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13 results on '"Radivojac P"'

1. CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

Author

Jain, Shantanu, Bakolitsa, Constantina, Brenner, Steven E, Radivojac, Predrag, Moult, John, Repo, Susanna, Hoskins, Roger A, Andreoletti, Gaia, Barsky, Daniel, Chellapan, Ajithavalli, Chu, Hoyin, Dabbiru, Navya, Kollipara, Naveen K, Ly, Melissa, Neumann, Andrew J, Pal, Lipika R, Odell, Eric, Pandey, Gaurav, Peters-Petrulewicz, Robin C, Srinivasan, Rajgopal, Yee, Stephen F, Yeleswarapu, Sri Jyothsna, Zuhl, Maya, Adebali, Ogun, Patra, Ayoti, Beer, Michael A, Hosur, Raghavendra, Peng, Jian, Bernard, Brady M, Berry, Michael, Dong, Shengcheng, Boyle, Alan P, Adhikari, Aashish, Chen, Jingqi, Hu, Zhiqiang, Wang, Robert, Wang, Yaqiong, Miller, Maximilian, Wang, Yanran, Bromberg, Yana, Turina, Paola, Capriotti, Emidio, Han, James J, Ozturk, Kivilcim, Carter, Hannah, Babbi, Giulia, Bovo, Samuele, Di Lena, Pietro, Martelli, Pier Luigi, Savojardo, Castrense, Casadio, Rita, Cline, Melissa S, De Baets, Greet, Bonache, Sandra, Diez, Orland, Gutierrez-Enriquez, Sara, Fernandez, Alejandro, Montalban, Gemma, Ootes, Lars, Ozkan, Selen, Padilla, Natalia, Riera, Casandra, De la Cruz, Xavier, Diekhans, Mark, Huwe, Peter J, Wei, Qiong, Xu, Qifang, Dunbrack, Roland L, Gotea, Valer, Elnitski, Laura, Margolin, Gennady, Fariselli, Piero, Kulakovskiy, Ivan V, Makeev, Vsevolod J, Penzar, Dmitry D, Vorontsov, Ilya E, Favorov, Alexander V, Forman, Julia R, Hasenahuer, Marcia, Fornasari, Maria S, Parisi, Gustavo, Avsec, Ziga, Celik, Muhammed H, Thi, Yen Duong Nguyen, Gagneur, Julien, Shi, Fang-Yuan, Edwards, Matthew D, Guo, Yuchun, Tian, Kevin, Zeng, Haoyang, Gifford, David K, Goke, Jonathan, Zaucha, Jan, Gough, Julian, Ritchie, Graham RS, Frankish, Adam, Mudge, Jonathan M, Harrow, Jennifer, Young, Erin L, and Yu, Yao

Subjects
Biological Sciences, Genetics, Human Genome, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Computational Biology, Mutation, Missense, Phenotype, Critical Assessment of Genome Interpretation Consortium, Environmental Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundThe Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.ResultsPerformance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.ConclusionsResults show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Published
2024

2. Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project

Author

Sarah L. Stenton, Melanie C. O’Leary, Gabrielle Lemire, Grace E. VanNoy, Stephanie DiTroia, Vijay S. Ganesh, Emily Groopman, Emily O’Heir, Brian Mangilog, Ikeoluwa Osei-Owusu, Lynn S. Pais, Jillian Serrano, Moriel Singer-Berk, Ben Weisburd, Michael W. Wilson, Christina Austin-Tse, Marwa Abdelhakim, Azza Althagafi, Giulia Babbi, Riccardo Bellazzi, Samuele Bovo, Maria Giulia Carta, Rita Casadio, Pieter-Jan Coenen, Federica De Paoli, Matteo Floris, Manavalan Gajapathy, Robert Hoehndorf, Julius O. B. Jacobsen, Thomas Joseph, Akash Kamandula, Panagiotis Katsonis, Cyrielle Kint, Olivier Lichtarge, Ivan Limongelli, Yulan Lu, Paolo Magni, Tarun Karthik Kumar Mamidi, Pier Luigi Martelli, Marta Mulargia, Giovanna Nicora, Keith Nykamp, Vikas Pejaver, Yisu Peng, Thi Hong Cam Pham, Maurizio S. Podda, Aditya Rao, Ettore Rizzo, Vangala G. Saipradeep, Castrense Savojardo, Peter Schols, Yang Shen, Naveen Sivadasan, Damian Smedley, Dorian Soru, Rajgopal Srinivasan, Yuanfei Sun, Uma Sunderam, Wuwei Tan, Naina Tiwari, Xiao Wang, Yaqiong Wang, Amanda Williams, Elizabeth A. Worthey, Rujie Yin, Yuning You, Daniel Zeiberg, Susanna Zucca, Constantina Bakolitsa, Steven E. Brenner, Stephanie M. Fullerton, Predrag Radivojac, Heidi L. Rehm, and Anne O’Donnell-Luria

Subjects
Rare disease, Genome sequencing, Genome interpretation, Variant prioritization, Best practices, Medicine, Genetics, QH426-470
Abstract

Abstract Background A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. Results Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. Conclusions Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

Published
2024
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3. A roadmap for the functional annotation of protein families: a community perspective

Author

de Crécy-lagard, Valérie, de Hegedus, Rocio Amorin, Arighi, Cecilia, Babor, Jill, Bateman, Alex, Blaby, Ian, Blaby-Haas, Crysten, Bridge, Alan J, Burley, Stephen K, Cleveland, Stacey, Colwell, Lucy J, Conesa, Ana, Dallago, Christian, Danchin, Antoine, de Waard, Anita, Deutschbauer, Adam, Dias, Raquel, Ding, Yousong, Fang, Gang, Friedberg, Iddo, Gerlt, John, Goldford, Joshua, Gorelik, Mark, Gyori, Benjamin M, Henry, Christopher, Hutinet, Geoffrey, Jaroch, Marshall, Karp, Peter D, Kondratova, Liudmyla, Lu, Zhiyong, Marchler-Bauer, Aron, Martin, Maria-Jesus, McWhite, Claire, Moghe, Gaurav D, Monaghan, Paul, Morgat, Anne, Mungall, Christopher J, Natale, Darren A, Nelson, William C, O’Donoghue, Seán, Orengo, Christine, O’Toole, Katherine H, Radivojac, Predrag, Reed, Colbie, Roberts, Richard J, Rodionov, Dmitri, Rodionova, Irina A, Rudolf, Jeffrey D, Saleh, Lana, Sheynkman, Gloria, Thibaud-Nissen, Francoise, Thomas, Paul D, Uetz, Peter, Vallenet, David, Carter, Erica Watson, Weigele, Peter R, Wood, Valerie, Wood-Charlson, Elisha M, and Xu, Jin

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Generic health relevance, Base Sequence, Computational Biology, Genome, Genomics, Molecular Sequence Annotation, Proteins, Data Format, Library and Information Studies, Bioinformatics and computational biology, Data management and data science
Abstract

Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

Published
2022

4. HIP2: An online database of human plasma proteins from healthy individuals

Author

Shen Changyu, Harrison Scott H, Saha Sudipto, Tang Haixu, Radivojac Predrag, Arnold Randy J, Zhang Xiang, and Chen Jake

Subjects
Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background With the introduction of increasingly powerful mass spectrometry (MS) techniques for clinical research, several recent large-scale MS proteomics studies have sought to characterize the entire human plasma proteome with a general objective for identifying thousands of proteins leaked from tissues in the circulating blood. Understanding the basic constituents, diversity, and variability of the human plasma proteome is essential to the development of sensitive molecular diagnosis and treatment monitoring solutions for future biomedical applications. Biomedical researchers today, however, do not have an integrated online resource in which they can search for plasma proteins collected from different mass spectrometry platforms, experimental protocols, and search software for healthy individuals. The lack of such a resource for comparisons has made it difficult to interpret proteomics profile changes in patients' plasma and to design protein biomarker discovery experiments. Description To aid future protein biomarker studies of disease and health from human plasma, we developed an online database, HIP2 (Healthy Human Individual's Integrated Plasma Proteome). The current version contains 12,787 protein entries linked to 86,831 peptide entries identified using different MS platforms. Conclusion This web-based database will be useful to biomedical researchers involved in biomarker discovery research. This database has been developed to be the comprehensive collection of healthy human plasma proteins, and has protein data captured in a relational database schema built to contain mappings of supporting peptide evidence from several high-quality and high-throughput mass-spectrometry (MS) experimental data sets. Users can search for plasma protein/peptide annotations, peptide/protein alignments, and experimental/sample conditions with options for filter-based retrieval to achieve greater analytical power for discovery and validation.

Published
2008
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5. Assessment of blind predictions of the clinical significance of BRCA1 and BRCA2 variants

Author

Cline, Melissa S, Babbi, Giulia, Bonache, Sandra, Cao, Yue, Casadio, Rita, de la Cruz, Xavier, Díez, Orland, Gutiérrez‐Enríquez, Sara, Katsonis, Panagiotis, Lai, Carmen, Lichtarge, Olivier, Martelli, Pier L, Mishne, Gilad, Moles‐Fernández, Alejandro, Montalban, Gemma, Mooney, Sean D, O'Conner, Robert, Ootes, Lars, Özkan, Selen, Padilla, Natalia, Pagel, Kymberleigh A, Pejaver, Vikas, Radivojac, Predrag, Riera, Casandra, Savojardo, Castrense, Shen, Yang, Sun, Yuanfei, Topper, Scott, Parsons, Michael T, Spurdle, Amanda B, Goldgar, David E, and Consortium, The ENIGMA

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Breast Cancer, Cancer, Ovarian Cancer, Rare Diseases, BRCA1 Protein, BRCA2 Protein, Breast Neoplasms, Computational Biology, Early Detection of Cancer, Female, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Humans, Models, Genetic, Ovarian Neoplasms, BRCA, BRCA1, BRCA2, CAGI, CAGI5, variant interpretation, ENIGMA Consortium, Clinical Sciences, Genetics & Heredity, Clinical sciences
Abstract

Testing for variation in BRCA1 and BRCA2 (commonly referred to as BRCA1/2), has emerged as a standard clinical practice and is helping countless women better understand and manage their heritable risk of breast and ovarian cancer. Yet the increased rate of BRCA1/2 testing has led to an increasing number of Variants of Uncertain Significance (VUS), and the rate of VUS discovery currently outpaces the rate of clinical variant interpretation. Computational prediction is a key component of the variant interpretation pipeline. In the CAGI5 ENIGMA Challenge, six prediction teams submitted predictions on 326 newly-interpreted variants from the ENIGMA Consortium. By evaluating these predictions against the new interpretations, we have gained a number of insights on the state of the art of variant prediction and specific steps to further advance this state of the art.

Published
2019

6. Assessing computational predictions of the phenotypic effect of cystathionine‐beta‐synthase variants

Author

Kasak, Laura, Bakolitsa, Constantina, Hu, Zhiqiang, Yu, Changhua, Rine, Jasper, Dimster‐Denk, Dago F, Pandey, Gaurav, Baets, Greet, Bromberg, Yana, Cao, Chen, Capriotti, Emidio, Casadio, Rita, Durme, Joost, Giollo, Manuel, Karchin, Rachel, Katsonis, Panagiotis, Leonardi, Emanuela, Lichtarge, Olivier, Martelli, Pier Luigi, Masica, David, Mooney, Sean D, Olatubosun, Ayodeji, Radivojac, Predrag, Rousseau, Frederic, Pal, Lipika R, Savojardo, Castrense, Schymkowitz, Joost, Thusberg, Janita, Tosatto, Silvio CE, Vihinen, Mauno, Väliaho, Jouni, Repo, Susanna, Moult, John, Brenner, Steven E, and Friedberg, Iddo

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Networking and Information Technology R&D (NITRD), Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Good Health and Well Being, Amino Acid Substitution, Computational Biology, Cystathionine, Cystathionine beta-Synthase, Homocysteine, Humans, Phenotype, Precision Medicine, CAGI challenge, critical assessment, cystathionine-beta-synthase, machine learning, phenotype prediction, single amino acid substitution, Clinical Sciences, Genetics & Heredity, Clinical sciences
Abstract

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

Published
2019

7. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Naihui Zhou, Yuxiang Jiang, Timothy R. Bergquist, Alexandra J. Lee, Balint Z. Kacsoh, Alex W. Crocker, Kimberley A. Lewis, George Georghiou, Huy N. Nguyen, Md Nafiz Hamid, Larry Davis, Tunca Dogan, Volkan Atalay, Ahmet S. Rifaioglu, Alperen Dalkıran, Rengul Cetin Atalay, Chengxin Zhang, Rebecca L. Hurto, Peter L. Freddolino, Yang Zhang, Prajwal Bhat, Fran Supek, José M. Fernández, Branislava Gemovic, Vladimir R. Perovic, Radoslav S. Davidović, Neven Sumonja, Nevena Veljkovic, Ehsaneddin Asgari, Mohammad R.K. Mofrad, Giuseppe Profiti, Castrense Savojardo, Pier Luigi Martelli, Rita Casadio, Florian Boecker, Heiko Schoof, Indika Kahanda, Natalie Thurlby, Alice C. McHardy, Alexandre Renaux, Rabie Saidi, Julian Gough, Alex A. Freitas, Magdalena Antczak, Fabio Fabris, Mark N. Wass, Jie Hou, Jianlin Cheng, Zheng Wang, Alfonso E. Romero, Alberto Paccanaro, Haixuan Yang, Tatyana Goldberg, Chenguang Zhao, Liisa Holm, Petri Törönen, Alan J. Medlar, Elaine Zosa, Itamar Borukhov, Ilya Novikov, Angela Wilkins, Olivier Lichtarge, Po-Han Chi, Wei-Cheng Tseng, Michal Linial, Peter W. Rose, Christophe Dessimoz, Vedrana Vidulin, Saso Dzeroski, Ian Sillitoe, Sayoni Das, Jonathan Gill Lees, David T. Jones, Cen Wan, Domenico Cozzetto, Rui Fa, Mateo Torres, Alex Warwick Vesztrocy, Jose Manuel Rodriguez, Michael L. Tress, Marco Frasca, Marco Notaro, Giuliano Grossi, Alessandro Petrini, Matteo Re, Giorgio Valentini, Marco Mesiti, Daniel B. Roche, Jonas Reeb, David W. Ritchie, Sabeur Aridhi, Seyed Ziaeddin Alborzi, Marie-Dominique Devignes, Da Chen Emily Koo, Richard Bonneau, Vladimir Gligorijević, Meet Barot, Hai Fang, Stefano Toppo, Enrico Lavezzo, Marco Falda, Michele Berselli, Silvio C.E. Tosatto, Marco Carraro, Damiano Piovesan, Hafeez Ur Rehman, Qizhong Mao, Shanshan Zhang, Slobodan Vucetic, Gage S. Black, Dane Jo, Erica Suh, Jonathan B. Dayton, Dallas J. Larsen, Ashton R. Omdahl, Liam J. McGuffin, Danielle A. Brackenridge, Patricia C. Babbitt, Jeffrey M. Yunes, Paolo Fontana, Feng Zhang, Shanfeng Zhu, Ronghui You, Zihan Zhang, Suyang Dai, Shuwei Yao, Weidong Tian, Renzhi Cao, Caleb Chandler, Miguel Amezola, Devon Johnson, Jia-Ming Chang, Wen-Hung Liao, Yi-Wei Liu, Stefano Pascarelli, Yotam Frank, Robert Hoehndorf, Maxat Kulmanov, Imane Boudellioua, Gianfranco Politano, Stefano Di Carlo, Alfredo Benso, Kai Hakala, Filip Ginter, Farrokh Mehryary, Suwisa Kaewphan, Jari Björne, Hans Moen, Martti E.E. Tolvanen, Tapio Salakoski, Daisuke Kihara, Aashish Jain, Tomislav Šmuc, Adrian Altenhoff, Asa Ben-Hur, Burkhard Rost, Steven E. Brenner, Christine A. Orengo, Constance J. Jeffery, Giovanni Bosco, Deborah A. Hogan, Maria J. Martin, Claire O’Donovan, Sean D. Mooney, Casey S. Greene, Predrag Radivojac, and Iddo Friedberg

Subjects
Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Published
2019
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8. New Drosophila Long-Term Memory Genes Revealed by Assessing Computational Function Prediction Methods

Author

Balint Z. Kacsoh, Stephen Barton, Yuxiang Jiang, Naihui Zhou, Sean D. Mooney, Iddo Friedberg, Predrag Radivojac, Casey S. Greene, and Giovanni Bosco

Subjects
D. melanogaster, Parasitoid wasp, Learning and memory, Long-term memory, Behavior, Bioinformatics, Gene function prediction, Critical assessment, Genetics, QH426-470
Abstract

A major bottleneck to our understanding of the genetic and molecular foundation of life lies in the ability to assign function to a gene and, subsequently, a protein. Traditional molecular and genetic experiments can provide the most reliable forms of identification, but are generally low-throughput, making such discovery and assignment a daunting task. The bottleneck has led to an increasing role for computational approaches. The Critical Assessment of Functional Annotation (CAFA) effort seeks to measure the performance of computational methods. In CAFA3, we performed selected screens, including an effort focused on long-term memory. We used homology and previous CAFA predictions to identify 29 key Drosophila genes, which we tested via a long-term memory screen. We identify 11 novel genes that are involved in long-term memory formation and show a high level of connectivity with previously identified learning and memory genes. Our study provides first higher-order behavioral assay and organism screen used for CAFA assessments and revealed previously uncharacterized roles of multiple genes as possible regulators of neuronal plasticity at the boundary of information acquisition and memory formation.

Published
2019
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9. CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases

Author

Zhiqiang Hu, Jesse M. Hunter, Olivier Lichtarge, Sean D. Mooney, Aashish N. Adhikari, Steven E. Brenner, Rita Casadio, Yizhou Yin, Lipika R. Pal, Uma Sunderam, Panagiotis Katsonis, Predrag Radivojac, Thomas Joseph, Giulia Babbi, Naveen Sivadasan, Constantina Bakolitsa, Vangala G. Saipradeep, Laura Kasak, John Moult, Julian Gough, M. Stephen Meyn, Pier Luigi Martelli, Jennifer Poitras, Rupa A Udani, Jan Zaucha, Rafael F. Guerrero, Yuxiang Jiang, Aditya Rao, Sujatha Kotte, Kunal Kundu, Kasak L., Hunter J.M., Udani R., Bakolitsa C., Hu Z., Adhikari A.N., Babbi G., Casadio R., Gough J., Guerrero R.F., Jiang Y., Joseph T., Katsonis P., Kotte S., Kundu K., Lichtarge O., Martelli P.L., Mooney S.D., Moult J., Pal L.R., Poitras J., Radivojac P., Rao A., Sivadasan N., Sunderam U., Saipradeep V.G., Yin Y., Zaucha J., Brenner S.E., and Meyn M.S.

Subjects
Male, Adolescent, In silico, Genomic data, Computational biology, Biology, Undiagnosed Diseases, Genome, Article, 03 medical and health sciences, Databases, Genetic, SickKid, pediatric rare disease, Genetics, Humans, Computer Simulation, Genetic Predisposition to Disease, Child, Gene, Genetics (clinical), 030304 developmental biology, Disease gene, 0303 health sciences, Whole Genome Sequencing, variant interpretation, 030305 genetics & heredity, Computational Biology, Genetic Variation, Pathogenicity, Phenotype, ddc, phenotype prediction, Child, Preschool, New disease, CAGI, Female, whole-genome sequencing data
Abstract

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

Published
2019
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10. Assessing the performance of in-silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer

Author

Rita Casadio, Panagiotis Katsonis, Susan L. Neuhausen, Alin Voskanian, Predrag Radivojac, Yao Yu, Steven E. Brenner, Yue Cao, Yana Bromberg, Yuanfei Sun, Erin Young, Giulia Babbi, Elad Ziv, Castrense Savojardo, Maricel G. Kann, Max Miller, Yanran Wang, Olivier Lichtarge, Aditi Garg, Pier Luigi Martelli, Yang Shen, Emidio Capriotti, Debnath Pal, Gaia Andreoletti, Sean V. Tavtigian, Sean D. Mooney, Vikas Pejaver, Lipika R. Pal, Chad D. Huff, Voskanian A., Katsonis P., Lichtarge O., Pejaver V., Radivojac P., Mooney S.D., Capriotti E., Bromberg Y., Wang Y., Miller M., Martelli P.L., Savojardo C., Babbi G., Casadio R., Cao Y., Sun Y., Shen Y., Garg A., Pal D., Yu Y., Huff C.D., Tavtigian S.V., Young E., Neuhausen S.L., Ziv E., Pal L.R., Andreoletti G., Brenner S.E., and Kann M.G.

Subjects
Adult, In silico, Breast Neoplasms, Computational biology, Disease, Biology, Genome, Polymorphism, Single Nucleotide, Article, Odds, 03 medical and health sciences, breast cancer, Breast cancer, SNV, Exome Sequencing, Genetics, medicine, Humans, Computer Simulation, Genetic Predisposition to Disease, CHEK2, Genetics (clinical), 030304 developmental biology, Aged, 0303 health sciences, 030305 genetics & heredity, Computational Biology, Hispanic or Latino, Hispanic women, Middle Aged, medicine.disease, Precision medicine, United States, Checkpoint Kinase 2, Case-Control Studies, Linear Models, CAGI, Identification (biology), Female
Abstract

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.

Published
2019

11. Assessment of methods for predicting the effects of PTEN and TPMT protein variants

Author

Lukas Folkman, Kunal Kundu, Yaoqi Zhou, Rita Casadio, Olivier Lichtarge, Yana Bromberg, Giulia Babbi, Yizhou Yin, Lipika R. Pal, Panagiotis Katsonis, Castrense Savojardo, Predrag Radivojac, Maximilian Miller, John Moult, Pier Luigi Martelli, Vikas Pejaver, Pejaver V., Babbi G., Casadio R., Folkman L., Katsonis P., Kundu K., Lichtarge O., Martelli P.L., Miller M., Moult J., Pal L.R., Savojardo C., Yin Y., Zhou Y., Radivojac P., and Bromberg Y.

Subjects
Nonsynonymous substitution, VAMP-seq, Computational biology, Article, Stability change, 03 medical and health sciences, Genetics, PTEN, Humans, thiopurine S-methyl transferase, TPMT, Genetics (clinical), 030304 developmental biology, 0303 health sciences, Thiopurine methyltransferase, biology, Protein Stability, 030305 genetics & heredity, PTEN Phosphohydrolase, Computational Biology, High-Throughput Nucleotide Sequencing, Methyltransferases, variant stability profiling, Mutation, biology.protein, Molecular mechanism, CAGI, Critical assessment, Experimental methods, phosphatase and tensin homolog, PTEN
Abstract

Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation-induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation (CAGI), we explored the utility of high-throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state-of-the-art predictors against over 7,000 non-synonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerged as top-performers depending on the metric, it is non-trivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability-affecting. Furthermore, it is unclear to what extent VSP abundance scores were reasonable proxies for the stability-related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

Published
2019

12. Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants

Author

Ayodeji Olatubosun, Dago F Dimster-Denk, Zhiqiang Hu, Pier Luigi Martelli, Mauno Vihinen, Olivier Lichtarge, Frederic Rousseau, Iddo Friedberg, Castrense Savojardo, Sean D. Mooney, Emanuela Leonardi, Greet De Baets, Manuel Giollo, Jouni Väliaho, Yana Bromberg, Rachel Karchin, Chen Cao, Janita Thusberg, Changhua Yu, Susanna Repo, Rita Casadio, David L. Masica, Laura Kasak, Emidio Capriotti, Jasper Rine, Gaurav Pandey, Silvio C. E. Tosatto, John Moult, Lipika R. Pal, Steven E. Brenner, Predrag Radivojac, Panagiotis Katsonis, Joost Schymkowitz, Joost Van Durme, Constantina Bakolitsa, Kasak L., Bakolitsa C., Hu Z., Yu C., Rine J., Dimster-Denk D.F., Pandey G., De Baets G., Bromberg Y., Cao C., Capriotti E., Casadio R., Van Durme J., Giollo M., Karchin R., Katsonis P., Leonardi E., Lichtarge O., Martelli P.L., Masica D., Mooney S.D., Olatubosun A., Radivojac P., Rousseau F., Pal L.R., Savojardo C., Schymkowitz J., Thusberg J., Tosatto S.C.E., Vihinen M., Valiaho J., Repo S., Moult J., Brenner S.E., and Friedberg I.

Subjects
Homocysteine, IMPACT, ved/biology.organism_classification_rank.species, Transsulfuration pathway, chemistry.chemical_compound, 2.1 Biological and endogenous factors, Single amino acid, Aetiology, Precision Medicine, Genetics (clinical), Genetics & Heredity, PROTEIN FUNCTION, 0303 health sciences, biology, 030305 genetics & heredity, CAGI challenge, SNAP, Phenotype, machine learning, Networking and Information Technology R&D (NITRD), phenotype prediction, critical assessment, Life Sciences & Biomedicine, cystathionine-beta-synthase, ENZYME, Clinical Sciences, Cystathionine beta-Synthase, Homocystinuria, Computational biology, single amino acid substitution, CLASSIFICATION, Article, 03 medical and health sciences, Cystathionine, Genetics, medicine, Humans, Model organism, 030304 developmental biology, SERVER, TOOLS, Science & Technology, MUTATIONS, business.industry, ved/biology, Computational Biology, medicine.disease, Cystathionine beta synthase, Good Health and Well Being, chemistry, Amino Acid Substitution, biology.protein, Generic health relevance, Personalized medicine, business, PATHOGENICITY
Abstract

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges. ispartof: HUMAN MUTATION vol:40 issue:9 pages:1530-1545 ispartof: location:United States status: published

Published
2019

13. The Cafa Challenge Reports Improved Protein Function Prediction And New Functional Annotations For Hundreds Of Genes Through Experimental Screens

Author

Heiko Schoof, Ahmet Sureyya Rifaioglu, Ian Sillitoe, Shanfeng Zhu, Marco Carraro, Naihui Zhou, Asa Ben-Hur, Rui Fa, Alice C. McHardy, David W. Ritchie, George Georghiou, Filip Ginter, Haixuan Yang, Alex A. Freitas, Constance J. Jeffery, Tapio Salakoski, Radoslav Davidovic, Huy N Nguyen, Devon Johnson, Yotam Frank, Alexandra J. Lee, Sean D. Mooney, Marco Falda, Marie-Dominique Devignes, Gianfranco Politano, David T. Jones, Silvio C. E. Tosatto, Renzhi Cao, Zihan Zhang, Sabeur Aridhi, Stefano Pascarelli, Vedrana Vidulin, Qizhong Mao, Balint Z. Kacsoh, Patricia C. Babbitt, Giovanni Bosco, Farrokh Mehryary, Florian Boecker, Alfonso E. Romero, Angela D. Wilkins, Saso Dzeroski, Richard Bonneau, Hans Moen, Chengxin Zhang, Prajwal Bhat, Giuliano Grossi, Martti Tolvanen, Matteo Re, Meet Barot, Mohammad R. K. Mofrad, Predrag Radivojac, Stefano Di Carlo, Tatyana Goldberg, Branislava Gemovic, Suyang Dai, Pier Luigi Martelli, Giorgio Valentini, Maxat Kulmanov, Maria Jesus Martin, Claire O'Donovan, Dallas J. Larsen, Alexandre Renaux, Alan Medlar, Jeffrey M. Yunes, Erica Suh, Volkan Atalay, Vladimir Gligorijević, Fran Supek, Elaine Zosa, Wei-Cheng Tseng, Nafiz Hamid, Marco Mesiti, Tunca Doğan, Petri Törönen, Hafeez Ur Rehman, Jose Manuel Rodriguez, Alessandro Petrini, Sayoni Das, Burkhard Rost, Miguel Amezola, Mateo Torres, Jianlin Cheng, Daisuke Kihara, Liisa Holm, Marco Frasca, Steven E. Brenner, Stefano Toppo, Adrian M. Altenhoff, Chenguang Zhao, Daniel B. Roche, Alperen Dalkiran, Alex W. Crocker, Marco Notaro, Iddo Friedberg, Michal Linial, Julian Gough, Damiano Piovesan, Slobodan Vucetic, Natalie Thurlby, Olivier Lichtarge, Jari Björne, Jonas Reeb, Rabie Saidi, Yuxiang Jiang, Christophe Dessimoz, Jie Hou, Ronghui You, Tomislav Šmuc, Paolo Fontana, Michele Berselli, Jia-Ming Chang, Deborah A. Hogan, Larry Davis, Ehsaneddin Asgari, Shuwei Yao, Zheng Wang, Fabio Fabris, Michael L. Tress, Caleb Chandler, Christine A. Orengo, Rengul Cetin Atalay, Castrense Savojardo, Danielle A Brackenridge, Peter W. Rose, Yang Zhang, Dane Jo, Gage S. Black, Shanshan Zhang, Aashish Jain, Liam J. McGuffin, Timothy Bergquist, Peter L. Freddolino, Robert Hoehndorf, Rita Casadio, Da Chen Emily Koo, Mark N. Wass, Hai Fang, Casey S. Greene, Suwisa Kaewphan, Magdalena Antczak, Wen-Hung Liao, Enrico Lavezzo, Neven Sumonja, Ashton Omdahl, José M. Fernández, Ilya Novikov, Jonathan B. Dayton, Feng Zhang, Vladimir Perovic, Cen Wan, Jonathan G. Lees, Kai Hakala, Weidong Tian, Alex Warwick Vesztrocy, Domenico Cozzetto, Nevena Veljkovic, Yi-Wei Liu, Imane Boudellioua, Po-Han Chi, Kimberley A. Lewis, Seyed Ziaeddin Alborzi, Giuseppe Profiti, Alberto Paccanaro, Itamar Borukhov, Alfredo Benso, Indika Kahanda, Rebecca L. Hurto, Bilgisayar Mühendisliği, National Science Foundation (United States), Gordon and Betty Moore Foundation, United States of Department of Health & Human Services, Cystic Fibrosis Foundation, Consejo Nacional de Ciencia y Tecnología (México), Deutsche Forschungsgemeinschaft (Alemania), European Research Council, Ministerio de Ciencia e Innovación (España), Unión Europea, University of Turku (Finlandia), Finlands Akademi (Finlandia), National Natural Science Foundation of China, Nanjing Agricultural University. The Academy of Science. National Key Research & Development Program of China, Ministero dell Istruzione, dell Universita e della Ricerca (Italia), Shanghai Municipal Science and Technology Major Project, Biotechnology and Biological Sciences Research Council (Reino Unido), Extreme Science and Engineering Discovery Environment, Ministry of Education, Science and Technological Development (Serbia), Ministry of Science and Technology, Ministry for Education (Baviera) (Alemania), Yad Hanadiv, University of Milan (Italia), Swiss National Science Foundation, Unión Europea. European Cooperation in Science and Technology (COST), Plataforma ISCIII de Bioinformática (España), Scientific and Technological Research Council of Turkey, Ministry of Education (China), University of Padua (Italia), Mühendislik ve Doğa Bilimleri Fakültesi -- Bilgisayar Mühendisliği Bölümü, Rifaioğlu, Ahmet Süreyya, Zhou N., Jiang Y., Bergquist T.R., Lee A.J., Kacsoh B.Z., Crocker A.W., Lewis K.A., Georghiou G., Nguyen H.N., Hamid M.N., Davis L., Dogan T., Atalay V., Rifaioglu A.S., Dalklran A., Cetin Atalay R., Zhang C., Hurto R.L., Freddolino P.L., Zhang Y., Bhat P., Supek F., Fernandez J.M., Gemovic B., Perovic V.R., Davidovic R.S., Sumonja N., Veljkovic N., Asgari E., Mofrad M.R.K., Profiti G., Savojardo C., Martelli P.L., Casadio R., Boecker F., Schoof H., Kahanda I., Thurlby N., McHardy A.C., Renaux A., Saidi R., Gough J., Freitas A.A., Antczak M., Fabris F., Wass M.N., Hou J., Cheng J., Wang Z., Romero A.E., Paccanaro A., Yang H., Goldberg T., Zhao C., Holm L., Toronen P., Medlar A.J., Zosa E., Borukhov I., Novikov I., Wilkins A., Lichtarge O., Chi P.-H., Tseng W.-C., Linial M., Rose P.W., Dessimoz C., Vidulin V., Dzeroski S., Sillitoe I., Das S., Lees J.G., Jones D.T., Wan C., Cozzetto D., Fa R., Torres M., Warwick Vesztrocy A., Rodriguez J.M., Tress M.L., Frasca M., Notaro M., Grossi G., Petrini A., Re M., Valentini G., Mesiti M., Roche D.B., Reeb J., Ritchie D.W., Aridhi S., Alborzi S.Z., Devignes M.-D., Koo D.C.E., Bonneau R., Gligorijevic V., Barot M., Fang H., Toppo S., Lavezzo E., Falda M., Berselli M., Tosatto S.C.E., Carraro M., Piovesan D., Ur Rehman H., Mao Q., Zhang S., Vucetic S., Black G.S., Jo D., Suh E., Dayton J.B., Larsen D.J., Omdahl A.R., McGuffin L.J., Brackenridge D.A., Babbitt P.C., Yunes J.M., Fontana P., Zhang F., Zhu S., You R., Zhang Z., Dai S., Yao S., Tian W., Cao R., Chandler C., Amezola M., Johnson D., Chang J.-M., Liao W.-H., Liu Y.-W., Pascarelli S., Frank Y., Hoehndorf R., Kulmanov M., Boudellioua I., Politano G., Di Carlo S., Benso A., Hakala K., Ginter F., Mehryary F., Kaewphan S., Bjorne J., Moen H., Tolvanen M.E.E., Salakoski T., Kihara D., Jain A., Smuc T., Altenhoff A., Ben-Hur A., Rost B., Brenner S.E., Orengo C.A., Jeffery C.J., Bosco G., Hogan D.A., Martin M.J., O'Donovan C., Mooney S.D., Greene C.S., Radivojac P., Friedberg I., Faculty of Economic and Social Sciences and Solvay Business School, Faculty of Sciences and Bioengineering Sciences, Faculty of Engineering, Computational genomics, Institute of Biotechnology, Bioinformatics, Genetics, Helsinki Institute of Life Science HiLIFE, Discovery Research Group/Prof. Hannu Toivonen, Iowa State University (ISU), European Bioinformatics Institute, École Polytechnique de Montréal (EPM), Vinča Institute of Nuclear Sciences, University of Belgrade [Belgrade], University of Bologna, Max Planck Institute for Plant Breeding Research (MPIPZ), European Virus Bioinformatics Center [Jena], Université libre de Bruxelles (ULB), Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes (LIMOS), SIGMA Clermont (SIGMA Clermont)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Department of Computer Science, University of Bristol [Bristol], Department of Computer Science [Columbia], University of Missouri [Columbia] (Mizzou), University of Missouri System-University of Missouri System, Yale School of Public Health (YSPH), Departamento de Geometría y Topología, Universidad de Granada (UGR), Tumor Biology Center, Centre for Nephrology [London, UK], University College of London [London] (UCL), Baylor College of Medicine (BCM), Baylor University, Department of Knowledge Technologies, Structural and Molecular Biology Department, University College London, Queen Mary University of London (QMUL), Spanish National Cancer Research Center (CNIO), Dipartimento di Informatica, Università degli Studi di Milano [Milano] (UNIMI), Dipartimento di Scienze dell'Informazione [Milano], United States Naval Academy, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Department of Molecular Medicine, Universita degli Studi di Padova, Centro de Regulación Genómica (CRG), Universitat Pompeu Fabra [Barcelona] (UPF), Physics Department, National Tsing Hua University [Hsinchu] (NTHU), Dipartimento di Automatica e Informatica [Torino] (DAUIN), Politecnico di Torino = Polytechnic of Turin (Polito), University of Turku, Bioinformatics Laboratory, University of Turku-Turku Center for Computer Science, Toyota Technological Institute at Chicago [Chicago] (TTIC), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL), Department of Computer Science [Colorado State University], Colorado State University [Fort Collins] (CSU), Centre for Plant Integrative Biology [Nothingham] (CPIB), University of Nottingham, UK (UON), BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany., University of Bologna/Università di Bologna, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Université d'Auvergne - Clermont-Ferrand I (UdA)-SIGMA Clermont (SIGMA Clermont)-Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS), Universidad de Granada = University of Granada (UGR), Università degli Studi di Milano = University of Milan (UNIMI), Università degli Studi di Padova = University of Padua (Unipd), and Université de Lausanne = University of Lausanne (UNIL)

Subjects
Library, Male, Identification, Candida-albicans, Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Procedures, Genome, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, Candida albicans, Molecular genetics, lcsh:QH301-705.5, ComputingMilieux_MISCELLANEOUS, Biological ontology, Settore BIO/11 - BIOLOGIA MOLECOLARE, 0303 health sciences, 318 Medical biotechnology, Biotechnology & applied microbiology, Ontology, Expectation, Genetics & heredity, Plant leaf, ddc, 3. Good health, Drosophila melanogaster, Human experiment, Fungal genome, Pseudomonas aeruginosa, Female, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Genome, Fungal, BIOINFORMATICS, Long-Term memory, Locomotion, Human, Adult, Memory, Long-Term, lcsh:QH426-470, Bioinformatics, Long term memory, Generation, Bacterial genome, Computational biology, Biology, Article, 03 medical and health sciences, Annotation, Big data, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Pseudomonas, Genetics, Animals, Humans, Gene, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Animal, Research, Experimental data, Molecular Sequence Annotation, Cell Biology, Nonhuman, Human genetics, lcsh:Genetics, lcsh:Biology (General), Biofilms, Proteins | Genes | Protein functions, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery, Function (biology), Genome, Bacterial
Abstract

Tosatto, Silvio/0000-0003-4525-7793; Zhang, Feng/0000-0003-3447-897X; Gonzalez, Jose Maria Fernandez/0000-0002-4806-5140; Devignes, Marie-Dominique/0000-0002-0399-8713; Wass, Mark/0000-0001-5428-6479; Falda, Marco/0000-0003-2642-519X; Thurlby, Natalie/0000-0002-1007-0286; Zosa, Elaine/0000-0003-2482-0663; Dessimoz, Christophe/0000-0002-2170-853X; Yunes, Jeffrey/0000-0003-1869-3231; Hamid, Md Nafiz/0000-0001-8681-6526; Hoehndorf, Robert/0000-0001-8149-5890; Dogan, Tunca/0000-0002-1298-9763; NOTARO, MARCO/0000-0003-4309-2200; Cozzetto, Domenico/0000-0001-6752-5432; Lewis, Kimberley/0000-0003-3010-8453; Roche, Daniel/0000-0002-9204-1840; Martin, Maria-Jesus/0000-0001-5454-2815; Tress, Michael/0000-0001-9046-6370; Tolvanen, Martti/0000-0003-3434-7646; Cheng, Jianlin/0000-0003-0305-2853; Rose, Peter/0000-0001-9981-9750; Renaux, Alexandre/0000-0002-4339-2791; Kacsoh, Balint/0000-0001-9171-0611; O'Donovan, Claire/0000-0001-8051-7429; Kulmanov, Maxat/0000-0003-1710-1820; Friedberg, Iddo/0000-0002-1789-8000; Zhou, Naihui/0000-0001-6268-6149, WOS: 000498615000001, PubMed ID: 31744546, Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens., National Science FoundationNational Science Foundation (NSF) [DBI1564756, DBI-1458359, DBI-1458390, DMS1614777, CMMI1825941, NSF 1458390]; Gordon and Betty Moore FoundationGordon and Betty Moore Foundation [GBMF 4552]; National Institutes of Health NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P20 GM113132]; Cystic Fibrosis Foundation [CFRDP STANTO19R0]; BBSRCBiotechnology and Biological Sciences Research Council (BBSRC) [BB/K004131/1, BB/F00964X/1, BB/M025047/1, BB/M015009/1]; Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT)Consejo Nacional de Ciencia y Tecnologia (CONACyT) [14-INV-088, PINV15-315]; NSFNational Science Foundation (NSF) [1660648, DBI 1759934, IIS1763246, DBI-1458477, 0965768, DMR-1420073, DBI-1458443]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM093123, DP1MH110234, UL1 TR002319, U24 TR002306]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155 "RESIST"German Research Foundation (DFG) [39087428]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM123055, R01GM60595, R15GM120650, GM083107, GM116960, AI134678, NIH R35-GM128637, R00-GM097033]; ERCEuropean Research Council (ERC) [StG 757700]; Spanish Ministry of Science, Innovation and Universities [BFU2017-89833-P]; Severo Ochoa award; Centre of Excellence project "BioProspecting of Adriatic Sea"; Croatian Government; European Regional Development FundEuropean Union (EU) [KK.01.1.1.01.0002]; ATT Tieto kayttoon grant; Academy of FinlandAcademy of Finland; University of Turku; CSC-IT Center for Science Ltd.; University of Miami; National Cancer Institute of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [U01CA198942]; Helsinki Institute for Life Sciences; Academy of FinlandAcademy of Finland [292589]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31671367, 31471245, 91631301, 61872094, 61572139]; National Key Research and Development Program of China [2016YFC1000505, 2017YFC0908402]; Italian Ministry of Education, University and Research (MIUR) PRIN 2017 projectMinistry of Education, Universities and Research (MIUR) [2017483NH8]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 2018SHZDZX01]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019431/1, BB/L020505/1, BB/L002817/1]; Elsevier; Extreme Science and Engineering Discovery Environment (XSEDE) award [MCB160101, MCB160124]; Ministry of Education, Science and Technological Development of the Republic of Serbia [173001]; Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2]; Montana State University; Bavarian Ministry for Education; Simons Foundation; NIH NINDSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [1R21NS103831-01]; University of Illinois at Chicago (UIC) Cancer Center award; UIC College of Liberal Arts and Sciences Faculty Award; UIC International Development Award; Yad Hanadiv [9660/2019]; National Institute of General Medical Science of the National Institute of Health [GM066099, GM079656]; Research Supporting Plan (PSR) of University of Milan [PSR2018-DIP-010-MFRAS]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [150654]; EMBL-European Bioinformatics Institute core funds; CAFA BBSRC [BB/N004876/1]; European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grantEuropean Union (EU) [778247]; COST ActionEuropean Cooperation in Science and Technology (COST) [BM1405]; NIH/NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R01 GM071749]; National Human Genome Research Institute of the National of Health [U41 HG007234]; INB Grant (ISCIII-SGEFI/ERDF) [PT17/0009/0001]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [EEEAG-116E930]; KanSil [2016K121540]; Universita degli Studi di Milano; 111 ProjectMinistry of Education, China - 111 Project [B18015]; key project of Shanghai Science Technology [16JC1420402]; ZJLab; project Ribes Network POR-FESR 3S4H [TOPP-ALFREVE18-01]; PRID/SID of University of Padova [TOPP-SID19-01]; NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R15GM120650]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/3454-01-01, URF/1/3790-01-01]; "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education; National Center for High-performance ComputingIstanbul Technical University, The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124.; The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443.

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2019
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