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                                    "term": "SAM"
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                    "term": "Data management"
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                "C"
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                    "note": "HTSlib: C library for reading/writing high-throughput sequencing data.",
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                        "title": "HTSlib: C library for reading/writing high-Throughput sequencing data",
                        "abstract": "Background: Since the original publication of the VCF and SAM formats, an explosion of software tools have been created to process these data files. To facilitate this a library was produced out of the original SAMtools implementation, with a focus on performance and robustness. The file formats themselves have become international standards under the jurisdiction of the Global Alliance for Genomics and Health. Findings: We present a software library for providing programmatic access to sequencing alignment and variant formats. It was born out of the widely used SAMtools and BCFtools applications. Considerable improvements have been made to the original code plus many new features including newer access protocols, the addition of the CRAM file format, better indexing and iterators, and better use of threading. Conclusion: Since the original Samtools release, performance has been considerably improved, with a BAM read-write loop running 5 times faster and BAM to SAM conversion 13 times faster (both using 16 threads, compared to Samtools 0.1.19). Widespread adoption has seen HTSlib downloaded >1 million times from GitHub and conda. The C library has been used directly by an estimated 900 GitHub projects and has been incorporated into Perl, Python, Rust, and R, significantly expanding the number of uses via other languages. HTSlib is open source and is freely available from htslib.org under MIT/BSD license.",
                        "date": "2021-02-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Bonfield J.K."
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                            {
                                "name": "Marshall J."
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                            {
                                "name": "Danecek P."
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                            {
                                "name": "Li H."
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                            {
                                "name": "Ohan V."
                            },
                            {
                                "name": "Whitwham A."
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                            {
                                "name": "Keane T."
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                                "term": "Nucleic acid sequence"
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                    "term": "Phylogeny"
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                "Mac",
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                    "url": "https://packages.debian.org/unstable/proteinortho",
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                    "note": "Installation with dpkg (root privileges are required)",
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            "publication": [
                {
                    "doi": "10.1186/1471-2105-12-124",
                    "pmid": "21526987",
                    "pmcid": "PMC3114741",
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                    "version": null,
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                    "metadata": {
                        "title": "Proteinortho: Detection of (Co-)orthologs in large-scale analysis",
                        "abstract": "Background: Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases.Results: The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes.Conclusions: Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware. © 2011 Lechner et al; licensee BioMed Central Ltd.",
                        "date": "2011-04-28T00:00:00Z",
                        "citationCount": 656,
                        "authors": [
                            {
                                "name": "Lechner M."
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                            {
                                "name": "Findeiss S."
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                            {
                                "name": "Steiner L."
                            },
                            {
                                "name": "Marz M."
                            },
                            {
                                "name": "Stadler P.F."
                            },
                            {
                                "name": "Prohaska S.J."
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                        "journal": "BMC Bioinformatics"
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                    "name": "Marcus Lechner",
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                },
                {
                    "name": "Sonja J Prohaska",
                    "email": null,
                    "url": null,
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            "name": "HIV-TRACE",
            "description": "HIV-TRACE is an application that identifies potential transmission clusters within a supplied FASTA file.",
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3495",
                                "term": "RNA sequence"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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            "language": [
                "JavaScript"
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            "name": "PredGPI",
            "description": "Prediction system for GPI-anchored proteins.",
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            "version": [
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3351",
                            "term": "Molecular surface analysis"
                        }
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
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                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
                                "term": "Protein report"
                            },
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                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
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                    ],
                    "note": "Prediction",
                    "cmd": null
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            "toolType": [
                "Web application"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3542",
                    "term": "Protein secondary structure"
                },
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
                }
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                "Linux",
                "Windows",
                "Mac"
            ],
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            "collectionID": [
                "Bologna Biocomputing Group"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
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            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "http://gpcr.biocomp.unibo.it/predgpi",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/1471-2105-9-392",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PredGPI: A GPI-anchor predictor",
                        "abstract": "Background: Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called ω-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes. Results: Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the ω-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature. Conclusion: PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes. © 2008 Pierleoni et al; licensee BioMed Central Ltd.",
                        "date": "2008-09-23T00:00:00Z",
                        "citationCount": 463,
                        "authors": [
                            {
                                "name": "Pierleoni A."
                            },
                            {
                                "name": "Martelli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
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                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
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                    "typeEntity": "Institute",
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                {
                    "name": "Andrea Pierleoni",
                    "email": "andrea@biocomp.unibo.it",
                    "url": null,
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                {
                    "name": "Rita Casadio",
                    "email": "casadio@biocomp.unibo.it",
                    "url": null,
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                {
                    "name": "Pier Luigi Martelli",
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                    "url": null,
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                    "name": "Castrense Savojardo",
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                        "title": "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields",
                        "abstract": "Motivation: Targeting peptides are the most important signal controlling the import of nuclear encoded proteins into mitochondria and plastids. In the lack of experimental information, their prediction is an essential step when proteomes are annotated for inferring both the localization and the sequence of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting peptides based on Grammatical-Restrained Hidden Conditional Random Fields. TPpred is trained on a non-redundant dataset of proteins where the presence of a target peptide was experimentally validated, comprising 297 sequences. When tested on the 297 positive and some other 8010 negative examples, TPpred outperformed available methods in both accuracy and Matthews correlation index (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%), TPpred is, therefore, well suited for large-scale analyses at the proteome level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis thaliana and yeast proteomes contain targeting peptides and are, therefore, likely to be localized in mitochondria and plastids. TPpred predictions correlate to a good extent with the experimental annotation of the subcellular localization, when available. TPpred was also trained and tested to predict the cleavage site of the organelle-targeting peptide: on this task, the average error of TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively. This value is lower than the error reported by other methods currently available. © 2013 The Author.",
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                            {
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                        "title": "MemLoci: Predicting subcellular localization of membrane proteins in eukaryotes",
                        "abstract": "Motivation: Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However, predictive tools are necessary when the localization of homologs is not known. This is so particularly for membrane proteins. Furthermore, most of the available predictors of subcellular localization are specifically trained on globular proteins and poorly perform on membrane proteins. Results: Here we develop MemLoci, a new support vector machinebased tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50. © The Author 2011. Published by Oxford University Press. All rights reserved.",
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                        "title": "MemPype: A pipeline for the annotation of eukaryotic membrane proteins",
                        "abstract": "MemPype is a Python-based pipeline including previously published methods for the prediction of signal peptides (SPEP), glycophosphatidylinositol (GPI) anchors (PredGPI), all-alpha membrane topology (ENSEMBLE), and a recent method (MemLoci) that specifically discriminates the localization of eukaryotic membrane proteins in: 'cell membrane', 'internal membranes', 'organelle membranes'. MemLoci scores with accuracy of 70 and generalized correlation coefficient (GCC) of 0.50 on a rigorous homology-unbiased validation set and overpasses other predictors for subcellular localization. The annotation process is based both on inheritance through homology and computational methods. Each submitted protein first retrieves, when available, up to 25 similar proteins (with sequence identity ≥50 and alignment coverage ≥50 on both sequences). This helps the identification of membrane-associated proteins and detailed localization tags. Each protein is also filtered for the presence of a GPI anchor [0.8 false positive rate (FPR)]. A positive score of GPI anchor prediction labels the sequence as exposed to 'Cell surface'. Concomitantly the sequence is analysed for the presence of a signal peptide and classified with MemLoci into one of three discriminated classes. Finally the sequence is filtered for predicting its putative all-alpha protein membrane topology (FPR<1). The web server is available at: http://mu2py.biocomp.unibo.it/ mempype. © 2011 The Author(s).",
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                    "doi": "10.1093/bioinformatics/btt089",
                    "pmid": "23428638",
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields",
                        "abstract": "Motivation: Targeting peptides are the most important signal controlling the import of nuclear encoded proteins into mitochondria and plastids. In the lack of experimental information, their prediction is an essential step when proteomes are annotated for inferring both the localization and the sequence of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting peptides based on Grammatical-Restrained Hidden Conditional Random Fields. TPpred is trained on a non-redundant dataset of proteins where the presence of a target peptide was experimentally validated, comprising 297 sequences. When tested on the 297 positive and some other 8010 negative examples, TPpred outperformed available methods in both accuracy and Matthews correlation index (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%), TPpred is, therefore, well suited for large-scale analyses at the proteome level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis thaliana and yeast proteomes contain targeting peptides and are, therefore, likely to be localized in mitochondria and plastids. TPpred predictions correlate to a good extent with the experimental annotation of the subcellular localization, when available. TPpred was also trained and tested to predict the cleavage site of the organelle-targeting peptide: on this task, the average error of TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively. This value is lower than the error reported by other methods currently available. © 2013 The Author.",
                        "date": "2013-04-15T00:00:00Z",
                        "citationCount": 14,
                        "authors": [
                            {
                                "name": "Indio V."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
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                {
                    "name": "Rita Casadio",
                    "email": "rita.casadio@unibo.it",
                    "url": null,
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                    "note": null
                },
                {
                    "name": "Piero Fariselli",
                    "email": "piero.fariselli@unito.it",
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                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
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        {
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            "description": "Predicting the impact of mutations on protein stability from sequence and structure.",
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                            "term": "Protein sequence analysis"
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                            "term": "Genetic variation analysis"
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                    ],
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                                    "term": "HTML"
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                    "note": "Prediction of the impact of non-synonymous polymorphisms on protein stability from sequence.",
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                },
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                            "uri": "http://edamontology.org/operation_2406",
                            "term": "Protein structure analysis"
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                            "term": "Genetic variation analysis"
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                    ],
                    "input": [
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                    "note": "Prediction of the impact of non-synonymous polymorphisms on protein stability from structure.",
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            ],
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                    "uri": "http://edamontology.org/topic_0130",
                    "term": "Protein folding, stability and design"
                },
                {
                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
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                    "uri": "http://edamontology.org/topic_3325",
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            "documentation": [
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                    "url": "https://inpsmd.biocomp.unibo.it/inpsSuite",
                    "type": [
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                    "note": null
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            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btv291",
                    "pmid": "25957347",
                    "pmcid": null,
                    "type": [
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "INPS: Predicting the impact of non-synonymous variations on protein stability from sequence",
                        "abstract": "Motivation: A tool for reliably predicting the impact of variations on protein stability is extremely important for both protein engineering and for understanding the effects of Mendelian and somatic mutations in the genome. Next Generation Sequencing studies are constantly increasing the number of protein sequences. Given the huge disproportion between protein sequences and structures, there is a need for tools suited to annotate the effect of mutations starting from protein sequence without relying on the structure. Here, we describe INPS, a novel approach for annotating the effect of non-synonymous mutations on the protein stability from its sequence. INPS is based on SVM regression and it is trained to predict the thermodynamic free energy change upon single-point variations in protein sequences. Results: We show that INPS performs similarly to the state-of-the-art methods based on protein structure when tested in cross-validation on a non-redundant dataset. INPS performs very well also on a newly generated dataset consisting of a number of variations occurring in the tumor suppressor protein p53. Our results suggest that INPS is a tool suited for computing the effect of non-synonymous polymorphisms on protein stability when the protein structure is not available. We also show that INPS predictions are complementary to those of the state-of-the-art, structure-based method mCSM. When the two methods are combined, the overall prediction on the p53 set scores significantly higher than those of the single methods.",
                        "date": "2015-02-06T00:00:00Z",
                        "citationCount": 94,
                        "authors": [
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btw192",
                    "pmid": "27153629",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "INPS-MD: A web server to predict stability of protein variants from sequence and structure",
                        "abstract": "Motivation: Protein function depends on its structural stability. The effects of single point variations on protein stability can elucidate the molecular mechanisms of human diseases and help in developing new drugs. Recently, we introduced INPS, a method suited to predict the effect of variations on protein stability from protein sequence and whose performance is competitive with the available state-of-the-art tools. Results: In this article, we describe INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension), a web server for the prediction of protein stability changes upon single point variation from protein sequence and/or structure. Here, we complement INPS with a new predictor (INPS3D) that exploits features derived from protein 3D structure. INPS3D scores with Pearson's correlation to experimental ΔΔG values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based INPS scores slightly lower than the structure-based INPS3D and both on the same blind test sets well compare with the state-of-the-art methods.",
                        "date": "2016-08-15T00:00:00Z",
                        "citationCount": 147,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
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                {
                    "name": "Castrense Savojardo",
                    "email": "savojard@biocomp.unibo.it",
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                },
                {
                    "name": "Piero Fariselli",
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                },
                {
                    "name": "Castrense Savojardo",
                    "email": "savojard@biocomp.unibo.it",
                    "url": "http://biocomp.unibo.it/savojard/",
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        },
        {
            "name": "PhD-SNP",
            "description": "Support Vector Machines based Predictor of human Deleterious Single Nucleotide Polymorphisms.",
            "homepage": "http://snps.biofold.org/phd-snp/",
            "biotoolsID": "phd-snp",
            "biotoolsCURIE": "biotools:phd-snp",
            "version": [
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            "function": [
                {
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                        {
                            "uri": "http://edamontology.org/operation_3225",
                            "term": "Variant classification"
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                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
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                                    "term": "FASTA"
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                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1622",
                                "term": "Disease report"
                            },
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                    "note": "Prediction",
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            "topic": [
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                    "term": "DNA polymorphism"
                }
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            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btl423",
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                    "metadata": {
                        "title": "Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information",
                        "abstract": "Motivation: Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Altzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence. Results: In this paper, we develop a method based on support vector machines (SVMs) that starting from the protein sequence information can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans. Using a dataset of 21 185 single point mutations, 61% of which are disease-related, out of 3587 proteins, we show that our predictor can reach more than 74% accuracy in the specific task of predicting whether a single point mutation can be disease related or not. Our method, although based on less information, outperforms other web-available predictors implementing different approaches. © 2006 Oxford University Press.",
                        "date": "2006-11-15T00:00:00Z",
                        "citationCount": 664,
                        "authors": [
                            {
                                "name": "Capriotti E."
                            },
                            {
                                "name": "Calabrese R."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
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                },
                {
                    "name": "Emidio Capriotti",
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                    "url": null,
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                },
                {
                    "name": "Rita Casadio",
                    "email": "casadio@biocomp.unibo.it",
                    "url": null,
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