Search

Your search keyword '"Profiti, Giuseppe"' showing total 12 results
Start Over Author "Profiti, Giuseppe" Topic software
12 results on '"Profiti, Giuseppe"'

1. Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome.

Author

Capriotti E, Montanucci L, Profiti G, Rossi I, Giannuzzi D, Aresu L, and Fariselli P

Subjects
Algorithms, Animals, Dogs, Genetic Variation, Genome-Wide Association Study, Genotype, Internet, Genome genetics, Genomics, Polymorphism, Single Nucleotide genetics, Software
Abstract

As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Published
2019
Full Text
View/download PDF

2. BUSCA: an integrative web server to predict subcellular localization of proteins.

Author

Savojardo C, Martelli PL, Fariselli P, Profiti G, and Casadio R

Subjects
Bacteria chemistry, Bacteria ultrastructure, Benchmarking, Cell Membrane chemistry, Cell Membrane ultrastructure, Cell Nucleus chemistry, Cell Nucleus ultrastructure, Chloroplasts chemistry, Chloroplasts ultrastructure, Eukaryota chemistry, Eukaryota ultrastructure, Eukaryotic Cells ultrastructure, Gene Expression, Gene Ontology, Internet, Membrane Proteins metabolism, Mitochondria chemistry, Mitochondria ultrastructure, Mitochondrial Proteins metabolism, Molecular Sequence Annotation, Prokaryotic Cells ultrastructure, Protein Sorting Signals genetics, Eukaryotic Cells chemistry, Membrane Proteins genetics, Mitochondrial Proteins genetics, Prokaryotic Cells chemistry, Software
Abstract

Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization.

Published
2018
Full Text
View/download PDF

3. The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation.

Author

Profiti G, Martelli PL, and Casadio R

Subjects
Cluster Analysis, Internet, Proteins chemistry, Proteins physiology, Molecular Sequence Annotation, Sequence Analysis, Protein, Software
Abstract

BAR 3.0 updates our server BAR (Bologna Annotation Resource) for predicting protein structural and functional features from sequence. We increase data volume, query capabilities and information conveyed to the user. The core of BAR 3.0 is a graph-based clustering procedure of UniProtKB sequences, following strict pairwise similarity criteria (sequence identity ≥40% with alignment coverage ≥90%). Each cluster contains the available annotation downloaded from UniProtKB, GO, PFAM and PDB. After statistical validation, GO terms and PFAM domains are cluster-specific and annotate new sequences entering the cluster after satisfying similarity constraints. BAR 3.0 includes 28 869 663 sequences in 1 361 773 clusters, of which 22.2% (22 241 661 sequences) and 47.4% (24 555 055 sequences) have at least one validated GO term and one PFAM domain, respectively. 1.4% of the clusters (36% of all sequences) include PDB structures and the cluster is associated to a hidden Markov model that allows building template-target alignment suitable for structural modeling. Some other 3 399 026 sequences are singletons. BAR 3.0 offers an improved search interface, allowing queries by UniProtKB-accession, Fasta sequence, GO-term, PFAM-domain, organism, PDB and ligand/s. When evaluated on the CAFA2 targets, BAR 3.0 largely outperforms our previous version and scores among state-of-the-art methods. BAR 3.0 is publicly available and accessible at http://bar.biocomp.unibo.it/bar3., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Published
2017
Full Text
View/download PDF

4. An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

Author

Jiang Y, Oron TR, Clark WT, Bankapur AR, D'Andrea D, Lepore R, Funk CS, Kahanda I, Verspoor KM, Ben-Hur A, Koo da CE, Penfold-Brown D, Shasha D, Youngs N, Bonneau R, Lin A, Sahraeian SM, Martelli PL, Profiti G, Casadio R, Cao R, Zhong Z, Cheng J, Altenhoff A, Skunca N, Dessimoz C, Dogan T, Hakala K, Kaewphan S, Mehryary F, Salakoski T, Ginter F, Fang H, Smithers B, Oates M, Gough J, Törönen P, Koskinen P, Holm L, Chen CT, Hsu WL, Bryson K, Cozzetto D, Minneci F, Jones DT, Chapman S, Bkc D, Khan IK, Kihara D, Ofer D, Rappoport N, Stern A, Cibrian-Uhalte E, Denny P, Foulger RE, Hieta R, Legge D, Lovering RC, Magrane M, Melidoni AN, Mutowo-Meullenet P, Pichler K, Shypitsyna A, Li B, Zakeri P, ElShal S, Tranchevent LC, Das S, Dawson NL, Lee D, Lees JG, Sillitoe I, Bhat P, Nepusz T, Romero AE, Sasidharan R, Yang H, Paccanaro A, Gillis J, Sedeño-Cortés AE, Pavlidis P, Feng S, Cejuela JM, Goldberg T, Hamp T, Richter L, Salamov A, Gabaldon T, Marcet-Houben M, Supek F, Gong Q, Ning W, Zhou Y, Tian W, Falda M, Fontana P, Lavezzo E, Toppo S, Ferrari C, Giollo M, Piovesan D, Tosatto SC, Del Pozo A, Fernández JM, Maietta P, Valencia A, Tress ML, Benso A, Di Carlo S, Politano G, Savino A, Rehman HU, Re M, Mesiti M, Valentini G, Bargsten JW, van Dijk AD, Gemovic B, Glisic S, Perovic V, Veljkovic V, Veljkovic N, Almeida-E-Silva DC, Vencio RZ, Sharan M, Vogel J, Kansakar L, Zhang S, Vucetic S, Wang Z, Sternberg MJ, Wass MN, Huntley RP, Martin MJ, O'Donovan C, Robinson PN, Moreau Y, Tramontano A, Babbitt PC, Brenner SE, Linial M, Orengo CA, Rost B, Greene CS, Mooney SD, Friedberg I, and Radivojac P

Subjects
Algorithms, Databases, Protein, Gene Ontology, Humans, Molecular Sequence Annotation, Proteins genetics, Computational Biology, Proteins chemistry, Software, Structure-Activity Relationship
Abstract

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging., Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2., Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

Published
2016
Full Text
View/download PDF

5. AlignBucket: a tool to speed up 'all-against-all' protein sequence alignments optimizing length constraints.

Author

Profiti G, Fariselli P, and Casadio R

Subjects
Humans, Algorithms, Computational Biology methods, Databases, Protein, Proteins chemistry, Sequence Alignment methods, Software
Abstract

Motivation: The next-generation sequencing era requires reliable, fast and efficient approaches for the accurate annotation of the ever-increasing number of biological sequences and their variations. Transfer of annotation upon similarity search is a standard approach. The procedure of all-against-all protein comparison is a preliminary step of different available methods that annotate sequences based on information already present in databases. Given the actual volume of sequences, methods are necessary to pre-process data to reduce the time of sequence comparison., Results: We present an algorithm that optimizes the partition of a large volume of sequences (the whole database) into sets where sequence length values (in residues) are constrained depending on a bounded minimal and expected alignment coverage. The idea is to optimally group protein sequences according to their length, and then computing the all-against-all sequence alignments among sequences that fall in a selected length range. We describe a mathematically optimal solution and we show that our method leads to a 5-fold speed-up in real world cases., Availability and Implementation: The software is available for downloading at http://www.biocomp.unibo.it/∼giuseppe/partitioning.html., Contact: giuseppe.profiti2@unibo.it., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Published
2015
Full Text
View/download PDF

6. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T, Bankapur, Asma R, D'Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S, Kahanda, Indika, Verspoor, Karin M, Ben-Hur, Asa, Koo, Da Chen Emily, Penfold-Brown, Duncan, Shasha, Dennis, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed ME, Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nives, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T, Chapman, Samuel, Bkc, Dukka, Khan, Ishita K, Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amos, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E, Hieta, Reija, Legge, Duncan, Lovering, Ruth C, Magrane, Michele, Melidoni, Anna N, Mutowo-Meullenet, Prudence, Pichler, Klemens, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, ElShal, Sarah, Tranchevent, Léon-Charles, Das, Sayoni, Dawson, Natalie L, Lee, David, Lees, Jonathan G, Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamás, Romero, Alfonso E, Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E, Pavlidis, Paul, Feng, Shou, Cejuela, Juan M, Goldberg, Tatyana, Hamp, Tobias, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, and Giollo, Manuel

Subjects
q-bio.QM, Bioinformatics, Protein, 1.1 Normal biological development and functioning, Proteins, Computational Biology, Molecular Sequence Annotation, Biological Sciences, Disease gene prioritization, Structure-Activity Relationship, Databases, Gene Ontology, Networking and Information Technology R&D (NITRD), Underpinning research, Information and Computing Sciences, Genetics, Humans, Generic health relevance, Protein function prediction, Algorithms, Software, Environmental Sciences
Abstract

BackgroundA major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.ResultsWe conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.ConclusionsThe top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

Published
2016

7. Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome

Author

Giuseppe Profiti, Diana Giannuzzi, Ludovica Montanucci, Emidio Capriotti, Ivan Rossi, Luca Aresu, Piero Fariselli, Capriotti, Emidio, Montanucci, Ludovica, Profiti, Giuseppe, Rossi, Ivan, Giannuzzi, Diana, Aresu, Luca, and Fariselli, Piero

Subjects
genomic variants, variant interpretation, dog genome, machine learning, Genotype, Genomics, Single-nucleotide polymorphism, Genome-wide association study, Computational biology, Biology, Genome, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, Dogs, Genetics, SNP, Animals, 030304 developmental biology, 0303 health sciences, Internet, Genetic Variation, Matthews correlation coefficient, Binary classification, Web Server Issue, Human genome, 030217 neurology & neurosurgery, Algorithms, Software, Genome-Wide Association Study
Abstract

As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve.

Published
2019

8. BUSCA: an integrative web server to predict subcellular localization of proteins

Author

Pier Luigi Martelli, Giuseppe Profiti, Piero Fariselli, Castrense Savojardo, Rita Casadio, Savojardo, Castrense, Martelli, Pier Luigi, Fariselli, Piero, Profiti, Giuseppe, and Casadio, Rita

Subjects
0106 biological sciences, 0301 basic medicine, Web server, Chloroplasts, Protein sequence analysis, Gene Expression, Protein subcellular localization prediction, protein sequence analysis, machine learning, Computational biology, Biology, Protein Sorting Signals, computer.software_genre, 01 natural sciences, Bacterial protein, Mitochondrial Proteins, 03 medical and health sciences, Annotation, Genetics, Cell Nucleus, Internet, Bacteria, Cell Membrane, Eukaryota, Membrane Proteins, Molecular Sequence Annotation, Subcellular localization, Protein subcellular localization prediction, Mitochondria, Transmembrane domain, Benchmarking, 030104 developmental biology, Eukaryotic Cells, Gene Ontology, Membrane protein, Prokaryotic Cells, Web Server Issue, computer, Software, 010606 plant biology & botany
Abstract

Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization.

Published
2018

9. The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation

Author

Giuseppe Profiti, Pier Luigi Martelli, Rita Casadio, Profiti, Giuseppe, Martelli, Pier Luigi, and Casadio, Rita

Subjects
0301 basic medicine, protein structure rpediction, Bar (music), Biology, Bioinformatics, 03 medical and health sciences, Annotation, Similarity (network science), Sequence Analysis, Protein, Genetics, Cluster Analysis, protein interaction, Cluster analysis, Hidden Markov model, Sequence, Internet, Information retrieval, Proteins, Molecular Sequence Annotation, sequence similarity, 030104 developmental biology, Web Server Issue, Graph (abstract data type), UniProt, Software, protein function prediction, clustering
Abstract

BAR 3.0 updates our server BAR (Bologna Annotation Resource) for predicting protein structural and functional features from sequence. We increase data volume, query capabilities and information conveyed to the user. The core of BAR 3.0 is a graph-based clustering procedure of UniProtKB sequences, following strict pairwise similarity criteria (sequence identity ≥40% with alignment coverage ≥90%). Each cluster contains the available annotation downloaded from UniProtKB, GO, PFAM and PDB. After statistical validation, GO terms and PFAM domains are cluster-specific and annotate new sequences entering the cluster after satisfying similarity constraints. BAR 3.0 includes 28 869 663 sequences in 1 361 773 clusters, of which 22.2% (22 241 661 sequences) and 47.4% (24 555 055 sequences) have at least one validated GO term and one PFAM domain, respectively. 1.4% of the clusters (36% of all sequences) include PDB structures and the cluster is associated to a hidden Markov model that allows building template-target alignment suitable for structural modeling. Some other 3 399 026 sequences are singletons. BAR 3.0 offers an improved search interface, allowing queries by UniProtKB-accession, Fasta sequence, GO-term, PFAM-domain, organism, PDB and ligand/s. When evaluated on the CAFA2 targets, BAR 3.0 largely outperforms our previous version and scores among state-of-the-art methods. BAR 3.0 is publicly available and accessible at http://bar.biocomp.unibo.it/bar3.

Published
2017

10. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Yuxiang Jiang, Tal Ronnen Oron, Wyatt T. Clark, Asma R. Bankapur, Daniel D’Andrea, Rosalba Lepore, Christopher S. Funk, Indika Kahanda, Karin M. Verspoor, Asa Ben-Hur, Da Chen Emily Koo, Duncan Penfold-Brown, Dennis Shasha, Noah Youngs, Richard Bonneau, Alexandra Lin, Sayed M. E. Sahraeian, Pier Luigi Martelli, Giuseppe Profiti, Rita Casadio, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Adrian Altenhoff, Nives Skunca, Christophe Dessimoz, Tunca Dogan, Kai Hakala, Suwisa Kaewphan, Farrokh Mehryary, Tapio Salakoski, Filip Ginter, Hai Fang, Ben Smithers, Matt Oates, Julian Gough, Petri Törönen, Patrik Koskinen, Liisa Holm, Ching-Tai Chen, Wen-Lian Hsu, Kevin Bryson, Domenico Cozzetto, Federico Minneci, David T. Jones, Samuel Chapman, Dukka BKC, Ishita K. Khan, Daisuke Kihara, Dan Ofer, Nadav Rappoport, Amos Stern, Elena Cibrian-Uhalte, Paul Denny, Rebecca E. Foulger, Reija Hieta, Duncan Legge, Ruth C. Lovering, Michele Magrane, Anna N. Melidoni, Prudence Mutowo-Meullenet, Klemens Pichler, Aleksandra Shypitsyna, Biao Li, Pooya Zakeri, Sarah ElShal, Léon-Charles Tranchevent, Sayoni Das, Natalie L. Dawson, David Lee, Jonathan G. Lees, Ian Sillitoe, Prajwal Bhat, Tamás Nepusz, Alfonso E. Romero, Rajkumar Sasidharan, Haixuan Yang, Alberto Paccanaro, Jesse Gillis, Adriana E. Sedeño-Cortés, Paul Pavlidis, Shou Feng, Juan M. Cejuela, Tatyana Goldberg, Tobias Hamp, Lothar Richter, Asaf Salamov, Toni Gabaldon, Marina Marcet-Houben, Fran Supek, Qingtian Gong, Wei Ning, Yuanpeng Zhou, Weidong Tian, Marco Falda, Paolo Fontana, Enrico Lavezzo, Stefano Toppo, Carlo Ferrari, Manuel Giollo, Damiano Piovesan, Silvio C.E. Tosatto, Angela del Pozo, José M. Fernández, Paolo Maietta, Alfonso Valencia, Michael L. Tress, Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino, Hafeez Ur Rehman, Matteo Re, Marco Mesiti, Giorgio Valentini, Joachim W. Bargsten, Aalt D. J. van Dijk, Branislava Gemovic, Sanja Glisic, Vladmir Perovic, Veljko Veljkovic, Nevena Veljkovic, Danillo C. Almeida-e-Silva, Ricardo Z. N. Vencio, Malvika Sharan, Jörg Vogel, Lakesh Kansakar, Shanshan Zhang, Slobodan Vucetic, Zheng Wang, Michael J. E. Sternberg, Mark N. Wass, Rachael P. Huntley, Maria J. Martin, Claire O’Donovan, Peter N. Robinson, Yves Moreau, Anna Tramontano, Patricia C. Babbitt, Steven E. Brenner, Michal Linial, Christine A. Orengo, Burkhard Rost, Casey S. Greene, Sean D. Mooney, Iddo Friedberg, Predrag Radivojac, Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T., Bankapur, Asma R., D’Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Kahanda, Indika, Verspoor, Karin M., Ben-Hur, Asa, Koo, Da Chen Emily, Penfold-Brown, Duncan, Shasha, Denni, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nive, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T., Chapman, Samuel, Bkc, Dukka, Khan, Ishita K., Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amo, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E., Hieta, Reija, Legge, Duncan, Lovering, Ruth C., Magrane, Michele, Melidoni, Anna N., Mutowo-Meullenet, Prudence, Pichler, Klemen, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, Elshal, Sarah, Tranchevent, Léon-Charle, Das, Sayoni, Dawson, Natalie L., Lee, David, Lees, Jonathan G., Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamá, Romero, Alfonso E., Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E., Pavlidis, Paul, Feng, Shou, Cejuela, Juan M., Goldberg, Tatyana, Hamp, Tobia, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio C.E., del Pozo, Angela, Fernández, José M., Maietta, Paolo, Valencia, Alfonso, Tress, Michael L., Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco, Savino, Alessandro, Rehman, Hafeez Ur, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W., van Dijk, Aalt D. J., Gemovic, Branislava, Glisic, Sanja, Perovic, Vladmir, Veljkovic, Veljko, Veljkovic, Nevena, Almeida-e-Silva, Danillo C., Vencio, Ricardo Z. N., Sharan, Malvika, Vogel, Jörg, Kansakar, Lakesh, Zhang, Shanshan, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael J. E., Wass, Mark N., Huntley, Rachael P., Martin, Maria J., O’Donovan, Claire, Robinson, Peter N., Moreau, Yve, Tramontano, Anna, Babbitt, Patricia C., Brenner, Steven E., Linial, Michal, Orengo, Christine A., Rost, Burkhard, Greene, Casey S., Mooney, Sean D., Friedberg, Iddo, Radivojac, Predrag, Friedberg, Iddo [0000-0002-1789-8000], Apollo - University of Cambridge Repository, (ukupan broj autora: 147), Biotechnology and Biological Sciences Research Council (BBSRC), National Science Foundation (Estados Unidos), United States of Department of Health & Human Services, National Natural Science Foundation of China, Natural Sciences and Engineering Research Council (Canadá), São Paulo Research Foundation, Ministerio de Economía y Competitividad (España), Biotechnology and Biological Sciences Research Council (Reino Unido), Katholieke Universiteit Leuven (Bélgica), Newton International Fellowship Scheme of the Royal Society grant, British Heart Foundation, Ministry of Education, Science and Technological Development (Serbia), Office of Biological and Environmental Research (Estados Unidos), Australian Research Council, University of Padua (Italia), Swiss National Science Foundation, Institute of Biotechnology, Computational genomics, and Bioinformatics

Subjects
0301 basic medicine, Computer science, Disease gene prioritization, Protein function prediction, Ecology, Evolution, Behavior and Systematics, Genetics, Cell Biology, 05 Environmental Sciences, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit, computer.software_genre, Quantitative Biology - Quantitative Methods, Wiskundige en Statistische Methoden - Biometris, Field (computer science), Laboratorium voor Plantenveredeling, Function (engineering), Databases, Protein, 1183 Plant biology, microbiology, virology, Quantitative Methods (q-bio.QM), media_common, Genetics & Heredity, Settore BIO/11 - BIOLOGIA MOLECOLARE, Ecology, SISTA, 1184 Genetics, developmental biology, physiology, Life Sciences & Biomedicine, Algorithms, Bioinformatics, Evolution, media_common.quotation_subject, BIOINFORMÁTICA, Machine learning, Bottleneck, Set (abstract data type), BIOS Applied Bioinformatics, 03 medical and health sciences, Annotation, Structure-Activity Relationship, Behavior and Systematics, Human Phenotype Ontology, Humans, ddc:610, DISINTEGRIN, Mathematical and Statistical Methods - Biometris, BIOINFORMATICS, 08 Information And Computing Sciences, Science & Technology, business.industry, Research, ADAM, Proteins, Computational Biology, Molecular Sequence Annotation, 06 Biological Sciences, Data set, ONTOLOGY, Plant Breeding, 030104 developmental biology, Gene Ontology, Biotechnology & Applied Microbiology, FOS: Biological sciences, Artificial intelligence, business, computer, Software
Abstract

BACKGROUND: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. RESULTS: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. CONCLUSIONS: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent., We acknowledge the contributions of Maximilian Hecht, Alexander Grün, Julia Krumhoff, My Nguyen Ly, Jonathan Boidol, Rene Schoeffel, Yann Spöri, Jessika Binder, Christoph Hamm and Karolina Worf. This work was partially supported by the following grants: National Science Foundation grants DBI-1458477 (PR), DBI-1458443 (SDM), DBI-1458390 (CSG), DBI-1458359 (IF), IIS-1319551 (DK), DBI-1262189 (DK), and DBI-1149224 (JC); National Institutes of Health grants R01GM093123 (JC), R01GM097528 (DK), R01GM076990 (PP), R01GM071749 (SEB), R01LM009722 (SDM), and UL1TR000423 (SDM); the National Natural Science Foundation of China grants 3147124 (WT) and 91231116 (WT); the National Basic Research Program of China grant 2012CB316505 (WT); NSERC grant RGPIN 371348-11 (PP); FP7 infrastructure project TransPLANT Award 283496 (ADJvD); Microsoft Research/FAPESP grant 2009/53161-6 and FAPESP fellowship 2010/50491-1 (DCAeS); Biotechnology and Biological Sciences Research Council grants BB/L020505/1 (DTJ), BB/F020481/1 (MJES), BB/K004131/1 (AP), BB/F00964X/1 (AP), and BB/L018241/1 (CD); the Spanish Ministry of Economics and Competitiveness grant BIO2012-40205 (MT); KU Leuven CoE PFV/10/016 SymBioSys (YM); the Newton International Fellowship Scheme of the Royal Society grant NF080750 (TN). CSG was supported in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative grant GBMF4552. Computational resources were provided by CSC – IT Center for Science Ltd., Espoo, Finland (TS). This work was supported by the Academy of Finland (TS). RCL and ANM were supported by British Heart Foundation grant RG/13/5/30112. PD, RCL, and REF were supported by Parkinson’s UK grant G-1307, the Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, and the Ministry of Education, Science and Technological Development of the Republic of Serbia grant 173001. This work was a Technology Development effort for ENIGMA – Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, which is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research grant DE-AC02-05CH11231. ENIGMA only covers the application of this work to microbial proteins. NSF DBI-0965616 and Australian Research Council grant DP150101550 (KMV). NSF DBI-0965768 (ABH). NIH T15 LM00945102 (training grant for CSF). FP7 FET grant MAESTRA ICT-2013-612944 and FP7 REGPOT grant InnoMol (FS). NIH R01 GM60595 (PCB). University of Padova grants CPDA138081/13 (ST) and GRIC13AAI9 (EL). Swiss National Science Foundation grant 150654 and UK BBSRC grant BB/M015009/1 (COD). PRB2 IPT13/0001 - ISCIII-SGEFI / FEDER (JMF)., This is the final version of the article. It first appeared from BioMed Central at http://dx.doi.org/10.1186/s13059-016-1037-6.

Published
2016

11. AlignBucket: a tool to speed up 'all-against-all' protein sequence alignments optimizing length constraints

Author

Giuseppe Profiti, Rita Casadio, Piero Fariselli, Profiti, Giuseppe, Fariselli, Piero, and Casadio, Rita

Subjects
Statistics and Probability, Computer science, Nearest neighbor search, Sequence alignment, Bioinformatics, Biochemistry, Protein sequencing, Humans, Databases, Protein, sequence alignment, optimization, constraints, similarity search, sequence alignment, optimization, constraints, similarity search, Molecular Biology, Sequence, Computational Biology, Proteins, Partition (database), Computer Science Applications, Computational Mathematics, Range (mathematics), Computational Theory and Mathematics, Algorithm, Algorithms, Software
Abstract

Motivation: The next-generation sequencing era requires reliable, fast and efficient approaches for the accurate annotation of the ever-increasing number of biological sequences and their variations. Transfer of annotation upon similarity search is a standard approach. The procedure of all-against-all protein comparison is a preliminary step of different available methods that annotate sequences based on information already present in databases. Given the actual volume of sequences, methods are necessary to pre-process data to reduce the time of sequence comparison. Results: We present an algorithm that optimizes the partition of a large volume of sequences (the whole database) into sets where sequence length values (in residues) are constrained depending on a bounded minimal and expected alignment coverage. The idea is to optimally group protein sequences according to their length, and then computing the all-against-all sequence alignments among sequences that fall in a selected length range. We describe a mathematically optimal solution and we show that our method leads to a 5-fold speed-up in real world cases. Availability and implementation: The software is available for downloading at http://www.biocomp.unibo.it/∼giuseppe/partitioning.html. Contact: giuseppe.profiti2@unibo.it Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2015

12. Tools and data services registry: a community effort to document bioinformatics resources

Author

Callum Smith, Paolo Uva, Thomas Gatter, Peter Løngreen, Peter Juvan, Hans Ienasescu, Giuseppe Profiti, Aleksandra Nenadic, Kristoffer Rapacki, Chris Morris, Paola Roncaglia, Steffen Möller, Laura Emery, Søren Brunak, Maria Maddalena Sperotto, Heinz Stockinger, Kristian Davidsen, Federico Zambelli, Helen Parkinson, Olivia Doppelt-Azeroual, Luana Licata, Tatyana Goldberg, Andrea Schafferhans, Elisabeth Gasteiger, Emil Karol Rydza, Camille Laibe, Victor De La Torre, Marie Grosjean, Manuela Helmer-Citterich, Hervé Ménager, Radka Svobodová Vařeková, Rafael C. Jimenez, Martin Closter Jespersen, Anthony Bretaudeau, Jan Brezovsky, Tunca Doğan, Matúš Kalaš, Peter M. Rice, Ivan Mičetić, Rune Møllegaard Friborg, Maximilian Koch, Silvio C. E. Tosatto, Nick Juty, Björn Grüning, Gianmauro Cuccuru, Frederik Coppens, Gianni Cesareni, Jon Ison, Rabie Saidi, Sébastien Moretti, Rita Casadio, Gert Vriend, Guy Yachdav, Niall Beard, Timothy F. Booth, Michael Cornell, Piotr Jaroslaw Chmura, Veit Schwämmle, Karel Berka, Dan Bolser, Vassilios Ioannidis, Jing-Woei Li, Burkhard Rost, Gianluca Della Vedova, Fabien Mareuil, Hedi Peterson, Allegra Via, Paolo Romano, Christian Anthon, Technical University of Denmark [Lyngby] (DTU), Institut Pasteur de Madagascar, Réseau International des Instituts Pasteur (RIIP), University of Bergen (UIB), University of Copenhagen = Københavns Universitet (KU), University of Manchester, Palacky University, European Bioinformatics Institute, NEBC Wallingford, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Masaryk University, University of Bologna, Università degli Studi di Roma Tor Vergata [Roma], Ghent University [Belgium] (UGENT), Flanders Institute for Biotechnology, CRS4 Bioinformat, Università degli studi di Milano-Bicocca, Swiss Institute of Bioinformatics, Universität Bielefeld = Bielefeld University, Tumor Biology Center, Centre National de la Recherche Scientifique (CNRS), University of Freiburg, University of Ljubljana, The Chinese University of Hong Kong [Hong Kong], Universita degli Studi di Padova, Bioinformatics Research Centre, Université de Lausanne, CCLRC Daresbury Laboratory, Universität zu Lübeck [Lübeck] - University of Lübeck [Lübeck], Universität Rostock, University of Tartu, Imperial College London, IRCCS Azienda Ospedaliera Universitaria Integrata San Martino (IRCCS AOU San Martino), University of Southern Denmark (SDU), WTCHG, Central European Institute of Technology [Brno] (CEITEC), Instituto Nacional de Bioinformática, Sapienza University of Rome (DIAG), Consiglio Nazionale delle Ricerche, University of Milan, Radboud University Nijmegen, Ison, J, Rapacki, K, Ménager, H, Kalaš, M, Rydza, E, Chmura, P, Anthon, C, Beard, N, Berka, K, Bolser, D, Booth, T, Bretaudeau, A, Brezovsky, J, Casadio, R, Cesareni, G, Coppens, F, Cornell, M, Cuccuru, G, Davidsen, K, DELLA VEDOVA, G, Dogan, T, Doppelt Azeroual, O, Emery, L, Gasteiger, E, Gatter, T, Goldberg, T, Grosjean, M, Grüning, B, Helmer Citterich, M, Ienasescu, H, Ioannidis, V, Jespersen, M, Jimenez, R, Juty, N, Juvan, P, Koch, M, Laibe, C, Li, J, Licata, L, Mareuil, F, Mičetić, I, Friborg, R, Moretti, S, Morris, C, Möller, S, Nenadic, A, Peterson, H, Profiti, G, Rice, P, Romano, P, Roncaglia, P, Saidi, R, Schafferhans, A, Schwämmle, V, Smith, C, Sperotto, M, Stockinger, H, Vařeková, R, Tosatto, S, de la Torre, V, Uva, P, Via, A, Yachdav, G, Zambelli, F, Vriend, G, Rost, B, Parkinson, H, Løngreen, P, Brunak, S, University of Bergen (UiB), Palacky University Olomouc, 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), Masaryk University [Brno] (MUNI), Universiteit Gent = Ghent University [Belgium] (UGENT), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL), Universität zu Lübeck [Lübeck], Central European Institute of Technology [Brno] (CEITEC MU), Brno University of Technology [Brno] (BUT), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), University of Copenhagen = Københavns Universitet (UCPH), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, University of Bologna/Università di Bologna, Universiteit Gent = Ghent University (UGENT), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Université de Lausanne = University of Lausanne (UNIL), Università degli Studi di Padova = University of Padua (Unipd), Universität zu Lübeck = University of Lübeck [Lübeck], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Università degli Studi di Milano = University of Milan (UNIMI), Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thoma, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Han, Ioannidis, Vassilio, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chri, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C E, de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, and Brunak, Søren

Subjects
0301 basic medicine, [SDV]Life Sciences [q-bio], registry, Bioinformatics, computer.software_genre, Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Systemutvikling og -arbeid: 426 [VDP], Task (project management), Documentation, Data and Information, Database Issue, Registries, bioinformatique, Data Curation, base de données, Settore BIO/11, gestion de données, tool, SOFTWARE-DEVELOPMENT, bioinformatics, ddc, outil informatique, Tools and data services registry, SEQANSWERS, Web service, MOLECULAR-BIOLOGY, Biology, Ecology and Environment, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Genetics, Implementation, Dissemination, Bioinformatikk / Bioinformatics, Data curation, bioinformatic, business.industry, Computational Biology, Software, Software development, bioinformatics, tools, registry, elixir, Biology and Life Sciences, Mathematics and natural scienses: 400::Information and communication science: 420::System development and design: 426 [VDP], FRAMEWORK, ELIXIR, Settore BIO/18 - Genetica, 030104 developmental biology, tools, Data as a service, COMPILATION, business, COLLECTION, Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19], computer, WEB SERVICES, LIFE SCIENCES
Abstract

Contains fulltext : 171819.pdf (Publisher’s version ) (Open Access) Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.

Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results