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Pangenomes may be represented as graphical structures, e.g. compacted colored de Bruijn graphs, which offer a low memory usage and facilitate reference-free sequence comparisons. While sequence-to-graph mapping to graphical pangenomes has been studied for some time, no local alignment search tool in the vein of BLAST has been proposed yet. Results: We present a new heuristic method to find maximum scoring local alignments of a DNA query sequence to a pangenome represented as a compacted colored de Bruijn graph. Our approach additionally allows a comparison of similarity among sequences within the pangenome. We show that local alignment scores follow an exponential-tail distribution similar to BLAST scores, and we discuss how to estimate its parameters to separate local alignments representing sequence homology from spurious findings. An implementation of our method is presented, and its performance and usability are shown. 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Pharmacophore, which is the spatial arrangement of features that is essential for a molecule to interact with a specific target receptor, is an important model for achieving this goal. We present a freely available web server, named PharmaGist, for pharmacophore detection. The employed method is ligand based. Namely, it does not require the structure of the target receptor. Instead, the input is a set of structures of drug-like molecules that are known to bind to the receptor. The output consists of candidate pharmacophores that are computed by multiple flexible alignment of the input ligands. The method handles the flexibility of the input ligands explicitly and in deterministic manner within the alignment process. PharmaGist is also highly efficient, where a typical run with up to 32 drug-like molecules takes seconds to a few minutes on a stardard PC. Another important characteristic is the capability of detecting pharmacophores shared by different subsets of input molecules. 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Although ESI spectra of multiply charged protein molecules also can be measured on this type of devices, only average spectra are produced for the majority of naturally occurring proteins. Evaluating such ESI protein spectra would provide valuable information about the native state of investigated proteins. However, no suitable and freely available software could be found which allows the charge state determination and molecular weight calculation of single proteins from average ESI-MS data. Therefore, an algorithm based on standard deviation optimization (scatter minimization) was implemented for the analysis of protein ESI-MS data. The resulting software ESIprot was tested with ESI-MS data of six intact reference proteins between 12.4 and 66.7kDa. In all cases, the correct charge states could be determined. The obtained absolute mass errors were in a range between -0.2 and 1.2Da, the relative errors below 30ppm. The possible mass accuracy allows for valid conclusions about the actual condition of proteins. Moreover, the ESIprot algorithm demonstrates an extraordinary robustness and allows spectral interpretation from as little as two peaks, given sufficient quality of the provided m/z data, without the necessity for peak intensity data. ESIprot is independent from the raw data format and the computer platform, making it a versatile tool for mass spectrometrists. The program code was released under the open-source GPLv3 license to support future developments of mass spectrometry software. © 2010 John Wiley & Sons, Ltd.", "date": "2010-01-01T00:00:00Z", "citationCount": 75, "authors": [ { "name": "Winkler R." } ], "journal": "Rapid Communications in Mass Spectrometry" } } ], "credit": [ { "name": "Robert Winkler", "email": "robert.winkler@ira.cinvestav.mx", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Maintainer" ], "note": null }, { "name": null, "email": "webmaster@ms-utils.org", "url": "http://ms-utils.org", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Documentor" ], "note": null } ], "owner": "msutils_import", "additionDate": "2017-01-17T14:50:59Z", "lastUpdate": "2026-02-24T10:43:29.547210Z", "editPermission": { "type": "group", "authors": [ "proteomics.bio.tools", "kilpejen" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "PSIPRED", "description": "The PSIPRED Protein Analysis Workbench unites many available analysis tools into a single web based framework. 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The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method. Availability: Freely available to non-commercial users at http://globin.bio.warwick.ac.uk/psipred/.", "date": "2000-01-01T00:00:00Z", "citationCount": 3053, "authors": [ { "name": "McGuffin L.J." }, { "name": "Bryson K." }, { "name": "Jones D.T." } ], "journal": "Bioinformatics" } }, { "doi": "10.1006/jmbi.1999.3091", "pmid": "10493868", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Protein secondary structure prediction based on position-specific scoring matrices", "abstract": "A two-stage neural network has been used to predict protein secondary structure based on the position specific scoring matrices generated by PSI-BLAST. Despite the simplicity and convenience of the approach used, the results are found to be superior to those produced by other methods, including the popular PHD method according to our own benchmarking results and the results from the recent Critical Assessment of Techniques for Protein Structure Prediction experiment (CASP3), where the method was evaluated by stringent blind testing. Using a new testing set based on a set of 187 unique folds, and three-way cross-validation based on structural similarity criteria rather than sequence similarity criteria used previously (no similar folds were present in both the testing and training sets) the method presented here (PSIPRED) achieved an average Q3 score of between 76.5% to 78.3% depending on the precise definition of observed secondary structure used, which is the highest published score for any method to date. Given the success of the method in CASP3, it is reasonable to be confident that the evaluation presented here gives a fair indication of the performance of the method in general.", "date": "1999-09-17T00:00:00Z", "citationCount": 4685, "authors": [ { "name": "Jones D.T." } ], "journal": "Journal of Molecular Biology" } }, { "doi": "10.1093/nar/gkt381", "pmid": "23748958", "pmcid": "PMC3692098", "type": [], "version": null, "note": null, "metadata": { "title": "Scalable web services for the PSIPRED Protein Analysis Workbench.", "abstract": "Here, we present the new UCL Bioinformatics Group's PSIPRED Protein Analysis Workbench. The Workbench unites all of our previously available analysis methods into a single web-based framework. The new web portal provides a greatly streamlined user interface with a number of new features to allow users to better explore their results. We offer a number of additional services to enable computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web server access and new HADOOP packages. 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How is specificity in substrate recognition and phosphorylation by protein kinases achieved? Here, we present an artificial neural network method that predicts phosphorylation sites in independent sequences with a sensitivity in the range from 69% to 96%. As an example, we predict novel phosphorylation sites in the p300/CBP protein that may regulate interaction with transcription factors and histone acetyltransferase activity. In addition, serine and threonine residues in p300/CBP that can be modified by O-linked glycosylation with N-acetylglucosamine are identified. Glycosylation may prevent phosphorplation at these sites, a mechanism named yin-yang regulation. 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