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Jetset: selecting the optimal microarray probe set to represent a gene
Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous express...
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Published in: | BMC bioinformatics 2011-12, Vol.12 (1), p.474-474, Article 474 |
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creator | Li, Qiyuan Birkbak, Nicolai J Gyorffy, Balazs Szallasi, Zoltan Eklund, Aron C |
description | Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task.
We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance.
This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest. |
doi_str_mv | 10.1186/1471-2105-12-474 |
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We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance.
This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest.</description><identifier>ISSN: 1471-2105</identifier><identifier>EISSN: 1471-2105</identifier><identifier>DOI: 10.1186/1471-2105-12-474</identifier><identifier>PMID: 22172014</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Breast Neoplasms - genetics ; Design ; DNA microarrays ; Exons ; Female ; Gene expression ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Genes ; Health sciences ; Humans ; Methodology ; Oligonucleotide Array Sequence Analysis - methods ; Physiological aspects ; Receptor, ErbB-2 - genetics ; Receptors, Estrogen - genetics ; Sensitivity and Specificity ; Studies</subject><ispartof>BMC bioinformatics, 2011-12, Vol.12 (1), p.474-474, Article 474</ispartof><rights>COPYRIGHT 2011 BioMed Central Ltd.</rights><rights>2011 Li et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright ©2011 Li et al; licensee BioMed Central Ltd. 2011 Li et al; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b715t-9a761aad69670935eb39e9d09801e64580780b600f09fe5212a21d72438619363</citedby><cites>FETCH-LOGICAL-b715t-9a761aad69670935eb39e9d09801e64580780b600f09fe5212a21d72438619363</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3266307/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/917561561?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22172014$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Qiyuan</creatorcontrib><creatorcontrib>Birkbak, Nicolai J</creatorcontrib><creatorcontrib>Gyorffy, Balazs</creatorcontrib><creatorcontrib>Szallasi, Zoltan</creatorcontrib><creatorcontrib>Eklund, Aron C</creatorcontrib><title>Jetset: selecting the optimal microarray probe set to represent a gene</title><title>BMC bioinformatics</title><addtitle>BMC Bioinformatics</addtitle><description>Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task.
We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance.
This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest.</description><subject>Breast Neoplasms - genetics</subject><subject>Design</subject><subject>DNA microarrays</subject><subject>Exons</subject><subject>Female</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Genes</subject><subject>Health sciences</subject><subject>Humans</subject><subject>Methodology</subject><subject>Oligonucleotide Array Sequence Analysis - methods</subject><subject>Physiological aspects</subject><subject>Receptor, ErbB-2 - genetics</subject><subject>Receptors, Estrogen - genetics</subject><subject>Sensitivity and Specificity</subject><subject>Studies</subject><issn>1471-2105</issn><issn>1471-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp1kk1v1DAQhiMEoqVw54QiOFQcUjyOY8cckKoVhUWVkPg4W44zSbPKxlvbi9p_zyxbVg0qiqVY42dej-edLHsJ7Ayglu9AKCg4sKoAXgglHmXHh9Dje_uj7FmMK8ZA1ax6mh1xDoozEMfZxRdMEdP7POKILg1Tn6crzP0mDWs75uvBBW9DsLf5JvgGCUt58nnATcCIU8pt3uOEz7MnnR0jvrj7n2Q_Lz7-WHwuLr9-Wi7OL4tGQZUKbZUEa1uppWK6rLApNeqW6ZoBSlHVjApsJGMd0x1WHLjl0CouylqCLmV5ki33uq23K7MJVGS4Nd4O5k_Ah97YkAY3opFCubpWitvKCVHXWjgrQTgumWVN60jrw15rs23W2Dp6TbDjTHR-Mg1Xpve_TMmlLJkigcVeoBn8fwTmJ86vzc4Ts_PEADdkGamc3pUR_PUWYzLrITocRzuh30ajoa5UqVlF5Ot_yJXfhon6TZCqJNAi6M0e6i01YZg6T1e7naQ559RdkErvLj17gKKvRbLcT9gNFJ8lvJ0lEJPwJvV2G6NZfv82Z9mepdmJMWB3aAkws5vbh5rw6r4Xh4S_g1r-BlKl44c</recordid><startdate>20111215</startdate><enddate>20111215</enddate><creator>Li, Qiyuan</creator><creator>Birkbak, Nicolai J</creator><creator>Gyorffy, Balazs</creator><creator>Szallasi, Zoltan</creator><creator>Eklund, Aron C</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20111215</creationdate><title>Jetset: selecting the optimal microarray probe set to represent a gene</title><author>Li, Qiyuan ; Birkbak, Nicolai J ; Gyorffy, Balazs ; Szallasi, Zoltan ; Eklund, Aron C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b715t-9a761aad69670935eb39e9d09801e64580780b600f09fe5212a21d72438619363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Breast Neoplasms - genetics</topic><topic>Design</topic><topic>DNA microarrays</topic><topic>Exons</topic><topic>Female</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Genes</topic><topic>Health sciences</topic><topic>Humans</topic><topic>Methodology</topic><topic>Oligonucleotide Array Sequence Analysis - methods</topic><topic>Physiological aspects</topic><topic>Receptor, ErbB-2 - genetics</topic><topic>Receptors, Estrogen - genetics</topic><topic>Sensitivity and Specificity</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Qiyuan</creatorcontrib><creatorcontrib>Birkbak, Nicolai J</creatorcontrib><creatorcontrib>Gyorffy, Balazs</creatorcontrib><creatorcontrib>Szallasi, Zoltan</creatorcontrib><creatorcontrib>Eklund, Aron C</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Science (Gale in Context)</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>BMC bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Qiyuan</au><au>Birkbak, Nicolai J</au><au>Gyorffy, Balazs</au><au>Szallasi, Zoltan</au><au>Eklund, Aron C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Jetset: selecting the optimal microarray probe set to represent a gene</atitle><jtitle>BMC bioinformatics</jtitle><addtitle>BMC Bioinformatics</addtitle><date>2011-12-15</date><risdate>2011</risdate><volume>12</volume><issue>1</issue><spage>474</spage><epage>474</epage><pages>474-474</pages><artnum>474</artnum><issn>1471-2105</issn><eissn>1471-2105</eissn><abstract>Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver inconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a pre-specified gene can be a nontrivial but essential task.
We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and robustness against transcript degradation. We used these scores to select a single representative probe set for each gene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated concordance between protein measurements and gene expression values, and between sets of genes whose expression is known to be correlated. For both test cases, we identified genes that were nominally detected by multiple probe sets, and we found that the probe set chosen by our method showed stronger concordance.
This method provides a simple, unambiguous mapping to allow assessment of the expression levels of specific genes of interest.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>22172014</pmid><doi>10.1186/1471-2105-12-474</doi><oa>free_for_read</oa></addata></record> |
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subjects | Breast Neoplasms - genetics Design DNA microarrays Exons Female Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Genes Health sciences Humans Methodology Oligonucleotide Array Sequence Analysis - methods Physiological aspects Receptor, ErbB-2 - genetics Receptors, Estrogen - genetics Sensitivity and Specificity Studies |
title | Jetset: selecting the optimal microarray probe set to represent a gene |
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