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            "name": "plantRNA",
            "description": "The PlantRNA database compiles tRNA gene sequences retrieved from fully annotated nuclear, plastidial and mitochondrial genomes of photosynthetic organisms.",
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                {
                    "uri": "http://edamontology.org/topic_0780",
                    "term": "Plant biology"
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            "license": "CC-BY-SA-4.0",
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                {
                    "doi": "10.1111/tpj.15997",
                    "pmid": null,
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                    "type": [
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                    "version": "2.0",
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                    "metadata": {
                        "title": "PlantRNA 2.0: an updated database dedicated to tRNAs of photosynthetic eukaryotes",
                        "abstract": "PlantRNA (http://plantrna.ibmp.cnrs.fr/) is a comprehensive database of transfer RNA (tRNA) gene sequences retrieved from fully annotated nuclear, plastidial and mitochondrial genomes of photosynthetic organisms. In the first release (PlantRNA 1.0), tRNA genes from 11 organisms were annotated. In this second version, the annotation was implemented to 51 photosynthetic species covering the whole phylogenetic tree of photosynthetic organisms, from the most basal group of Archeplastida, the glaucophyte Cyanophora paradoxa, to various land plants. tRNA genes from lower photosynthetic organisms such as streptophyte algae or lycophytes as well as extremophile photosynthetic species such as Eutrema parvulum were incorporated in the database. As a whole, about 37 000 tRNA genes were accurately annotated. In the frame of the tRNA genes annotation from the genome of the Rhodophyte Chondrus crispus, non-canonical splicing sites in the D- or T-regions of tRNA molecules were identified and experimentally validated. As for PlantRNA 1.0, comprehensive biological information including 5′- and 3′-flanking sequences, A and B box sequences, region of transcription initiation and poly(T) transcription termination stretches, tRNA intron sequences and tRNA mitochondrial import are included.",
                        "date": "2022-11-01T00:00:00Z",
                        "citationCount": 7,
                        "authors": [
                            {
                                "name": "Cognat V."
                            },
                            {
                                "name": "Pawlak G."
                            },
                            {
                                "name": "Pflieger D."
                            },
                            {
                                "name": "Drouard L."
                            }
                        ],
                        "journal": "Plant Journal"
                    }
                },
                {
                    "doi": "10.1093/nar/gks935",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "PlantRNA, a database for tRNAs of photosynthetic eukaryotes",
                        "abstract": "PlantRNA database (http://plantrna.ibmp.cnrs.fr/) compiles transfer RNA (tRNA) gene sequences retrieved from fully annotated plant nuclear, plastidial and mitochondrial genomes. The set of annotated tRNA gene sequences has been manually curated for maximum quality and confidence. The novelty of this database resides in the inclusion of biological information relevant to the function of all the tRNAs entered in the library. This includes 5′- and 3′-flanking sequences, A and B box sequences, region of transcription initiation and poly(T) transcription termination stretches, tRNA intron sequences, aminoacyl-tRNA synthetases and enzymes responsible for tRNA maturation and modification. Finally, data on mitochondrial import of nuclear-encoded tRNAs as well as the bibliome for the respective tRNAs and tRNA-binding proteins are also included. The current annotation concerns complete genomes from 11 organisms: five flowering plants (Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Medicago truncatula and Brachypodium distachyon), a moss (Physcomitrella patens), two green algae (Chlamydomonas rein-hardtii and Ostreococcus tauri), one glaucophyte (Cyanophora paradoxa), one brown alga (Ectocarpus siliculosus) and a pennate diatom (Phaeodactylum tricornutum). The database will be regularly updated and implemented with new plant genome annotations so as to provide extensive information on tRNA biology to the research community. © The Author(s) 2012.",
                        "date": "2013-01-01T00:00:00Z",
                        "citationCount": 55,
                        "authors": [
                            {
                                "name": "Cognat V."
                            },
                            {
                                "name": "Pawlak G."
                            },
                            {
                                "name": "Duchene A.-M."
                            },
                            {
                                "name": "Daujat M."
                            },
                            {
                                "name": "Gigant A."
                            },
                            {
                                "name": "Salinas T."
                            },
                            {
                                "name": "Michaud M."
                            },
                            {
                                "name": "Gutmann B."
                            },
                            {
                                "name": "Giege P."
                            },
                            {
                                "name": "Gobert A."
                            },
                            {
                                "name": "Marechal-Drouard L."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Valerie Cognat",
                    "email": "valerie.cognat@cnrs.fr",
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            "name": "DeepSig",
            "description": "Prediction of secretory signal peptides in protein sequences",
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                            "term": "Protein signal peptide detection"
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                                "term": "Protein sequence (raw)"
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                            "data": {
                                "uri": "http://edamontology.org/data_3028",
                                "term": "Taxonomy"
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
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                    "uri": "http://edamontology.org/topic_3307",
                    "term": "Computational biology"
                },
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
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                    "type": "Source code",
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                {
                    "url": "https://hub.docker.com/r/bolognabiocomp/deepsig",
                    "type": "Container file",
                    "note": null,
                    "version": null
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                {
                    "url": "https://github.com/BolognaBiocomp/deepsig",
                    "type": [
                        "Command-line options"
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                    "note": null
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            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btx818",
                    "pmid": "29280997",
                    "pmcid": "PMC5946842",
                    "type": [
                        "Primary"
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                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "DeepSig: Deep learning improves signal peptide detection in proteins",
                        "abstract": "Motivation The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website.",
                        "date": "2018-05-15T00:00:00Z",
                        "citationCount": 77,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
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                    "email": null,
                    "url": "http://biocomp.unibo.it",
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                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
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                },
                {
                    "name": "Pier Luigi Martelli",
                    "email": "pierluigi.martelli@unibo.it",
                    "url": "http://biocomp.unibo.it",
                    "orcidid": "https://orcid.org/0000-0002-0274-5669",
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        {
            "name": "Meta-SNP",
            "description": "Meta-predictor of disease causing variants.",
            "homepage": "http://snps.biofold.org/meta-snp",
            "biotoolsID": "meta-snp",
            "biotoolsCURIE": "biotools:meta-snp",
            "version": [
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3225",
                            "term": "Variant classification"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
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                            "format": [
                                {
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                                    "term": "FASTA"
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                            "data": {
                                "uri": "http://edamontology.org/data_2209",
                                "term": "Mutation ID"
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                            "format": [
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                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
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                            ]
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1622",
                                "term": "Disease report"
                            },
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                                    "uri": "http://edamontology.org/format_2331",
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
                },
                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
                }
            ],
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                "Linux",
                "Windows",
                "Mac"
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                "Italy"
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            "documentation": [
                {
                    "url": "http://snps.biofold.org/meta-snp/pages/help.html",
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                    "doi": null,
                    "pmid": "23819846",
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                    "metadata": {
                        "title": "Collective judgment predicts disease-associated single nucleotide variants.",
                        "abstract": "In recent years the number of human genetic variants deposited into the publicly available databases has been increasing exponentially. The latest version of dbSNP, for example, contains ~50 million validated Single Nucleotide Variants (SNVs). SNVs make up most of human variation and are often the primary causes of disease. The non-synonymous SNVs (nsSNVs) result in single amino acid substitutions and may affect protein function, often causing disease. Although several methods for the detection of nsSNV effects have already been developed, the consistent increase in annotated data is offering the opportunity to improve prediction accuracy. Here we present a new approach for the detection of disease-associated nsSNVs (Meta-SNP) that integrates four existing methods: PANTHER, PhD-SNP, SIFT and SNAP. We first tested the accuracy of each method using a dataset of 35,766 disease-annotated mutations from 8,667 proteins extracted from the SwissVar database. The four methods reached overall accuracies of 64%-76% with a Matthew's correlation coefficient (MCC) of 0.38-0.53. We then used the outputs of these methods to develop a machine learning based approach that discriminates between disease-associated and polymorphic variants (Meta-SNP). In testing, the combined method reached 79% overall accuracy and 0.59 MCC, ~3% higher accuracy and ~0.05 higher correlation with respect to the best-performing method. Moreover, for the hardest-to-define subset of nsSNVs, i.e. variants for which half of the predictors disagreed with the other half, Meta-SNP attained 8% higher accuracy than the best predictor. Here we find that the Meta-SNP algorithm achieves better performance than the best single predictor. This result suggests that the methods used for the prediction of variant-disease associations are orthogonal, encoding different biologically relevant relationships. Careful combination of predictions from various resources is therefore a good strategy for the selection of high reliability predictions. Indeed, for the subset of nsSNVs where all predictors were in agreement (46% of all nsSNVs in the set), our method reached 87% overall accuracy and 0.73 MCC. Meta-SNP server is freely accessible at http://snps.biofold.org/meta-snp.",
                        "date": "2013-01-01T00:00:00Z",
                        "citationCount": 186,
                        "authors": [
                            {
                                "name": "Capriotti E."
                            },
                            {
                                "name": "Altman R.B."
                            },
                            {
                                "name": "Bromberg Y."
                            }
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                        "journal": "BMC genomics"
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        },
        {
            "name": "compareMS2",
            "description": "compareMS2 is a tool for comparing sets of (tandem) mass spectra for clustering samples, molecular phylogenetics, identification of biological species or tissues, and quality control. compareMS2 currently consumes Mascot Generic Format, or MGF, and produces output in a variety of common image and distance matrix formats.",
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                            "uri": "http://edamontology.org/operation_2424",
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                            "uri": "http://edamontology.org/operation_0567",
                            "term": "Phylogenetic tree visualisation"
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                    "metadata": {
                        "title": "Molecular phylogenetics by direct comparison of tandem mass spectra",
                        "abstract": "Rationale: Molecular phylogenetics is the study of evolution and relatedness of organisms or genes. Mass spectrometry is used routinely for bacterial identification and has also been used for phylogenetic analysis, for instance from bone material. Unfortunately, only a small fraction of the acquired tandem mass spectra allow direct interpretation. Methods: We describe a new algorithm and software for molecular phylogenetics using pairwise comparisons of tandem mass spectra from enzymatically digested proteins. The spectra need not be annotated and all acquired data is used in the analysis. To demonstrate the method, we analyzed tryptic digests of sera from four great apes and two other primates. Results: The distribution of spectra dot products for thousands of tandem mass spectra collected from two samples provides a measure on the fraction of shared peptides between the two samples. When inverted, this becomes a distance metric. By pairwise comparison between species and averaging over four individuals per species, it was possible to reconstruct the unique correct phylogenetic tree for the great apes and other primates. Conclusions: The new method described here has several attractive features compared with existing methods, among them simplicity, the unbiased use of all acquired data rather than a small subset of spectra, and the potential use of heavily degraded proteins or proteins with a priori unknown modifications. © 2012 John Wiley & Sons, Ltd.",
                        "date": "2012-04-15T00:00:00Z",
                        "citationCount": 28,
                        "authors": [
                            {
                                "name": "Palmblad M."
                            },
                            {
                                "name": "Deelder A.M."
                            }
                        ],
                        "journal": "Rapid Communications in Mass Spectrometry"
                    }
                },
                {
                    "doi": "10.1021/acs.jproteome.2c00457",
                    "pmid": "36173614",
                    "pmcid": "PMC9903320",
                    "type": [
                        "Other"
                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets",
                        "abstract": "It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.",
                        "date": "2023-02-03T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Marissen R."
                            },
                            {
                                "name": "Varunjikar M.S."
                            },
                            {
                                "name": "Laros J.F.J."
                            },
                            {
                                "name": "Rasinger J.D."
                            },
                            {
                                "name": "Neely B.A."
                            },
                            {
                                "name": "Palmblad M."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                },
                {
                    "doi": "10.1021/acs.jproteome.1c00528",
                    "pmid": "34523928",
                    "pmcid": "PMC8491155",
                    "type": [
                        "Review"
                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics Experiments in the Light of Proteomics",
                        "abstract": "Science is full of overlooked and undervalued research waiting to be rediscovered. Proteomics is no exception. In this perspective, we follow the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing tryptic peptides across animal species. This pioneering work directly led to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics. In the decades following, proteins continued to provide essential clues on evolutionary history. While technology has continued to improve, contemporary proteomics has strayed from this larger biological context, rarely comparing species or asking how protein structure, function, and interactions have evolved. Here we recombine proteomics with molecular phylogenetics, highlighting the value of framing proteomic results in a larger biological context and how almost forgotten research, though technologically surpassed, can still generate new ideas and illuminate our work from a different perspective. Though it is infeasible to read all research published on a large topic, looking up older papers can be surprisingly rewarding when rediscovering a \"gem\"at the end of a long citation chain, aided by digital collections and perpetually helpful librarians. Proper literature study reduces unnecessary repetition and allows research to be more insightful and impactful by truly standing on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/) as dataset MSV000087993.",
                        "date": "2021-10-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Neely B.A."
                            },
                            {
                                "name": "Palmblad M."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                }
            ],
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                    "email": null,
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                    "note": null
                },
                {
                    "name": "Magnus Palmblad",
                    "email": "magnus.palmblad@gmail.com",
                    "url": "https://github.com/magnuspalmblad",
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        {
            "name": "REDItools",
            "description": "Python scripts to detect RNA editing in deep transcriptome sequencing data (RNAseq)",
            "homepage": "https://github.com/BioinfoUNIBA/REDItools",
            "biotoolsID": "reditools",
            "biotoolsCURIE": "biotools:reditools",
            "version": [
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                "2.0"
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                    "type": "rrid",
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                            "uri": "http://edamontology.org/operation_2945",
                            "term": "Analysis"
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1916",
                                "term": "Alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2572",
                                    "term": "BAM"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2526",
                                "term": "Text data"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": "Run REDItools on RNA sequences",
                    "cmd": "REDItoolDnaRna.py -i rnaseq.bam -f myreference.fa -o myoutputfolder"
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Mac"
            ],
            "language": [
                "Python"
            ],
            "license": "MIT",
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                "ELIXIR-ITA-CNR"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
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            "documentation": [
                {
                    "url": "https://github.com/BioinfoUNIBA/REDItools",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btt287",
                    "pmid": "23742983",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0-1.3",
                    "note": null,
                    "metadata": {
                        "title": "REDItools: High-throughput RNA editing detection made easy",
                        "abstract": "The reliable detection of RNA editing sites from massive sequencing data remains challenging and, although several methodologies have been proposed, no computational tools have been released to date. Here, we introduce REDItools a suite of python scripts to perform high-throughput investigation of RNA editing using next-generation sequencing data.Availability and implementation: REDItools are in python programming language and freely available at http://code.google. com/p/reditools/.Contact: or graziano.pesole@uniba.itSupplementary information: Supplementary data are available at Bioinformatics online. © 2013 The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.",
                        "date": "2013-07-15T00:00:00Z",
                        "citationCount": 201,
                        "authors": [
                            {
                                "name": "Picardi E."
                            },
                            {
                                "name": "Pesole G."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1186/s12859-020-03562-x",
                    "pmid": "32838738",
                    "pmcid": "PMC7446188",
                    "type": [],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "HPC-REDItools: A novel HPC-aware tool for improved large scale RNA-editing analysis",
                        "abstract": "Background: RNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity. The automatic detection of RNA-editing from RNA-seq data is computational intensive and limited to small data sets, thus preventing a reliable genome-wide characterisation of such process. Results: In this work we introduce HPC-REDItools, an upgraded tool for accurate RNA-editing events discovery from large dataset repositories. Availability: https://github.com/BioinfoUNIBA/REDItools2. Conclusions: HPC-REDItools is dramatically faster than the previous version, REDItools, enabling big-data analysis by means of a MPI-based implementation and scaling almost linearly with the number of available cores.",
                        "date": "2020-08-21T00:00:00Z",
                        "citationCount": 21,
                        "authors": [
                            {
                                "name": "Flati T."
                            },
                            {
                                "name": "Gioiosa S."
                            },
                            {
                                "name": "Spallanzani N."
                            },
                            {
                                "name": "Tagliaferri I."
                            },
                            {
                                "name": "Diroma M.A."
                            },
                            {
                                "name": "Pesole G."
                            },
                            {
                                "name": "Chillemi G."
                            },
                            {
                                "name": "Picardi E."
                            },
                            {
                                "name": "Castrignano T."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1038/s41596-019-0279-7",
                    "pmid": "31996844",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal",
                        "abstract": "RNA editing is a widespread post-transcriptional mechanism able to modify transcripts through insertions/deletions or base substitutions. It is prominent in mammals, in which millions of adenosines are deaminated to inosines by members of the ADAR family of enzymes. A-to-I RNA editing has a plethora of biological functions, but its detection in large-scale transcriptome datasets is still an unsolved computational task. To this aim, we developed REDItools, the first software package devoted to the RNA editing profiling in RNA-sequencing (RNAseq) data. It has been successfully used in human transcriptomes, proving the tissue and cell type specificity of RNA editing as well as its pervasive nature. Outcomes from large-scale REDItools analyses on human RNAseq data have been collected in our specialized REDIportal database, containing more than 4.5 million events. Here we describe in detail two bioinformatic procedures based on our computational resources, REDItools and REDIportal. In the first procedure, we outline a workflow to detect RNA editing in the human cell line NA12878, for which transcriptome and whole genome data are available. In the second procedure, we show how to identify dysregulated editing at specific recoding sites in post-mortem brain samples of Huntington disease donors. On a 64-bit computer running Linux with ≥32 GB of random-access memory (RAM), both procedures should take ~76 h, using 4 to 24 cores. Our protocols have been designed to investigate RNA editing in different organisms with available transcriptomic and/or genomic reads. Scripts to complete both procedures and a docker image are available at https://github.com/BioinfoUNIBA/REDItools.",
                        "date": "2020-03-01T00:00:00Z",
                        "citationCount": 69,
                        "authors": [
                            {
                                "name": "Lo Giudice C."
                            },
                            {
                                "name": "Tangaro M.A."
                            },
                            {
                                "name": "Pesole G."
                            },
                            {
                                "name": "Picardi E."
                            }
                        ],
                        "journal": "Nature Protocols"
                    }
                },
                {
                    "doi": "10.1007/978-1-0716-1307-8_14",
                    "pmid": "33835447",
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "RNA Editing Detection in HPC Infrastructures",
                        "abstract": "RNA editing by A-to-I deamination is a relevant co/posttranscriptional modification carried out by ADAR enzymes. In humans, it has pivotal cellular effects and its deregulation has been linked to a variety of human disorders including neurological and neurodegenerative diseases and cancer. Despite its biological relevance, the detection of RNA editing variants in large transcriptome sequencing experiments (RNAseq) is yet a challenging computational task. To drastically reduce computing times we have developed a novel REDItools version able to identify A-to-I events in huge amount of RNAseq data employing High Performance Computing (HPC) infrastructures. Here we show how to use REDItools v2 in HPC systems.",
                        "date": "2021-01-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Giudice C.L."
                            },
                            {
                                "name": "Mansi L."
                            },
                            {
                                "name": "Flati T."
                            },
                            {
                                "name": "Gioiosa S."
                            },
                            {
                                "name": "Chillemi G."
                            },
                            {
                                "name": "Libro P."
                            },
                            {
                                "name": "Castrignano T."
                            },
                            {
                                "name": "Pesole G."
                            },
                            {
                                "name": "Picardi E."
                            }
                        ],
                        "journal": "Methods in Molecular Biology"
                    }
                },
                {
                    "doi": "10.1002/0471250953.bi1212s49",
                    "pmid": "25754992",
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Using REDItools to detect RNA editing events in NGS datasets",
                        "abstract": "RNAediting is a post-transcriptional/co-transcriptional molecular phenomenon whereby a genetic message is modified from the corresponding DNA template by means of substitutions, insertions, and/or deletions. It occurs in a variety of organisms and different cellular locations through evolutionally and biochemically unrelated proteins. RNA editing has a plethora of biological effects including the modulation of alternative splicing and fine-tuning of gene expression. RNA editing events by base substitutions can be detected on a genomic scale by NGS technologies through the REDItools package, an ad hoc suite of Python scripts to study RNA editing using RNA-Seq and DNA-Seq data or RNA-Seq data alone. REDItools implement effective filters to minimize biases due to sequencing errors, mapping errors, and SNPs. The package is freely available at Google Code repository (http://code.google.com/p/reditools/) and released under the MIT license. In the present unit we show three basic protocols corresponding to three main REDItools scripts.",
                        "date": "2015-01-01T00:00:00Z",
                        "citationCount": 25,
                        "authors": [
                            {
                                "name": "Picardi E."
                            },
                            {
                                "name": "D'Erchia A.M."
                            },
                            {
                                "name": "A. Antonio"
                            },
                            {
                                "name": "G. Graziano"
                            }
                        ],
                        "journal": "Current Protocols in Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
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                    "email": null,
                    "url": null,
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                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Ernesto Picardi",
                    "email": "ernesto.picardi@uniba.it",
                    "url": "https://www.uniba.it/docenti/picardi-ernesto/en",
                    "orcidid": "https://orcid.org/0000-0002-6549-0114",
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        },
        {
            "name": "Mass Dynamics",
            "description": "A software platform for analysis and visualization of label-free and tandem mass tag (TMT) data-dependent acquisition (DDA) bottom-up proteomics data, including pathway and gene set enrichment analyses.",
            "homepage": "https://massdynamics.com/",
            "biotoolsID": "mass_dynamics",
            "biotoolsCURIE": "biotools:mass_dynamics",
            "version": [
                "1.0",
                "2.0"
            ],
            "otherID": [],
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            "function": [
                {
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                            "uri": "http://edamontology.org/operation_2945",
                            "term": "Analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2536",
                                "term": "Mass spectrometry data"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": []
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
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            "publication": [
                {
                    "doi": "10.1021/acs.jproteome.1c00683",
                    "pmid": "34647461",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "Mass Dynamics 1.0: A Streamlined, Web-Based Environment for Analyzing, Sharing, and Integrating Label-Free Data",
                        "abstract": "Label-free quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis, and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyze and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes a cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but also annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardization in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/.",
                        "date": "2021-11-05T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Bloom J."
                            },
                            {
                                "name": "Triantafyllidis A."
                            },
                            {
                                "name": "Quaglieri A."
                            },
                            {
                                "name": "Burton Ngov P."
                            },
                            {
                                "name": "Infusini G."
                            },
                            {
                                "name": "Webb A."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                },
                {
                    "doi": "10.1101/2022.12.12.517480",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": null
                }
            ],
            "credit": [],
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            "owner": "n.m.palmblad@lumc.nl",
            "additionDate": "2024-01-18T10:05:20.317333Z",
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        },
        {
            "name": "MaCPepDB - Mass Centric Peptide Database",
            "description": "A Database to Quickly Access All Tryptic Peptides of the UniProtKB",
            "homepage": "https://macpepdb.mpc.rub.de/",
            "biotoolsID": "macpepdb_-_mass_centric_peptide_database",
            "biotoolsCURIE": "biotools:macpepdb_-_mass_centric_peptide_database",
            "version": [
                "2.3.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [],
            "toolType": [
                "Database portal"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Python",
                "JavaScript",
                "SQL"
            ],
            "license": null,
            "collectionID": [
                "CUBiMed.RUB",
                "BioInfra.Prot"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Germany"
            ],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/mpc-bioinformatics/macpepdb",
                    "type": [
                        "Repository"
                    ],
                    "note": "Backend implementation written in Flask"
                },
                {
                    "url": "https://github.com/mpc-bioinformatics/macpepdb",
                    "type": [
                        "Repository"
                    ],
                    "note": "Frontend implementation written in NuxtJS"
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "https://macpepdb.mpc.rub.de/docs/api",
                    "type": [
                        "API documentation"
                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/mpc-bioinformatics/macpepdb/blob/main/Readme.md",
                    "type": [
                        "Installation instructions"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1021/acs.jproteome.0c00967",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
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                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "MaCPepDB: A Database to Quickly Access All Tryptic Peptides of the UniProtKB",
                        "abstract": "Protein sequence databases play a crucial role in the majority of the currently applied mass-spectrometry-based proteomics workflows. Here UniProtKB serves as one of the major sources, as it combines the information of several smaller databases and enriches the entries with additional biological information. For the identification of peptides in a sample by tandem mass spectra, as generated by data-dependent acquisition, protein sequence databases provide the basis for most spectrum identification search engines. In addition, for targeted proteomics approaches like selected reaction monitoring (SRM) and parallel reaction monitoring (PRM), knowledge of the peptide sequences, their masses, and whether they are unique for a protein is essential. Because most bottom-up proteomics approaches use trypsin to cleave the proteins in a sample, the tryptic peptides contained in a protein database are of great interest. We present a database, called MaCPepDB (mass-centric peptide database), that consists of the complete tryptic digest of the Swiss-Prot and TrEMBL parts of UniProtKB. This database is especially designed to not only allow queries of peptide sequences and return the respective information about connected proteins and thus whether a peptide is unique but also allow queries of specific masses of peptides or precursors of MS/MS spectra. Furthermore, posttranslational modifications can be considered in a query as well as different mass deviations for posttranslational modifications. Hence the database can be used by a sequence query not only to, for example, check in which proteins of the UniProt database a tryptic peptide can be found but also to find possibly interfering peptides in PRM/SRM experiments using the mass query. The complete database contains currently 5 939 244 990 peptides from 185 561 610 proteins (UniProt version 2020_03), for which a single query usually takes less than 1 s. For easy exploration of the data, a web interface was developed. A REST application programming interface (API) for programmatic and workflow access is also available at https://macpepdb.mpc.rub.de.",
                        "date": "2021-04-02T00:00:00Z",
                        "citationCount": 4,
                        "authors": [
                            {
                                "name": "Uszkoreit J."
                            },
                            {
                                "name": "Winkelhardt D."
                            },
                            {
                                "name": "Barkovits K."
                            },
                            {
                                "name": "Wulf M."
                            },
                            {
                                "name": "Roocke S."
                            },
                            {
                                "name": "Marcus K."
                            },
                            {
                                "name": "Eisenacher M."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                }
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                {
                    "name": "PD Dr. Martin Eisenacher",
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                    "term": "Metagenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3512",
                    "term": "Gene transcripts"
                },
                {
                    "uri": "http://edamontology.org/topic_3299",
                    "term": "Evolutionary biology"
                },
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
                }
            ],
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                "Linux",
                "Windows"
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                    "version": "1.0",
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                }
            ],
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                    "name": "Fotis E. Psomopoulos",
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                    "biotoolsID": "prinseq",
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                            "term": "Read pre-processing"
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                            "uri": "http://edamontology.org/operation_0292",
                            "term": "Sequence alignment"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3197",
                            "term": "Genetic variation analysis"
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                    ],
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2975",
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                        }
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                                    "uri": "http://edamontology.org/format_1929",
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                    "cmd": "curl -O 'https://raw.githubusercontent.com/cbg-ethz/V-pipe/master/utils/quick_install.sh'\nbash quick_install.sh -b sars-cov2 -p V-pipe-installation/ -w working/\ncd V-pipe-installation/working/\nmkdir -p samples/patient_1/20200409/raw_data\ncp ~/Download/reads_R{1,2}.fastq.gz samples/patient_1/20200409/raw_data/\n./vpipe -p --cores 2\n\nls samples/patient_1/20200409/variants/SNVs/snvs.vcf\nls samples//variants/SNVs/REGION_*/support/*.fasgz"
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                    "note": "V-pipe was used to process a major part of the Swiss samples in this pan-European study.",
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                    "note": "The waste water samples were analyzed using V-pipe and additional custom scripts.",
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                    "note": "In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each 'omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets.",
                    "metadata": {
                        "title": "Planet Microbe: A platform for marine microbiology to discover and analyze interconnected 'omics and environmental data'",
                        "abstract": "In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and ongoing oceanographic sequencing efforts. In Planet Microbe, each 'omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/.",
                        "date": "2021-01-08T00:00:00Z",
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                            {
                                "name": "Ponsero A.J."
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