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

Author

Zhou, Naihui, Jiang, Yuxiang, Bergquist, Timothy R, Lee, Alexandra J, Kacsoh, Balint Z, Crocker, Alex W, Lewis, Kimberley A, Georghiou, George, Nguyen, Huy N, Hamid, Md Nafiz, Davis, Larry, Dogan, Tunca, Atalay, Volkan, Rifaioglu, Ahmet S, Dalkıran, Alperen, Cetin Atalay, Rengul, Zhang, Chengxin, Hurto, Rebecca L, Freddolino, Peter L, Zhang, Yang, Bhat, Prajwal, Supek, Fran, Fernández, José M, Gemovic, Branislava, Perovic, Vladimir R, Davidović, Radoslav S, Sumonja, Neven, Veljkovic, Nevena, Asgari, Ehsaneddin, Mofrad, Mohammad RK, Profiti, Giuseppe, Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Boecker, Florian, Schoof, Heiko, Kahanda, Indika, Thurlby, Natalie, McHardy, Alice C, Renaux, Alexandre, Saidi, Rabie, Gough, Julian, Freitas, Alex A, Antczak, Magdalena, Fabris, Fabio, Wass, Mark N, Hou, Jie, Cheng, Jianlin, Wang, Zheng, Romero, Alfonso E, Paccanaro, Alberto, Yang, Haixuan, Goldberg, Tatyana, Zhao, Chenguang, Holm, Liisa, Törönen, Petri, Medlar, Alan J, Zosa, Elaine, Borukhov, Itamar, Novikov, Ilya, Wilkins, Angela, Lichtarge, Olivier, Chi, Po-Han, Tseng, Wei-Cheng, Linial, Michal, Rose, Peter W, Dessimoz, Christophe, Vidulin, Vedrana, Dzeroski, Saso, Sillitoe, Ian, Das, Sayoni, Lees, Jonathan Gill, Jones, David T, Wan, Cen, Cozzetto, Domenico, Fa, Rui, Torres, Mateo, Warwick Vesztrocy, Alex, Rodriguez, Jose Manuel, Tress, Michael L, Frasca, Marco, Notaro, Marco, Grossi, Giuliano, Petrini, Alessandro, Re, Matteo, Valentini, Giorgio, Mesiti, Marco, Roche, Daniel B, Reeb, Jonas, Ritchie, David W, Aridhi, Sabeur, Alborzi, Seyed Ziaeddin, Devignes, Marie-Dominique, Koo, Da Chen Emily, Bonneau, Richard, Gligorijević, Vladimir, Barot, Meet, Fang, Hai, Toppo, Stefano, and Lavezzo, Enrico

Subjects
Human Genome, Networking and Information Technology R&D (NITRD), Genetics, Generic health relevance, Animals, Biofilms, Candida albicans, Drosophila melanogaster, Genome, Bacterial, Genome, Fungal, Humans, Locomotion, Memory, Long-Term, Molecular Sequence Annotation, Pseudomonas aeruginosa, Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, 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.ConclusionWe 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

9. Additional file 2 of The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

Author

Alsheikh, Ammar J., Wollenhaupt, Sabrina, King, Emily A., Reeb, Jonas, Ghosh, Sujana, Stolzenburg, Lindsay R., Tamim, Saleh, Lazar, Jozef, Davis, J. Wade, and Jacob, Howard J.

Abstract

Additional file 2 contains exact terms and phrases used to setup the seven filters that were used to narrow down the broad search results.

Published
2022
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10. Evaluierung von Vorhersagemethoden der Auswirkung von Sequenzvarianten

Author

Reeb, Jonas, Rost, Burkhard (Prof. Dr.), and Grimm, Dominik (Prof. Dr.)

Subjects
Biowissenschaften, Biologie, ddc:570
Abstract

The thesis discusses the challenges faced by methods which predict the effect of protein sequence variants. The analyses illustrate that methods are biased towards deleterious, high effect variants which exhibit strong sequence conservation. Furthermore, variants with beneficial effect are generally neglected. Overall, no method performs best under all circumstances. Accounting for deficiencies will allow methods to support clinical action and drive large-scale adoption of precision medicine. Diese Arbeit befasst sich mit Herausforderungen für Methoden, die den Effekt von Proteinsequenzvarianten vorhersagen. Die Analysen zeigen, dass sie dazu neigen, Varianten mit starkem, schädlichem Effekt zu detektieren. Des Weiteren werden Varianten mit positivem Effekt generell vernachlässigt. Keine einzelne Methode zeigt unter allen Umständen die beste Leistung. Zukünftige Anstrengungen werden klinische Anwendungen und damit die umfassende Einführung von Präzisionsmedizin unterstützen.

Published
2021

11. Assessment of variant effect prediction

Author

Grimm, Dominik (Prof. Dr.), Rost, Burkhard (Prof. Dr.), Reeb, Jonas, Grimm, Dominik (Prof. Dr.), Rost, Burkhard (Prof. Dr.), and Reeb, Jonas

Abstract

The thesis discusses the challenges faced by methods which predict the effect of protein sequence variants. The analyses illustrate that methods are biased towards deleterious, high effect variants which exhibit strong sequence conservation. Furthermore, variants with beneficial effect are generally neglected. Overall, no method performs best under all circumstances. Accounting for deficiencies will allow methods to support clinical action and drive large-scale adoption of precision medicine., Diese Arbeit befasst sich mit Herausforderungen für Methoden, die den Effekt von Proteinsequenzvarianten vorhersagen. Die Analysen zeigen, dass sie dazu neigen, Varianten mit starkem, schädlichem Effekt zu detektieren. Des Weiteren werden Varianten mit positivem Effekt generell vernachlässigt. Keine einzelne Methode zeigt unter allen Umständen die beste Leistung. Zukünftige Anstrengungen werden klinische Anwendungen und damit die umfassende Einführung von Präzisionsmedizin unterstützen.

Published
2021

12. Assessment of variant effect prediction

Author

Rost, Burkhard (Prof. Dr.), Rost, Burkhard (Prof. Dr.);Grimm, Dominik (Prof. Dr.), Reeb, Jonas, Rost, Burkhard (Prof. Dr.), Rost, Burkhard (Prof. Dr.);Grimm, Dominik (Prof. Dr.), and Reeb, Jonas

Abstract

The thesis discusses the challenges faced by methods which predict the effect of protein sequence variants. The analyses illustrate that methods are biased towards deleterious, high effect variants which exhibit strong sequence conservation. Furthermore, variants with beneficial effect are generally neglected. Overall, no method performs best under all circumstances. Accounting for deficiencies will allow methods to support clinical action and drive large-scale adoption of precision medicine., Diese Arbeit befasst sich mit Herausforderungen für Methoden, die den Effekt von Proteinsequenzvarianten vorhersagen. Die Analysen zeigen, dass sie dazu neigen, Varianten mit starkem, schädlichem Effekt zu detektieren. Des Weiteren werden Varianten mit positivem Effekt generell vernachlässigt. Keine einzelne Methode zeigt unter allen Umständen die beste Leistung. Zukünftige Anstrengungen werden klinische Anwendungen und damit die umfassende Einführung von Präzisionsmedizin unterstützen.

Published
2021

13. Common sequence variants affect molecular function more than rare variants?

Author

Mahlich, Yannick, Reeb, Jonas, Hecht, Maximilian, Schelling, Maria, De Beer, Tjaart Andries Petrus, Bromberg, Yana, and Rost, Burkhard

Subjects
Proteome, Science, Mutation, Genetic Variation, Humans, Medicine, Article, Software
Abstract

Any two unrelated individuals differ by about 10,000 single amino acid variants (SAVs). Do these impact molecular function? Experimental answers cannot answer comprehensively, while state-of-the-art prediction methods can. We predicted the functional impacts of SAVs within human and for variants between human and other species. Several surprising results stood out. Firstly, four methods (CADD, PolyPhen-2, SIFT, and SNAP2) agreed within 10 percentage points on the percentage of rare SAVs predicted with effect. However, they differed substantially for the common SAVs: SNAP2 predicted, on average, more effect for common than for rare SAVs. Given the large ExAC data sets sampling 60,706 individuals, the differences were extremely significant (p-value

Published
2017

16. MOESM1 of The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Naihui Zhou, Yuxiang Jiang, Bergquist, Timothy, Lee, Alexandra, Balint Kacsoh, Crocker, Alex, Lewis, Kimberley, Georghiou, George, Nguyen, Huy, Md Nafiz Hamid, Davis, Larry, Tunca Dogan, Atalay, Volkan, Rifaioglu, Ahmet, Dalkıran, Alperen, Rengul Cetin Atalay, Chengxin Zhang, Hurto, Rebecca, Freddolino, Peter, Zhang, Yang, Prajwal Bhat, Supek, Fran, Fernández, José, Gemovic, Branislava, Perovic, Vladimir, Davidović, Radoslav, Sumonja, Neven, Veljkovic, Nevena, Ehsaneddin Asgari, Mofrad, Mohammad, Profiti, Giuseppe, Castrense Savojardo, Martelli, Pier Luigi, Casadio, Rita, Boecker, Florian, Schoof, Heiko, Indika Kahanda, Thurlby, Natalie, McHardy, Alice, Renaux, Alexandre, Saidi, Rabie, Gough, Julian, Freitas, Alex, Antczak, Magdalena, Fabris, Fabio, Wass, Mark, Hou, Jie, Jianlin Cheng, Wang, Zheng, Romero, Alfonso, Paccanaro, Alberto, Haixuan Yang, Goldberg, Tatyana, Chenguang Zhao, Holm, Liisa, Törönen, Petri, Medlar, Alan, Zosa, Elaine, Borukhov, Itamar, Novikov, Ilya, Wilkins, Angela, Lichtarge, Olivier, Po-Han Chi, Tseng, Wei-Cheng, Linial, Michal, Rose, Peter, Dessimoz, Christophe, Vidulin, Vedrana, Saso Dzeroski, Sillitoe, Ian, Sayoni Das, Lees, Jonathan Gill, Jones, David, Wan, Cen, Cozzetto, Domenico, Fa, Rui, Torres, Mateo, Vesztrocy, Alex Warwick, Rodriguez, Jose Manuel, Tress, Michael, Frasca, Marco, Notaro, Marco, Grossi, Giuliano, Petrini, Alessandro, Re, Matteo, Valentini, Giorgio, Mesiti, Marco, Roche, Daniel, Reeb, Jonas, Ritchie, David, Sabeur Aridhi, Alborzi, Seyed Ziaeddin, Marie-Dominique Devignes, Koo, Da Chen Emily, Bonneau, Richard, Gligorijević, Vladimir, Meet Barot, Fang, Hai, Toppo, Stefano, Lavezzo, Enrico, Falda, Marco, Berselli, Michele, Tosatto, Silvio, Carraro, Marco, Piovesan, Damiano, Hafeez Ur Rehman, Qizhong Mao, Shanshan Zhang, Vucetic, Slobodan, Black, Gage, Jo, Dane, Suh, Erica, Dayton, Jonathan, Larsen, Dallas, Omdahl, Ashton, McGuffin, Liam, Brackenridge, Danielle, Babbitt, Patricia, Yunes, Jeffrey, Fontana, Paolo, Zhang, Feng, Shanfeng Zhu, Ronghui You, Zihan Zhang, Suyang Dai, Shuwei Yao, Weidong Tian, Renzhi Cao, Chandler, Caleb, Amezola, Miguel, Johnson, Devon, Chang, Jia-Ming, Wen-Hung Liao, Liu, Yi-Wei, Pascarelli, Stefano, Yotam Frank, Hoehndorf, Robert, Kulmanov, Maxat, Boudellioua, Imane, Politano, Gianfranco, Carlo, Stefano Di, Benso, Alfredo, Hakala, Kai, Ginter, Filip, Mehryary, Farrokh, Suwisa Kaewphan, Björne, Jari, Moen, Hans, Tolvanen, Martti, Salakoski, Tapio, Kihara, Daisuke, Aashish Jain, Šmuc, Tomislav, Altenhoff, Adrian, Ben-Hur, Asa, Rost, Burkhard, Brenner, Steven, Orengo, Christine, Jeffery, Constance, Bosco, Giovanni, Hogan, Deborah, Martin, Maria, O’Donovan, Claire, Mooney, Sean, Greene, Casey, Radivojac, Predrag, and Friedberg, Iddo

Subjects
Pharmacology, FOS: Biological sciences, Data_FILES, Genetics, Biochemistry, Molecular Biology, 69999 Biological Sciences not elsewhere classified, Developmental Biology
Abstract

Additional file 1 Additional figures and tables referenced in the article.

Published
2019
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18. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

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

Published
2019
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21. Genome Landscapes of Disease: Strategies to Predict the Phenotypic Consequences of Human Germline and Somatic Variation

Author

Reeb, Jonas, Hecht, Maximilian, Mahlich, Yannick, Bromberg, Yana, and Rost, Burkhard

Subjects
QH301-705.5, Sequence Databases, Mouse Models, Protein Sequencing, Research and Analysis Methods, Polymorphism, Single Nucleotide, Database and Informatics Methods, Mice, Model Organisms, Mathematical and Statistical Techniques, Genetics, Animals, Humans, Genetic Predisposition to Disease, Animal Models of Disease, Biology (General), Statistical Methods, Molecular Biology Techniques, Sequencing Techniques, Databases, Protein, Molecular Biology, Biology and Life Sciences, Computational Biology, Proteins, Animal Models, Disease Models, Animal, Biological Databases, Mutagenesis, Physical Sciences, Mutation Databases, Mutation, Animal Studies, Sequence Analysis, Sequence Alignment, Mathematics, Statistics (Mathematics), Research Article, Forecasting
Abstract

Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease., Author Summary The variations in the genetic sequence between individuals affect the gene-product, i.e. the protein differently. Some variants have no measurable effect (are neutral), while others affect protein function. Some of those effects are so severe they cause so called monogenic Mendelian diseases, i.e. diseases triggered by a single letter change. Some in silico methods predict the molecular impact of sequence variation. However, both experimental and computational analyses struggle to generalize from the effect upon molecular protein function to the effect upon the organism such as a disease. Here, we confirmed that methods predicting molecular effects correctly capture the type of effects causing Mendelian diseases in human and introduced a data set for animal diseases that was also captured by predictions methods. Predicted effects were less when in silico testing human variants in an animal model (here mouse). This is important to know because “mouse models” are common to study human diseases. Overall, we provided some evidence for a link between the molecular level and some type of disease.

Published
2016

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