2.50
Hdl Handle:
http://hdl.handle.net/10541/68763
Title:
Exon level integration of proteomics and microarray data.
Authors:
Bitton, Danny A; Okoniewski, Michal J; Connolly, Yvonne; Miller, Crispin J
Abstract:
BACKGROUND: Previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. We hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing. RESULTS: Using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with Affymetrix Exon array data at the level of individual exons. We found significantly higher degrees of correlation than have been previously observed (r = 0.808). The study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance. CONCLUSION: We conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. The approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome.
Affiliation:
Cancer Research UK, Applied Computational Biology and Bioinformatics Group, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Wilmslow Road, Manchester, M20 4BX, UK. dbitton@picr.man.ac.uk
Citation:
Exon level integration of proteomics and microarray data. 2008, 9:118 BMC Bioinformatics
Journal:
BMC Bioinformatics
Issue Date:
2008
URI:
http://hdl.handle.net/10541/68763
DOI:
10.1186/1471-2105-9-118
PubMed ID:
18298841
Type:
Article
Language:
en
ISSN:
1471-2105
Appears in Collections:
Applied Computational Biology and Bioinformatics; All Paterson Institute for Cancer Research; Molecular Biology Core Facility

Full metadata record

DC FieldValue Language
dc.contributor.authorBitton, Danny A-
dc.contributor.authorOkoniewski, Michal J-
dc.contributor.authorConnolly, Yvonne-
dc.contributor.authorMiller, Crispin J-
dc.date.accessioned2009-05-22T13:05:50Z-
dc.date.available2009-05-22T13:05:50Z-
dc.date.issued2008-
dc.identifier.citationExon level integration of proteomics and microarray data. 2008, 9:118 BMC Bioinformaticsen
dc.identifier.issn1471-2105-
dc.identifier.pmid18298841-
dc.identifier.doi10.1186/1471-2105-9-118-
dc.identifier.urihttp://hdl.handle.net/10541/68763-
dc.description.abstractBACKGROUND: Previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. We hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing. RESULTS: Using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with Affymetrix Exon array data at the level of individual exons. We found significantly higher degrees of correlation than have been previously observed (r = 0.808). The study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance. CONCLUSION: We conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. The approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome.en
dc.language.isoenen
dc.subjectMicroarray Dataen
dc.subject.meshAlgorithms-
dc.subject.meshGene Expression Profiling-
dc.subject.meshOligonucleotide Array Sequence Analysis-
dc.subject.meshPeptide Mapping-
dc.subject.meshProteome-
dc.subject.meshProteomics-
dc.subject.meshRNA Splice Sites-
dc.subject.meshReproducibility of Results-
dc.subject.meshSensitivity and Specificity-
dc.subject.meshSystems Integration-
dc.titleExon level integration of proteomics and microarray data.en
dc.typeArticleen
dc.contributor.departmentCancer Research UK, Applied Computational Biology and Bioinformatics Group, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Wilmslow Road, Manchester, M20 4BX, UK. dbitton@picr.man.ac.uken
dc.identifier.journalBMC Bioinformaticsen

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