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Abacus: A computational tool for extracting and pre-processing spectral count data for label-free quantitative proteomic analysis

dc.contributor.authorFermin, Damianen_US
dc.contributor.authorBasrur, Venkateshaen_US
dc.contributor.authorYocum, Anastasia K.en_US
dc.contributor.authorNesvizhskii, Alexey I.en_US
dc.date.accessioned2011-04-07T18:52:37Z
dc.date.accessioned2011-04-07T18:52:37Z
dc.date.available2012-05-14T20:40:08Zen_US
dc.date.issued2011-04en_US
dc.identifier.citationFermin, Damian; Basrur, Venkatesha; Yocum, Anastasia K.; Nesvizhskii, Alexey I. (2011). "Abacus: A computational tool for extracting and pre-processing spectral count data for label-free quantitative proteomic analysis." PROTEOMICS 11(7): 1340-1345. <http://hdl.handle.net/2027.42/83477>en_US
dc.identifier.issn1615-9853en_US
dc.identifier.issn1615-9861en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/83477
dc.description.abstractWe describe Abacus, a computational tool for extracting spectral counts from MS/MS data sets. The program aggregates data from multiple experiments, adjusts spectral counts to accurately account for peptides shared across multiple proteins, and performs common normalization steps. It can also output the spectral count data at the gene level, thus simplifying the integration and comparison between gene and protein expression data. Abacus is compatible with the widely used Trans-Proteomic Pipeline suite of tools and comes with a graphical user interface making it easy to interact with the program. The main aim of Abacus is to streamline the analysis of spectral count data by providing an automated, easy to use solution for extracting this information from proteomic data sets for subsequent, more sophisticated statistical analysis.en_US
dc.publisherWILEY-VCH Verlagen_US
dc.subject.otherLife and Medical Sciencesen_US
dc.titleAbacus: A computational tool for extracting and pre-processing spectral count data for label-free quantitative proteomic analysisen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelBiological Chemistryen_US
dc.subject.hlbsecondlevelChemical Engineeringen_US
dc.subject.hlbsecondlevelChemistryen_US
dc.subject.hlbsecondlevelMaterials Science and Engineeringen_US
dc.subject.hlbsecondlevelMolecular, Cellular and Developmental Biologyen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Pathology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Pathology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Pathology, University of Michigan, Ann Arbor, MI, USAen_US
dc.contributor.affiliationumDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA ; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA ; Department of Pathology, University of Michigan, 4237 Medical Science I, Ann Arbor, MI 48109, USA Fax: +1-734-615-2500en_US
dc.identifier.pmid21360675en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/83477/1/1340_ftp.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/83477/2/pmic_201000650_sm_SupplInfo.pdf
dc.identifier.doi10.1002/pmic.201000650en_US
dc.identifier.sourcePROTEOMICSen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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