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MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.", "publicationTitle": "Nature Biotechnology", "publisher": "", "place": "", "date": "Marzo 2001", "volume": "19", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "242-247", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Nat Biotech", "DOI": "10.1038/85686", "citationKey": "", "url": "http://www.nature.com.pros.lib.unimi.it/nbt/journal/v19/n3/full/nbt0301_242.html", "accessDate": "2013-08-05T15:28:38Z", "PMID": "", "PMCID": "", "ISSN": "1087-0156", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com.pros.lib.unimi.it", "callNumber": "", "rights": "© 2001 Nature Publishing Group", "extra": "", "tags": [], "collections": [ "ZM3I3WEI" ], "relations": {}, "dateAdded": "2013-08-05T15:28:38Z", "dateModified": "2013-08-05T15:28:38Z" } }, { "key": "GJBTZXVC", "version": 220, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/GJBTZXVC", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/GJBTZXVC", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/81857/items/AKPWC3VM", "type": "application/json" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "GJBTZXVC", "version": 220, "parentItem": "AKPWC3VM", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2013-08-05T14:54:37Z", "url": "http://onlinelibrary.wiley.com.pros.lib.unimi.it/store/10.1002/jms.1064/asset/1064_ftp.pdf?v=1&t=hjzsvnvu&s=2d987d6dcf93fa93b90eb5703ed7c7610ac9d7a8", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Lo et al. - 2006 - Identification of microbial mixtures by LC-selecti.pdf", "md5": "4ae082eb397a9d6cc61c377d9f1e4aff", "mtime": 1375714479381, "tags": [], "relations": {}, "dateAdded": "2013-08-05T14:54:37Z", "dateModified": "2013-08-05T14:54:39Z" } }, { "key": "AKPWC3VM", "version": 219, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/AKPWC3VM", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/AKPWC3VM", "type": "text/html" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "creatorSummary": "Lo et al.", "parsedDate": "2006", "numChildren": 1 }, "data": { "key": "AKPWC3VM", "version": 219, "itemType": "journalArticle", "title": "Identification of microbial mixtures by LC-selective proteotypic-peptide analysis(SPA)", "creators": [ { "creatorType": "author", "firstName": "Alan A.-L.", "lastName": "Lo" }, { "creatorType": "author", "firstName": "Anren", "lastName": "Hu" }, { "creatorType": "author", "firstName": "Yen-Peng", "lastName": "Ho" } ], "abstractNote": "This paper describes a method—using a combination of LC-MS/MS of selected bacteria-specific peptides and database search—for determining the species of bacteria present in a mixture. We identified the proteotypic peptides that were associated with specific bacteria by searching protein databases for the LC-MS/MS data. The retention time windows for specific peptide markers were used as an extra constraint so that the peptide markers of many bacterial species could be analyzed in a single LC-selective proteotypic-peptide analysis (SPA). We performed LC-MS/MS analyses on the proteolytic digest of cell extracts and monitored only the selected marker peptide ions at given elution time windows. The corresponding bacterial species could be characterized when the selected peptides that eluted at expected elution windows were identified correctly from the database. We managed to identify up to eight bacterial species simultaneously during a single LC-MS/MS analysis, as well as bacteria mixed in various abundances. Two marker ions having similar values of m/z, but obtained from two different bacterial samples, which would otherwise be selected as precursors within mass tolerance and would complicate the MS/MS data, were time-resolved using LC and then used to correctly identify their bacterial sources. The coupling of selective MS/MS monitoring with separation methods, such as LC, provides a highly selective and accurate analytical method for characterizing complex mixtures of bacterial species. Copyright © 2006 John Wiley & Sons, Ltd.", "publicationTitle": "Journal of Mass Spectrometry", "publisher": "", "place": "", "date": "2006", "volume": "41", "issue": "8", "section": "", "partNumber": "", "partTitle": "", "pages": "1049–1060", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1002/jms.1064", "citationKey": "", "url": "http://onlinelibrary.wiley.com.pros.lib.unimi.it/doi/10.1002/jms.1064/abstract", "accessDate": "2013-08-05T14:54:37Z", "PMID": "", "PMCID": "", "ISSN": "1096-9888", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "Copyright © 2006 John Wiley & Sons, Ltd.", "extra": "", "tags": [ { "tag": "Bacteria", "type": 1 }, { "tag": "Peptides", "type": 1 }, { "tag": "biomarker", "type": 1 }, { "tag": "liquid chromatography", "type": 1 }, { "tag": "tandem mass spectrometry", "type": 1 } ], "collections": [ "ZM3I3WEI" ], "relations": {}, "dateAdded": "2013-08-05T14:54:37Z", "dateModified": "2013-08-05T14:54:37Z" } }, { "key": "NR2B9ZEK", "version": 218, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/NR2B9ZEK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/NR2B9ZEK", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/81857/items/MTHE6B3V", "type": "application/json" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "NR2B9ZEK", "version": 218, "parentItem": "MTHE6B3V", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2013-08-05T14:32:06Z", "url": "http://genomebiology.com/content/pdf/gb-2004-6-1-r9.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Desiere et al. - 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Here we describe the mapping of peptides derived from accurate interpretations of protein tandem mass spectrometry (MS) data to eukaryotic genomes and the generation of an expandable resource for integration of data from many diverse proteomics experiments. Furthermore, we demonstrate that peptide identifications obtained from high-throughput proteomics can be integrated on a large scale with the human genome. 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Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).", "publicationTitle": "Nature Biotechnology", "publisher": "", "place": "", "date": "Febbraio 2007", "volume": "25", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "125-131", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Nat Biotech", "DOI": "10.1038/nbt1275", "citationKey": "", "url": "http://www.nature.com.pros.lib.unimi.it/nbt/journal/v25/n1/full/nbt1275.html", "accessDate": "2013-07-31T15:59:53Z", "PMID": "", "PMCID": "", "ISSN": "1087-0156", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com.pros.lib.unimi.it", "callNumber": "", "rights": "© 2006 Nature Publishing Group", "extra": "", "tags": [], "collections": [ "ZM3I3WEI" ], "relations": {}, "dateAdded": "2013-07-31T15:59:53Z", "dateModified": "2013-07-31T15:59:53Z" } }, { "key": "TKIV3CX3", "version": 214, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/TKIV3CX3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/TKIV3CX3", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/81857/items/NZJPDQ72", "type": "application/json" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "TKIV3CX3", "version": 214, "parentItem": "NZJPDQ72", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2013-07-31T15:59:17Z", "url": "http://www.nature.com.pros.lib.unimi.it/nbt/journal/v25/n1/pdf/nbt1275.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Mallick et al. - 2007 - Computational prediction of proteotypic peptides f.pdf", "md5": "98cdb90d8394661d7b57d0d378b936dc", "mtime": 1375286364171, "tags": [], "relations": {}, "dateAdded": "2013-07-31T15:59:17Z", "dateModified": "2013-07-31T15:59:24Z" } }, { "key": "NZJPDQ72", "version": 214, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/NZJPDQ72", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/NZJPDQ72", "type": "text/html" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "creatorSummary": "Mallick et al.", "parsedDate": "2007-02", "numChildren": 2 }, "data": { "key": "NZJPDQ72", "version": 214, "itemType": "journalArticle", "title": "Computational prediction of proteotypic peptides for quantitative proteomics", "creators": [ { "creatorType": "author", "firstName": "Parag", "lastName": "Mallick" }, { "creatorType": "author", "firstName": "Markus", "lastName": "Schirle" }, { "creatorType": "author", "firstName": "Sharon S.", "lastName": "Chen" }, { "creatorType": "author", "firstName": "Mark R.", "lastName": "Flory" }, { "creatorType": "author", "firstName": "Hookeun", "lastName": "Lee" }, { "creatorType": "author", "firstName": "Daniel", "lastName": "Martin" }, { "creatorType": "author", "firstName": "Jeffrey", "lastName": "Ranish" }, { "creatorType": "author", "firstName": "Brian", "lastName": "Raught" }, { "creatorType": "author", "firstName": "Robert", "lastName": "Schmitt" }, { "creatorType": "author", "firstName": "Thilo", "lastName": "Werner" }, { "creatorType": "author", "firstName": "Bernhard", "lastName": "Kuster" }, { "creatorType": "author", "firstName": "Ruedi", "lastName": "Aebersold" } ], "abstractNote": "Mass spectrometry–based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).", "publicationTitle": "Nature Biotechnology", "publisher": "", "place": "", "date": "Febbraio 2007", "volume": "25", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "125-131", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Nat Biotech", "DOI": "10.1038/nbt1275", "citationKey": "", "url": "http://www.nature.com.pros.lib.unimi.it/nbt/journal/v25/n1/full/nbt1275.html", "accessDate": "2013-07-31T15:59:17Z", "PMID": "", "PMCID": "", "ISSN": "1087-0156", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com.pros.lib.unimi.it", "callNumber": "", 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"NZJPDQ72", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2013-07-31T15:59:17Z", "url": "http://www.nature.com.pros.lib.unimi.it/nbt/journal/v25/n1/full/nbt1275.html", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "nbt1275.html", "md5": "2d99579ada646db9e4a2794ae1eb0bff", "mtime": 1375286357953, "tags": [], "relations": {}, "dateAdded": "2013-07-31T15:59:17Z", "dateModified": "2013-07-31T15:59:17Z" } }, { "key": "DXNGUXXS", "version": 13, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/DXNGUXXS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/DXNGUXXS", "type": "text/html" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "creatorSummary": "Di Silvestre et al.", "parsedDate": "2013", "numChildren": 0 }, "data": { "key": "DXNGUXXS", "version": 13, "itemType": "bookSection", "title": "Processing of Mass Spectrometry Data in Clinical Applications", "creators": [ { "creatorType": "author", "firstName": "Dario", "lastName": "Di Silvestre" }, { "creatorType": "author", "firstName": "Pietro", "lastName": "Brunetti" }, { "creatorType": "author", "firstName": "Pierluigi", "lastName": "Mauri" } ], "abstractNote": "", "bookTitle": "Bioinformatics of Human Proteomics", "series": "Translational Bioinformatics", "seriesNumber": "", "volume": "3", "numberOfVolumes": "", "edition": "", "date": "2013", "publisher": "Wang, Xiangdong", "place": "Dordrecht", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "", "ISBN": "978-94-007-5811-7", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2013-01-28T11:26:00Z", "dateModified": "2013-01-28T11:32:56Z" } }, { "key": "SAFVAHPZ", "version": 1, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/SAFVAHPZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/SAFVAHPZ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/81857/items/38PTNRWJ", "type": "application/json" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } } }, "data": { "key": "SAFVAHPZ", "version": 1, "parentItem": "38PTNRWJ", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed Link", "accessDate": "2013-01-28T11:20:58Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/21598388", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2013-01-28T11:20:58Z", "dateModified": "2013-01-28T11:20:58Z" } }, { "key": "38PTNRWJ", "version": 1, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/38PTNRWJ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/38PTNRWJ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "creatorSummary": "Comunian et al.", "parsedDate": "2011-06", "numChildren": 1 }, "data": { "key": "38PTNRWJ", "version": 1, "itemType": "journalArticle", "title": "A comparative MudPIT analysis identifies different expression profiles in heart compartments", "creators": [ { "creatorType": "author", "firstName": "Claudio", "lastName": "Comunian" }, { "creatorType": "author", "firstName": "Francesca", "lastName": "Rusconi" }, { "creatorType": "author", "firstName": "Antonella", "lastName": "De Palma" }, { "creatorType": "author", "firstName": "Pietro", "lastName": "Brunetti" }, { "creatorType": "author", "firstName": "Daniele", "lastName": "Catalucci" }, { "creatorType": "author", "firstName": "Pier L", "lastName": "Mauri" } ], "abstractNote": "Cardiomyopathies indistinctly affect atrial and ventricular cardiac compartments with alterations of their mechanical and/or electrical activity. To understand the main mechanisms involved in these pathological alterations, a detailed knowledge of the physiology of the healthy heart is critical. In the present work, we utilize multidimensional protein identification technology to characterize the murine left ventricle (LV), right ventricle (RV), and atria (A) proteomes, identifying thousands of distinct proteins. Moreover, using multidimensional algorithm protein map tool, relative abundances of proteins among the heart chambers were investigated. In sum, we found 16 and 55 proteins were more abundant in LV compared to RV and A, respectively; 47 and 60 proteins were more abundant in RV than LV and A, respectively; and, 81 and 74 proteins were more abundant in A than LV and RV, respectively. This detailed characterization of myocardial compartment proteome represents an important advancement in the knowledge of heart physiology, and may contribute to the identification of key features underlying the onset of cardiomyopathy.", "publicationTitle": "Proteomics", "publisher": "", "place": "", "date": "Jun 2011", "volume": "11", "issue": "11", "section": "", "partNumber": "", "partTitle": "", "pages": "2320-2328", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Proteomics", "DOI": "10.1002/pmic.201000479", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1615-9861", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "PMID: 21598388", "tags": [ { "tag": "Analysis of Variance", "type": 1 }, { "tag": "Animals", "type": 1 }, { "tag": "Blotting, Western", "type": 1 }, { "tag": "Heart Atria", "type": 1 }, { "tag": "Heart Ventricles", "type": 1 }, { "tag": "Male", "type": 1 }, { "tag": "Mice", "type": 1 }, { "tag": "Organ Specificity", "type": 1 }, { "tag": "Peptide Fragments", "type": 1 }, { "tag": "Proteins", "type": 1 }, { "tag": "Proteome", "type": 1 }, { "tag": "Trypsin", "type": 1 }, { "tag": "proteomics", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2013-01-28T11:20:58Z", "dateModified": "2013-01-28T11:20:58Z" } }, { "key": "TUWNNXRJ", "version": 1, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/TUWNNXRJ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/TUWNNXRJ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "creatorSummary": "Bergamini et al.", "parsedDate": "2012-03", "numChildren": 0 }, "data": { "key": "TUWNNXRJ", "version": 1, "itemType": "journalArticle", "title": "MudPIT analysis of released proteins in Pseudomonas aeruginosa laboratory and clinical strains in relation to pro-inflammatory effects", "creators": [ { "creatorType": "author", "firstName": "Gabriella", "lastName": "Bergamini" }, { "creatorType": "author", "firstName": "Dario", "lastName": "Di Silvestre" }, { "creatorType": "author", "firstName": "Pierluigi", "lastName": "Mauri" }, { "creatorType": "author", "firstName": "Cristina", "lastName": "Cigana" }, { "creatorType": "author", "firstName": "Alessandra", "lastName": "Bragonzi" }, { "creatorType": "author", "firstName": "Antonella", "lastName": "De Palma" }, { "creatorType": "author", "firstName": "Louise", "lastName": "Benazzi" }, { "creatorType": "author", "firstName": "Gerd", "lastName": "Döring" }, { "creatorType": "author", "firstName": "Baroukh Maurice", "lastName": "Assael" }, { "creatorType": "author", "firstName": "Paola", "lastName": "Melotti" }, { "creatorType": "author", "firstName": "Claudio", "lastName": "Sorio" } ], "abstractNote": "Pseudomonas aeruginosa (Pa) is the most common virulent pathogen contributing to the pathogenesis of cystic fibrosis (CF). During bacterial lung colonization, the products of its metabolism are released in the extracellular space contributing to the pathogenic events associated with its presence. To gain insights on the mechanisms involved in the Pa pathogenesis we focused our attention on proteins released by Pa using a MudPIT approach combined with cell biology assays. Conditioned medium (CM) collected under aerobic and microaerobic conditions from Pa clinical strains (in early and late colonization), unlike the laboratory strain, induced expression of IL-8 mRNA in CF airway epithelial cells. We have identified proteins released by clinically relevant Pa strains, focusing on the pro-inflammatory effects as metalloproteases (MMPs). In fact, their expression pattern was associated with the highest pro-inflammatory activity measured in the early clinically isolated strain. The relation was further supported by the result of the analysis of a larger and independent set of Pa isolates derived from sporadically and chronically infected CF patients: 76% of sporadic samples expressed protease activity (n = 44), while only 27% scored positive in the chronically infected individuals (n = 38, p < 0.0001, Fisher's exact test). Finally, looking for a possible mechanism of action of bacterial MMPs, we found that CM from early clinical isolates can cleave CXCR1 on the surface of human neutrophils, suggesting a potential role for the bacterially released MMPs in the protection of the pathogen from the host's response.", "publicationTitle": "Integrative biology: quantitative biosciences from nano to macro", "publisher": "", "place": "", "date": "Mar 2012", "volume": "4", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "270-279", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Integr Biol (Camb)", "DOI": "10.1039/c2ib00127f", "citationKey": "", "url": "http://www.ncbi.nlm.nih.gov/pubmed/22298109", "accessDate": "2012-08-13T13:50:09Z", "PMID": "", "PMCID": "", "ISSN": "1757-9708", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "0000", "rights": "", "extra": "PMID: 22298109", "tags": [ { "tag": "Bacterial Proteins", "type": 1 }, { "tag": "Bacteriological Techniques", "type": 1 }, { "tag": "Cell Line", "type": 1 }, { "tag": "Culture Media, Conditioned", "type": 1 }, { "tag": "Cystic Fibrosis", "type": 1 }, { "tag": "Gene Expression", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Inflammation Mediators", "type": 1 }, { "tag": "Interleukin-8", "type": 1 }, { "tag": "Metalloproteases", "type": 1 }, { "tag": "Neutrophils", "type": 1 }, { "tag": "Pseudomonas Infections", "type": 1 }, { "tag": "Pseudomonas aeruginosa", "type": 1 }, { "tag": "RNA, Messenger", "type": 1 }, { "tag": "Receptors, Interleukin-8A", "type": 1 }, { "tag": "Virulence", "type": 1 }, { "tag": "proteomics", "type": 1 }, { "tag": "systems biology", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-08-13T13:50:09Z", "dateModified": "2012-08-13T13:50:10Z" } }, { "key": "72I99RT5", "version": 1, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/72I99RT5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/72I99RT5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "creatorSummary": "Levner", "parsedDate": "2005", "numChildren": 0 }, "data": { "key": "72I99RT5", "version": 1, "itemType": "journalArticle", "title": "Feature selection and nearest centroid classification for protein mass spectrometry", "creators": [ { "creatorType": "author", "firstName": "Ilya", "lastName": "Levner" } ], "abstractNote": "BACKGROUND\n\nThe use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the \"curse of dimensionality\" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry.\n\n\nRESULTS\n\nThis study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets.\n\n\nCONCLUSION\n\nThis study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation. The revealed inconsistencies provide clear evidence that algorithm evaluation should be performed on several data sets using a consistent (i.e., non-randomized, stratified) cross-validation procedure in order for the conclusions to be statistically sound.", "publicationTitle": "BMC bioinformatics", "publisher": "", "place": "", "date": "2005", "volume": "6", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "68", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "BMC Bioinformatics", "DOI": "10.1186/1471-2105-6-68", "citationKey": "", "url": "http://www.ncbi.nlm.nih.gov/pubmed/15788095", "accessDate": "2012-08-13T12:10:53Z", "PMID": "", "PMCID": "", "ISSN": "1471-2105", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "0092", "rights": "", "extra": "PMID: 15788095", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Artificial Intelligence", "type": 1 }, { "tag": "Cluster Analysis", "type": 1 }, { "tag": "Data Interpretation, Statistical", "type": 1 }, { "tag": "Databases, Genetic", "type": 1 }, { "tag": "Female", "type": 1 }, { "tag": "Gene Expression Profiling", "type": 1 }, { "tag": "Gene Expression Regulation, Neoplastic", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Information Storage and Retrieval", "type": 1 }, { "tag": "Male", "type": 1 }, { "tag": "Mass Spectrometry", "type": 1 }, { "tag": "Models, Genetic", "type": 1 }, { "tag": "Models, Statistical", "type": 1 }, { "tag": "Neural Networks (Computer)", "type": 1 }, { "tag": "Oligonucleotide Array Sequence Analysis", "type": 1 }, { "tag": "Ovarian Neoplasms", "type": 1 }, { "tag": "Pattern Recognition, Automated", "type": 1 }, { "tag": "Prostatic Neoplasms", "type": 1 }, { "tag": "Proteins", "type": 1 }, { "tag": "Reproducibility of Results", "type": 1 }, { "tag": "Sensitivity and Specificity", "type": 1 }, { "tag": "Software", "type": 1 }, { "tag": "Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization", "type": 1 }, { "tag": "Time Factors", "type": 1 }, { "tag": "computational biology", "type": 1 }, { "tag": "proteomics", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-08-13T12:10:53Z", "dateModified": "2012-08-13T12:10:54Z" } }, { "key": "ZVGVAI9W", "version": 1, "library": { "type": "group", "id": 81857, "name": "ITB_CNR_proteomic_team", "links": { "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/81857/items/ZVGVAI9W", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/itb_cnr_proteomic_team/items/ZVGVAI9W", "type": "text/html" } }, "meta": { "createdByUser": { "id": 749137, "username": "gunzapper", "name": "Pietro", "links": { "alternate": { "href": "https://www.zotero.org/gunzapper", "type": "text/html" } } }, "creatorSummary": "Yu et al.", "parsedDate": "2006-09", "numChildren": 0 }, "data": { "key": "ZVGVAI9W", "version": 1, "itemType": "journalArticle", "title": "Multiple peak alignment in sequential data analysis: a scale-space-based approach", "creators": [ { "creatorType": "author", "firstName": "Weichuan", "lastName": "Yu" }, { "creatorType": "author", "firstName": "Xiaoye", "lastName": "Li" }, { "creatorType": "author", "firstName": "Junfeng", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Baolin", "lastName": "Wu" }, { "creatorType": "author", "firstName": "Kenneth R", "lastName": "Williams" }, { "creatorType": "author", "firstName": "Hongyu", "lastName": "Zhao" } ], "abstractNote": "In this paper, we address the multiple peak alignment problem in sequential data analysis with an approach based on the Gaussian scale-space theory. We assume that multiple sets of detected peaks are the observed samples of a set of common peaks. We also assume that the locations of the observed peaks follow unimodal distributions (e.g., normal distribution) with their means equal to the corresponding locations of the common peaks and variances reflecting the extension of their variations. Under these assumptions, we convert the problem of estimating locations of the unknown number of common peaks from multiple sets of detected peaks into a much simpler problem of searching for local maxima in the scale-space representation. The optimization of the scale parameter is achieved using an energy minimization approach. We compare our approach with a hierarchical clustering method using both simulated data and real mass spectrometry data. We also demonstrate the merit of extending the binary peak detection method (i.e., a candidate is considered either as a peak or as a nonpeak) with a quantitative scoring measure-based approach (i.e., we assign to each candidate a possibility of being a peak).", "publicationTitle": "IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM", "publisher": "", "place": "", "date": "2006 Jul-Sep", "volume": "3", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "208-219", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "IEEE/ACM Trans Comput Biol Bioinform", "DOI": "10.1109/TCBB.2006.41", "citationKey": "", "url": "http://www.ncbi.nlm.nih.gov/pubmed/17048459", "accessDate": "2012-08-13T12:05:56Z", "PMID": "", "PMCID": "", "ISSN": "1545-5963", "archive": "", "archiveLocation": "", "shortTitle": "Multiple peak alignment in sequential data analysis", "language": "", "libraryCatalog": "NCBI PubMed", "callNumber": "0021", "rights": "", "extra": "PMID: 17048459", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Amino Acid Sequence", "type": 1 }, { "tag": "Molecular Sequence Data", "type": 1 }, { "tag": "Proteins", "type": 1 }, { "tag": "Sequence Alignment", "type": 1 }, { "tag": "Sequence Analysis, Protein", "type": 1 }, { "tag": "Sequence Homology, Amino Acid", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2012-08-13T12:05:56Z", "dateModified": "2012-08-13T12:05:57Z" } } ]