, Carlos P [0000-0001-5263-533X], Giner-Lamia, Joaquín [0000-0003-1553-8295], Mende, Daniel R. [0000-0001-6831-4557], Orakov, Askarbek [0000-0001-6823-5269], Letunic, Ivica [0000-0003-3560-4288], Hildebrand, Falk [0000-0002-0078-8948], Van Rossum, Thea [0000-0002-3598-5001], Forslund, Sofia K [0000-0003-4285-6993], Khedkar, Supriya [0000-0001-6606-2202], Maistrenko, Oleksandr M [0000-0003-1961-7548], Pan, Shaojun [0000-0002-5270-5614], Jia, Longhao [0000-0002-3490-840X], Ferretti, Pamela [0000-0002-1707-9013], Sunagawa, Shinichi [0000-0003-3065-0314], Nielsen, Henrik Bjørn [0000-0003-2281-5713], Huerta-Cepas, Jaime [0000-0003-4195-5025], Bork, Peer [0000-0002-2627-833X], Coelho, Luis Pedro, Alves, Renato, Del Río, Álvaro Rodríguez, Myers, Pernille Neve, Cantalapiedra, Carlos P, Giner-Lamia, Joaquín, Schmidt, Thomas Sebastian, Mende, Daniel R., Orakov, Askarbek, Letunic, Ivica, Hildebrand, Falk, Van Rossum, Thea, Forslund, Sofía K., Khedkar, Supriya, Maistrenko, Oleksandr M., Pan, Shaojun, Jia, Longhao, Ferretti, Pamela, Sunagawa, Shinichi, Zhao, Xing-Ming, Nielsen, Henrik Bjørn, Huerta-Cepas, Jaime, and Bork, Peer

Abstract

Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats1,2,3, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.

Published
2021

32. Role for urea in nitrification by polar marine Archaea

Author

Alonso-Sáez, Laura, Waller, Alison S., Mende, Daniel R., Bakker, Kevin, Farnelid, Hanna, Yager, Patricia L., Lovejoy, Connie, Tremblay, Jean-Éric, Potvin, Marianne, Heinrich, Friederike, Estrada, Marta, Riemann, Lasse, Bork, Peer, Pedrós-Alió, Carlos, and Bertilsson, Stefan

Published
2012

33. Complex marine microbial communities partition metabolism of scarce resources over the diel cycle

Author

Muratore, Daniel, Boysen, Angela K., Harke, Matthew J., Becker, Kevin W., Casey, John R., Coesel, Sacha N., Mende, Daniel R., Wilson, Samuel T., Aylward, Frank O., Eppley, John M., Vislova, Alice, Peng, Shengyun, Rodriguez-Gonzalez, Rogelio A., Beckett, Stephen J., Virginia Armbrust, E., DeLong, Edward F., Karl, David M., White, Angelicque E., Zehr, Jonathan P., Van Mooy, Benjamin A. S., Dyhrman, Sonya T., Ingalls, Anitra E., Weitz, Joshua S., Muratore, Daniel, Boysen, Angela K., Harke, Matthew J., Becker, Kevin W., Casey, John R., Coesel, Sacha N., Mende, Daniel R., Wilson, Samuel T., Aylward, Frank O., Eppley, John M., Vislova, Alice, Peng, Shengyun, Rodriguez-Gonzalez, Rogelio A., Beckett, Stephen J., Virginia Armbrust, E., DeLong, Edward F., Karl, David M., White, Angelicque E., Zehr, Jonathan P., Van Mooy, Benjamin A. S., Dyhrman, Sonya T., Ingalls, Anitra E., and Weitz, Joshua S.

Abstract

Complex assemblages of microbes in the surface ocean are responsible for approximately half of global carbon fixation. The persistence of high taxonomic diversity despite competition for a small suite of relatively homogeneously distributed nutrients, that is, 'the paradox of the plankton', represents a long-standing challenge for ecological theory. Here we find evidence consistent with temporal niche partitioning of nitrogen assimilation processes over a diel cycle in the North Pacific Subtropical Gyre. We jointly analysed transcript abundances, lipids and metabolites and discovered that a small number of diel archetypes can explain pervasive periodic dynamics. Metabolic pathway analysis of identified diel signals revealed asynchronous timing in the transcription of nitrogen uptake and assimilation genes among different microbial groups-cyanobacteria, heterotrophic bacteria and eukaryotes. This temporal niche partitioning of nitrogen uptake emerged despite synchronous transcription of photosynthesis and central carbon metabolism genes and associated macromolecular abundances. Temporal niche partitioning may be a mechanism by which microorganisms in the open ocean mitigate competition for scarce resources, supporting community coexistence.

Published
2022
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34. Critical Assessment of Metagenome Interpretation:the second round of challenges

Author

Meyer, Fernando, Fritz, Adrian, Deng, Zhi-Luo, Koslicki, David, Lesker, Till Robin, Gurevich, Alexey, Robertson, Gary, Alser, Mohammed, Antipov, Dmitry, Beghini, Francesco, Bertrand, Denis, Brito, Jaqueline J., Brown, C. Titus, Buchmann, Jan, Buluç, Aydin, Chen, Bo, Chikhi, Rayan, Clausen, Philip T.L.C., Cristian, Alexandru, Dabrowski, Piotr Wojciech, Darling, Aaron E., Egan, Rob, Eskin, Eleazar, Georganas, Evangelos, Goltsman, Eugene, Gray, Melissa A., Hansen, Lars Hestbjerg, Hofmeyr, Steven, Huang, Pingqin, Irber, Luiz, Jia, Huijue, Jørgensen, Tue Sparholt, Kieser, Silas D., Klemetsen, Terje, Kola, Axel, Kolmogorov, Mikhail, Korobeynikov, Anton, Kwan, Jason, LaPierre, Nathan, Lemaitre, Claire, Li, Chenhao, Limasset, Antoine, Malcher-Miranda, Fabio, Mangul, Serghei, Marcelino, Vanessa R., Marchet, Camille, Marijon, Pierre, Meleshko, Dmitry, Mende, Daniel R., Milanese, Alessio, Nagarajan, Niranjan, Nissen, Jakob, Nurk, Sergey, Oliker, Leonid, Paoli, Lucas, Peterlongo, Pierre, Piro, Vitor C., Porter, Jacob S., Rasmussen, Simon, Rees, Evan R., Reinert, Knut, Renard, Bernhard, Robertsen, Espen Mikal, Rosen, Gail L., Ruscheweyh, Hans-Joachim, Sarwal, Varuni, Segata, Nicola, Seiler, Enrico, Shi, Lizhen, Sun, Fengzhu, Sunagawa, Shinichi, Sørensen, Søren Johannes, Thomas, Ashleigh, Tong, Chengxuan, Trajkovski, Mirko, Tremblay, Julien, Uritskiy, Gherman, Vicedomini, Riccardo, Wang, Zhengyang, Wang, Ziye, Wang, Zhong, Warren, Andrew, Willassen, Nils Peder, Yelick, Katherine, You, Ronghui, Zeller, Georg, Zhao, Zhengqiao, Zhu, Shanfeng, Zhu, Jie, Garrido-Oter, Ruben, Gastmeier, Petra, Hacquard, Stephane, Häußler, Susanne, Khaledi, Ariane, Maechler, Friederike, Mesny, Fantin, Radutoiu, Simona, Schulze-Lefert, Paul, Smit, Nathiana, Strowig, Till, Bremges, Andreas, Sczyrba, Alexander, McHardy, Alice Carolyn, Meyer, Fernando, Fritz, Adrian, Deng, Zhi-Luo, Koslicki, David, Lesker, Till Robin, Gurevich, Alexey, Robertson, Gary, Alser, Mohammed, Antipov, Dmitry, Beghini, Francesco, Bertrand, Denis, Brito, Jaqueline J., Brown, C. Titus, Buchmann, Jan, Buluç, Aydin, Chen, Bo, Chikhi, Rayan, Clausen, Philip T.L.C., Cristian, Alexandru, Dabrowski, Piotr Wojciech, Darling, Aaron E., Egan, Rob, Eskin, Eleazar, Georganas, Evangelos, Goltsman, Eugene, Gray, Melissa A., Hansen, Lars Hestbjerg, Hofmeyr, Steven, Huang, Pingqin, Irber, Luiz, Jia, Huijue, Jørgensen, Tue Sparholt, Kieser, Silas D., Klemetsen, Terje, Kola, Axel, Kolmogorov, Mikhail, Korobeynikov, Anton, Kwan, Jason, LaPierre, Nathan, Lemaitre, Claire, Li, Chenhao, Limasset, Antoine, Malcher-Miranda, Fabio, Mangul, Serghei, Marcelino, Vanessa R., Marchet, Camille, Marijon, Pierre, Meleshko, Dmitry, Mende, Daniel R., Milanese, Alessio, Nagarajan, Niranjan, Nissen, Jakob, Nurk, Sergey, Oliker, Leonid, Paoli, Lucas, Peterlongo, Pierre, Piro, Vitor C., Porter, Jacob S., Rasmussen, Simon, Rees, Evan R., Reinert, Knut, Renard, Bernhard, Robertsen, Espen Mikal, Rosen, Gail L., Ruscheweyh, Hans-Joachim, Sarwal, Varuni, Segata, Nicola, Seiler, Enrico, Shi, Lizhen, Sun, Fengzhu, Sunagawa, Shinichi, Sørensen, Søren Johannes, Thomas, Ashleigh, Tong, Chengxuan, Trajkovski, Mirko, Tremblay, Julien, Uritskiy, Gherman, Vicedomini, Riccardo, Wang, Zhengyang, Wang, Ziye, Wang, Zhong, Warren, Andrew, Willassen, Nils Peder, Yelick, Katherine, You, Ronghui, Zeller, Georg, Zhao, Zhengqiao, Zhu, Shanfeng, Zhu, Jie, Garrido-Oter, Ruben, Gastmeier, Petra, Hacquard, Stephane, Häußler, Susanne, Khaledi, Ariane, Maechler, Friederike, Mesny, Fantin, Radutoiu, Simona, Schulze-Lefert, Paul, Smit, Nathiana, Strowig, Till, Bremges, Andreas, Sczyrba, Alexander, and McHardy, Alice Carolyn

Abstract

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Published
2022

35. Metagenomic DNA sequencing for semi-quantitative pathogen detection from urine:a prospective, laboratory-based, proof-of-concept study

Author

Janes, Victoria A, Matamoros, Sébastien, Munk, Patrick, Clausen, Philip T L C, Koekkoek, Sylvie M, Koster, Linda A M, Jakobs, Marja E, de Wever, Bob, Visser, Caroline E, Aarestrup, Frank M, Lund, Ole, de Jong, Menno D, Bossuyt, Patrick M M, Mende, Daniel R, Schultsz, Constance, Janes, Victoria A, Matamoros, Sébastien, Munk, Patrick, Clausen, Philip T L C, Koekkoek, Sylvie M, Koster, Linda A M, Jakobs, Marja E, de Wever, Bob, Visser, Caroline E, Aarestrup, Frank M, Lund, Ole, de Jong, Menno D, Bossuyt, Patrick M M, Mende, Daniel R, and Schultsz, Constance

Abstract

Background Semi-quantitative bacterial culture is the reference standard to diagnose urinary tract infection, but culture is time-consuming and can be unreliable if patients are receiving antibiotics. Metagenomics could increase diagnostic accuracy and speed by sequencing the microbiota and resistome directly from urine. We aimed to compare metagenomics to culture for semi-quantitative pathogen and resistome detection from urine. Methods In this proof-of-concept study, we prospectively included consecutive urine samples from a clinical diagnostic laboratory in Amsterdam. Urine samples were screened by DNA concentration, followed by PCR-free metagenomic sequencing of randomly selected samples with a high concentration of DNA (culture positive and negative). A diagnostic index was calculated as the product of DNA concentration and fraction of pathogen reads. We compared results with semi-quantitative culture using area under the receiver operating characteristic curve (AUROC) analyses. We used ResFinder and PointFinder for resistance gene detection and compared results to phenotypic antimicrobial susceptibility testing for six antibiotics commonly used for urinary tract infection treatment: nitrofurantoin, ciprofloxacin, fosfomycin, cotrimoxazole, ceftazidime, and ceftriaxone. Findings We screened 529 urine samples of which 86 were sequenced (43 culture positive and 43 culture negative). The AUROC of the DNA concentration-based screening was 0·85 (95% CI 0·81-0·89). At a cutoff value of 6·0 ng/mL, culture positivity was ruled out with a negative predictive value of 91% (95% CI 87-93; 26 of 297 samples), reducing the number of samples requiring sequencing by 56% (297 of 529 samples). The AUROC of the diagnostic index was 0·87 (95% CI 0·79-0·95). A diagnostic index cutoff value of 17·2 yielded a positive predictive value of 93% (95% CI 85-97) and a negative predictive value of 69% (55-80), correcting for a culture-pos

Published
2022

37. GUNC Poster at ISME 2022

Author

Orakov, Askarbek, Fullam, Anthony, Coelho, Luis Pedro, Khedkar, Supriya, Szklarczyk, Damian, Mende, Daniel R., Sebastian Benedikt Schmidt, Thomas, and Bork, Peer

Abstract

It's a poster to present the GUNC tool at ISME 2022 Conference. GUNC (the Genome UNClutterer) is a tool that accurately detects and quantifies prokaryotic genome chimerism based on the lineage homogeneity of individual contigs using a genome’s full complement of genes.

Published
2022
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38. Disentangling the impact of environmental and phylogenetic constraints on prokaryotic within-species diversity

Author

European Commission, EMBO, Maistrenko, Oleksandr M. [0000-0003-1961-7548], Mende, Daniel R. [0000-0001-6831-4557], Hildebrand, Falk [0000-0002-0078-8948], Schmidt, Thomas Sebastian [0000-0001-8587-4177], Li, Simone S. [0000-0002-0073-3656], Rodrigues, Joao Frederico Matias [0000-0001-8413-9920], von Mering, Christian [0000-0001-7734-9102], Coelho, Luis Pedro [0000-0002-9280-7885], Huerta-Cepas, Jaime [0000-0003-4195-5025], Sunagawa, Shinichi [0000-0003-3065-0314], Bork, Peer [0000-0002-2627-833X], Maistrenko, Oleksandr M., Mende, Daniel R., Luetge, Mechthild, Hildebrand, Falk, Schmidt, Thomas Sebastian, Li, Simone S., Rodrigues, Joao Frederico Matias, von Mering, Christian, Coelho, Luis Pedro, Huerta-Cepas, Jaime, Sunagawa, Shinichi, Bork, Peer, European Commission, EMBO, Maistrenko, Oleksandr M. [0000-0003-1961-7548], Mende, Daniel R. [0000-0001-6831-4557], Hildebrand, Falk [0000-0002-0078-8948], Schmidt, Thomas Sebastian [0000-0001-8587-4177], Li, Simone S. [0000-0002-0073-3656], Rodrigues, Joao Frederico Matias [0000-0001-8413-9920], von Mering, Christian [0000-0001-7734-9102], Coelho, Luis Pedro [0000-0002-9280-7885], Huerta-Cepas, Jaime [0000-0003-4195-5025], Sunagawa, Shinichi [0000-0003-3065-0314], Bork, Peer [0000-0002-2627-833X], Maistrenko, Oleksandr M., Mende, Daniel R., Luetge, Mechthild, Hildebrand, Falk, Schmidt, Thomas Sebastian, Li, Simone S., Rodrigues, Joao Frederico Matias, von Mering, Christian, Coelho, Luis Pedro, Huerta-Cepas, Jaime, Sunagawa, Shinichi, and Bork, Peer

Abstract

Microbial organisms inhabit virtually all environments and encompass a vast biological diversity. The pangenome concept aims to facilitate an understanding of diversity within defined phylogenetic groups. Hence, pangenomes are increasingly used to characterize the strain diversity of prokaryotic species. To understand the interdependence of pangenome features (such as the number of core and accessory genes) and to study the impact of environmental and phylogenetic constraints on the evolution of conspecific strains, we computed pangenomes for 155 phylogenetically diverse species (from ten phyla) using 7,000 high-quality genomes to each of which the respective habitats were assigned. Species habitat ubiquity was associated with several pangenome features. In particular, core-genome size was more important for ubiquity than accessory genome size. In general, environmental preferences had a stronger impact on pangenome evolution than phylogenetic inertia. Environmental preferences explained up to 49% of the variance for pangenome features, compared with 18% by phylogenetic inertia. This observation was robust when the dataset was extended to 10,100 species (59 phyla). The importance of environmental preferences was further accentuated by convergent evolution of pangenome features in a given habitat type across different phylogenetic clades. For example, the soil environment promotes expansion of pangenome size, while host-associated habitats lead to its reduction. Taken together, we explored the global principles of pangenome evolution, quantified the influence of habitat, and phylogenetic inertia on the evolution of pangenomes and identified criteria governing species ubiquity and habitat specificity.

Published
2020

39. proGenomes2: an improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes

Author

European Commission, Comunidad de Madrid, Agencia Estatal de Investigación (España), Mende, Daniel R. [0000-0001-6831-4557], Letunic, Ivica [0000-0003-3560-4288], Maistrenko, Oleksandr M. [0000-0003-1961-7548], Schmidt, Thomas Sebastian [0000-0001-8587-4177], Milanese, Alessio [0000-0002-7050-2239], Paoli, Lucas [0000-0003-0771-8309], Orakov, Askarbek [0000-0001-6823-5269], Forslund, Sofía K. [0000-0003-4285-6993], Sunagawa, Shinichi [0000-0003-3065-0314], Zeller, Georg [0000-0003-1429-7485], Huerta-Cepas, Jaime [0000-0003-4195-5025], Coelho, Luis Pedro [0000-0002-9280-7885], Bork, Peer [0000-0002-2627-833X], Mende, Daniel R., Letunic, Ivica, Maistrenko, Oleksandr M., Schmidt, Thomas Sebastian, Milanese, Alessio, Paoli, Lucas, Hernández-Plaza, Ana, Orakov, Askarbek, Forslund, Sofía K., Sunagawa, Shinichi, Zeller, Georg, Huerta-Cepas, Jaime, Coelho, Luis Pedro, Bork, Peer, European Commission, Comunidad de Madrid, Agencia Estatal de Investigación (España), Mende, Daniel R. [0000-0001-6831-4557], Letunic, Ivica [0000-0003-3560-4288], Maistrenko, Oleksandr M. [0000-0003-1961-7548], Schmidt, Thomas Sebastian [0000-0001-8587-4177], Milanese, Alessio [0000-0002-7050-2239], Paoli, Lucas [0000-0003-0771-8309], Orakov, Askarbek [0000-0001-6823-5269], Forslund, Sofía K. [0000-0003-4285-6993], Sunagawa, Shinichi [0000-0003-3065-0314], Zeller, Georg [0000-0003-1429-7485], Huerta-Cepas, Jaime [0000-0003-4195-5025], Coelho, Luis Pedro [0000-0002-9280-7885], Bork, Peer [0000-0002-2627-833X], Mende, Daniel R., Letunic, Ivica, Maistrenko, Oleksandr M., Schmidt, Thomas Sebastian, Milanese, Alessio, Paoli, Lucas, Hernández-Plaza, Ana, Orakov, Askarbek, Forslund, Sofía K., Sunagawa, Shinichi, Zeller, Georg, Huerta-Cepas, Jaime, Coelho, Luis Pedro, and Bork, Peer

Abstract

Microbiology depends on the availability of annotated microbial genomes for many applications. Comparative genomics approaches have been a major advance, but consistent and accurate annotations of genomes can be hard to obtain. In addition, newer concepts such as the pan-genome concept are still being implemented to help answer biological questions. Hence, we present proGenomes2, which provides 87 920 high-quality genomes in a user-friendly and interactive manner. Genome sequences and annotations can be retrieved individually or by taxonomic clade. Every genome in the database has been assigned to a species cluster and most genomes could be accurately assigned to one or multiple habitats. In addition, general functional annotations and specific annotations of antibiotic resistance genes and single nucleotide variants are provided. In short, proGenomes2 provides threefold more genomes, enhanced habitat annotations, updated taxonomic and functional annotation and improved linkage to the NCBI BioSample database. The database is available at http://progenomes.embl.de/.

Published
2020

40. Towards the biogeography of prokaryotic genes

Author

Coelho, Luis Pedro, primary, Alves, Renato, additional, del Río, Álvaro Rodríguez, additional, Myers, Pernille Neve, additional, Cantalapiedra, Carlos P., additional, Giner-Lamia, Joaquín, additional, Schmidt, Thomas Sebastian, additional, Mende, Daniel R., additional, Orakov, Askarbek, additional, Letunic, Ivica, additional, Hildebrand, Falk, additional, Van Rossum, Thea, additional, Forslund, Sofia K., additional, Khedkar, Supriya, additional, Maistrenko, Oleksandr M., additional, Pan, Shaojun, additional, Jia, Longhao, additional, Ferretti, Pamela, additional, Sunagawa, Shinichi, additional, Zhao, Xing-Ming, additional, Nielsen, Henrik Bjørn, additional, Huerta-Cepas, Jaime, additional, and Bork, Peer, additional

Published
2021
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41. Genomic variation landscape of the human gut microbiome

Author

Schloissnig, Siegfried, Arumugam, Manimozhiyan, Sunagawa, Shinichi, Mitreva, Makedonka, Tap, Julien, Zhu, Ana, Waller, Alison, Mende, Daniel R., Kultima, Jens Roat, Martin, John, Kota, Karthik, Sunyaev, Shamil R., Weinstock, George M., and Bork, Peer

Subjects
Genetic markers -- Identification and classification, Microbiota (Symbiotic organisms) -- Observations -- Genetic aspects, Genetic variation -- Research, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Whereas large-scale efforts have rapidly advanced the understanding and practical impact of human genomic variation, the practical impact of variation is largely unexplored in the human microbiome. We therefore developed a framework for metagenomic variation analysis and applied it to 252 faecal metagenomes of 207 individuals from Europe and North America. Using 7.4 billion reads aligned to 101 reference species, we detected 10.3 million single nucleotide polymorphisms (SNPs), 107,991 short insertions/deletions, and 1,051 structural variants. The average ratio of non- synonymous to synonymous polymorphism rates of 0.11 was more variable between gut microbial species than across human hosts. Subjects sampled at varying time intervals exhibited individuality and temporal stability of SNP variation patterns, despite considerable composition changes of their gut microbiota. This indicates that individual-specific strains are not easily replaced and that an individual might have a unique metagenomic genotype, which may be exploitable for personalized diet or drug intake., With the increasing availability of individual human genomes, various theoretical and practical aspects of genomic variation can be deduced for individuals and the human population as a whole (1,2). Like [...]

Published
2013
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42. Additional file 12 of Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage

Author

Boeuf, Dominique, Eppley, John M., Mende, Daniel R., Malmstrom, Rex R., Woyke, Tanja, and DeLong, Edward F.

Subjects
food and beverages
Abstract

Additional file 11: Supplementary Figure 8. Synteny map of the genic neighborhood of Xanthorhodopsin-like coding genes retrieved in SAR324 genomes. Target gene is displayed in red, enzyme-coding gene in orange, hypothetical enzyme-coding gene in yellow, transporter-coding gene in green, protein-coding gene in grey and hypothetical coding gene in white. tRNA are displayed by black bars. Contigs are identified as follow: Population genome identifier - GenBank assemblies (GCA) identifier (contig identifier). Name background was colored according to subclades.

Published
2021
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43. Additional file 6 of Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage

Author

Boeuf, Dominique, Eppley, John M., Mende, Daniel R., Malmstrom, Rex R., Woyke, Tanja, and DeLong, Edward F.

Abstract

Additional file 5: Supplementary Figure 2. Depth distribution of SAR324 average coverage in the same samples from which the SAGs have been isolated (black) and in HOT time-series (grey). Depth distribution of physical and chemical parameters at each month of 2016 are displayed in red and the average profile in blue. T: temperature, S: salinity, O: oxygen concentration, NO2+NO3: Nitrite and nitrate concentration, P: phosphorus concentration.

Published
2021
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44. Additional file 8 of Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage

Author

Boeuf, Dominique, Eppley, John M., Mende, Daniel R., Malmstrom, Rex R., Woyke, Tanja, and DeLong, Edward F.

Abstract

Additional file 7: Supplementary Figure 4. Placement of SAR324 16S rRNA coding genes (red) into the SILVA 132 reference tree. Subclades as defined by the ANI in this study are displayed by the inner brackets. Outside brackets denote the official classification as based on the SILVA database. 16S rDNA sequences retrieved from population genomes were aligned using SINA and placed into the reference tree using ARB_add_by_parcimony as implemented in ARB software. Genes are identified as follow: Population genome identifier (this study) | GenBank assemblies (GCA) identifier | genome description.

Published
2021
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46. Additional file 9 of Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage

Author

Boeuf, Dominique, Eppley, John M., Mende, Daniel R., Malmstrom, Rex R., Woyke, Tanja, and DeLong, Edward F.

Abstract

Additional file 8: Supplementary Figure 5. Comparison between phylogenetic tree of 16S rRNA coding genes (left) and ANI classification (right). Phylogenetic tree of 16S rDNA genes was inferred from a MUSCLE alignment using Maximum Likelihood and General Time Reversible model with MEGA X software.

Published
2021
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48. Additional file 5 of Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage

Author

Boeuf, Dominique, Eppley, John M., Mende, Daniel R., Malmstrom, Rex R., Woyke, Tanja, and DeLong, Edward F.

Abstract

Additional file 4: Supplementary Figure 1. Venn diagrams of SAR324 genes pooled either by subclade (A) or by ecotype (B). The total of genes shared among the genomes constituting the subclade or the ecotype, are displayed in italic grey below the number of genes unique to the subclade or the ecotype. Venn diagrams have been generated from http://bioinformatics.psb.ugent.be/webtools/Venn .

Published
2021
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49. Additional file 1 of GUNC: detection of chimerism and contamination in prokaryotic genomes

Author

Orakov, Askarbek, Fullam, Anthony, Coelho, Luis Pedro, Khedkar, Supriya, Szklarczyk, Damian, Mende, Daniel R., Schmidt, Thomas S. B., and Bork, Peer

Abstract

Additional file 1: Figure S1. a Percent stacked bar chart of CheckM inferred marker lineage levels (colors) for type 3a simulated chimeric genomes (see Methods & Fig. 2a) across different: 1) divergence levels of source genomes (x-axis); 2) simulated portions of contamination (columns); and 3) scenarios of contamination (‘added’ vs ‘replaced’, rows; see Methods). In a and b, the first column (“0”) are clean (non-chimeric) genomes shown for comparison. b Average inferred CheckM marker lineage depth (y-axis) of simulated chimeric genomes under different contamination scenarios (‘added’ in dark blue; ‘replaced’ in light blue). The true taxonomic depth of divergence between source genomes are indicated in green. c Equivalent to a, but using chimeric genomes simulated from multiple sources (type 3b in Fig. 2a). Columns indicate the number of equally contributing source genomes (n_sources); rows indicate simulation setups (‘0.5’ if 50% of each source genome was used; ‘1/n_sources’ for equal source parts; see Methods). In c & d, the first column (“1”) are clean (non-chimeric) genomes, the second column (“2”) are type 3a genomes as in a & b, shown for comparison. d Average inferred CheckM marker lineage depths (y-axis) with different portions of contamination, equivalent to panel b. Figure S2. Comparison of median scores from GUNC and CheckM of simulations of genomes type 3a and 3b where source genomes make equal contributions summing 1 in total (e.g. 0.2 from each of 5 sources or 0.25 from each of 4 sources). This shows that the trend from Fig. 2b persists when multiple source genomes are mixed in a simulated chimeric genome. Figure S3. F-scores of distinction between clean and chimeric genomes across all divergence levels of source genomes for different simulation scenarios. MIMAG medium is CheckM contamination < 10% and CheckM completeness ≥50%. MIMAG high is CheckM contamination 90% and due to irrelevance to our simulations we decided that additional criteria of presence of rRNAs and tRNAs can be ignored here. “Cont” stands for CheckM contamination and GUNC means GUNC CSS of 0.45 & contamination >2%). The stacked bar plot on the right indicates the numbers of genomes from the overlap in each category. These categories do have overlaps and therefore genomes in them were counted and removed from the set used to count remaining categories in the following order of their genome counts: 71 > 34 > 86 > 187 & 29. Figure S8. a Cumulative plot summarizing genome qualities of various sets of genomes represented by lines of different colors. Any point in a plot indicates a portion of genomes retained in a set (y-axis) after filtering out genomes with GUNC CSS higher than the cutoff (x-axis) & GUNC contamination >5% (ignoring species-level scores). b Cumulative plot illustrating the number of species-level genome bins (SGBs) (from Pasolli et al. 2019). Lines indicate the portion of unique SGBs retained (y-axis) after filtering out SGBs where either “all” or “at least one” genome has GUNC CSS score higher than the cutoff (x-axis) & GUNC contamination >5%. Figure S9. Cumulative plots summarizing genome quality for various genome reference and MAG datasets. This plot is equivalent to main Fig. 3a, but using a reference set based on GTDB v95 instead of GUNC’s default based on proGenomes 2.1 (see Methods for details). Note that the Almeida, Pasolli and Nayfach sets were pre-filtered using variations of the MIMAG medium criterion based on CheckM estimates. GTDB, Genome Taxonomy Database; GMGC, Global Microbial Gene Catalogue. Figure S10. Alluvial illustration of the fate of genomes in GMGC based on filters by GUNC and CheckM. Three filters are: 1) CheckM contamination

Published
2021
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50. Potential of fecal microbiota for early‐stage detection of colorectal cancer

Author

Zeller, Georg, Tap, Julien, Voigt, Anita Y, Sunagawa, Shinichi, Kultima, Jens Roat, Costea, Paul I, Amiot, Aurélien, Böhm, Jürgen, Brunetti, Francesco, Habermann, Nina, Hercog, Rajna, Koch, Moritz, Luciani, Alain, Mende, Daniel R, Schneider, Martin A, Schrotz‐King, Petra, Tournigand, Christophe, Tran Van Nhieu, Jeanne, Yamada, Takuji, Zimmermann, Jürgen, Benes, Vladimir, Kloor, Matthias, Ulrich, Cornelia M, von Knebel Doeberitz, Magnus, Sobhani, Iradj, and Bork, Peer

Published
2014
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