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31 results on '"Meleshko D"'

1. The new and complete Belle II DEPFET pixel detector: Commissioning and previous operational experience

Author

Ahlburg, P., Andricek, L., Babu, V., Baur, A., Becherer, F., Bernlochner, F., Bilk, J., Bilka, T., Bolz, A., Cao, L., Dhayal, R., Dingfelder, J., Doležal, Z., Farkas, R., Frey, A., Gadow, K., Giakoustidis, G., Graf-Schreiber, M., Gruberova, Z., Han, Y., Hoek, M., Kehl, J., Khan, M., Kiesling, C., Kodyš, P., Koffmane, C., Krein, M., Kühn, W., Krüger, H., Kvasnička, P., Lange, J.S., Leitl, P., Liu, Q., Lück, T., Mariñas, C., Meleshko, D., Moser, H.G., Niebuhr, C., Ninkovic, J., Paschen, B., Pitzl, D., Reif, M., Reiter, S.P., Richter, R., Schmitz, J., Schwenker, B., Schwickardi, M., Sfienti, C., Skorupa, J., Spruck, B., Stefkova, S., Stever, R., Takahashi, M., Wang, B., Wermes, N., and Žlebčík, R.

Published
2024
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2. The Belle II Pixel Vertex Detector

Author

Baur, Anselm, primary, Ahlburg, P., additional, Andricek, L., additional, Ayad, R., additional, Babu, V., additional, Becherer, F., additional, Bernlochner, F., additional, Bierwirth, L., additional, Bilk, J., additional, Bilka, T., additional, Bolz, A., additional, Bozek, A., additional, Camien, C., additional, Cao, L., additional, Dhayal, R., additional, Dingfelder, J., additional, Doležal, Z., additional, Farkas, R., additional, Frey, A., additional, Gadow, K., additional, Giakoustidis, G., additional, Graf-Schreiber, M., additional, Greenwald, D., additional, Gruberova, Z., additional, Han, Y., additional, Hoek, M., additional, Huber, S., additional, Kapusta, P., additional, Karl, R., additional, Kehl, J., additional, Khan, M., additional, Kiesling, C., additional, Kisielewski, B., additional, Kodyš, P., additional, Koffmane, C., additional, Konorov, I., additional, Krein, M., additional, Kühn, W., additional, Krüger, H., additional, Kvasnicka, P., additional, Lange, J. S., additional, Leitl, P., additional, Levit, D., additional, Liu, Q., additional, Liu, Z., additional, Lück, T., additional, Mariñas, C., additional, Meleshko, D., additional, Moser, H. G., additional, Niebuhr, C., additional, Ninkovic, J., additional, Paschen, B., additional, Paul, S., additional, Peric, I., additional, Pitzl, D., additional, Rabusov, A., additional, Reiter, S. P., additional, Richter, R., additional, Ritzert, M., additional, Sanchez, J. G., additional, Schmitz, J., additional, Schwenker, B., additional, Schwickardi, M., additional, Sfienti, C., additional, Simon, F., additional, Skorupa, J., additional, Soloviev, Y., additional, Spruck, B., additional, Stefkova, S., additional, Stever, R., additional, Takahashi, M., additional, Vila, I., additional, Virto, A. L., additional, Wang, B. S., additional, Wang, C., additional, Wermes, N., additional, Zhao, J., additional, and Žlebčík, R., additional

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

Author

Meyer, F, Fritz, A, Deng, Z-L, Koslicki, D, Lesker, TR, Gurevich, A, Robertson, G, Alser, M, Antipov, D, Beghini, F, Bertrand, D, Brito, JJ, Brown, CT, Buchmann, J, Buluc, A, Chen, B, Chikhi, R, Clausen, PTLC, Cristian, A, Dabrowski, PW, Darling, AE, Egan, R, Eskin, E, Georganas, E, Goltsman, E, Gray, MA, Hansen, LH, Hofmeyr, S, Huang, P, Irber, L, Jia, H, Jorgensen, TS, Kieser, SD, Klemetsen, T, Kola, A, Kolmogorov, M, Korobeynikov, A, Kwan, J, LaPierre, N, Lemaitre, C, Li, C, Limasset, A, Malcher-Miranda, F, Mangul, S, Marcelino, VR, Marchet, C, Marijon, P, Meleshko, D, Mende, DR, Milanese, A, Nagarajan, N, Nissen, J, Nurk, S, Oliker, L, Paoli, L, Peterlongo, P, Piro, VC, Porter, JS, Rasmussen, S, Rees, ER, Reinert, K, Renard, B, Robertsen, EM, Rosen, GL, Ruscheweyh, H-J, Sarwal, V, Segata, N, Seiler, E, Shi, L, Sun, F, Sunagawa, S, Sorensen, SJ, Thomas, A, Tong, C, Trajkovski, M, Tremblay, J, Uritskiy, G, Vicedomini, R, Wang, Z, Warren, A, Willassen, NP, Yelick, K, You, R, Zeller, G, Zhao, Z, Zhu, S, Zhu, J, Garrido-Oter, R, Gastmeier, P, Hacquard, S, Haeussler, S, Khaledi, A, Maechler, F, Mesny, F, Radutoiu, S, Schulze-Lefert, P, Smit, N, Strowig, T, Bremges, A, Sczyrba, A, McHardy, AC, Meyer, F, Fritz, A, Deng, Z-L, Koslicki, D, Lesker, TR, Gurevich, A, Robertson, G, Alser, M, Antipov, D, Beghini, F, Bertrand, D, Brito, JJ, Brown, CT, Buchmann, J, Buluc, A, Chen, B, Chikhi, R, Clausen, PTLC, Cristian, A, Dabrowski, PW, Darling, AE, Egan, R, Eskin, E, Georganas, E, Goltsman, E, Gray, MA, Hansen, LH, Hofmeyr, S, Huang, P, Irber, L, Jia, H, Jorgensen, TS, Kieser, SD, Klemetsen, T, Kola, A, Kolmogorov, M, Korobeynikov, A, Kwan, J, LaPierre, N, Lemaitre, C, Li, C, Limasset, A, Malcher-Miranda, F, Mangul, S, Marcelino, VR, Marchet, C, Marijon, P, Meleshko, D, Mende, DR, Milanese, A, Nagarajan, N, Nissen, J, Nurk, S, Oliker, L, Paoli, L, Peterlongo, P, Piro, VC, Porter, JS, Rasmussen, S, Rees, ER, Reinert, K, Renard, B, Robertsen, EM, Rosen, GL, Ruscheweyh, H-J, Sarwal, V, Segata, N, Seiler, E, Shi, L, Sun, F, Sunagawa, S, Sorensen, SJ, Thomas, A, Tong, C, Trajkovski, M, Tremblay, J, Uritskiy, G, Vicedomini, R, Wang, Z, Warren, A, Willassen, NP, Yelick, K, You, R, Zeller, G, Zhao, Z, Zhu, S, Zhu, J, Garrido-Oter, R, Gastmeier, P, Hacquard, S, Haeussler, S, Khaledi, A, Maechler, F, Mesny, F, Radutoiu, S, Schulze-Lefert, P, Smit, N, Strowig, T, Bremges, A, Sczyrba, A, and McHardy, AC

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

6. 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

7. Critical Assessment of Metagenome Interpretation - the second round of challenges

Author

Meyer, F., primary, Fritz, A., additional, Deng, Z.-L., additional, Koslicki, D., additional, Gurevich, A., additional, Robertson, G., additional, Alser, M., additional, Antipov, D., additional, Beghini, F., additional, Bertrand, D., additional, Brito, J. J., additional, Brown, C.T., additional, Buchmann, J., additional, Buluç, A., additional, Chen, B., additional, Chikhi, R., additional, Clausen, P. T., additional, Cristian, A., additional, Dabrowski, P. W., additional, Darling, A. E., additional, Egan, R., additional, Eskin, E., additional, Georganas, E., additional, Goltsman, E., additional, Gray, M. A., additional, Hansen, L. H., additional, Hofmeyr, S., additional, Huang, P., additional, Irber, L., additional, Jia, H., additional, Jørgensen, T. S., additional, Kieser, S. D., additional, Klemetsen, T., additional, Kola, A., additional, Kolmogorov, M., additional, Korobeynikov, A., additional, Kwan, J., additional, LaPierre, N., additional, Lemaitre, C., additional, Li, C., additional, Limasset, A., additional, Malcher-Miranda, F., additional, Mangul, S., additional, Marcelino, V. R., additional, Marchet, C., additional, Marijon, P., additional, Meleshko, D., additional, Mende, D. R., additional, Milanese, A., additional, Nagarajan, N., additional, Nissen, J., additional, Nurk, S., additional, Oliker, L., additional, Paoli, L., additional, Peterlongo, P., additional, Piro, V. C., additional, Porter, J. S., additional, Rasmussen, S., additional, Rees, E. R., additional, Reinert, K., additional, Renard, B., additional, Robertsen, E. M., additional, Rosen, G. L., additional, Ruscheweyh, H.-J., additional, Sarwal, V., additional, Segata, N., additional, Seiler, E., additional, Shi, L., additional, Sun, F., additional, Sunagawa, S., additional, Sørensen, S. J., additional, Thomas, A., additional, Tong, C., additional, Trajkovski, M., additional, Tremblay, J., additional, Uritskiy, G., additional, Vicedomini, R., additional, Wang, Zi., additional, Wang, Zhe., additional, Wang, Zho., additional, Warren, A., additional, Willassen, N. P., additional, Yelick, K., additional, You, R., additional, Zeller, G., additional, Zhao, Z., additional, Zhu, S., additional, Zhu, J., additional, Garrido-Oter, R., additional, Gastmeier, P., additional, Hacquard, S., additional, Häußler, S., additional, Khaledi, A., additional, Maechler, F., additional, Mesny, F., additional, Radutoiu, S., additional, Schulze-Lefert, P., additional, Smit, N., additional, Strowig, T., additional, Bremges, A., additional, Sczyrba, A., additional, and McHardy, A. C., additional

Published
2021
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11. 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

12. CAMP: A modular metagenomics analysis system for integrated multi-step data exploration.

Author

Mak L, Tierney B, Ronkowski C, Toscan RB, Turhan B, Toomey M, Martinez JSA, Fu C, Lucaci AG, Solano AHB, Setubal JC, Henriksen JR, Zimmerman S, Kopbayeva M, Noyvert A, Iwan Z, Kar S, Nakazawa N, Meleshko D, Horyslavets D, Kantsypa V, Frolova A, Kahles A, Danko D, Elhaik E, Labaj P, Mangul S, Mason CE, and Hajirasouliha I

Abstract

Motivation: Computational analysis of large-scale metagenomics sequencing datasets have proven to be both incredibly valuable for extracting isolate-level taxonomic, and functional insights from complex microbial communities. However, due to an ever-expanding ecosystem of metagenomics-specific methods and file-formats, designing studies which implement seamless and scalable end-to-end workflows, and exploring the massive amounts of output data have become studies unto themselves. One-click bioinformatics pipelines have helped to organize these tools into targeted workflows, but they suffer from general compatibility and maintainability issues., Methods: To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed a module-based metagenomics analysis system: "Core Analysis Metagenomics Pipeline" (CAMP), written in Snakemake, a popular workflow management system, along with a standardized module and working directory architecture. Each module can be run independently or conjointly with a series of others to produce the target data format (ex. short-read preprocessing alone, or short-read preprocessing followed by de novo assembly), and outputs aggregated summary statistics reports and semi-guided Jupyter notebook-based visualizations., Results: We have applied CAMP to a set of ten metagenomics samples to demonstrate how a modular analysis system with built-in data visualization at intermediate steps facilitates rich and seamless inter-communication between output data from different analytic purposes., Availability: The module template as well as the modules described below can be found at https://github.com/MetaSUB-CAMP., Competing Interests: Competing Interests No competing interest is declared.

Published
2024
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13. cloudrnaSPAdes: isoform assembly using bulk barcoded RNA sequencing data.

Author

Meleshko D, Prjbelski AD, Raiko M, Tomescu AI, Tilgner H, and Hajirasouliha I

Subjects
Humans, Sequence Analysis, RNA methods, Protein Isoforms genetics, Protein Isoforms metabolism, RNA-Seq, High-Throughput Nucleotide Sequencing, Transcriptome, RNA genetics, Genomics methods
Abstract

Motivation: Recent advancements in long-read RNA sequencing have enabled the examination of full-length isoforms, previously uncaptured by short-read sequencing methods. An alternative powerful method for studying isoforms is through the use of barcoded short-read RNA reads, for which a barcode indicates whether two short-reads arise from the same molecule or not. Such techniques included the 10x Genomics linked-read based SParse Isoform Sequencing (SPIso-seq), as well as Loop-Seq, or Tell-Seq. Some applications, such as novel-isoform discovery, require very high coverage. Obtaining high coverage using long reads can be difficult, making barcoded RNA-seq data a valuable alternative for this task. However, most annotation pipelines are not able to work with a set of short reads instead of a single transcript, also not able to work with coverage gaps within a molecule if any. In order to overcome this challenge, we present an RNA-seq assembler that allows the determination of the expressed isoform per barcode., Results: In this article, we present cloudrnaSPAdes, a tool for assembling full-length isoforms from barcoded RNA-seq linked-read data in a reference-free fashion. Evaluating it on simulated and real human data, we found that cloudrnaSPAdes accurately assembles isoforms, even for genes with high isoform diversity., Availability and Implementation: cloudrnaSPAdes is a feature release of a SPAdes assembler and version used for this article is available at https://github.com/1dayac/cloudrnaSPAdes-release., (© The Author(s) 2024. Published by Oxford University Press.)

Published
2024
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14. Ariadne: synthetic long read deconvolution using assembly graphs.

Author

Mak L, Meleshko D, Danko DC, Barakzai WN, Maharjan S, Belchikov N, and Hajirasouliha I

Subjects
Genomics, Metagenome, Algorithms, Pentaerythritol Tetranitrate
Abstract

Synthetic long read sequencing techniques such as UST's TELL-Seq and Loop Genomics' LoopSeq combine 3[Formula: see text] barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3[Formula: see text] unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Published
2023
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15. Benchmarking State-of-the-Art Approaches for Norovirus Genome Assembly in Metagenome Sample.

Author

Meleshko D and Korobeynikov A

Abstract

A recently published article in BMCGenomics by Fuentes-Trillo et al. contains a comparison of assembly approaches of several noroviral samples via different tools and preprocessing strategies. It turned out that the study used outdated versions of tools as well as tools that were not designed for the viral assembly task. In order to improve the suboptimal assemblies, authors suggested different sophisticated preprocessing strategies that seem to make only minor contributions to the results. We have reproduced the analysis using state-of-the-art tools designed for viral assembly, and we demonstrate that tools from the SPAdes toolkit (rnaviralSPAdes and coronaSPAdes) allow one to assemble the samples from the original study into a single contig without any additional preprocessing.

Published
2023
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16. cloudrnaSPAdes: Isoform assembly using bulk barcoded RNA sequencing data.

Author

Meleshko D, Prjbelski AD, Raiko M, Tomescu AI, Tilgner H, and Hajirasouliha I

Abstract

Motivation: Recent advancements in long-read RNA sequencing have enabled the examination of full-length isoforms, previously uncaptured by short-read sequencing methods. An alternative powerful method for studying isoforms is through the use of barcoded short-read RNA reads, for which a barcode indicates whether two short-reads arise from the same molecule or not. Such techniques included the 10x Genomics linked-read based SParse Isoform Sequencing (SPIso-seq), as well as Loop-Seq, or Tell-Seq. Some applications, such as novel-isoform discovery, require very high coverage. Obtaining high coverage using long reads can be difficult, making barcoded RNA-seq data a valuable alternative for this task. However, most annotation pipelines are not able to work with a set of short reads instead of a single transcript, also not able to work with coverage gaps within a molecule if any. In order to overcome this challenge, we present an RNA-seq assembler allowing the determination of the expressed isoform per barcode., Results: In this paper, we present cloudrnaSPAdes, a tool for assembling full-length isoforms from barcoded RNA-seq linked-read data in a reference-free fashion. Evaluating it on simulated and real human data, we found that cloudrnaSPAdes accurately assembles isoforms, even for genes with high isoform diversity., Availability: cloudrnaSPAdes is a feature release of a SPAdes assembler and available at https://cab.spbu.ru/software/cloudrnaspades/., Competing Interests: Conflicts of interest: none declared.

Published
2023
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17. Cue: a deep-learning framework for structural variant discovery and genotyping.

Author

Popic V, Rohlicek C, Cunial F, Hajirasouliha I, Meleshko D, Garimella K, and Maheshwari A

Subjects
Humans, Genotype, Cues, Genomic Structural Variation, Genome, Human, Software, Deep Learning
Abstract

Structural variants (SVs) are a major driver of genetic diversity and disease in the human genome and their discovery is imperative to advances in precision medicine. Existing SV callers rely on hand-engineered features and heuristics to model SVs, which cannot scale to the vast diversity of SVs nor fully harness the information available in sequencing datasets. Here we propose an extensible deep-learning framework, Cue, to call and genotype SVs that can learn complex SV abstractions directly from the data. At a high level, Cue converts alignments to images that encode SV-informative signals and uses a stacked hourglass convolutional neural network to predict the type, genotype and genomic locus of the SVs captured in each image. We show that Cue outperforms the state of the art in the detection of several classes of SVs on synthetic and real short-read data and that it can be easily extended to other sequencing platforms, while achieving competitive performance., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Published
2023
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18. Efficient detection and assembly of non-reference DNA sequences with synthetic long reads.

Author

Meleshko D, Yang R, Marks P, Williams S, and Hajirasouliha I

Subjects
Algorithms, Base Sequence, Humans, Sequence Analysis, DNA methods, Genome, Human, High-Throughput Nucleotide Sequencing methods
Abstract

Recent pan-genome studies have revealed an abundance of DNA sequences in human genomes that are not present in the reference genome. A lion's share of these non-reference sequences (NRSs) cannot be reliably assembled or placed on the reference genome. Improvements in long-read and synthetic long-read (aka linked-read) technologies have great potential for the characterization of NRSs. While synthetic long reads require less input DNA than long-read datasets, they are algorithmically more challenging to use. Except for computationally expensive whole-genome assembly methods, there is no synthetic long-read method for NRS detection. We propose a novel integrated alignment-based and local assembly-based algorithm, Novel-X, that uses the barcode information encoded in synthetic long reads to improve the detection of such events without a whole-genome de novo assembly. Our evaluations demonstrate that Novel-X finds many non-reference sequences that cannot be found by state-of-the-art short-read methods. We applied Novel-X to a diverse set of 68 samples from the Polaris HiSeq 4000 PGx cohort. Novel-X discovered 16 691 NRS insertions of size > 300 bp (total length 18.2 Mb). Many of them are population specific or may have a functional impact., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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

Author

Meyer F, Fritz A, Deng ZL, Koslicki D, Lesker TR, Gurevich A, Robertson G, Alser M, Antipov D, Beghini F, Bertrand D, Brito JJ, Brown CT, Buchmann J, Buluç A, Chen B, Chikhi R, Clausen PTLC, Cristian A, Dabrowski PW, Darling AE, Egan R, Eskin E, Georganas E, Goltsman E, Gray MA, Hansen LH, Hofmeyr S, Huang P, Irber L, Jia H, Jørgensen TS, Kieser SD, Klemetsen T, Kola A, Kolmogorov M, Korobeynikov A, Kwan J, LaPierre N, Lemaitre C, Li C, Limasset A, Malcher-Miranda F, Mangul S, Marcelino VR, Marchet C, Marijon P, Meleshko D, Mende DR, Milanese A, Nagarajan N, Nissen J, Nurk S, Oliker L, Paoli L, Peterlongo P, Piro VC, Porter JS, Rasmussen S, Rees ER, Reinert K, Renard B, Robertsen EM, Rosen GL, Ruscheweyh HJ, Sarwal V, Segata N, Seiler E, Shi L, Sun F, Sunagawa S, Sørensen SJ, Thomas A, Tong C, Trajkovski M, Tremblay J, Uritskiy G, Vicedomini R, Wang Z, Wang Z, Wang Z, Warren A, Willassen NP, Yelick K, You R, Zeller G, Zhao Z, Zhu S, Zhu J, Garrido-Oter R, Gastmeier P, Hacquard S, Häußler S, Khaledi A, Maechler F, Mesny F, Radutoiu S, Schulze-Lefert P, Smit N, Strowig T, Bremges A, Sczyrba A, and McHardy AC

Subjects
Archaea genetics, Reproducibility of Results, Sequence Analysis, DNA, Software, Metagenome, Metagenomics methods
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., (© 2022. The Author(s).)

Published
2022
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20. Petabase-scale sequence alignment catalyses viral discovery.

Author

Edgar RC, Taylor B, Lin V, Altman T, Barbera P, Meleshko D, Lohr D, Novakovsky G, Buchfink B, Al-Shayeb B, Banfield JF, de la Peña M, Korobeynikov A, Chikhi R, and Babaian A

Subjects
Animals, Archives, Bacteriophages enzymology, Bacteriophages genetics, Biodiversity, Coronavirus classification, Coronavirus enzymology, Coronavirus genetics, Evolution, Molecular, Hepatitis Delta Virus enzymology, Hepatitis Delta Virus genetics, Humans, Models, Molecular, RNA Viruses classification, RNA Viruses enzymology, RNA-Dependent RNA Polymerase chemistry, RNA-Dependent RNA Polymerase genetics, Software, Cloud Computing, Databases, Genetic, RNA Viruses genetics, RNA Viruses isolation & purification, Sequence Alignment methods, Virology methods, Virome genetics
Abstract

Public databases contain a planetary collection of nucleic acid sequences, but their systematic exploration has been inhibited by a lack of efficient methods for searching this corpus, which (at the time of writing) exceeds 20 petabases and is growing exponentially 1 . Here we developed a cloud computing infrastructure, Serratus, to enable ultra-high-throughput sequence alignment at the petabase scale. We searched 5.7 million biologically diverse samples (10.2 petabases) for the hallmark gene RNA-dependent RNA polymerase and identified well over 10 5 novel RNA viruses, thereby expanding the number of known species by roughly an order of magnitude. We characterized novel viruses related to coronaviruses, hepatitis delta virus and huge phages, respectively, and analysed their environmental reservoirs. To catalyse the ongoing revolution of viral discovery, we established a free and comprehensive database of these data and tools. Expanding the known sequence diversity of viruses can reveal the evolutionary origins of emerging pathogens and improve pathogen surveillance for the anticipation and mitigation of future pandemics., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Published
2022
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21. coronaSPAdes: from biosynthetic gene clusters to RNA viral assemblies.

Author

Meleshko D, Hajirasouliha I, and Korobeynikov A

Subjects
Humans, Pandemics, Metagenome, Genome, Viral, Software, COVID-19
Abstract

Motivation: The COVID-19 pandemic has ignited a broad scientific interest in viral research in general and coronavirus research in particular. The identification and characterization of viral species in natural reservoirs typically involves de novo assembly. However, existing genome, metagenome and transcriptome assemblers often are not able to assemble many viruses (including coronaviruses) into a single contig. Coverage variation between datasets and within dataset, presence of close strains, splice variants and contamination set a high bar for assemblers to process viral datasets with diverse properties., Results: We developed coronaSPAdes, a novel assembler for RNA viral species recovery in general and coronaviruses in particular. coronaSPAdes leverages the knowledge about viral genome structures to improve assembly extending ideas initially implemented in biosyntheticSPAdes. We have shown that coronaSPAdes outperforms existing SPAdes modes and other popular short-read metagenome and viral assemblers in the recovery of full-length RNA viral genomes., Availability and Implementation: coronaSPAdes version used in this article is a part of SPAdes 3.15 release and is freely available at http://cab.spbu.ru/software/spades., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Published
2021
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22. A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion.

Author

Khosravi P, Lysandrou M, Eljalby M, Li Q, Kazemi E, Zisimopoulos P, Sigaras A, Brendel M, Barnes J, Ricketts C, Meleshko D, Yat A, McClure TD, Robinson BD, Sboner A, Elemento O, Chughtai B, and Hajirasouliha I

Subjects
Artificial Intelligence, Humans, Magnetic Resonance Imaging, Male, Retrospective Studies, Deep Learning, Prostatic Neoplasms diagnostic imaging, Radiology
Abstract

Background: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications., Purpose: To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information., Study Type: Retrospective., Population: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases)., Field Strength/sequence: 1.5 to 3.0 Tesla, T2-weighted image pulse sequences., Assessment: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor., Statistical Tests: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa., Results: Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high- vs. low-risk of prostate disease, respectively., Data Conclusion: AI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time., Level of Evidence: 1 TECHNICAL EFFICACY STAGE: 2., (© 2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC. on behalf of International Society for Magnetic Resonance in Medicine.)

Published
2021
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23. 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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24. A comprehensive metagenomics framework to characterize organisms relevant for planetary protection.

Author

Danko DC, Sierra MA, Benardini JN, Guan L, Wood JM, Singh N, Seuylemezian A, Butler DJ, Ryon K, Kuchin K, Meleshko D, Bhattacharya C, Venkateswaran KJ, and Mason CE

Subjects
Environment, Controlled, Humans, Metagenome, Spacecraft, Metagenomics, Space Flight
Abstract

Background: Clean rooms of the Space Assembly Facility (SAF) at the Jet Propulsion Laboratory (JPL) at NASA are the final step of spacecraft cleaning and assembly before launching into space. Clean rooms have stringent methods of air-filtration and cleaning to minimize microbial contamination for exoplanetary research and minimize the risk of human pathogens, but they are not sterile. Clean rooms make a selective environment for microorganisms that tolerate such cleaning methods. Previous studies have attempted to characterize the microbial cargo through sequencing and culture-dependent protocols. However, there is not a standardized metagenomic workflow nor analysis pipeline for spaceflight hardware cleanroom samples to identify microbial contamination. Additionally, current identification methods fail to characterize and profile the risk of low-abundance microorganisms., Results: A comprehensive metagenomic framework to characterize microorganisms relevant for planetary protection in multiple cleanroom classifications (from ISO-5 to ISO-8.5) and sample types (surface, filters, and debris collected via vacuum devices) was developed. Fifty-one metagenomic samples from SAF clean rooms were sequenced and analyzed to identify microbes that could potentially survive spaceflight based on their microbial features and whether the microbes expressed any metabolic activity or growth. Additionally, an auxiliary testing was performed to determine the repeatability of our techniques and validate our analyses. We find evidence that JPL clean rooms carry microbes with attributes that may be problematic in space missions for their documented ability to withstand extreme conditions, such as psychrophilia and ability to form biofilms, spore-forming capacity, radiation resistance, and desiccation resistance. Samples from ISO-5 standard had lower microbial diversity than those conforming to ISO-6 or higher filters but still carried a measurable microbial load., Conclusions: Although the extensive cleaning processes limit the number of microbes capable of withstanding clean room condition, it is important to quantify thresholds and detect organisms that can inform ongoing Planetary Protection goals, provide a biological baseline for assembly facilities, and guide future mission planning. Video Abstract.

Published
2021
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25. Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions.

Author

Butler D, Mozsary C, Meydan C, Foox J, Rosiene J, Shaiber A, Danko D, Afshinnekoo E, MacKay M, Sedlazeck FJ, Ivanov NA, Sierra M, Pohle D, Zietz M, Gisladottir U, Ramlall V, Sholle ET, Schenck EJ, Westover CD, Hassan C, Ryon K, Young B, Bhattacharya C, Ng DL, Granados AC, Santos YA, Servellita V, Federman S, Ruggiero P, Fungtammasan A, Chin CS, Pearson NM, Langhorst BW, Tanner NA, Kim Y, Reeves JW, Hether TD, Warren SE, Bailey M, Gawrys J, Meleshko D, Xu D, Couto-Rodriguez M, Nagy-Szakal D, Barrows J, Wells H, O'Hara NB, Rosenfeld JA, Chen Y, Steel PAD, Shemesh AJ, Xiang J, Thierry-Mieg J, Thierry-Mieg D, Iftner A, Bezdan D, Sanchez E, Campion TR Jr, Sipley J, Cong L, Craney A, Velu P, Melnick AM, Shapira S, Hajirasouliha I, Borczuk A, Iftner T, Salvatore M, Loda M, Westblade LF, Cushing M, Wu S, Levy S, Chiu C, Schwartz RE, Tatonetti N, Rennert H, Imielinski M, and Mason CE

Subjects
Adult, Aged, Angiotensin Receptor Antagonists pharmacology, Angiotensin-Converting Enzyme Inhibitors pharmacology, Antiviral Agents pharmacology, COVID-19 epidemiology, COVID-19 Nucleic Acid Testing, Drug Interactions, Female, Gene Expression Profiling, Genome, Viral, HLA Antigens genetics, Host Microbial Interactions drug effects, Host Microbial Interactions genetics, Humans, Male, Middle Aged, Molecular Diagnostic Techniques, New York City epidemiology, Nucleic Acid Amplification Techniques, Pandemics, RNA-Seq, SARS-CoV-2 classification, SARS-CoV-2 drug effects, COVID-19 Drug Treatment, COVID-19 genetics, COVID-19 virology, SARS-CoV-2 genetics
Abstract

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Published
2021
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26. Using SPAdes De Novo Assembler.

Author

Prjibelski A, Antipov D, Meleshko D, Lapidus A, and Korobeynikov A

Subjects
Bacteria genetics, Biosynthetic Pathways genetics, Databases, Genetic, Metagenome, Multigene Family, Plasmids genetics, RNA-Seq, Transcriptome genetics, Algorithms, Sequence Analysis, DNA methods
Abstract

SPAdes-St. Petersburg genome Assembler-was originally developed for de novo assembly of genome sequencing data produced for cultivated microbial isolates and for single-cell genomic DNA sequencing. With time, the functionality of SPAdes was extended to enable assembly of IonTorrent data, as well as hybrid assembly from short and long reads (PacBio and Oxford Nanopore). In this article we present protocols for five different assembly pipelines that comprise the SPAdes package and that are used for assembly of metagenomes and transcriptomes as well as assembly of putative plasmids and biosynthetic gene clusters from whole-genome sequencing and metagenomic datasets. In addition, we present guidelines for understanding results with use cases for each pipeline, and several additional support protocols that help in using SPAdes properly. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Assembling isolate bacterial datasets Basic Protocol 2: Assembling metagenomic datasets Basic Protocol 3: Assembling sets of putative plasmids Basic Protocol 4: Assembling transcriptomes Basic Protocol 5: Assembling putative biosynthetic gene clusters Support Protocol 1: Installing SPAdes Support Protocol 2: Providing input via command line Support Protocol 3: Providing input data via YAML format Support Protocol 4: Restarting previous run Support Protocol 5: Determining strand-specificity of RNA-seq data., (© 2020 Wiley Periodicals LLC.)

Published
2020
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27. Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions.

Author

Butler DJ, Mozsary C, Meydan C, Danko D, Foox J, Rosiene J, Shaiber A, Afshinnekoo E, MacKay M, Sedlazeck FJ, Ivanov NA, Sierra M, Pohle D, Zietz M, Gisladottir U, Ramlall V, Westover CD, Ryon K, Young B, Bhattacharya C, Ruggiero P, Langhorst BW, Tanner N, Gawrys J, Meleshko D, Xu D, Steel PAD, Shemesh AJ, Xiang J, Thierry-Mieg J, Thierry-Mieg D, Schwartz RE, Iftner A, Bezdan D, Sipley J, Cong L, Craney A, Velu P, Melnick AM, Hajirasouliha I, Horner SM, Iftner T, Salvatore M, Loda M, Westblade LF, Cushing M, Levy S, Wu S, Tatonetti N, Imielinski M, Rennert H, and Mason CE

Abstract

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets., Competing Interests: Conflicts of Interest Nathan Tanner and Bradley W. Langhorst are employees at New England Biolabs.

Published
2020
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28. BiosyntheticSPAdes: reconstructing biosynthetic gene clusters from assembly graphs.

Author

Meleshko D, Mohimani H, Tracanna V, Hajirasouliha I, Medema MH, Korobeynikov A, and Pevzner PA

Subjects
Contig Mapping, Datasets as Topic, Dental Plaque microbiology, Gingiva microbiology, Humans, Internet, Mouth Mucosa microbiology, Pharynx microbiology, Protein Biosynthesis, Tongue microbiology, Genes, Bacterial, Metagenome, Metagenomics methods, Multigene Family, Software
Abstract

Predicting biosynthetic gene clusters (BGCs) is critically important for discovery of antibiotics and other natural products. While BGC prediction from complete genomes is a well-studied problem, predicting BGCs in fragmented genomic assemblies remains challenging. The existing BGC prediction tools often assume that each BGC is encoded within a single contig in the genome assembly, a condition that is violated for most sequenced microbial genomes where BGCs are often scattered through several contigs, making it difficult to reconstruct them. The situation is even more severe in shotgun metagenomics, where the contigs are often short, and the existing tools fail to predict a large fraction of long BGCs. While it is difficult to assemble BGCs in a single contig, the structure of the genome assembly graph often provides clues on how to combine multiple contigs into segments encoding long BGCs. We describe biosyntheticSPAdes, a tool for predicting BGCs in assembly graphs and demonstrate that it greatly improves the reconstruction of BGCs from genomic and metagenomics data sets., (© 2019 Meleshko et al.; Published by Cold Spring Harbor Laboratory Press.)

Published
2019
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29. Minerva: an alignment- and reference-free approach to deconvolve Linked-Reads for metagenomics.

Author

Danko DC, Meleshko D, Bezdan D, Mason C, and Hajirasouliha I

Subjects
Algorithms, Metagenome, Metagenomics methods, Sequence Analysis, DNA methods, Software
Abstract

Emerging Linked-Read technologies (aka read cloud or barcoded short-reads) have revived interest in short-read technology as a viable approach to understand large-scale structures in genomes and metagenomes. Linked-Read technologies, such as the 10x Chromium system, use a microfluidic system and a specialized set of 3' barcodes (aka UIDs) to tag short DNA reads sourced from the same long fragment of DNA; subsequently, the tagged reads are sequenced on standard short-read platforms. This approach results in interesting compromises. Each long fragment of DNA is only sparsely covered by reads, no information about the ordering of reads from the same fragment is preserved, and 3' barcodes match reads from roughly 2-20 long fragments of DNA. However, compared to long-read technologies, the cost per base to sequence is far lower, far less input DNA is required, and the per base error rate is that of Illumina short-reads. In this paper, we formally describe a particular algorithmic issue common to Linked-Read technology: the deconvolution of reads with a single 3' barcode into clusters that represent single long fragments of DNA. We introduce Minerva, a graph-based algorithm that approximately solves the barcode deconvolution problem for metagenomic data (where reference genomes may be incomplete or unavailable). Additionally, we develop two demonstrations where the deconvolution of barcoded reads improves downstream results, improving the specificity of taxonomic assignments and of k -mer-based clustering. To the best of our knowledge, we are the first to address the problem of barcode deconvolution in metagenomics., (© 2019 Danko et al.; Published by Cold Spring Harbor Laboratory Press.)

Published
2019
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30. American Gut: an Open Platform for Citizen Science Microbiome Research.

Author

McDonald D, Hyde E, Debelius JW, Morton JT, Gonzalez A, Ackermann G, Aksenov AA, Behsaz B, Brennan C, Chen Y, DeRight Goldasich L, Dorrestein PC, Dunn RR, Fahimipour AK, Gaffney J, Gilbert JA, Gogul G, Green JL, Hugenholtz P, Humphrey G, Huttenhower C, Jackson MA, Janssen S, Jeste DV, Jiang L, Kelley ST, Knights D, Kosciolek T, Ladau J, Leach J, Marotz C, Meleshko D, Melnik AV, Metcalf JL, Mohimani H, Montassier E, Navas-Molina J, Nguyen TT, Peddada S, Pevzner P, Pollard KS, Rahnavard G, Robbins-Pianka A, Sangwan N, Shorenstein J, Smarr L, Song SJ, Spector T, Swafford AD, Thackray VG, Thompson LR, Tripathi A, Vázquez-Baeza Y, Vrbanac A, Wischmeyer P, Wolfe E, Zhu Q, and Knight R

Abstract

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.

Published
2018
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31. metaSPAdes: a new versatile metagenomic assembler.

Author

Nurk S, Meleshko D, Korobeynikov A, and Pevzner PA

Subjects
Genome, Bacterial, Contig Mapping methods, Genomics methods, Metagenome, Sequence Analysis, DNA methods, Software
Abstract

While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging, thus stifling biological discoveries. Moreover, recent studies revealed that complex bacterial populations may be composed from dozens of related strains, thus further amplifying the challenge of metagenomic assembly. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes. We benchmark metaSPAdes against other state-of-the-art metagenome assemblers and demonstrate that it results in high-quality assemblies across diverse data sets., (© 2017 Nurk et al.; Published by Cold Spring Harbor Laboratory Press.)

Published
2017
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