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2. The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Author

Rigoudy, Noa, Dussert, Gaspard, Benyoub, Abdelbaki, Besnard, Aurélien, Birck, Carole, Boyer, Jérome, Bollet, Yoann, Bunz, Yoann, Caussimont, Gérard, Chetouane, Elias, Carriburu, Jules Chiffard, Cornette, Pierre, Delestrade, Anne, De Backer, Nina, Dispan, Lucie, Le Barh, Maden, Duhayer, Jeanne, Elder, Jean-François, Fanjul, Jean-Baptiste, Fonderflick, Jocelyn, Froustey, Nicolas, Garel, Mathieu, Gaudry, William, Gérard, Agathe, Gimenez, Olivier, Hemery, Arzhela, Hemon, Audrey, Jullien, Jean-Michel, Knitter, Daniel, Malafosse, Isabelle, Marginean, Mircea, Ménard, Louise, Ouvrier, Alice, Pariset, Gwennaelle, Prunet, Vincent, Rabault, Julien, Randon, Malory, Raulet, Yann, Régnier, Antoine, Ribière, Romain, Ricci, Jean-Claude, Ruette, Sandrine, Schneylin, Yann, Sentilles, Jérôme, Siefert, Nathalie, Smith, Bethany, Terpereau, Guillaume, Touchet, Pierrick, Thuiller, Wilfried, Uzal, Antonio, Vautrain, Valentin, Vimal, Ruppert, Weber, Julian, Spataro, Bruno, Miele, Vincent, and Chamaillé-Jammes, Simon

Published
2023
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9. Nine tips for ecologists using machine learning

Author

Desprez, Marine, Miele, Vincent, and Gimenez, Olivier

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Biological sciences, Populations and Evolution (q-bio.PE), Quantitative Biology - Populations and Evolution, Machine Learning (cs.LG)
Abstract

Due to their high predictive performance and flexibility, machine learning models are an appropriate and efficient tool for ecologists. However, implementing a machine learning model is not yet a trivial task and may seem intimidating to ecologists with no previous experience in this area. Here we provide a series of tips to help ecologists in implementing machine learning models. We focus on classification problems as many ecological studies aim to assign data into predefined classes such as ecological states or biological entities. Each of the nine tips identifies a common error, trap or challenge in developing machine learning models and provides recommendations to facilitate their use in ecological studies.

Published
2023

10. Chapter 6: Using Latent Block Models to Detect Structure in Ecological Networks

Author

Aubert, Julie, Barbillon, Pierre, Donnet, Sophie, Miele, Vincent, Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP]
Abstract

International audience

Published
2022

15. Revisiting giraffe photo-identification using deep learning and network analysis

Author

Miele, Vincent, Dussert, Gaspard, Spataro, Bruno, Chamaillé-Jammes, Simon, Allainé, Dominique, Bonenfant, Christophe, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
SIFT, deep metric learning, opensource, [SDE]Environmental Sciences, image similarity networks, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, animal identification
Abstract

An increasing number of research programs rely on photographic capture-recapture (vs. direct marking) of individuals to study distribution and demography within animal populations. Photo-identification of individuals living in the wild is sometimes feasible using idiosyncratic coat or skin patterns, like for giraffes. When performed manually, the task is tedious and becomes almost impossible as populations grow in size. Computer vision techniques are an appealing and unavoidable help to tackle this apparently simple task in the big-data era. In this context, we propose to revisit giraffe re-identification using convolutional neural networks (CNNs).We first developed an end-to-end pipeline to retrieve a comprehensive set of re-identified giraffes from about 4, 000 raw photographs. To do so, we combined CNN-based object detection, SIFT pattern matching, and image similarity networks. We then quantified the performance of deep metric learning to retrieve the identity of known and unknown individuals. The re-identification performance of CNNs reached a top 5 accuracy of about 90%. Fully based on open-source software packages, our work paves the way for further attempts to build CNN-based pipelines for re-identification of individual animals, in giraffes but also in other species.

Published
2020

16. Deciphering the connectivity structure of biological networks using MixNet

Author

Miele Vincent, Picard Franck, Daudin Jean-Jacques, Cottret Ludovic, and Robin Stéphane

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles. Results We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the E. coli transcriptional regulatory network, the macaque cortex network, a foodweb network and the Buchnera aphidicola metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering. Conclusion We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.

Published
2009
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19. Toward community standards in the quest for orthologs

Author

Dessimoz, Christophe, Gabaldón, Toni, Roos, David S., Sonnhammer, Erik L. L., Herrero, Javier, Altenhoff, Adrian, Apweiler, Rolf, Ashburner, Michael, Blake, Judith, Boeckmann, Brigitte, Bridge, Alan, Bruford, Elspeth, Cherry, Mike, Conte, Matthieu, Dannie, Durand, Datta, Ruchira, Dessimoz, Christophe, Domelevo Entfellner, Jean-Baka, Ebersberger, Ingo, Gabaldón, Toni, Galperin, Michael, Herrero, Javier, Joseph, Jacob, Koestler, Tina, Kriventseva, Evgenia, Lecompte, Odile, Leunissen, Jack, Lewis, Suzanna, Linard, Benjamin, Livstone, Michael S., Lu, Hui-Chun, Martin, Maria, Mazumder, Raja, Messina, David, Miele, Vincent, Muffato, Matthieu, Perrière, Guy, Punta, Marco, Roos, David, Rouard, Mathieu, Schmitt, Thomas, Schreiber, Fabian, Silva, Alan, Sjölander, Kimmen, Škunca, Nives, Sonnhammer, Erik, Stanley, Eleanor, Szklarczyk, Radek, Thomas, Paul, Uchiyama, Ikuo, Van Bel, Michiel, Vandepoele, Klaas, Vilella, Albert J., Yates, Andrew, and Zdobnov, Evgeny

Published
2012
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22. An appraisal of graph embeddings for comparing trophic network architectures.

Author

Botella, Christophe, Dray, Stéphane, Matias, Catherine, Miele, Vincent, and Thuiller, Wilfried

Subjects
VECTOR spaces, ECOLOGISTS, ECOSYSTEMS
Abstract

Comparing the architecture of interaction networks in space or time is essential for understanding the assembly, trajectory, functioning and persistence of species communities. Graph embedding methods, which position networks into a vector space where nearby networks have similar architectures, could be ideal tools for this purposes.Here, we evaluated the ability of seven graph embedding methods to disentangle architectural similarities of interactions networks for supervised and unsupervised posterior analytic tasks. The evaluation was carried out over a large number of simulated trophic networks representing variations around six ecological properties and size.We did not find an overall best method and instead showed that the performance of the methods depended on the targeted ecological properties and thus on the research questions. We also highlighted the importance of normalising the embedding for network sizes for meaningful posterior unsupervised analyses.We concluded by orientating potential users to the most suited methods given the question, the targeted network ecological property, and outlined links between those ecological properties and three ecological processes: robustness to extinction, community persistence and ecosystem functioning. We hope this study will stimulate the appropriation of graph embedding methods by ecologists. [ABSTRACT FROM AUTHOR]

Published
2022
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30. Sport-Related Structural Brain Injury and Return to Play: Systematic Review and Expert Insight.

Author

Zuckerman, Scott L, Yengo-Kahn, Aaron M, Tang, Alan R, Bailes, Julian E, Beauchamp, Kathryn, Berger, Mitchel S, Bonfield, Christopher M, Camarata, Paul J, Cantu, Robert C, Davis, Gavin A, Ellenbogen, Richard G, Ellis, Michael J, Feuer, Hank, Guazzo, Eric, Harris, Odette A, Heppner, Peter, Honeybul, Stephen, Manley, Geoff, Maroon, Joseph C, and Miele, Vincent J

Published
2021
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31. List of Contributors

Author

Abtahi, Ali R., Adogwa, Owoicho, Ahmed, A. Karim, Ainechi, Ana, Alan, Nima, Albanese, Jessica, Aleem, Ilyas, Almeida, Joao Paulo, Alshabab, Basel Sheikh, Alvarado, Anthony M., Alvi, Mohammed Ali, Amer, Aboubakr, Ames, Christopher P., Ammanuel, Simon G., Anand, Neel, Anderson, Paul A., Angelov, Lilyana, Arnold, Paul M., Aubin, Carl-Eric, Bae, Junseok, Bain, Mark, Barber, Joshua, Baron, Eli M., Basi, Hersimren Kaur, Bawahab, Asef, Benzel, Edward C., Berven, Sigurd H., Bevan, Adam K., Bice, Miranda, Bilsky, Mark, Bisson, Erica F., Blaskiewicz, Donald, Boody, Barrett S., Bowles, Daniel, Branch, Charles, Brooks, Nathaniel, Buckland, Aaron J., Buell, Thomas J., Burch, Shane, Burke, John F., Butt, Bilal B., Bydon, Mohamad, Camara, Joaquin, Canseco, Jose A., Casper, David S., Cassidy, Matthew, Chao, Samuel T., Chatain, Grégoire P., Chieng, Lee Onn, Cho, Woojin, Choi, Hoon, Christopher, Susan R., Chung, Andrew S., Clark, Aaron J., Coric, Domagoj, Corriveau, Mark D., Cottrill, Ethan, Cychosz, Christopher, Daubs, Gregory, Daubs, Michael D., Davin, Sara, DeMicco, Russell C., de Padua, Ashley, Derman, Peter B., Dhall, Sanjay S., Diab, Mohammad, DiGiorgio, Anthony M., Dimar, John R., II, Dru, Alexander B., Dubousset, Jean, Dunbar, Melissa R., Ebrahim, Zeyd, Ehresman, Jeff, Elder, J. Bradley, Ellenbogen, Richard G., El Naga, Ashraf N., Emans, John B., Engstrom, John W., Errico, Thomas J., Farag, Ehab, Farber, S. Harrison, Fatima, Nida, Fedorak, Graham T., Fehlings, Michael G., Feigenbaum, Frank, Ferrara, Lisa, Fessler, Richard G., Finn, Michael, Fischgrund, Jeffrey S., Fisher, Mark D., Fontes, Ricardo B.V., Fox, Michael A., Freedman, Brett A., Frenkel, Mark, Fridley, Jared, Gabet, Joelle, Garg, Sumeet, German, John W., Ghogawala, Zoher, Gibbs, Christopher M., Gillick, John L., Gilligan, Christopher J., Gillis, Christopher C., Goel, Atul, Gokaslan, Ziya L., Goldstein, Zachary H., Grant, Gerald A., Grasso, Mackenzie, Grossbach, Andrew J., Guan, Jian, Guinn, Jeremy, Gupta, Raghav, Gussous, Yazeed M., Guyer, Richard D., Haddad, Alexander F., Hamilton, Kirsty, Harland, Tessa, Harrop, James S., Hart, David J., Hart, Robert A., Hassan, Waqaas A., Hayes, Amanda W., Heary, Robert F., Hedequist, Daniel, Henderson, Fraser C., Sr., Henzel, M. Kristi, Herring, Eric Z., Higgins, Dominique, Hilibrand, Alan S., Hindoyan, Kevork N., Hines, Kevin, Hitchon, Patrick W., Hoffmann, Jacob C., Hoh, Daniel J., Holt, Joshua, Hsia, Augusto, Hsieh, Jason, Hsu, Wellington K., Hurlbert, John, Hwang, Steven W., Iordanou, Jordan C., Jack, Andrew S., Janich, Karl, Jiang, Bowen, Jiang, Fan, Jimenez, Xavier F., Johnson, J. Patrick, Jones, G. Alexander, Jones, Kristen E., Joseph, Jacob R., Joshi, Rushikesh S., Juthani, Rupa G., Kalfas, Iain H., Kalra, Ricky R., Kang, James D., Kanter, Adam S., Kasliwal, Manish K., Kaushal, Mayank, Kerolus, Mena G., Kesler, Kyle, Kessler, Remi A., Khalaf, Tagreed, Khalil, Jad G., Kim, Terrence T., Kimball, Jon, Kisinde, Stanley, Klineberg, Eric O., Kondylis, Efstathios, Kramer, Dallas E., Krauss, William E., Krishnaney, Ajit A., Krogue, Justin, Kshettry, Varun R., Kumar, Neeraj, Kurpad, Shekar N., Labak, Collin M., Labelle, Hubert, Ladd, Bryan, Lafage, Virginie, Laratta, Joseph L., Lark, Robert, Lau, Darryl, Laufer, Ilya, Lavelle, William F., Le, Hai V., Lee, Andrew, Lee, Nathan J., Lee, Sang-Ho, Lehman, Ronald A., Jr., Lehner, Kurt, Lenke, Lawrence G., Li, Yingda, Lieberman, Isador H., Ling, Marcus Z., Lo, Victor P., Lollis, S. Scott, Lombardi, Joseph M., Long, Donlin, Long, Roger, Lotz, Jeffrey, Lyons, Joseph G., Mac-Thiong, Jean-Marc, Machado, Andre, Mallow, Gary M., Malone, David G., Manjila, Sunil, Maroon, Joseph C., Maslak, Joseph P., Massaad, Elie, Matsumoto, Morio, Martini, Michael L., Mayer, E. Kano, Mayer, Rory, Mazanec, Daniel J., McCormick, Kyle L., McCormick, Paul C., McGrath, Kyle, McNeill, Ian T., Medress, Zachary A., Mendelis, Joseph R., Mendis, Phillip G., Metz, Lionel, Midha, Rajiv, Miele, Vincent J., Mijatovic, Desimir, Mikula, Anthony L., Min, Elliot, Mizuno, Shuichi, Moghaddamjou, Ali, Molenda, Joseph E., Momin, Arbaz, Momin, Eric, Moore, Nina Z., Morris, Dylan, Morrissette, Cole R., Mroz, Thomas E., Mummaneni, Praveen V., Mundis, Gregory M., Munoz, Tess, Naderi, Sait, Nagel, Sean J., Nagoshi, Narihito, Nail, Tara Jayde, Nair, Dileep, NaPier, Zachary, Neifert, Sean N., Nemunaitis, Gregory, Nessim, Adam, Niu, Tianyi, Norwig, John A., Ong, Binnan, Onwuzulike, Kaine C., Oren, Jonathan, Orr, R. Douglas, O’Toole, John E., Ozpinar, Alp, Padua, Fortunato G., Page, Paul, Park, Paul J., Parmar, Vikas, Patel, Arati, Patel, Parthik, Patel, Rakesh, Pelle, Dominic, Peña, Enrique, Pendleton, Courtney, Pennicooke, Brenton, Pennington, Zach, Perez-Cruet, Mick J., Petersen-Fitts, Graysen R., Peterson, Thomas A., Piche, Joshua, Placide, Rick, Platt, Andrew, Polifka, Adam J., Polly, David W., Poree, Lawrence, Rajasekaran, S., Rammo, Richard, Rasouli, Jonathan J., Ratliff, John K., Rawlinson, Jeremy J., Ray, Wilson Z., Recinos, Pablo F., Recinos, Violette M., Reid, Patrick, Renfrow, Jackie, Resnick, Daniel K., Resser, Tina, Rhines, Laurence D., Richardson, Dusty, Riesenburger, Ron, Rispinto, Sarah, Rivera, Joshua, Rosenquist, Richard Wayne, Ross, Lindsey, Rossi, Vincent, Rudisill, Samuel S., Ruggieri, Paul, Ryu, Won Hyung A., Sabourin, Victor, Safaee, Michael M., Safain, Mina, Salaheen, Zaid, Samartzis, Dino, Samdani, Amer F., Samtani, Rahul G., Sasso, Rick, Satin, Alexander M., Savage, Jason W., Sawin, Paul D., Sawires, Andrew N., Sawyer, Aenor, Schmidt, Bradley T., Schmidt, Eric, Schmidt, Meic H., Schwab, Frank J., Sciubba, Daniel M., Sellin, Jonathan N., Shaaya, Elias, Shabani, Saman, Shaffrey, Christopher I., Shankar, Ganesh M., Shao, Jianning, Sharan, Alok D., Shaw, Jeremy D., Shen, Jian, Shenoy, Kartik, Shetty, Ajoy Prasad, Shin, John H., Shook, Steven J., Singh, Harminder, Singh, Rahul, Sissman, Ethan, Siyaji, Zakariah K., Smith, Gabriel A., Smith, John T., Smith, Justin S., Spinner, Robert J., Spurgas, Morgan P., Stefanelli, Anthony J., Steinmetz, Michael P., Sundar, Swetha J., Swarup, Ishaan, Takeoka, Yoshiki, Tatsui, Claudio E., Teferi, Nahom, Telfeian, Albert E., Theodore, Nicholas, Theologis, Alekos A., Thiyagarajah, Nishanth, Thorp, Brian D., Traynelis, Vincent C., Trost, Gregory R., Truong, Huy Q., Tsiang, John T., Tumialán, Luis M., Tymchak, Zane A., Udayasankar, Unni, Umansky, Daniel, Uribe, Juan S., Vaccaro, Alexander R., Vargovich, Alison M., Vaziri, Sasha, Veeravagu, Anand, Venezia, Michael, Verma, Kushagra, Vilarello, Brandon, Viljoen, Stephanus V., Viswanathan, Vibhu Krishnan, Volovetz, Josephine, Vorster, Sarel J., Walker, Corey T., Wallace, Daniel J., Wang, Anthony C., Wang, Jeffrey C., Wang, Marjorie C., Wang, Michael Y., Wang, Xiaoyu, Weinstein, Stuart L., Weisman, Michael H., Weissmann, Karen, Wentworth, Kelly, Whiting, Benjamin, Whitmore, Robert G., Wick, Joseph, Williams, Michelle, Wilson, James R., Wilson, Leslie, Witham, Timothy, Wolfla, Christopher E., Wolinsky, Jean-Paul, Woodard, Eric J., Wu, Hao-Hua, Yang, Michael, Yezdani, Samir G., Yoganandan, Narayan, Yolcu, Yagiz, Young, Robin, Zanation, Adam, Zehri, Aqib, and Zileli, Mehmet

Published
2022
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32. Ultra-fast sequence clustering from similarity networks with SiLiX

Author

Duret Laurent, Penel Simon, and Miele Vincent

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background The number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time. Results We present the software package SiLiX that implements a novel method which reconsiders single linkage clustering with a graph theoretical approach. A parallel version of the algorithms is also presented. As a demonstration of the ability of our software, we clustered more than 3 millions sequences from about 2 billion BLAST hits in 7 minutes, with a high clustering quality, both in terms of sensitivity and specificity. Conclusions Comparing state-of-the-art software, SiLiX presents the best up-to-date capabilities to face the problem of clustering large collections of sequences. SiLiX is freely available at http://lbbe.univ-lyon1.fr/SiLiX.

Published
2011
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33. Playing hide and seek with repeats in local and global de novo transcriptome assembly of short RNA-seq reads

Author

Lima, Leandro, Sinaimeri, Blerina, Sacomoto, Gustavo, Lopez-Maestre, Helene, Marchet, Camille, Miele, Vincent, Sagot, Marie-France, Lacroix, Vincent, Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Baobab, Département PEGASE [LBBE] (PEGASE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Eléments transposables, évolution, populations, Département génétique, interactions et évolution des génomes [LBBE] (GINSENG), Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)

Subjects
Transcriptome assembly, Applied Mathematics, [SDV]Life Sciences [q-bio], Enumeration algorithm, Repeats, Formal model for representing repeats, Alternative splicing, Assembly evaluation, De Bruijn graph topology, RNA-seq, Computational Theory and Mathematics, Structural Biology, Molecular Biology
Abstract

International audience; AbstractBackground The main challenge in de novo genome assembly of DNA-seq data is certainly to deal with repeats that are longer than the reads. In de novo transcriptome assembly of RNA-seq reads, on the other hand, this problem has been underestimated so far. Even though we have fewer and shorter repeated sequences in transcriptomics, they do create ambiguities and confuse assemblers if not addressed properly. Most transcriptome assemblers of short reads are based on de Bruijn graphs (DBG) and have no clear and explicit model for repeats in RNA-seq data, relying instead on heuristics to deal with them.ResultsThe results of this work are threefold. First, we introduce a formal model for representing high copy-number and low-divergence repeats in RNA-seq data and exploit its properties to infer a combinatorial characteristic of repeat-associated subgraphs. We show that the problem of identifying such subgraphs in a DBG is NP-complete. Second, we show that in the specific case of local assembly of alternative splicing (AS) events, we can implicitly avoid such subgraphs, and we present an efficient algorithm to enumerate AS events that are not included in repeats. Using simulated data, we show that this strategy is significantly more sensitive and precise than the previous version of KisSplice (Sacomoto et al. in WABI, pp 99–111, 1), Trinity (Grabherr et al. in Nat Biotechnol 29(7):644–652, 2), and Oases (Schulz et al. in Bioinformatics 28(8):1086–1092, 3), for the specific task of calling AS events. Third, we turn our focus to full-length transcriptome assembly, and we show that exploring the topology of DBGs can improve de novo transcriptome evaluation methods. Based on the observation that repeats create complicated regions in a DBG, and when assemblers try to traverse these regions, they can infer erroneous transcripts, we propose a measure to flag transcripts traversing such troublesome regions, thereby giving a confidence level for each transcript. The originality of our work when compared to other transcriptome evaluation methods is that we use only the topology of the DBG, and not read nor coverage information. We show that our simple method gives better results than Rsem-Eval (Li et al. in Genome Biol 15(12):553, 4) and TransRate (Smith-Unna et al. in Genome Res 26(8):1134–1144, 5) on both real and simulated datasets for detecting chimeras, and therefore is able to capture assembly errors missed by these methods.

Published
2017

34. Revisiting animal photo‐identification using deep metric learning and network analysis.

Author

Miele, Vincent, Dussert, Gaspard, Spataro, Bruno, Chamaillé‐Jammes, Simon, Allainé, Dominique, Bonenfant, Christophe, and Freckleton, Robert

Subjects
CONVOLUTIONAL neural networks, ENVIRONMENTAL monitoring, IMAGE registration, INTEGRATED software, PATTERN matching
Abstract

Copyright of Methods in Ecology & Evolution is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2021
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37. Toward community standards in the quest for orthologs

Author

Dessimoz, Christophe, Gabaldón, Toni, Roos, David S., Sonnhammer, Erik L. L., Herrero, Javier, Altenhoff, Adrian, Apweiler, Rolf, Ashburner, Michael, Blake, Judith, Boeckmann, Brigitte, Bridge, Alan, Bruford, Elspeth, Cherry, Mike, Conte, Matthieu, Dannie, Durand, Datta, Ruchira, Domelevo Entfellner, Jean-Baka, Ebersberger, Ingo, Galperin, Michael, Joseph, Jacob, Koestler, Tina, Kriventseva, Evgenia, Lecompte, Odile, Leunissen, Jack, Lewis, Suzanna, Linard, Benjamin, Livstone, Michael S., Lu, Hui-Chun, Martin, Maria, Mazumder, Raja, Messina, David, Miele, Vincent, Muffato, Matthieu, Perrière, Guy, Punta, Marco, Roos, David, Rouard, Mathieu, Schmitt, Thomas, Schreiber, Fabian, Silva, Alan, Sjölander, Kimmen, Škunca, Nives, Sonnhammer, Erik, Stanley, Eleanor, Szklarczyk, Radek, Thomas, Paul, Uchiyama, Ikuo, Van Bel, Michiel, Vandepoele, Klaas, Vilella, Albert J., Yates, Andrew, and Zdobnov, Evgeny

Abstract

The identification of orthologs—genes pairs descended from a common ancestor through speciation, rather than duplication—has emerged as an essential component of many bioinformatics applications, ranging from the annotation of new genomes to experimental target prioritization. Yet, the development and application of orthology inference methods is hampered by the lack of consensus on source proteomes, file formats and benchmarks. The second ‘Quest for Orthologs' meeting brought together stakeholders from various communities to address these challenges. We report on achievements and outcomes of this meeting, focusing on topics of particular relevance to the research community at large. The Quest for Orthologs consortium is an open community that welcomes contributions from all researchers interested in orthology research and applications. Contact: dessimoz@ebi.ac.uk

Published
2017

38. Inferring the timing of territoriality and rut in male roe deer from movements? Some preliminary results - and new perspectives

Author

Morellet, Nicolas, Miele, Vincent, Bonenfant, Christophe, Unité de recherche Comportement et Ecologie de la Faune Sauvage (CEFS), Institut National de la Recherche Agronomique (INRA), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017

40. Core–periphery dynamics in a plant–pollinator network.

Author

Miele, Vincent, Ramos‐Jiliberto, Rodrigo, Vázquez, Diego P., and Rodriguez‐Cabal, Mariano

Subjects
*POLLINATORS, *ECOSYSTEM management, *CORE & periphery (Economic theory), *STOCHASTIC models, *PLANT capacity
Abstract

Mutualistic networks are highly dynamic, characterized by high temporal turnover of species and interactions. Yet, we have a limited understanding of how the internal structure of these networks and the roles species play in them vary through time.We used 6 years of observation data and a novel statistical method (dynamic stochastic block models) to assess how network structure and species' structural position within the network change throughout subseasons of the flowering season and across years in a quantitative plant–pollinator network from a dryland ecosystem in Argentina.Our analyses revealed a core–periphery structure persistent through subseasons and years. Yet, species structural position as core or peripheral was highly dynamic: virtually all species that were at the core in some subseasons were also peripheral in other subseasons, while many other species always remained peripheral.Our results illuminate our understanding of the dynamics of mutualistic networks and have important implications for ecosystem management and conservation. [ABSTRACT FROM AUTHOR]

Published
2020
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41. Inferring the timing of territoriality and rut in male roe deer from movements?

Author

Morellet, Nicolas, Bonenfant, Christophe, Miele, Vincent, Heurich, Marco, Focardi, Stefano, ProdInra, Migration, Unité de recherche Comportement et Ecologie de la Faune Sauvage (CEFS), and Institut National de la Recherche Agronomique (INRA)

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2016

42. DNA Physical Properties and Nucleosome Positions Are Major Determinants of HIV-1 Integrase Selectivity

Author

Naughtin, Monica, Haftek-Terreau, Zofia, Xavier, Johan, Meyer, Sam, Silvain, Maud, Jaszczyszyn, Yan, Lévy, Nicolas, Miele, Vincent, Benleulmi, Mohamed, Ruff, Marc, Parissi, Vincent, Vaillant, Cédric, Lavigne, Marc, Laboratoire Joliot Curie, École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS), Chromatine et Régulation de la Pathogénie bactérienne (CRP), Microbiologie, adaptation et pathogénie (MAP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon, Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Microbiologie cellulaire et moléculaire et pathogénicité (MCMP), Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Virologie moléculaire et Vaccinologie, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), This work was supported by Agence Nationale de recherche sur le VIH et les hépatites: JX VP CV ML, Centre National de la Recherche Scientifique: MN, MR, VP, CV, ML, Agence Nationale de la Recherche: MSB, VP, MR, Sidaction: MR, Ecole Normale Supérieure de Lyon: CV, ML, and Institut Pasteur de Paris: ML, École normale supérieure de Lyon (ENS de Lyon)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Microbiologie Fondamentale et Pathogénicité (MFP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Lavigne, Marc, École normale supérieure - Lyon ( ENS Lyon ) -Centre National de la Recherche Scientifique ( CNRS ), Microbiologie, adaptation et pathogénie ( MAP ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Sud - Paris 11 ( UP11 ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Institut de Génétique et de Biologie Moléculaire et Cellulaire ( IGBMC ), Université de Strasbourg ( UNISTRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Microbiologie cellulaire et moléculaire et pathogénicité ( MCMP ), Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Physique de l'ENS Lyon ( Phys-ENS ), École normale supérieure - Lyon ( ENS Lyon ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique ( CNRS )

Subjects
Binding Sites, Virus Integration, lcsh:R, lcsh:Medicine, DNA, HIV Integrase, Chromatin, Nucleosomes, Substrate Specificity, DNA-Binding Proteins, [ SDV.BBM.BC ] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], Humans, lcsh:Q, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], lcsh:Science, [SDV.BBM.BC] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], Protein Binding, Research Article
Abstract

International audience; Retroviral integrases (INs) catalyse the integration of the reverse transcribed viral DNA into the host cell genome. This process is selective, and chromatin has been proposed to be a major factor regulating this step in the viral life cycle. However, the precise underlying mechanisms are still under investigation. We have developed a new in vitro integration assay using physiologically-relevant, reconstituted genomic acceptor chromatin and high-throughput determination of nucleosome positions and integration sites, in parallel. A quantitative analysis of the resulting data reveals a chromatin-dependent redistribution of the integration sites and establishes a link between integration sites and nucleosome positions. The co-activator LEDGF/ p75 enhanced integration but did not modify the integration sites under these conditions. We also conducted an in cellulo genome-wide comparative study of nucleosome positions and human immunodeficiency virus type-1 (HIV-1) integration sites identified experimentally in vivo. These studies confirm a preferential integration in nucleosome-covered regions. Using a DNA mechanical energy model, we show that the physical properties of DNA probed by IN binding are important in determining IN selectivity. These novel in vitro and in vivo approaches confirm that IN has a preference for integration into a nucleosome, and suggest the existence of two levels of IN selectivity. The first depends on the physical properties of the target DNA and notably, the energy required to fit DNA into the IN catalytic pocket. The second depends on the DNA deformation associated with DNA wrapping around a nucleosome. Taken together , these results indicate that HIV-1 IN is a shape-readout DNA binding protein.

Published
2015

43. Non-trophic interactions strengthen the diversity—functioning relationship in an ecological bioenergetic network model.

Author

Miele, Vincent, Guill, Christian, Ramos-Jiliberto, Rodrigo, and Kéfi, Sonia

Subjects
*BIOTIC communities, *ECOLOGICAL models, *SPECIES diversity, *BIOMASS production, *POPULATION biology, *COEXISTENCE of species
Abstract

Ecological communities are undeniably diverse, both in terms of the species that compose them as well as the type of interactions that link species to each other. Despite this long recognition of the coexistence of multiple interaction types in nature, little is known about the consequences of this diversity for community functioning. In the ongoing context of global change and increasing species extinction rates, it seems crucial to improve our understanding of the drivers of the relationship between species diversity and ecosystem functioning. Here, using a multispecies dynamical model of ecological communities including various interaction types (e.g. competition for space, predator interference, recruitment facilitation in addition to feeding), we studied the role of the presence and the intensity of these interactions for species diversity, community functioning (biomass and production) and the relationship between diversity and functioning.Taken jointly, the diverse interactions have significant effects on species diversity, whose amplitude and sign depend on the type of interactions involved and their relative abundance. They however consistently increase the slope of the relationship between diversity and functioning, suggesting that species losses might have stronger effects on community functioning than expected when ignoring the diversity of interaction types and focusing on feeding interactions only. [ABSTRACT FROM AUTHOR]

Published
2019
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44. Diversity indices for ecological networks: a unifying framework using Hill numbers.

Author

Ohlmann, Marc, Miele, Vincent, Dray, Stéphane, Chalmandrier, Loïc, O'Connor, Louise, Thuiller, Wilfried, and Drake, John

Subjects
*MOUNTAINS, *FOOD chains
Abstract

Describing how ecological interactions change over space and time and how they are shaped by environmental conditions is crucial to understand and predict ecosystem trajectories. However, it requires having an appropriate framework to measure network diversity locally, regionally and between samples (α‐, γ‐ and β‐diversity). Here, we propose a unifying framework that builds on Hill numbers and accounts both for the probabilistic nature of biotic interactions and the abundances of species or groups. We emphasise the importance of analysing network diversity across different species aggregation levels (e.g. from species to trophic groups) to get a better understanding of network structure. We illustrate our framework with a simulation experiment and an empirical analysis using a global food‐web database. We discuss further usages of the framework and show how it responds to recent calls on comparing ecological networks and analysing their variation across environmental gradients and time. [ABSTRACT FROM AUTHOR]

Published
2019
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45. Global survey of mobile DNA horizontal transfer in arthropods reveals Lepidoptera as a prime hotspot.

Author

Reiss, Daphné, Mialdea, Gladys, Miele, Vincent, de Vienne, Damien, Peccoud, Jean, Gilbert, Clément, Duret, Laurent, and Charlat, Sylvain

Subjects
MOBILE genetic elements, ARTHROPODA, TRANSPOSONS, DNA, LEPIDOPTERA
Abstract

More than any other genome components, Transposable Elements (TEs) have the capacity to move across species barriers through Horizontal Transfer (HT), with substantial evolutionary consequences. Previous large-scale surveys, based on full-genomes comparisons, have revealed the transposition mode as an important predictor of HT rates variation across TE superfamilies. However, host biology could represent another major explanatory factor, one that needs to be investigated through extensive taxonomic sampling. Here we test this hypothesis using a field collection of 460 Arthropod species from Tahiti and surrounding islands. Through targeted massive parallel sequencing, we uncover patterns of HT in three widely-distributed TE superfamilies with contrasted modes of transposition. In line with earlier findings, the DNA transposons under study (TC1-Mariner) were found to transfer horizontally at the highest frequency, closely followed by the LTR superfamily (Copia), in contrast with the non-LTR superfamily (Jockey), that mostly diversifies through vertical inheritance and persists longer within genomes. Strikingly, across all superfamilies, we observe a marked excess of HTs in Lepidoptera, an insect order that also commonly hosts baculoviruses, known for their ability to transport host TEs. These results turn the spotlight on baculoviruses as major potential vectors of TEs in Arthropods, and further emphasize the importance of non-vertical TE inheritance in genome evolution. [ABSTRACT FROM AUTHOR]

Published
2019
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46. Spatially-constrained clustering of ecological networks

Author

Miele, Vincent, Picard, Franck, and Dray, St��phane

Subjects
FOS: Computer and information sciences, Applications (stat.AP), Statistics - Applications
Abstract

Spatial ecological networks are widely used to model interactions between georeferenced biological entities (e.g., populations or communities). The analysis of such data often leads to a two-step approach where groups containing similar biological entities are firstly identified and the spatial information is used afterwards to improve the ecological interpretation. We develop an integrative approach to retrieve groups of nodes that are geographically close and ecologically similar. Our model-based spatially-constrained method embeds the geographical information within a regularization framework by adding some constraints to the maximum likelihood estimation of parameters. A simulation study and the analysis of real data demonstrate that our approach is able to detect complex spatial patterns that are ecologically meaningful. The model-based framework allows us to consider external information (e.g., geographic proximities, covariates) in the analysis of ecological networks and appears to be an appealing alternative to consider such data.

Published
2014

47. New developments in KisSplice: Combining local and global transcriptome assemblers to decipher splicing in RNA-seq data

Author

Julien-Laferriere, Alice, Sacomoto, Gustavo, Chikhi, Rayan, Scaon, Erwan, Parsons, David P., Sagot, Marie-France, Peterlongo, Pierre, Miele, Vincent, Lacroix, Vincent, Baobab, Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), An algorithmic view on genomes, cells, and environments (BAMBOO), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), SED [Grenoble], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Génétique et évolution des interactions hôtes-parasites, Département génétique, interactions et évolution des génomes [LBBE] (GINSENG), and Service Expérimentation et Développement (SED [Grenoble])

Subjects
[SDV]Life Sciences [q-bio], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2013

48. Multidisciplinary Implementation of Published Surgical Site Infection Prevention Recommendations Is Associated with Improved Outcomes in Neurosurgical Patients.

Author

Palladino, Katie J., Dixon, Heather, Yassin, Mohamed H., Semroc, Suzan, and Miele, Vincent

Abstract

Surgical site infections (SSIs) are among the most common, most expensive healthcare-associated infections. In 2019, spinal fusions and laminectomies are associated with the most excess SSIs at an urban, 420-bed acute care hospital. Adherence to published SSI prevention recommendations is unknown in this patient population and improving adherence may improve SSI rates, readmission rates, and patient satisfaction. In 2020, we established a multidisciplinary workgroup of subject-matter experts and champions responsible for implementing published SSI prevention recommendations through education, coaching, and validation. Validators reviewed medical records of a random sample of procedures to assess adherence to select SSI prevention recommendations. Adherence and SSI rates were reported to the workgroup monthly the first year, then quarterly. In this quasi-experimental study, we compared pre-intervention to post-intervention SSI rates, readmission rates, and patient satisfaction measures. We obtained SSI rates from the National Healthcare Safety Network. We calculated readmission rates via medical record abstraction. SSI and readmission rates pre- and post-intervention were compared using the mid-p value method. A mid-p value<0.05 was significant. We obtained measures of patient satisfaction from Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey responses. Surgeon-specific standardized infection ratio (SIR) decreased by 87% between 2019 and 2021 (p=0.0017). Facility neurosurgery SIR decreased by 82% between 2019 and 2021 (p=0.0003). From 2019 to 2020, neurosurgery-related 90-day and 30-day readmission rates decreased by 46% (p=0.10) and 74% (p=0.02), respectively. From 2019 to 2021, patient experience HCAHPS top box scores in this population increased in hospital rating (55.9% to 60.2%), hospital recommendation (55.4% to 62.8%), physician communication (73.1% to 74.5%), and care transition domains (47.9% to 54.2%). Formation of a multidisciplinary SSI prevention workgroup responsible for implementing, coaching, and routinely monitoring adherence to published SSI prevention recommendations is associated with decreased SSI and readmission rates and increased patient satisfaction. [ABSTRACT FROM AUTHOR]

Published
2023
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