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1. Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment

Author

Chng K. R., Li C., Bertrand D., Ng A. H. Q., Kwah J. S., Low H. M., Tong C., Natrajan M., Zhang M. H., Xu L., Ko K. K. K., Ho E. X. P., Av-Shalom T. V., Teo J. W. P., Khor C. C., Danko D., Bezdan D., Afshinnekoo E., Ahsanuddin S., Bhattacharya C., Butler D. J., De Filippis F., Hecht J., Kahles A., Karasikov M., Kyrpides N. C., Leung M. H. Y., Meleshko D., Mustafa H., Mutai B., Neches R. Y., Ng A., Nieto-Caballero M., Nikolayeva O., Nikolayeva T., Png E., Sanchez J. L., Shaaban H., Sierra M. A., Tong X., Young B., Alicea J., Bhattacharyya M., Blekhman R., Castro-Nallar E., Canas A. M., Chatziefthimiou A. D., Crawford R. W., Deng Y., Desnues C., Dias-Neto E., Donnellan D., Dybwad M., Elhaik E., Ercolini D., Frolova A., Graf A. B., Green D. C., Hajirasouliha I., Hernandez M., Iraola G., Jang S., Jones A., Kelly F. J., Knights K., Labaj P. P., Lee P. K. H., Shawn L., Ljungdahl P., Lyons A., Mason-Buck G., McGrath K., Mongodin E. F., Moraes M. O., Nagarajan N., Noushmehr H., Oliveira M., Ossowski S., Osuolale O. O., Ozcan O., Paez-Espino D., Rascovan N., Richard H., Ratsch G., Schriml L. M., Semmler T., Sezerman O. U., Shi L., Song L. H., Suzuki H., Court D. S., Thomas D., Tighe S. W., Udekwu K. I., Ugalde J. A., Valentine B., Vassilev D. I., Vayndorf E., Velavan T. P., Zambrano M. M., Zhu J., Zhu S., Mason C. E., Chen S. L., Ng O. T., Marimuthu K., Ang B., Genome Institute of Singapore (GIS), Singapore University of Technology and Design (SUTD), Singapore General Hospital, National University Hospital [Singapore] (NUH), Weill Cornell Medicine [Cornell University], Cornell University [New York], Nanyang Technological University [Singapour], Tan Tock Seng Hospital, Department of Computational and Systems Biology [Singapore], Funding for this work was provided by A*STAR (N.N.), and we are grateful for support from NMRC (NMRC CGAug16C005: O.T.N. and K.M.). C.E.M. acknowledges support from the WorldQuant Foundation, the Bill and Melinda Gates Foundation (OPP1151054) and the Alfred P. Sloan Foundation (G-2015-13964). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. We would like to thank J. Gilbert for insightful comments and feedback on this work., MetaSUB Consortium: David Danko, Daniela Bezdan, Ebrahim Afshinnekoo, Sofia Ahsanuddin, Chandrima Bhattacharya, Daniel J. Butler, Kern Rei Chng, Francesca De Filippis, Jochen Hecht, Andre Kahles, Mikhail Karasikov, Nikos C. Kyrpides, Marcus H. Y. Leung, Dmitry Meleshko, Harun Mustafa, Beth Mutai, Russell Y. Neches, Amanda Ng, Marina Nieto-Caballero, Olga Nikolayeva, Tatyana Nikolayeva, Eileen Png, Jorge L. Sanchez, Heba Shaaban, Maria A. Sierra, Xinzhao Tong, Ben Young, Josue Alicea, Malay Bhattacharyya, Ran Blekhman, Eduardo Castro-Nallar, Ana M. Cañas, Aspassia D. Chatziefthimiou, Robert W. Crawford, Youping Deng, Christelle Desnues, Emmanuel Dias-Neto, Daisy Donnellan, Marius Dybwad, Eran Elhaik, Danilo Ercolini, Alina Frolova, Alexandra B. Graf, David C. Green, Iman Hajirasouliha, Mark Hernandez, Gregorio Iraola, Soojin Jang, Angela Jones, Frank J. Kelly, Kaymisha Knights, Paweł P. Łabaj, Patrick K. H. Lee, Levy Shawn, Per Ljungdahl, Abigail Lyons, Gabriella Mason-Buck, Ken McGrath, Emmanuel F. Mongodin, Milton Ozorio Moraes, Niranjan Nagarajan, Houtan Noushmehr, Manuela Oliveira, Stephan Ossowski, Olayinka O. Osuolale, Orhan Özcan, David Paez-Espino, Nicolas Rascovan, Hugues Richard, Gunnar Rätsch, Lynn M. Schriml, Torsten Semmler, Osman U. Sezerman, Leming Shi, Le Huu Song, Haruo Suzuki, Denise Syndercombe Court, Dominique Thomas, Scott W. Tighe, Klas I. Udekwu, Juan A. Ugalde, Brandon Valentine, Dimitar I. Vassilev, Elena Vayndorf, Thirumalaisamy P. Velavan, María M. Zambrano, Jifeng Zhu, Sibo Zhu & Christopher E. Mason, Weill Cornell Medicine [New York], Chng, K. R., Li, C., Bertrand, D., Ng, A. H. Q., Kwah, J. S., Low, H. M., Tong, C., Natrajan, M., Zhang, M. H., Xu, L., Ko, K. K. K., Ho, E. X. P., Av-Shalom, T. V., Teo, J. W. P., Khor, C. C., Danko, D., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D. J., De Filippis, F., Hecht, J., Kahles, A., Karasikov, M., Kyrpides, N. C., Leung, M. H. Y., Meleshko, D., Mustafa, H., Mutai, B., Neches, R. Y., Ng, A., Nieto-Caballero, M., Nikolayeva, O., Nikolayeva, T., Png, E., Sanchez, J. L., Shaaban, H., Sierra, M. A., Tong, X., Young, B., Alicea, J., Bhattacharyya, M., Blekhman, R., Castro-Nallar, E., Canas, A. M., Chatziefthimiou, A. D., Crawford, R. W., Deng, Y., Desnues, C., Dias-Neto, E., Donnellan, D., Dybwad, M., Elhaik, E., Ercolini, D., Frolova, A., Graf, A. B., Green, D. C., Hajirasouliha, I., Hernandez, M., Iraola, G., Jang, S., Jones, A., Kelly, F. J., Knights, K., Labaj, P. P., Lee, P. K. H., Shawn, L., Ljungdahl, P., Lyons, A., Mason-Buck, G., Mcgrath, K., Mongodin, E. F., Moraes, M. O., Nagarajan, N., Noushmehr, H., Oliveira, M., Ossowski, S., Osuolale, O. O., Ozcan, O., Paez-Espino, D., Rascovan, N., Richard, H., Ratsch, G., Schriml, L. M., Semmler, T., Sezerman, O. U., Shi, L., Song, L. H., Suzuki, H., Court, D. S., Thomas, D., Tighe, S. W., Udekwu, K. I., Ugalde, J. A., Valentine, B., Vassilev, D. I., Vayndorf, E., Velavan, T. P., Zambrano, M. M., Zhu, J., Zhu, S., Mason, C. E., Chen, S. L., Ng, O. T., Marimuthu, K., Ang, B., and Acibadem University Dspace

Subjects
0301 basic medicine, Disease prevention, 030106 microbiology, Geographic Mapping, Drug resistance, Beds, Biology, Opportunistic Infections, Genome, General Biochemistry, Genetics and Molecular Biology, Article, Tertiary Care Centers, 03 medical and health sciences, Plasmid, Antibiotic resistance, Spatio-Temporal Analysis, Drug Resistance, Multiple, Bacterial, Drug Resistance, Bacterial, Patients' Rooms, Humans, Microbiome, Equipment and Supplies, Hospital, Genetics, Cross Infection, Infection Control, Singapore, Microbiota, General Medicine, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, 3. Good health, Resistome, Disinfection, 030104 developmental biology, Metagenomics, Biofilms, Equipment Contamination, Mobilome, Microbial genetics
Abstract

Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections., Spatiotemporal characterization of microbial diversity and antibiotic resistance in a tertiary-care hospital reveals broad distribution and persistence of antibiotic-resistant organisms that could cause opportunistic infections in a healthcare setting.

Published
2020
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2. A global metagenomic map of urban microbiomes and antimicrobial resistance

Author

Nadine Farhat, Tomoki Takeda, Astred Castro, Ken McGrath, Khaliun Sanchir, Iman Hajirasouliha, Eunice So, Laraib Zafar, Diana N. Nunes, Harun Mustafa, Amy Zhang, Priscilla Lisboa, Christian Schori, Marisano James, Jasna Chalangal, Sebastien Halary, Shahryar Rana, Yunmi Lee, Oli Schacher, Liliana Godoy, David A. Coil, Phanthira Pugdeethosal, Michelle D. Williams, German Marchandon, Angela Cantillo, Naoya Takahashi, Christopher Mozsary, Juana Gonzalez, Patrick K. H. Lee, Gerardo de Lamotte, Alessandro Robertiello, Steven Du, Fabienne Velter, Stefan G. Stark, Miguel Carbajo, Vincent Matthys, David A. Westfall, Julia Boeri, Irène Mauricette Mendy, Jonathan Cedillo, Francesco Oteri, Robert W. Crawford, Takayuki Ito, Tina Wunderlin, Maureen Muscat, David Paez-Espino, Carmen Urgiles, Aida Nesimi, Steffen Schaaf, Adan Ramirez-Rojas, Kunihiko Miyake, Christopher E. Mason, Anais Cardenas, Sharah Islam, Diego Benítez, Melissa Pool Pizzi, Kianna Ciaramella, Ciro Borrelli, Riham Islam, Dorottya Nagy-Szakal, Abd-Manaaf Bakere, Ait-hamlat Adel, Olha Lakhneko, Badamnyambuu Iderzorig, Ana Valeria Castro, Adam Phillips, Robert A. Petit, Flavia Corsi, Romain Conte, Krista Ryon, Soojin Jang, Joseph Benson, Fernanda de Souza Gomes Kehdy, Cindy Wang, Nicole Mathews, Jenn-Wei Chen, Rachel Paras, Paulina Pastuszek, Abigail Lyons, Paul Roldán, Muntaha Munia, Pierre Nicolas, Cassie L. Ettinger, Kyrylo Pyrshev, Katterinne N. Mendez, Eduardo Castro-Nallar, Valeriia Dotsenko, Michelle Tuz, Krizzy Mallari, Eileen Png, Yuya Sonohara, Tanja Miketic, Stéphane Delmas, Shu Zhang, Masaki Sato, Yuanting Zheng, Jifeng Zhu, Roland Häusler, Lucie Bittner, Savlatjon Rahmatulloev, Jonathan Foox, Bruno D'Alessandro, Alketa Plaku, Faisal Alquaddoomi, Yang Zhang, Kern Rei Chng, Juliana Lago, Allaeddine Chettouh, Tamera Henry, Houtan Noushmehr, Tranette Gregory, Sara Abdul Majid, Frank J. Kelly, Benjamin Pulatov, Laurie Casalot, Takema Kajita, Lennard Epping, Thais Fernanda Bartelli, Eftar Moniruzzaman, Renee Vivancos-Koopman, Thirumalaisamy P. Velavan, Tracy W. Liu, Yelyzaveta Tymoshenko, Alma Plaku, Nika Gurianova, Ambar Mendez, Anna Tomaselli, Sonia Dorado, Donato Giovannelli, Hira Choudhry, Synti Ng, Sheelta S. Kumar, Jennifer Q. Lu, Weijun Liang, Ellen Koag, Dennis Gankin, Maria João Amorim, Gwenola Simon, Kiyoshi Suganuma, Mikhail Karasikov, Christos A. Ouzounis, Madelyn May, Eran Elhaik, Stephan Ossowski, Kevin Bolzli, Matthew Arthur, Yuya Oto, Jananan Pathmanathan, Salah Mahmoud, Kou Takahashi, Brunna Marques, Kelly French, Felipe Sepúlveda, Shusei Yoshikawa, Paulo Thiago de Souza Santos, Andrew N. Gray, Juliana S Bernardes, Felipe Segato, Björn Brindefalk, George C. Yeh, Jhovana L. Velasco Flores, Jill Sullivan, Silva Baburyan, Denisse Flores, Russell Y. Neches, Sabrina Persaud, Rasheena Wright, Takumi Togashi, Verónica Antelo, Nao Kato, Skye Felice, Tatjana Mustac, Daisy Donnellan, Katerine Carrillo, Anna Litskevitch, Catalina García, Sota Ito, Naya Eady, Andrew Wan, Irene Meng, Sophie Guasco, Danilo Ercolini, Francesca De Filippis, Vincent Lemaire, Luice Fan, Lothar H. Wieler, Mariia Rybak, Jorge Sanchez, Jonathan S. Gootenberg, Itsuki Tomita, Maritza S Mosella, Laura Garcia, Natalka Makogon, Daisy Cheung, Hitler Francois Vasquez Arevalo, Freddy Asenjo, Gabriela P. Branco, Erika Cifuentes, Chloé Dequeker, Aspassia D. Chatziefthimiou, Alexis Terrero, Roy Meoded, Isabelle de Oliveira Moraes, Shaleni K. Singh, Orgil-Erdene Molomjamts, Karishma Miah, Laurent David, Wolfgang Haehr, Dao Phuong Giang, Romain Lannes, Prashanthi Ratnanandan, Ryota Yamanaka, Riccardo Vicedomini, Sadaf Ayaz, Oluwatosin M. Osuolale, Laura E. Vann, Gregory Chem, Andrea Gonzalez, Aszia Burrell, Ariel Chernomoretz, Sakura Ishizuka, Michelle Rivera, Avigdor Nosrati, Michelle B. Chen, Juliette Auvinet, Nils Ordioni, Tomoro Warashina, Guillaume Blanc, Tomislav Ivankovic, Christina Black, Lauren E. Hittle, David Hess-Homeier, Michael Kozhar, Hamood Suliman, Karobi Moitra, Saher Rahiel, Spyridon Gkotzis, Jenny Arevalo, Shaikh B. Iqbal, Beth Mutai, Mohammed Mohsin, Scott Tighe, Sylvie Collin, Yoshitaka Saito, Wayne Menary, Youping Deng, Lucy Lee, Esmeralda Jiminez, Ayuki Watanabe, Nikos C. Kyrpides, Natasha Mohan, Angelika Pupiec, Dedan Githae, Simone Cawthorne, Jonathan A. Eisen, Tomoki Iwashiro, Chiaki Homma, Thomas Saw Aung, Laura Molina, Marcus H. Y. Leung, Ophélie Da Silva, Yan Ling Wong, Hosna Noorzi, Mario Moreno, Alina Butova, Leming Shi, Brian W. Wong, Sarah S. Jackson, Moses Lin, Annabelle Meagher, Pujita Das, Catherine Burke, Mitsuki Ota, Maria Domenica Moccia, Nicolas Sprinsky, Catherine E. Pugh, David C. Green, Fazlina Fauzi, Erdenetsetseg Batdelger, Annie Geiger, Valeria Ventorino, Tolulope Oluwadare, Delisia Cuebas, Catalina Truong, Leonardo Posada, Michael Angelov, Tathiane M. Malta, Amanda Ng, Francesca Nadalin, Arya Hawkins-Zafarnia, Yuh Shiwa, Athena Mitsios, Milton Ozório Moraes, Manolo Laiola, Kalyn Ali, Jaden J.A. Hastings, Ikuto Saito, Maheen Shakil, Chisato Suzuki, Elena M. Vayndorf, Hubert Rehrauer, Ajay Menon, Kaitlan Russell, Aliyah Shari, Rebecca Smith, Gregorio Iraola, Max Priestman, Alan Briones, Silver A. Wolf, Camila Gonzalez-Poblete, Eleonora De Lazzari, Shirley Chiu, Michelle Ki, Irene Hoxie, Marianne Jaubert, Ayantu Jinfessa, Ryan J. King, Nghiem Xuan Hoan, Jalia Bynoe, Jacob Friedman, Aneisa Ramcharan, Pablo Fresia, Cristina Muñoz, Muhammad Afaq, Anyi Tang, Médine Benchouaia, Isabella Kuniko T. Takenaka, Anastasia Chasapi, Areeg Naeem, Hannah Benisty, Cecilia N. Cossio, Nathalie Hüsser, Mahfuza Sabina, Thais S. Sabedot, JoAnn Jacobs, Camila P. E. de Souza, Manuela Oliveira, Jean-Pierre Bouly, Mariko Usui, Wilson Miranda, Natalia Marciniak, Hiram Caballero, Samuel Weekes, Alexandra B. Graf, Emily Leong, Tatyana Nikolayeva, Dominique Thomas, Charlotte Greselle, Cecilia Salazar, Sreya Ray Chaudhuri, Kevin Becher, Sandra Roth, Ryusei Miura, Kari Oline Bøifot, Dimitri Manoir, Oliver Toth, Chandrima Bhattacharya, Manuel Perez, Isha Lamba, Takafumi Tsurumaki, Timothy D. Read, Anna-Lena M. Schinke, Ryan Sankar, Le Huu Song, Narasimha Rao Nedunuri, Emmanuel Dias-Neto, Ana Flávia Costa, Adiell Melamed, Christelle Desnues, Natalie R. Davidson, Aaron E. Darling, Hyung Jun Kim, Josephine Galipon, Jacqueline Orrego, Dimitar Vassilev, Michael Huber, Nur Hazlin Hazrin-Chong, Gaston H. Gonnet, Kaymisha Knights, Osman U. Sezerman, Dmitry Meleshko, Eunice Thambiraja, Jingcheng Yang, Aubin Fleiss, Gloria Nguyen, Katelyn Jackson, Nuria Aventin, Stephanie L. Hyland, Andrea Hässig, Catharine Aquino, Simona Lysakova, Israel O. Osuolale, Kasia Sluzek, Rania Siam, Alina Frolova, Samuel Hernandez, Yui Him Lo, Bazartseren Boldgiv, Ben Young, Maryna Korshevniuk, Majelia Ampadu, Yuk Man Tang, Amanda L. Muehlbauer, Sade Thomas, Gabriel Figueroa, Alexis Rivera, Lisbeth Pineda, Alexandra Dutan, Jennifer M. Tran, Chris K. Deng, Vedbar S. Khadka, Paola Florez de Sessions, Elizabeth Humphries, Hugues Richard, Hiba Naveed, Nora C. Toussaint, Mahshid Khavari, Maria del Mar Vivanco Ruiz, Antonin Thiébaut, Nicolás Rascovan, Marius Dybwad, Orhan Özcan, Lawrence Kwong, David Danko, Shaira Khan, Andrea Tassinari, Silvia Beurmann, Tsoi Ying Lai, Nanami Kubota, Tieliu Shi, Diana Chicas, Evan E. Afshin, Hirokazu Yano, Jonas Krebs, Mayuko Nakagawa, Hyun Jung Lee, Irene González Navarrete, Rachid Ounit, Lucia E. Alvarado-Arnez, Masaki Nasu, Allison Chan, Harilanto Andrianjakarivony, Jennifer Amachee, Mahdi Taye, Wan Chiew Ng, Kathryn O’Brien, Shino Ishikawa, Tristan Bitard-Feildel, Sora Takagi, Felix Hartkopf, Niamh B. O’Hara, Marcos A. S. Fonseca, Subhamitra Pakrashi, Amrit Kaur, Eva Hell, Patricia Vera-Wolf, Naimah Munim, Luiza Ferreira de Araújo, Mizuki Igarashi, Brianna Pompa-Hogan, Alessandra Carbone, Anne-Sophie Benoiston, Eric Helfrich, Michael A. Suarez-Villamil, Omar O. Abudayyeh, Natasha Abdullah, Jaime J. Fuentes, Juan Carlos Forero, Tetiana Yeskova, Denis Bertrand, Sambhawa Priya, Denisse Maldonado, Agier Nicolas, Ana Valeria B Castro, Starr Chatziefthimiou, André Kahles, Aaishah Francis, Fernanda Arredondo, Emilio Tarcitano, Irvind Buttar, Alex Alexiev, Jennifer Molinet, Sarah Shalaby, Itunu A. Oluwadare, Jason Sperry, Katrin Bakhl, Ana M. Cañas, Sofia Ahsanuddin, Miar Elaskandrany, Elodie Laine, Sven Bönigk, Johannes Werner, Stephen Eduard Boja Ruiz, Gargi Dayama, Paulina Buczansla, Brandon Valentine, Bharath Prithiviraj, Toni Bode, Stas Zubenko, Jake Cohen, Guilllaume Jospin, Zulena Saravi, Per O. Ljungdahl, Inderjit Kaur, Mauricio Moldes, Giuseppe KoLoMonaco, Denise Syndercombe Court, Sonia Bouchard, Sonia Losim, Sookwon Moon, Heba Shaaban, Suraj Patel, Sibo Zhu, Sarh Aly, Arif Asyraf Md Supie, LaShonda Dorsey, Juan Guerra, François Baudon, Rantimi A. Olawoyin, Alexia Bordigoni, Iqra Faiz, Mathilde Garcia, Gabriella Mason-Buck, María Gabriela Portilla, Niranjan Nagarajan, Fumie Takahara, Nancy Merino, Watson Andrew, Gina Kim, Yuma Sato, Hyenah Shim, Marie-Laure Jerier, Affifah Saadah Ahmad Kassim, Katerina Kuchin, Daniel Butler, Paweł P. Łabaj, Nadezhda Kobko-Litskevitch, Emmanuel F. Mongodin, Yuto Togashi, Paula Rodríguez, Pilar Lopez Hernandez, Xiaoqing Chen, Maria A. Sierra, Olga Nikolayeva, Manon Loubens, Colleen Conger, Hikaru Shirahata, Chenhao Li, Timothy Donahoe, Youngja Park, Lucia Elena Alvarado Arnez, Salama Chaker, Francisco Chavez, Alessandra Breschi, Jorge L. Sanchez, Kaung Myat San, Nayra Aguilar Rojas, Marcos Abraao, Kai Sasaki, Bryan Nazario, Olena Yemets, Klas I. Udekwu, Lynn M. Schriml, Anisia Peters, Aliaksei Holik, Mark Hernandez, Emile Faure, Malay Bhattacharyya, Josef W. Moser, Núria Andreu Somavilla, María Mercedes Zambrano, Kannan Rajendran, Gabriela E. Albuquerque, Tao Qing, Kazutoshi Tsuda, Ymke De Jong, Princess Osma, Mayra Arauco Livia, Javier Quilez Oliete, Carl Chrispin, Hyun Woo Joo, Ingrid Lafontaine, Nala An, Seisuke Sato, Felipe Segato Dezem, Andrew Maltez Thomas, Alexandre Desert, Xiao Wen Cai, O. Osuolale, Jun Wu, Coral Pardo-Esté, Courtney Robinson, Yuri Matsuzaki, Marina Nieto-Caballero, Cem Meydan, Ralph Schlapbach, Mark Menor, Sofia Castro, Rachel Kwong, Brittany Blyther, Olexandr Lykhenko, Jason R. Schriml, Christian Brion, Jenessa Orpilla, Juan A. Ugalde, Elsy Mankah Ngwa, Álvaro Aranguren, Lauren Mak, Matías Giménez, Ashanti Narce, Torsten Semmler, Stefan I. Tsonev, Abdollahi Nika, Katherine E. Dahlhausen, Monika Devi, Gunnar Rätsch, Oasima Muner, Carla Bello, Muhammad Al-Fath Amran, Anyelic Rosario, Melissa Ortega, Andrea Patrignani, Ante Peros, Elias McComb, Ryo Sato, Ireen Alam, Clara N. Dias, Soma Tanaka, Dayana Calderon, Ran Blekhman, Mathilde Mignotte, Alicia Boyd, Jochen Hecht, Thomas Neff, Xinzhao Tong, Josue Alicea, Kiara Olmeda, Sonia Marinovic, Carme Arnan, Kohei Ito, Samantha L. Goldman, Marianna S. Serpa, Renee Richer, Kaisei Sato, Jordana M. Silva, Akash Keluth Chavan, Sangwan Kim, Laís Pereira Ferreira, Sophie Vacant, Nowshin Sayara, Haruo Suzuki, Madeline Leahy, Juan C. Severyn, Sierra Vincent, Masaru Tomita, Maliha Mamun, Lucinda B. Davenport, Gabriella Oken, Dagmara Lewandowska, Gustavo Adolfo Malca Salas, Andrii Kuklin, Tyler Wong, Charlie Feigin, Eric Minwei Liu, Sonia L. Ghose, Daniela Bezdan, Antonietta La Storia, Juan P. Escalera-Antezana, Nuno Rufino de Sousa, Samuel M. Gerner, Weill Cornell Medicine [New York], Icahn School of Medicine at Mount Sinai [New York] (MSSM), Genome Institute of Singapore (GIS), Centre for Genomic Regulation [Barcelona] (CRG), Universitat Pompeu Fabra [Barcelona] (UPF)-Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), AUTRES, Massachusetts Institute of Technology (MIT), Indian Statistical Institute [Kolkata], University of Minnesota System, Universidad Andrés Bello [Santiago] (UNAB), California State University [Sacramento], University of Naples Federico II, University of Hawaii, Institut méditerranéen d'océanologie (MIO), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN), Medical Genomics Group, University College of London [London] (UCL)-UCL Cancer Institute, Norwegian Defence Research Establishment (FFI), Lund University [Lund], Institute of Molecular Biology and Genetics of National Academy of Sciences of Ukraine, University of Vienna [Vienna], King‘s College London, University of Colorado [Boulder], Institut Pasteur de Montevideo, Réseau International des Instituts Pasteur (RIIP), Institut Pasteur Korea - Institut Pasteur de Corée, Fudan University [Shanghai], City University of Hong Kong [Hong Kong] (CUHK), Stockholm University, University of Maryland School of Medicine, University of Maryland System, Fundação Oswaldo Cruz (FIOCRUZ), University of São Paulo (USP), Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Barcelona Institute of Science and Technology (BIST), Elizade University, Acibadem Mehmet Ali Aydınlar University, Paléogénomique microbienne - Microbial paleogenomics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Robert Koch Institute [Berlin] (RKI), East China Normal University [Shangaï] (ECNU), Cairo University, Vietnamese-German Center for Medical Research, Keio University, Université du Vermont, Universidad del Desarrollo, University of Sofia, University of Alaska [Fairbanks] (UAF), Universitätsklinikum Tübingen - University Hospital of Tübingen, Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Corporación Corpogen-Research Center, Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Weill Cornell Medicine [Cornell University], Cornell University [New York], University of Naples Federico II = Università degli studi di Napoli Federico II, Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Fundação Oswaldo Cruz / Oswaldo Cruz Foundation (FIOCRUZ), Universidade de São Paulo = University of São Paulo (USP), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Софийски университет = Sofia University, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Universidad Andrés Bello - UNAB (CHILE), Acibadem University Dspace, Danko, D., Bezdan, D., Afshin, E. E., Ahsanuddin, S., Bhattacharya, C., Butler, D. J., Chng, K. R., Donnellan, D., Hecht, J., Jackson, K., Kuchin, K., Karasikov, M., Lyons, A., Mak, L., Meleshko, D., Mustafa, H., Mutai, B., Neches, R. Y., Ng, A., Nikolayeva, O., Nikolayeva, T., Png, E., Ryon, K. A., Sanchez, J. L., Shaaban, H., Sierra, M. A., Thomas, D., Young, B., Abudayyeh, O. O., Alicea, J., Bhattacharyya, M., Blekhman, R., Castro-Nallar, E., Canas, A. M., Chatziefthimiou, A. D., Crawford, R. W., De Filippis, F., Deng, Y., Desnues, C., Dias-Neto, E., Dybwad, M., Elhaik, E., Ercolini, D., Frolova, A., Gankin, D., Gootenberg, J. S., Graf, A. B., Green, D. C., Hajirasouliha, I., Hastings, J. J. A., Hernandez, M., Iraola, G., Jang, S., Kahles, A., Kelly, F. J., Knights, K., Kyrpides, N. C., Labaj, P. P., Lee, P. K. H., Leung, M. H. Y., Ljungdahl, P. O., Mason-Buck, G., Mcgrath, K., Meydan, C., Mongodin, E. F., Moraes, M. O., Nagarajan, N., Nieto-Caballero, M., Noushmehr, H., Oliveira, M., Ossowski, S., Osuolale, O. O., Ozcan, O., Paez-Espino, D., Rascovan, N., Richard, H., Ratsch, G., Schriml, L. M., Semmler, T., Sezerman, O. U., Shi, L., Shi, T., Siam, R., Song, L. H., Suzuki, H., Court, D. S., Tighe, S. W., Tong, X., Udekwu, K. I., Ugalde, J. A., Valentine, B., Vassilev, D. I., Vayndorf, E. M., Velavan, T. P., Wu, J., Zambrano, M. M., Zhu, J., Zhu, S., Mason, C. E., Abdullah, N., Abraao, M., Adel, A. -H., Afaq, M., Al-Quaddoomi, F. S., Alam, I., Albuquerque, G. E., Alexiev, A., Ali, K., Alvarado-Arnez, L. E., Aly, S., Amachee, J., Amorim, M. G., Ampadu, M., Amran, M. A. -F., An, N., Andrew, W., Andrianjakarivony, H., Angelov, M., Antelo, V., Aquino, C., Aranguren, A., Araujo, L. F., Vasquez Arevalo, H. F., Arevalo, J., Arnan, C., Alvarado Arnez, L. E., Arredondo, F., Arthur, M., Asenjo, F., Aung, T. S., Auvinet, J., Aventin, N., Ayaz, S., Baburyan, S., Bakere, A. -M., Bakhl, K., Bartelli, T. F., Batdelger, E., Baudon, F., Becher, K., Bello, C., Benchouaia, M., Benisty, H., Benoiston, A. -S., Benson, J., Benitez, D., Bernardes, J., Bertrand, D., Beurmann, S., Bitard-Feildel, T., Bittner, L., Black, C., Blanc, G., Blyther, B., Bode, T., Boeri, J., Boldgiv, B., Bolzli, K., Bordigoni, A., Borrelli, C., Bouchard, S., Bouly, J. -P., Boyd, A., Branco, G. P., Breschi, A., Brindefalk, B., Brion, C., Briones, A., Buczansla, P., Burke, C. M., Burrell, A., Butova, A., Buttar, I., Bynoe, J., Bonigk, S., Boifot, K. O., Caballero, H., Cai, X. W., Calderon, D., Cantillo, A., Carbajo, M., Carbone, A., Cardenas, A., Carrillo, K., Casalot, L., Castro, S., Castro, A. V., Castro, A., Castro, A. V. B., Cawthorne, S., Cedillo, J., Chaker, S., Chalangal, J., Chan, A., Chasapi, A. I., Chatziefthimiou, S., Chaudhuri, S. R., Chavan, A. K., Chavez, F., Chem, G., Chen, X., Chen, M., Chen, J. -W., Chernomoretz, A., Chettouh, A., Cheung, D., Chicas, D., Chiu, S., Choudhry, H., Chrispin, C., Ciaramella, K., Cifuentes, E., Cohen, J., Coil, D. A., Collin, S., Conger, C., Conte, R., Corsi, F., Cossio, C. N., Costa, A. F., Cuebas, D., D'Alessandro, B., Dahlhausen, K. E., Darling, A. E., Das, P., Davenport, L. B., David, L., Davidson, N. R., Dayama, G., Delmas, S., Deng, C. K., Dequeker, C., Desert, A., Devi, M., Dezem, F. S., Dias, C. N., Donahoe, T. R., Dorado, S., Dorsey, L., Dotsenko, V., Du, S., Dutan, A., Eady, N., Eisen, J. A., Elaskandrany, M., Epping, L., Escalera-Antezana, J. P., Ettinger, C. L., Faiz, I., Fan, L., Farhat, N., Faure, E., Fauzi, F., Feigin, C., Felice, S., Ferreira, L. P., Figueroa, G., Fleiss, A., Flores, D., Velasco Flores, J. L., Fonseca, M. A. S., Foox, J., Forero, J. C., Francis, A., French, K., Fresia, P., Friedman, J., Fuentes, J. J., Galipon, J., Garcia, M., Garcia, L., Garcia, C., Geiger, A., Gerner, S. M., Ghose, S. L., Giang, D. P., Gimenez, M., Giovannelli, D., Githae, D., Gkotzis, S., Godoy, L., Goldman, S., Gonnet, G. H., Gonzalez, J., Gonzalez, A., Gonzalez-Poblete, C., Gray, A., Gregory, T., Greselle, C., Guasco, S., Guerra, J., Gurianova, N., Haehr, W., Halary, S., Hartkopf, F., Hawkins-Zafarnia, A., Hazrin-Chong, N. H., Helfrich, E., Hell, E., Henry, T., Hernandez, S., Hernandez, P. L., Hess-Homeier, D., Hittle, L. E., Hoan, N. X., Holik, A., Homma, C., Hoxie, I., Huber, M., Humphries, E., Hyland, S., Hassig, A., Hausler, R., Husser, N., Petit, R. A., Iderzorig, B., Igarashi, M., Iqbal, S. B., Ishikawa, S., Ishizuka, S., Islam, S., Islam, R., Ito, K., Ito, S., Ito, T., Ivankovic, T., Iwashiro, T., Jackson, S., Jacobs, J., James, M., Jaubert, M., Jerier, M. -L., Jiminez, E., Jinfessa, A., De Jong, Y., Joo, H. W., Jospin, G., Kajita, T., Ahmad Kassim, A. S., Kato, N., Kaur, A., Kaur, I., de Souza Gomes Kehdy, F., Khadka, V. S., Khan, S., Khavari, M., Ki, M., Kim, G., Kim, H. J., Kim, S., King, R. J., Kolomonaco, G., Koag, E., Kobko-Litskevitch, N., Korshevniuk, M., Kozhar, M., Krebs, J., Kubota, N., Kuklin, A., Kumar, S. S., Kwong, R., Kwong, L., Lafontaine, I., Lago, J., Lai, T. Y., Laine, E., Laiola, M., Lakhneko, O., Lamba, I., de Lamotte, G., Lannes, R., De Lazzari, E., Leahy, M., Lee, H., Lee, Y., Lee, L., Lemaire, V., Leong, E., Lewandowska, D., Li, C., Liang, W., Lin, M., Lisboa, P., Litskevitch, A., Liu, E. M., Liu, T., Livia, M. A., Lo, Y. H., Losim, S., Loubens, M., Lu, J., Lykhenko, O., Lysakova, S., Mahmoud, S., Majid, S. A., Makogon, N., Maldonado, D., Mallari, K., Malta, T. M., Mamun, M., Manoir, D., Marchandon, G., Marciniak, N., Marinovic, S., Marques, B., Mathews, N., Matsuzaki, Y., Matthys, V., May, M., Mccomb, E., Meagher, A., Melamed, A., Menary, W., Mendez, K. N., Mendez, A., Mendy, I. M., Meng, I., Menon, A., Menor, M., Meoded, R., Merino, N., Miah, K., Mignotte, M., Miketic, T., Miranda, W., Mitsios, A., Miura, R., Miyake, K., Moccia, M. D., Mohan, N., Mohsin, M., Moitra, K., Moldes, M., Molina, L., Molinet, J., Molomjamts, O. -E., Moniruzzaman, E., Moon, S., de Oliveira Moraes, I., Moreno, M., Mosella, M. S., Moser, J. W., Mozsary, C., Muehlbauer, A. L., Muner, O., Munia, M., Munim, N., Muscat, M., Mustac, T., Munoz, C., Nadalin, F., Naeem, A., Nagy-Szakal, D., Nakagawa, M., Narce, A., Nasu, M., Navarrete, I. G., Naveed, H., Nazario, B., Nedunuri, N. R., Neff, T., Nesimi, A., Ng, W. C., Ng, S., Nguyen, G., Ngwa, E., Nicolas, A., Nicolas, P., Nika, A., Noorzi, H., Nosrati, A., Nunes, D. N., O'Brien, K., O'Hara, N. B., Oken, G., Olawoyin, R. A., Oliete, J. Q., Olmeda, K., Oluwadare, T., Oluwadare, I. A., Ordioni, N., Orpilla, J., Orrego, J., Ortega, M., Osma, P., Osuolale, I. O., Osuolale, O. M., Ota, M., Oteri, F., Oto, Y., Ounit, R., Ouzounis, C. A., Pakrashi, S., Paras, R., Pardo-Este, C., Park, Y. -J., Pastuszek, P., Patel, S., Pathmanathan, J., Patrignani, A., Perez, M., Peros, A., Persaud, S., Peters, A., Phillips, A., Pineda, L., Pizzi, M. P., Plaku, A., Pompa-Hogan, B., Portilla, M. G., Posada, L., Priestman, M., Prithiviraj, B., Priya, S., Pugdeethosal, P., Pugh, C. E., Pulatov, B., Pupiec, A., Pyrshev, K., Qing, T., Rahiel, S., Rahmatulloev, S., Rajendran, K., Ramcharan, A., Ramirez-Rojas, A., Rana, S., Ratnanandan, P., Read, T. D., Rehrauer, H., Richer, R., Rivera, A., Rivera, M., Robertiello, A., Robinson, C., Rodriguez, P., Rojas, N. A., Roldan, P., Rosario, A., Roth, S., Ruiz, M., Boja Ruiz, S. E., Russell, K., Rybak, M., Sabedot, T. S., Sabina, M., Saito, I., Saito, Y., Malca Salas, G. A., Salazar, C., San, K. M., Sanchez, J., Sanchir, K., Sankar, R., de Souza Santos, P. T., Saravi, Z., Sasaki, K., Sato, Y., Sato, M., Sato, S., Sato, R., Sato, K., Sayara, N., Schaaf, S., Schacher, O., Schinke, A. -L. M., Schlapbach, R., Schori, C., Schriml, J. R., Segato, F., Sepulveda, F., Serpa, M. S., De Sessions, P. F., Severyn, J. C., Shakil, M., Shalaby, S., Shari, A., Shim, H., Shirahata, H., Shiwa, Y., Da Silva, O., Silva, J. M., Simon, G., Singh, S. K., Sluzek, K., Smith, R., So, E., Andreu Somavilla, N., Sonohara, Y., Rufino de Sousa, N., Souza, C., Sperry, J., Sprinsky, N., Stark, S. G., La Storia, A., Suganuma, K., Suliman, H., Sullivan, J., Supie, A. A. M., Suzuki, C., Takagi, S., Takahara, F., Takahashi, N., Takahashi, K., Takeda, T., Takenaka, I. K., Tanaka, S., Tang, A., Man Tang, Y., Tarcitano, E., Tassinari, A., Taye, M., Terrero, A., Thambiraja, E., Thiebaut, A., Thomas, S., Thomas, A. M., Togashi, Y., Togashi, T., Tomaselli, A., Tomita, M., Tomita, I., Toth, O., Toussaint, N. C., Tran, J. M., Truong, C., Tsonev, S. I., Tsuda, K., Tsurumaki, T., Tuz, M., Tymoshenko, Y., Urgiles, C., Usui, M., Vacant, S., Vann, L. E., Velter, F., Ventorino, V., Vera-Wolf, P., Vicedomini, R., Suarez-Villamil, M. A., Vincent, S., Vivancos-Koopman, R., Wan, A., Wang, C., Warashina, T., Watanabe, A., Weekes, S., Werner, J., Westfall, D., Wieler, L. H., Williams, M., Wolf, S. A., Wong, B., Wong, Y. L., Wong, T., Wright, R., Wunderlin, T., Yamanaka, R., Yang, J., Yano, H., Yeh, G. C., Yemets, O., Yeskova, T., Yoshikawa, S., Zafar, L., Zhang, Y., Zhang, S., Zhang, A., Zheng, Y., and Zubenko, S.

Subjects
Urban Population, Drug Resistance, Sequence assembly, Microbiologia, microbiome, global health, computer.software_genre, Medical and Health Sciences, shotgun sequencing, BGC, 0302 clinical medicine, Databases, Genetic, 11. Sustainability, Global health, AMR, 11 Medical and Health Sciences, ComputingMilieux_MISCELLANEOUS, 0303 health sciences, built environment, metagenome, antimicrobial resistance, NGS, de novo assembly, biology, Shotgun sequencing, Microbiota, built Environment, Bacterial, Biodiversity, Biological Sciences, 3. Good health, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Infection, Biotechnology, Geospatial analysis, [SDV.BID]Life Sciences [q-bio]/Biodiversity, Article, General Biochemistry, Genetics and Molecular Biology, Databases, 03 medical and health sciences, Antibiotic resistance, Genetic, Drug Resistance, Bacterial, International MetaSUB Consortium, Genetics, Humans, Microbiome, 030304 developmental biology, Human Genome, 06 Biological Sciences, 15. Life on land, biology.organism_classification, Resistènica als medicaments antiinfecciosos, SAÚDE PÚBLICA, Genòmica, 13. Climate action, Evolutionary biology, Metagenomics, Antimicrobial Resistance, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, computer, 030217 neurology & neurosurgery, Archaea, Developmental Biology
Abstract

Summary We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities., Graphical abstract, Highlights • Cities possess a consistent “core” set of non-human microbes • Urban microbiomes echo important features of cities and city-life • Antimicrobial resistance genes are widespread in cities • Cities contain many novel bacterial and viral species, This systematic, worldwide catalog of urban microbiomes represents a metagenomic atlas important for understanding the ecology, virulence, and antibiotic resistance of city-specific microbial communities.

Published
2021
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3. Label-guided seed-chain-extend alignment on annotated De Bruijn graphs.

Author

Mustafa H, Karasikov M, Mansouri Ghiasi N, Rätsch G, and Kahles A

Subjects
Software, Computational Biology methods, Sequence Analysis, DNA methods, Databases, Genetic, Algorithms, Sequence Alignment methods
Abstract

Motivation: Exponential growth in sequencing databases has motivated scalable De Bruijn graph-based (DBG) indexing for searching these data, using annotations to label nodes with sample IDs. Low-depth sequencing samples correspond to fragmented subgraphs, complicating finding the long contiguous walks required for alignment queries. Aligners that target single-labelled subgraphs reduce alignment lengths due to fragmentation, leading to low recall for long reads. While some (e.g. label-free) aligners partially overcome fragmentation by combining information from multiple samples, biologically irrelevant combinations in such approaches can inflate the search space or reduce accuracy., Results: We introduce a new scoring model, 'multi-label alignment' (MLA), for annotated DBGs. MLA leverages two new operations: To promote biologically relevant sample combinations, 'Label Change' incorporates more informative global sample similarity into local scores. To improve connectivity, 'Node Length Change' dynamically adjusts the DBG node length during traversal. Our fast, approximate, yet accurate MLA implementation has two key steps: a single-label seed-chain-extend aligner (SCA) and a multi-label chainer (MLC). SCA uses a traditional scoring model adapting recent chaining improvements to assembly graphs and provides a curated pool of alignments. MLC extracts seed anchors from SCAs alignments, produces multi-label chains using MLA scoring, then finally forms multi-label alignments. We show via substantial improvements in taxonomic classification accuracy that MLA produces biologically relevant alignments, decreasing average weighted UniFrac errors by 63.1%-66.8% and covering 45.5%-47.4% (median) more long-read query characters than state-of-the-art aligners. MLAs runtimes are competitive with label-combining alignment and substantially faster than single-label alignment., Availability and Implementation: The data, scripts, and instructions for generating our results are available at https://github.com/ratschlab/mla., (© The Author(s) 2024. Published by Oxford University Press.)

Published
2024
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4. Lossless indexing with counting de Bruijn graphs.

Author

Karasikov M, Mustafa H, Rätsch G, and Kahles A

Subjects
Humans, Algorithms, Software, Genome, Human, RNA-Seq methods, Molecular Sequence Annotation methods, Sequence Analysis, RNA methods, High-Throughput Nucleotide Sequencing methods, Computational Biology methods
Abstract

Sequencing data are rapidly accumulating in public repositories. Making this resource accessible for interactive analysis at scale requires efficient approaches for its storage and indexing. There have recently been remarkable advances in building compressed representations of annotated (or colored ) de Bruijn graphs for efficiently indexing k -mer sets. However, approaches for representing quantitative attributes such as gene expression or genome positions in a general manner have remained underexplored. In this work, we propose counting de Bruijn graphs , a notion generalizing annotated de Bruijn graphs by supplementing each node-label relation with one or many attributes (e.g., a k -mer count or its positions). Counting de Bruijn graphs index k -mer abundances from 2652 human RNA-seq samples in over eightfold smaller representations compared with state-of-the-art bioinformatics tools and is faster to construct and query. Furthermore, counting de Bruijn graphs with positional annotations losslessly represent entire reads in indexes on average 27% smaller than the input compressed with gzip for human Illumina RNA-seq and 57% smaller for Pacific Biosciences (PacBio) HiFi sequencing of viral samples. A complete searchable index of all viral PacBio SMRT reads from NCBI's Sequence Read Archive (SRA) (152,884 samples, 875 Gbp) comprises only 178 GB. Finally, on the full RefSeq collection, we generate a lossless and fully queryable index that is 4.6-fold smaller than the MegaBLAST index. The techniques proposed in this work naturally complement existing methods and tools using de Bruijn graphs, and significantly broaden their applicability: from indexing k -mer counts and genome positions to implementing novel sequence alignment algorithms on top of highly compressed graph-based sequence indexes., (© 2022 Karasikov et al.; Published by Cold Spring Harbor Laboratory Press.)

Published
2022
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5. Biosynthetic potential of the global ocean microbiome.

Author

Paoli L, Ruscheweyh HJ, Forneris CC, Hubrich F, Kautsar S, Bhushan A, Lotti A, Clayssen Q, Salazar G, Milanese A, Carlström CI, Papadopoulou C, Gehrig D, Karasikov M, Mustafa H, Larralde M, Carroll LM, Sánchez P, Zayed AA, Cronin DR, Acinas SG, Bork P, Bowler C, Delmont TO, Gasol JM, Gossert AD, Kahles A, Sullivan MB, Wincker P, Zeller G, Robinson SL, Piel J, and Sunagawa S

Subjects
Bacteria classification, Bacteria genetics, Genomics, Multigene Family genetics, Phylogeny, Biosynthetic Pathways genetics, Microbiota genetics, Oceans and Seas
Abstract

Natural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups 1 , this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds 2,3 . However, studying this diversity to identify genomic pathways for the synthesis of such compounds 4 and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters ('Candidatus Eudoremicrobiaceae') that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments., (© 2022. The Author(s).)

Published
2022
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6. Topology-based sparsification of graph annotations.

Author

Danciu D, Karasikov M, Mustafa H, Kahles A, and Rätsch G

Subjects
Software, Algorithms, Biomedical Research
Abstract

Motivation: Since the amount of published biological sequencing data is growing exponentially, efficient methods for storing and indexing this data are more needed than ever to truly benefit from this invaluable resource for biomedical research. Labeled de Bruijn graphs are a frequently-used approach for representing large sets of sequencing data. While significant progress has been made to succinctly represent the graph itself, efficient methods for storing labels on such graphs are still rapidly evolving., Results: In this article, we present RowDiff, a new technique for compacting graph labels by leveraging expected similarities in annotations of vertices adjacent in the graph. RowDiff can be constructed in linear time relative to the number of vertices and labels in the graph, and in space proportional to the graph size. In addition, construction can be efficiently parallelized and distributed, making the technique applicable to graphs with trillions of nodes. RowDiff can be viewed as an intermediary sparsification step of the original annotation matrix and can thus naturally be combined with existing generic schemes for compressed binary matrices. Experiments on 10 000 RNA-seq datasets show that RowDiff combined with multi-BRWT results in a 30% reduction in annotation footprint over Mantis-MST, the previously known most compact annotation representation. Experiments on the sparser Fungi subset of the RefSeq collection show that applying RowDiff sparsification reduces the size of individual annotation columns stored as compressed bit vectors by an average factor of 42. When combining RowDiff with a multi-BRWT representation, the resulting annotation is 26 times smaller than Mantis-MST., Availability and Implementation: RowDiff is implemented in C++ within the MetaGraph framework. The source code and the data used in the experiments are publicly available at https://github.com/ratschlab/row_diff., (© The Author(s) 2021. Published by Oxford University Press.)

Published
2021
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7. A global metagenomic map of urban microbiomes and antimicrobial resistance.

Author

Danko D, Bezdan D, Afshin EE, Ahsanuddin S, Bhattacharya C, Butler DJ, Chng KR, Donnellan D, Hecht J, Jackson K, Kuchin K, Karasikov M, Lyons A, Mak L, Meleshko D, Mustafa H, Mutai B, Neches RY, Ng A, Nikolayeva O, Nikolayeva T, Png E, Ryon KA, Sanchez JL, Shaaban H, Sierra MA, Thomas D, Young B, Abudayyeh OO, Alicea J, Bhattacharyya M, Blekhman R, Castro-Nallar E, Cañas AM, Chatziefthimiou AD, Crawford RW, De Filippis F, Deng Y, Desnues C, Dias-Neto E, Dybwad M, Elhaik E, Ercolini D, Frolova A, Gankin D, Gootenberg JS, Graf AB, Green DC, Hajirasouliha I, Hastings JJA, Hernandez M, Iraola G, Jang S, Kahles A, Kelly FJ, Knights K, Kyrpides NC, Łabaj PP, Lee PKH, Leung MHY, Ljungdahl PO, Mason-Buck G, McGrath K, Meydan C, Mongodin EF, Moraes MO, Nagarajan N, Nieto-Caballero M, Noushmehr H, Oliveira M, Ossowski S, Osuolale OO, Özcan O, Paez-Espino D, Rascovan N, Richard H, Rätsch G, Schriml LM, Semmler T, Sezerman OU, Shi L, Shi T, Siam R, Song LH, Suzuki H, Court DS, Tighe SW, Tong X, Udekwu KI, Ugalde JA, Valentine B, Vassilev DI, Vayndorf EM, Velavan TP, Wu J, Zambrano MM, Zhu J, Zhu S, and Mason CE

Subjects
Biodiversity, Databases, Genetic, Humans, Drug Resistance, Bacterial genetics, Metagenomics, Microbiota genetics, Urban Population
Abstract

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities., Competing Interests: Declaration of interests C.E.M. is co-founder of Biotia and Onegevity Health. D.B. is co-founder and CSO of Poppy Health Inc. The other authors declare they have no competing interests that impacted this study., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Published
2021
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8. Sparse Binary Relation Representations for Genome Graph Annotation.

Author

Karasikov M, Mustafa H, Joudaki A, Javadzadeh-No S, Rätsch G, and Kahles A

Subjects
Algorithms, Computational Biology, Data Compression, Humans, Molecular Sequence Annotation methods, Genome genetics, High-Throughput Nucleotide Sequencing methods, Sequence Analysis, DNA methods, Software
Abstract

High-throughput DNA sequencing data are accumulating in public repositories, and efficient approaches for storing and indexing such data are in high demand. In recent research, several graph data structures have been proposed to represent large sets of sequencing data and to allow for efficient querying of sequences. In particular, the concept of labeled de Bruijn graphs has been explored by several groups. Although there has been good progress toward representing the sequence graph in small space, methods for storing a set of labels on top of such graphs are still not sufficiently explored. It is also currently not clear how characteristics of the input data, such as the sparsity and correlations of labels, can help to inform the choice of method to compress the graph labeling. In this study, we present a new compression approach, Multi-binary relation wavelet tree (BRWT) , which is adaptive to different kinds of input data. We show an up to 29% improvement in compression performance over the basic BRWT method, and up to a 68% improvement over the current state-of-the-art for de Bruijn graph label compression. To put our results into perspective, we present a systematic analysis of five different state-of-the-art annotation compression schemes, evaluate key metrics on both artificial and real-world data, and discuss how different data characteristics influence the compression performance. We show that the improvements of our new method can be robustly reproduced for different representative real-world data sets.

Published
2020
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10. Assessment of chemical-crosslink-assisted protein structure modeling in CASP13.

Author

Fajardo JE, Shrestha R, Gil N, Belsom A, Crivelli SN, Czaplewski C, Fidelis K, Grudinin S, Karasikov M, Karczyńska AS, Kryshtafovych A, Leitner A, Liwo A, Lubecka EA, Monastyrskyy B, Pagès G, Rappsilber J, Sieradzan AK, Sikorska C, Trabjerg E, and Fiser A

Subjects
Algorithms, Chromatography, Liquid, Models, Chemical, Reproducibility of Results, Tandem Mass Spectrometry, Computational Biology methods, Cross-Linking Reagents chemistry, Models, Molecular, Protein Conformation, Proteins chemistry
Abstract

With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest-to-date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows., (© 2019 Wiley Periodicals, Inc.)

Published
2019
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11. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

Author

Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG, Gerguri T, Bates PA, Laine E, Carbone A, Grudinin S, Kong R, Liu RR, Xu XM, Shi H, Chang S, Eisenstein M, Karczynska A, Czaplewski C, Lubecka E, Lipska A, Krupa P, Mozolewska M, Golon Ł, Samsonov S, Liwo A, Crivelli S, Pagès G, Karasikov M, Kadukova M, Yan Y, Huang SY, Rosell M, Rodríguez-Lumbreras LA, Romero-Durana M, Díaz-Bueno L, Fernandez-Recio J, Christoffer C, Terashi G, Shin WH, Aderinwale T, Maddhuri Venkata Subraman SR, Kihara D, Kozakov D, Vajda S, Porter K, Padhorny D, Desta I, Beglov D, Ignatov M, Kotelnikov S, Moal IH, Ritchie DW, Chauvot de Beauchêne I, Maigret B, Devignes MD, Ruiz Echartea ME, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Cao Y, Shen Y, Baek M, Park T, Woo H, Seok C, Braitbard M, Bitton L, Scheidman-Duhovny D, Dapkūnas J, Olechnovič K, Venclovas Č, Kundrotas PJ, Belkin S, Chakravarty D, Badal VD, Vakser IA, Vreven T, Vangaveti S, Borrman T, Weng Z, Guest JD, Gowthaman R, Pierce BG, Xu X, Duan R, Qiu L, Hou J, Ryan Merideth B, Ma Z, Cheng J, Zou X, Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue L, Jiménez-García B, van Noort CW, Honorato RV, Bonvin AMJJ, and Wodak SJ

Subjects
Algorithms, Binding Sites genetics, Databases, Protein, Models, Molecular, Protein Binding genetics, Protein Interaction Mapping, Proteins chemistry, Proteins genetics, Structural Homology, Protein, Computational Biology, Protein Conformation, Proteins ultrastructure, Software
Abstract

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements., (© 2019 Wiley Periodicals, Inc.)

Published
2019
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12. Smooth orientation-dependent scoring function for coarse-grained protein quality assessment.

Author

Karasikov M, Pagès G, and Grudinin S

Subjects
Models, Molecular, Protein Conformation, Grain Proteins chemistry
Abstract

Motivation: Protein quality assessment (QA) is a crucial element of protein structure prediction, a fundamental and yet open problem in structural bioinformatics. QA aims at ranking predicted protein models to select the best candidates. The assessment can be performed based either on a single model or on a consensus derived from an ensemble of models. The latter strategy can yield very high performance but substantially depends on the pool of available candidate models, which limits its applicability. Hence, single-model QA methods remain an important research target, also because they can assist the sampling of candidate models., Results: We present a novel single-model QA method called SBROD. The SBROD (Smooth Backbone-Reliant Orientation-Dependent) method uses only the backbone protein conformation, and hence it can be applied to scoring coarse-grained protein models. The proposed method deduces its scoring function from a training set of protein models. The SBROD scoring function is composed of four terms related to different structural features: residue-residue orientations, contacts between backbone atoms, hydrogen bonding and solvent-solute interactions. It is smooth with respect to atomic coordinates and thus is potentially applicable to continuous gradient-based optimization of protein conformations. Furthermore, it can also be used for coarse-grained protein modeling and computational protein design. SBROD proved to achieve similar performance to state-of-the-art single-model QA methods on diverse datasets (CASP11, CASP12 and MOULDER)., Availability and Implementation: The standalone application implemented in C++ and Python is freely available at https://gitlab.inria.fr/grudinin/sbrod and supported on Linux, MacOS and Windows., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Published
2019
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13. Dynamic compression schemes for graph coloring.

Author

Mustafa H, Schilken I, Karasikov M, Eickhoff C, Rätsch G, and Kahles A

Subjects
Algorithms, Color, Genomics, High-Throughput Nucleotide Sequencing, Computational Biology, Data Compression, Software
Abstract

Motivation: Technological advancements in high-throughput DNA sequencing have led to an exponential growth of sequencing data being produced and stored as a byproduct of biomedical research. Despite its public availability, a majority of this data remains hard to query for the research community due to a lack of efficient data representation and indexing solutions. One of the available techniques to represent read data is a condensed form as an assembly graph. Such a representation contains all sequence information but does not store contextual information and metadata., Results: We present two new approaches for a compressed representation of a graph coloring: a lossless compression scheme based on a novel application of wavelet tries as well as a highly accurate lossy compression based on a set of Bloom filters. Both strategies retain a coloring even when adding to the underlying graph topology. We present construction and merge procedures for both methods and evaluate their performance on a wide range of different datasets. By dropping the requirement of a fully lossless compression and using the topological information of the underlying graph, we can reduce memory requirements by up to three orders of magnitude. Representing individual colors as independently stored modules, our approaches can be efficiently parallelized and provide strategies for dynamic use. These properties allow for an easy upscaling to the problem sizes common to the biomedical domain., Availability and Implementation: We provide prototype implementations in C++, summaries of our experiments as well as links to all datasets publicly at https://github.com/ratschlab/graph_annotation., Supplementary Information: Supplementary data are available at Bioinformatics online.

Published
2019
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