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                        "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."
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                            {
                                "name": "Casadio R."
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                    "name": "Andrea Pierleoni",
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                    "name": "Rita Casadio",
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                    "name": "Pier Luigi Martelli",
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                            "term": "Protein subcellular localisation prediction"
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                                "uri": "http://edamontology.org/data_0849",
                                "term": "Sequence record"
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                    "note": "Prediction for the subcellular localization",
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                {
                    "uri": "http://edamontology.org/topic_0140",
                    "term": "Protein targeting and localisation"
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                {
                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
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                    "metadata": {
                        "title": "BaCelLo: A balanced subcellular localization predictor",
                        "abstract": "Motivation. The knowledge of the subcellular localization of a protein is fundamental for elucidating its function. It is difficult to determine the subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the subcellular location of proteins in large scale genomic projects. Results. BaCelLo is a predictor for five classes of subcellular localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequence and from the evolutionary information contained in alignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the subcellular localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment. © 2006 Oxford University Press.",
                        "date": "2006-07-15T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Pierleoni A."
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                            {
                                "name": "Martelli P.L."
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                            {
                                "name": "Fariselli P."
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                            {
                                "name": "Casadio R."
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                        "journal": "Bioinformatics"
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                        "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.",
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                    "note": "For each heterogeneous or polygenic disease, eDGAR provides information on the relationship among the proteins encoded by the involved genes.",
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                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
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                {
                    "uri": "http://edamontology.org/topic_3574",
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                    "term": "Rare diseases"
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                    "metadata": {
                        "title": "eDGAR: A database of disease-gene associations with annotated relationships among genes",
                        "abstract": "Background: Genetic investigations, boosted by modern sequencing techniques, allow dissecting the genetic component of different phenotypic traits. These efforts result in the compilation of lists of genes related to diseases and show that an increasing number of diseases is associated with multiple genes. Investigating functional relations among genes associated with the same disease contributes to highlighting molecular mechanisms of the pathogenesis. Results: We present eDGAR, a database collecting and organizing the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar. For each disease-associated gene, eDGAR collects information on its annotation. Specifically, for lists of genes, eDGAR provides information on: i) interactions retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG and REACTOME pathways; v) enriched functional annotations computed with NET-GE; vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes and/or co-localisation in neighboring loci. The present release of eDGAR includes 2672 diseases, related to 3658 different genes, for a total number of 5729 gene-disease associations. 71% of the genes are linked to 621 multigenic diseases and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical and regulatory interactions. eDGAR includes a network based enrichment method for detecting statistically significant functional terms associated to groups of genes. Conclusions: eDGAR offers a resource to analyze disease-gene associations. In multigenic diseases genes can share physical interactions and/or co-occurrence in the same functional processes. eDGAR is freely available at: edgar. biocomp.unibo.it.",
                        "date": "2017-01-01T00:00:00Z",
                        "citationCount": 45,
                        "authors": [
                            {
                                "name": "Babbi G."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Profiti G."
                            },
                            {
                                "name": "Bovo S."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "BMC Genomics"
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                    "name": "Giulia Babbi",
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        {
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                                "uri": "http://edamontology.org/data_2048",
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                },
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                    "type": "Downloads page",
                    "note": "Installation with dpkg (root privileges are required)",
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                    "type": "Downloads page",
                    "note": "conda install proteinortho",
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                    "note": "brew install proteinortho",
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                    "pmid": null,
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                    "type": [
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                    ],
                    "version": "version 6",
                    "note": "For the version 6 of proteinortho",
                    "metadata": {
                        "title": "Proteinortho6: pseudo-reciprocal best alignment heuristic for graph-based detection of (co-)orthologs",
                        "abstract": "Proteinortho is a widely used tool to predict (co)-orthologous groups of genes for any set of species. It finds application in comparative and functional genomics, phylogenomics, and evolutionary reconstructions. With a rapidly increasing number of available genomes, the demand for large-scale predictions is also growing. In this contribution, we evaluate and implement major algorithmic improvements that significantly enhance the speed of the analysis without reducing precision. Graph-based detection of (co-)orthologs is typically based on a reciprocal best alignment heuristic that requires an all vs. all comparison of proteins from all species under study. The initial identification of similar proteins is accelerated by introducing an alternative search tool along with a revised search strategy—the pseudo-reciprocal best alignment heuristic—that reduces the number of required sequence comparisons by one-half. The clustering algorithm was reworked to efficiently decompose very large clusters and accelerate processing. Proteinortho6 reduces the overall processing time by an order of magnitude compared to its predecessor while maintaining its small memory footprint and good predictive quality.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Klemm P."
                            },
                            {
                                "name": "Stadler P.F."
                            },
                            {
                                "name": "Lechner M."
                            }
                        ],
                        "journal": "Frontiers in Bioinformatics"
                    }
                },
                {
                    "doi": "10.1186/1471-2105-12-124",
                    "pmid": "21526987",
                    "pmcid": "PMC3114741",
                    "type": [],
                    "version": "version 4 to 5",
                    "note": "For version 4 to 5 of proteinortho",
                    "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": 799,
                        "authors": [
                            {
                                "name": "Lechner M."
                            },
                            {
                                "name": "Findeiss S."
                            },
                            {
                                "name": "Steiner L."
                            },
                            {
                                "name": "Marz M."
                            },
                            {
                                "name": "Stadler P.F."
                            },
                            {
                                "name": "Prohaska S.J."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1371/journal.pone.0105015",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
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                    ],
                    "version": null,
                    "note": "The synteny extension PoFF (-syteny option)",
                    "metadata": {
                        "title": "Orthology detection combining clustering and synteny for very large datasets",
                        "abstract": "The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets. © 2014 Lechner et al.",
                        "date": "2014-08-19T00:00:00Z",
                        "citationCount": 67,
                        "authors": [
                            {
                                "name": "Lechner M."
                            },
                            {
                                "name": "Hernandez-Rosales M."
                            },
                            {
                                "name": "Doerr D."
                            },
                            {
                                "name": "Wieseke N."
                            },
                            {
                                "name": "Thevenin A."
                            },
                            {
                                "name": "Stoye J."
                            },
                            {
                                "name": "Hartmann R.K."
                            },
                            {
                                "name": "Prohaska S.J."
                            },
                            {
                                "name": "Stadler P.F."
                            }
                        ],
                        "journal": "PLoS ONE"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Marcus Lechner",
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                    "term": "Structural genomics"
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                    "term": "Nucleic acid sites, features and motifs"
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                    "term": "Whole genome sequencing"
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                    "term": "Immunoproteins and antigens"
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                        "title": "Performance and accuracy of four open-source tools for in silico serotyping of salmonella spp. Based on whole-genome short-read sequencing data",
                        "abstract": "We compared the performance of four open-source in silico Salmonella typing tools (SeqSero, SeqSero2, Salmonella In Silico Typing Resource [SISTR], and Metric Oriented Sequence Typer [MOST]) to assess their potential for replacing laboratory serological testing with serovar predictions from whole-genome sequencing data. We conducted a retrospective analysis of 1,624 Salmonella isolates of 72 serovars submitted to the German National Salmonella Reference Laboratory between 1999 and 2019. All isolates are derived from animal and foodstuff origins. We conducted Illumina short-read sequencing and compared the in silico serovar prediction results with the results of routine laboratory serotyping. We found the bestperforming in silico serovar prediction tool to be SISTR, with 94% correctly typed isolates, followed by SeqSero2 (87%), SeqSero (81%), and MOST (79%). Furthermore, we found that mapping-based tools like SeqSero and SeqSero2 (allele mode) were more reliable for the prediction of monophasic variants, while sequence type and clusterbased methods like MOST and SISTR (core-genome multilocus sequence type [cg-MLST]), showed greater resilience when confronted with GC-biased sequencing data. We showed that the choice of library preparation kit could substantially affect O antigen detection, due to the low GC content of the wzx and wzy genes. Although the accuracy of computational serovar predictions is still not quite on par with traditional serotyping by Salmonella reference laboratories, the command-line tools investigated in this study perform a rapid, efficient, inexpensive, and reproducible analysis, which can be integrated into in-house characterization pipelines. Based on our results, we find SISTR most suitable for automated, routine serotyping for public health surveillance of Salmonella. IMPORTANCE Salmonella spp. are important foodborne pathogens. To reduce the number of infected patients, it is essential to understand which subtypes of the bacteria cause disease outbreaks. Traditionally, characterization of Salmonella requires serological testing, a laboratory method by which Salmonella isolates can be classified into over 2,600 distinct subtypes, called serovars. Due to recent advances in whole-genome sequencing, many tools have been developed to replace traditional testing methods with computational analysis of genome sequences. It is crucial to validate that these tools, many already in use for routine surveillance, deliver accurate and reliable serovar information. In this study, we set out to compare which of the currently available open-source command-line tools is most suitable to replace serological testing. A thorough evaluation of the differing computational approaches is highly important to ensure the backward compatibility of serotyping data and to maintain comparability between laboratories.",
                        "date": "2020-03-01T00:00:00Z",
                        "citationCount": 34,
                        "authors": [
                            {
                                "name": "Uelze L."
                            },
                            {
                                "name": "Borowiak M."
                            },
                            {
                                "name": "Deneke C."
                            },
                            {
                                "name": "Szabo I."
                            },
                            {
                                "name": "Fischer J."
                            },
                            {
                                "name": "Tausch S.H."
                            },
                            {
                                "name": "Malorny B."
                            }
                        ],
                        "journal": "Applied and Environmental Microbiology"
                    }
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