Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme
Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualiz...
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Veröffentlicht in: | Genome medicine 2010-09, Vol.2 (9), p.65-65, Article 65 |
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creator | Ovaska, Kristian Laakso, Marko Haapa-Paananen, Saija Louhimo, Riku Chen, Ping Aittomäki, Viljami Valo, Erkka Núñez-Fontarnau, Javier Rantanen, Ville Karinen, Sirkku Nousiainen, Kari Lahesmaa-Korpinen, Anna-Maria Miettinen, Minna Saarinen, Lilli Kohonen, Pekka Wu, Jianmin Westermarck, Jukka Hautaniemi, Sampsa |
description | Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed.
We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.
We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.
Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/ |
doi_str_mv | 10.1186/gm186 |
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We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.
We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.
Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/</description><identifier>ISSN: 1756-994X</identifier><identifier>EISSN: 1756-994X</identifier><identifier>DOI: 10.1186/gm186</identifier><identifier>PMID: 20822536</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Analysis ; Glioblastoma multiforme ; Information management</subject><ispartof>Genome medicine, 2010-09, Vol.2 (9), p.65-65, Article 65</ispartof><rights>COPYRIGHT 2010 BioMed Central Ltd.</rights><rights>Copyright ©2010 Ovaska et al.; licensee BioMed Central Ltd. 2010 Ovaska et al.; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b607t-9161f9c949b44c5c692443c6664d062470207c94d8c9ad2fe6971afa1f1186463</citedby><cites>FETCH-LOGICAL-b607t-9161f9c949b44c5c692443c6664d062470207c94d8c9ad2fe6971afa1f1186463</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/PMC3092116/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3092116/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20822536$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ovaska, Kristian</creatorcontrib><creatorcontrib>Laakso, Marko</creatorcontrib><creatorcontrib>Haapa-Paananen, Saija</creatorcontrib><creatorcontrib>Louhimo, Riku</creatorcontrib><creatorcontrib>Chen, Ping</creatorcontrib><creatorcontrib>Aittomäki, Viljami</creatorcontrib><creatorcontrib>Valo, Erkka</creatorcontrib><creatorcontrib>Núñez-Fontarnau, Javier</creatorcontrib><creatorcontrib>Rantanen, Ville</creatorcontrib><creatorcontrib>Karinen, Sirkku</creatorcontrib><creatorcontrib>Nousiainen, Kari</creatorcontrib><creatorcontrib>Lahesmaa-Korpinen, Anna-Maria</creatorcontrib><creatorcontrib>Miettinen, Minna</creatorcontrib><creatorcontrib>Saarinen, Lilli</creatorcontrib><creatorcontrib>Kohonen, Pekka</creatorcontrib><creatorcontrib>Wu, Jianmin</creatorcontrib><creatorcontrib>Westermarck, Jukka</creatorcontrib><creatorcontrib>Hautaniemi, Sampsa</creatorcontrib><title>Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme</title><title>Genome medicine</title><addtitle>Genome Med</addtitle><description>Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed.
We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.
We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.
Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/</description><subject>Analysis</subject><subject>Glioblastoma multiforme</subject><subject>Information management</subject><issn>1756-994X</issn><issn>1756-994X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNqFktFqFDEUhgdRbK19BQmI9mpqkslkJjdCKa0WFrxR8C5kMiez0WSyJjNbfPtm3LrsQkUCScj5zn9y_qQozgm-JKTlHwaf52fFKWlqXgrBvj8_2J8Ur1L6gTFnlDUvixOKW0rrip8WcqXiAGXSygHq1aSQHScYoppsGJGJysN9iD_RJoat7SEhhXTwmwhrGJPdAtpauEcZHZwNnVNpCl4hP7vJmhA9vC5eGOUSnD-uZ8W325uv15_L1ZdPd9dXq7LjuJlKQTgxQgsmOsZ0rbmgjFWac856zPOlMcVNDvetFqqnBrhoiDKKmKV5xquz4uNOdzN3HnoN4xSVk5tovYq_ZVBWHkdGu5ZD2MoKC0rIItDuBDob_iFwHMk2yD-m59SLx9ox_JohTdLbpME5NUKYk2yb3AGrWf1fsuGY1Kyp20y-3ZFDfhhpRxNyUb3Q8opWDWW5a5ypyyeoPHrwVocRjM3nRwnvDxLWoNy0TsHNy2unY_DdDtQxpBTB7J0gWC6e73t_c-j7nvr7w6oHOpvRcA</recordid><startdate>20100907</startdate><enddate>20100907</enddate><creator>Ovaska, Kristian</creator><creator>Laakso, Marko</creator><creator>Haapa-Paananen, Saija</creator><creator>Louhimo, Riku</creator><creator>Chen, Ping</creator><creator>Aittomäki, Viljami</creator><creator>Valo, Erkka</creator><creator>Núñez-Fontarnau, Javier</creator><creator>Rantanen, Ville</creator><creator>Karinen, Sirkku</creator><creator>Nousiainen, Kari</creator><creator>Lahesmaa-Korpinen, Anna-Maria</creator><creator>Miettinen, Minna</creator><creator>Saarinen, Lilli</creator><creator>Kohonen, Pekka</creator><creator>Wu, Jianmin</creator><creator>Westermarck, Jukka</creator><creator>Hautaniemi, Sampsa</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>5PM</scope></search><sort><creationdate>20100907</creationdate><title>Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme</title><author>Ovaska, Kristian ; Laakso, Marko ; Haapa-Paananen, Saija ; Louhimo, Riku ; Chen, Ping ; Aittomäki, Viljami ; Valo, Erkka ; Núñez-Fontarnau, Javier ; Rantanen, Ville ; Karinen, Sirkku ; Nousiainen, Kari ; Lahesmaa-Korpinen, Anna-Maria ; Miettinen, Minna ; Saarinen, Lilli ; Kohonen, Pekka ; Wu, Jianmin ; Westermarck, Jukka ; Hautaniemi, Sampsa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b607t-9161f9c949b44c5c692443c6664d062470207c94d8c9ad2fe6971afa1f1186463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Analysis</topic><topic>Glioblastoma multiforme</topic><topic>Information management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ovaska, Kristian</creatorcontrib><creatorcontrib>Laakso, Marko</creatorcontrib><creatorcontrib>Haapa-Paananen, Saija</creatorcontrib><creatorcontrib>Louhimo, Riku</creatorcontrib><creatorcontrib>Chen, Ping</creatorcontrib><creatorcontrib>Aittomäki, Viljami</creatorcontrib><creatorcontrib>Valo, Erkka</creatorcontrib><creatorcontrib>Núñez-Fontarnau, Javier</creatorcontrib><creatorcontrib>Rantanen, Ville</creatorcontrib><creatorcontrib>Karinen, Sirkku</creatorcontrib><creatorcontrib>Nousiainen, Kari</creatorcontrib><creatorcontrib>Lahesmaa-Korpinen, Anna-Maria</creatorcontrib><creatorcontrib>Miettinen, Minna</creatorcontrib><creatorcontrib>Saarinen, Lilli</creatorcontrib><creatorcontrib>Kohonen, Pekka</creatorcontrib><creatorcontrib>Wu, Jianmin</creatorcontrib><creatorcontrib>Westermarck, Jukka</creatorcontrib><creatorcontrib>Hautaniemi, Sampsa</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Genome medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ovaska, Kristian</au><au>Laakso, Marko</au><au>Haapa-Paananen, Saija</au><au>Louhimo, Riku</au><au>Chen, Ping</au><au>Aittomäki, Viljami</au><au>Valo, Erkka</au><au>Núñez-Fontarnau, Javier</au><au>Rantanen, Ville</au><au>Karinen, Sirkku</au><au>Nousiainen, Kari</au><au>Lahesmaa-Korpinen, Anna-Maria</au><au>Miettinen, Minna</au><au>Saarinen, Lilli</au><au>Kohonen, Pekka</au><au>Wu, Jianmin</au><au>Westermarck, Jukka</au><au>Hautaniemi, Sampsa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme</atitle><jtitle>Genome medicine</jtitle><addtitle>Genome Med</addtitle><date>2010-09-07</date><risdate>2010</risdate><volume>2</volume><issue>9</issue><spage>65</spage><epage>65</epage><pages>65-65</pages><artnum>65</artnum><issn>1756-994X</issn><eissn>1756-994X</eissn><abstract>Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed.
We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.
We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.
Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>20822536</pmid><doi>10.1186/gm186</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Glioblastoma multiforme Information management |
title | Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme |
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