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Missing data in open-data era – a barrier to multiomics integration

Missing data in open-data era – a barrier to multiomics integration Currently, high-throughput omics data are popular for describing and analyzing biological processes. They provide information on, for example, the level of gene expression (GEO and ArrayExpress databases), the relation of protein, or metabolites (KEGG, Reactome, and String). Based on modern analytical technology, it is possible to interrogate the status of chromatin modification, gene transcription and translation, protein modification, and activity on tissue and single-cell levels. ‘Omics data analysis has had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs and is widely recognized as a tool for precision medicine [1], [2], [3], [4], [5], [6], [7], [8].This became possible thanks to rapidly developing techniques and methods for data analysis focusing on the interpretation of a specific signal (e.g. sequencing reads).Nevertheless, the multidimensional analysis remains challenging partly because of the size and complex data structure. In ‘omics experiments, typically thousands of hypotheses are tested simultaneously, each often based on few independent replicates. Traditional statistical tests perform poorly with this new type of data [9], [10], [11]. Second, most often analyses are performed on one type of data at a time, providing only limited insights although adding confidence and clarity to biological interpretation through multiple independent pieces of evidence.Despite successes in streamlining the bioinformatics tools and the flow of information between databases [12] that made data analysis accessible to a wider scientific community, quality approaches leading to a breakthrough in personalized medical diagnostics and treatment is yet to come. More recently, attempts have been made to integrate heterogeneous biological data sets with the aim to improve the interpretability of biological signals that might span multiple tissues and timescales and represent systemic changes related to, for instance, the treatment or disease-related changes.To capture these complex relationships, new data analysis tools and data integration platforms are emerging based on well-known and newly developed methodologies and computational algorithms [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. Although this information is very valuable, it does not allow the evaluation of biological processes for a particular case (conditions) with both qualitative description of biological relation and quantitative multidimensional, statistical look. There are very few sets of qualitative data (relation described in biochemical pathways, the interaction of biomolecules, etc.) and quantitative data (level of gene expression, level of metabolites, etc.) available in which data points are derived comparably (e.g. collected from the same individuals or scoring units). This makes the integration of biostatistical data difficult and it is not necessarily convertible into applications (e.g. in medicine).To move the field forward, it is therefore crucial to comprehensively collect data on both molecular ‘omics (genomics, metabolomics, etc.) and phenomics levels, including all necessary clinical and epidemiological metadata and with the number of biological and technical replicates matching criteria set by appropriate study design. High-quality, interpreted, and validated data are needed as a base for algorithm development and testing.Currently, the best data coverage of open-data sets is achieved for array-based transcriptomics and sequencing, mostly in humans. Both ArrayExpress and MetaboLights are popular repositories and widely suggested by scientific journals to deposit paper accompanying data sets. The content summary reflects the current landscape of the publicly available functional ‘omics data. ArrayExpress provides to date the most comprehensive overview of molecular assay comprising experiments. As of 29 November 2017, it includes 70,521 mostly transcriptomics experiments (57,588 transcription profiling, including 8134 RNA-seq coding and 1831 RNA-seq noncoding experiments) and a minor fraction of DNA data (205 DNA-seq and 5294 ChIP-seq experiments), proteomics (188 proteomic and 23 translation profiling), and a number of other data types. Complementary ‘omics assays are in the minority and cover mostly sequencing data (1927 paired sequencing assays; e.g. RNA-seq for both coding and noncoding sequences; RNA-seq and ChIP-seq assays), except for four experiments that cover both transcriptomics and proteomic profiling data sets. MetaboLights is a major repository for metabolomics data, but only 313 experiments are publicly shared and none clearly indicated as being accompanied by other data sets. The European Nucleotide Archive and Sequence Read Archive contains a vast space of sequencing data (891.8 sequences deposited ENA), but its lack of functional metadata makes it very difficult to search multiple data types across studies, except finding coding or noncoding sequence but without information about experiment type (transcription profiling vs. genotyping).Therefore, a broad application of FAIR data principles [24] is necessary to ensure date reusability.Despite the necessity of data findability and accessibility, a data sharing process that would provide straightforward compatibility with other data sets and tools has been advocated over the last decade, as not much has been done to practically lower barriers for data reuse. It is partially because data standardization, management, and sharing to appropriately explore ‘omics data proved to be also a big challenge [25], but most importantly because the data are not reaching the public domain where it could be found. For all of this to happen, it is necessary that publishers start demanding more transparency in relation to data generation and analysis and ideally adopt recent developments provided by F1000 that allow users to interact with the data while reading [26], [27].Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.Research funding: None declared.Employment or leadership: None declared.Honorarium: None declared.Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.References[1]Tebani A, Afonso C, Marret S, Bekri S. Omics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations.. Int J Mol Sci. 2016;17(9): E1555.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000385525500175&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f310.3390/ijms17091555TebaniAAfonsoCMarretSBekriSOmics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations.Int J Mol Sci2016179E1555[2]Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for the masses: the future of metabolomics in a personalized world. New Horizons Transl Med [Internet] 2017;3:294–305.TrivediDKHollywoodKAGoodacreRMetabolomics for the masses: the future of metabolomics in a personalized worldNew Horizons Transl Med [Internet]20173294305[3]Chen HH, Kuo MT. Improving radiotherapy in cancer treatment: promises and challenges [Internet]. Oncotarget 2017;8:62742–58.ChenHHKuoMTImproving radiotherapy in cancer treatment: promises and challenges [Internet]Oncotarget201786274258[4]Weinshilboum RM, Wang L. Pharmacogenomics: precision medicine and drug response [Internet]. Mayo Clin Proc 2017;92:1711–22.10.1016/j.mayocp.2017.09.001WeinshilboumRMWangLPharmacogenomics: precision medicine and drug response [Internet]Mayo Clin Proc201792171122[5]Neavin D, Kaddurah-Daouk R, Weinshilboum R. Pharmacometabolomics informs pharmacogenomics [Internet]. Metabolomics 2016;12:121.10.1007/s11306-016-1066-xNeavinDKaddurah-DaoukRWeinshilboumRPharmacometabolomics informs pharmacogenomics [Internet]Metabolomics201612121[6]Senft D, Leiserson MD, Ruppin E, Ronai ZA. Precision oncology: the road ahead [Internet]. Trends Mol Med 2017;23:874–98.10.1016/j.molmed.2017.08.003SenftDLeisersonMDRuppinERonaiZAPrecision oncology: the road ahead [Internet]Trends Mol Med20172387498[7]Kan M, Shumyatcher M, Himes BE. Using omics approaches to understand pulmonary diseases [Internet]. Respir Res 2017;18:149.10.1186/s12931-017-0631-9KanMShumyatcherMHimesBEUsing omics approaches to understand pulmonary diseases [Internet]Respir Res201718149[8]Moran S, Martinez-Cardús A, Boussios S, Esteller M. Precision medicine based on epigenomics: the paradigm of carcinoma of unknown primary [Internet]. Nat Rev Clin Oncol 2017;14:682–94.10.1038/nrclinonc.2017.97MoranSMartinez-CardúsABoussiosSEstellerMPrecision medicine based on epigenomics: the paradigm of carcinoma of unknown primary [Internet]Nat Rev Clin Oncol20171468294[9]Dunkler D, Sánchez-Cabo F, Heinze G. Statistical analysis principles for omics data. Methods Mol Biol 2011;719:113–31.10.1007/978-1-61779-027-0_521370081http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000288923900005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3DunklerDSánchez-CaboFHeinzeGStatistical analysis principles for omics dataMethods Mol Biol201171911331[10]Gottardo R, Pannucci JA, Kuske CR, Brettin T. Statistical analysis of microarray data: a Bayesian approach. Biostatistics 2003;4:597–620.10.1093/biostatistics/4.4.597GottardoRPannucciJAKuskeCRBrettinTStatistical analysis of microarray data: a Bayesian approachBiostatistics20034597620[11]Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L, Vineis P, et al. Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers [Internet]. Environ Mol Mutagen 2013;54:542–57.10.1002/em.21797Chadeau-HyamMCampanellaGJombartTBottoloLPortengenLVineisPDeciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers [Internet]Environ Mol Mutagen20135454257[12]Leipzig J. A review of bioinformatic pipeline frameworks [Internet]. Brief Bioinform 2016;18:bbw020.LeipzigJA review of bioinformatic pipeline frameworks [Internet]Brief Bioinform201618bbw020[13]Merrick BA, London RE, Bushel PR, Grissom SF, Paules RS. Platforms for biomarker analysis using high-throughput approaches in genomics, transcriptomics, proteomics, metabolomics, and bioinformatics. IARC Sci Publ. 2011;(163):121–42.22997859MerrickBALondonREBushelPRGrissomSFPaulesRSPlatforms for biomarker analysis using high-throughput approaches in genomics, transcriptomics, proteomics, metabolomics, and bioinformaticsIARC Sci Publ201116312142[14]Waller T, Gubała T, Sarapata K, Piwowar M, Jurkowski W. DNA microarray integromics analysis platform [Internet]. BioData Min 2015;8:18.10.1186/s13040-015-0052-6WallerTGubałaTSarapataKPiwowarMJurkowskiWDNA microarray integromics analysis platform [Internet]BioData Min2015818[15]Grene R, Klumas C, Suren H, Yang K, Collakova E, Myers E, et al. Mining and visualization of microarray and metabolomic data reveal extensive cell wall remodeling during winter hardening in Sitka spruce (Picea sitchensis). Front Plant Sci 2012;3:241.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000208837900237&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f323112803GreneRKlumasCSurenHYangKCollakovaEMyersEMining and visualization of microarray and metabolomic data reveal extensive cell wall remodeling during winter hardening in Sitka spruce (Picea sitchensis)Front Plant Sci20123241[16]Li S, Todor A, Luo R. Blood transcriptomics and metabolomics for personalized medicine. Comput Struct Biotechnol J 2016;14:1–7.10.1016/j.csbj.2015.10.00526702339http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000392630000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3LiSTodorALuoRBlood transcriptomics and metabolomics for personalized medicineComput Struct Biotechnol J20161417[17]Su G, Burant CF, Beecher CW, Athey BD, Meng F, Ferrara C, et al. Integrated metabolome and transcriptome analysis of the NCI60 dataset. BMC Bioinform 2011;12:S36.10.1186/1471-2105-12-S1-S36SuGBurantCFBeecherCWAtheyBDMengFFerraraCIntegrated metabolome and transcriptome analysis of the NCI60 datasetBMC Bioinform201112S36[18]Kamburov A, Cavill R, Ebbels TM, Herwig R, Keun HC. Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA [Internet]. Bioinformatics 2011;27:2917–8.10.1093/bioinformatics/btr49921893519KamburovACavillREbbelsTMHerwigRKeunHCIntegrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA [Internet]Bioinformatics20112729178[19]Kuo T-C, Tian T-F, Tseng Y, Kolbe A, Oliver S, Fernie A, et al. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol 2013;7:64.10.1186/1752-0509-7-6423875761http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000322266000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3KuoT-CTianT-FTsengYKolbeAOliverSFernieA3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic dataBMC Syst Biol2013764[20]Lin D, Zhang J, Li J, Xu C, Deng H-W, Wang Y-P. An integrative imputation method based on multi-omics datasets. BMC Bioinform 2016;17:247.10.1186/s12859-016-1122-6LinDZhangJLiJXuCDengH-WWangY-PAn integrative imputation method based on multi-omics datasetsBMC Bioinform201617247[21]Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, et al. Multi-omics factor analysis disentangles heterogeneity in blood cancer [Internet]. bioRxiv 2017. Article no: 217554. https://www.biorxiv.org/content/early/2017/11/10/217554.ArgelaguetRVeltenBArnolDDietrichSZenzTMarioniJCMulti-omics factor analysis disentangles heterogeneity in blood cancer [Internet]bioRxiv2017217554https://www.biorxiv.org/content/early/2017/11/10/217554[22]Acharjee A, Ament Z, West JA, Stanley E, Griffin JL. Integration of metabolomics, lipidomics and clinical data using a machine learning method. BMC Bioinform 2016;17:440.10.1186/s12859-016-1292-2AcharjeeAAmentZWestJAStanleyEGriffinJLIntegration of metabolomics, lipidomics and clinical data using a machine learning methodBMC Bioinform201617440[23]Acharjee A, Kloosterman B, de Vos RC, Werij JS, Bachem CW, Visser RG, et al. Data integration and network reconstruction with ∼omics data using random forest regression in potato. Anal Chim Acta 2011;705:56–63.10.1016/j.aca.2011.03.050http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000295993900009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3AcharjeeAKloostermanBde VosRCWerijJSBachemCWVisserRGData integration and network reconstruction with ∼omics data using random forest regression in potatoAnal Chim Acta20117055663[24]Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship [Internet]. Sci Data 2016;3. Article no: 160018.WilkinsonMDDumontierMAalbersbergIJAppletonGAxtonMBaakAThe FAIR Guiding Principles for scientific data management and stewardship [Internet]Sci Data20161600183[25]Schneider MV, Orchard S. Omics technologies, data and bioinformatics principles. Methods Mol Biol 2011;719:3–30.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000288923900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f32137007710.1007/978-1-61779-027-0_1SchneiderMVOrchardSOmics technologies, data and bioinformatics principlesMethods Mol Biol2011719330[26]Fletcher B. First paper with “living figure” published. Available at: https://www.biosciencetechnology.com/article/2015/04/first-paper-living-figure-published. Accessed: 24 Jan 2018.FletcherBFirst paper with “living figure” publishedAvailable at: https://www.biosciencetechnology.com/article/2015/04/first-paper-living-figure-publishedAccessed: 24 Jan 2018[27]Colomb J, Brembs B. Sub-strains of Drosophila Canton-S differ markedly in their locomotor behavior [Version 2]. F1000Res. 2015;3:176. DOI: 10.12688/f1000research.4263.ColombJBrembsBSub-strains of Drosophila Canton-S differ markedly in their locomotor behavior [Version 2]F1000Res2015317610.12688/f1000research.4263 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bio-Algorithms and Med-Systems de Gruyter

Missing data in open-data era – a barrier to multiomics integration

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de Gruyter
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©2018 Walter de Gruyter GmbH, Berlin/Boston
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1896-530X
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1896-530X
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10.1515/bams-2017-0026
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Abstract

Currently, high-throughput omics data are popular for describing and analyzing biological processes. They provide information on, for example, the level of gene expression (GEO and ArrayExpress databases), the relation of protein, or metabolites (KEGG, Reactome, and String). Based on modern analytical technology, it is possible to interrogate the status of chromatin modification, gene transcription and translation, protein modification, and activity on tissue and single-cell levels. ‘Omics data analysis has had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs and is widely recognized as a tool for precision medicine [1], [2], [3], [4], [5], [6], [7], [8].This became possible thanks to rapidly developing techniques and methods for data analysis focusing on the interpretation of a specific signal (e.g. sequencing reads).Nevertheless, the multidimensional analysis remains challenging partly because of the size and complex data structure. In ‘omics experiments, typically thousands of hypotheses are tested simultaneously, each often based on few independent replicates. Traditional statistical tests perform poorly with this new type of data [9], [10], [11]. Second, most often analyses are performed on one type of data at a time, providing only limited insights although adding confidence and clarity to biological interpretation through multiple independent pieces of evidence.Despite successes in streamlining the bioinformatics tools and the flow of information between databases [12] that made data analysis accessible to a wider scientific community, quality approaches leading to a breakthrough in personalized medical diagnostics and treatment is yet to come. More recently, attempts have been made to integrate heterogeneous biological data sets with the aim to improve the interpretability of biological signals that might span multiple tissues and timescales and represent systemic changes related to, for instance, the treatment or disease-related changes.To capture these complex relationships, new data analysis tools and data integration platforms are emerging based on well-known and newly developed methodologies and computational algorithms [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. Although this information is very valuable, it does not allow the evaluation of biological processes for a particular case (conditions) with both qualitative description of biological relation and quantitative multidimensional, statistical look. There are very few sets of qualitative data (relation described in biochemical pathways, the interaction of biomolecules, etc.) and quantitative data (level of gene expression, level of metabolites, etc.) available in which data points are derived comparably (e.g. collected from the same individuals or scoring units). This makes the integration of biostatistical data difficult and it is not necessarily convertible into applications (e.g. in medicine).To move the field forward, it is therefore crucial to comprehensively collect data on both molecular ‘omics (genomics, metabolomics, etc.) and phenomics levels, including all necessary clinical and epidemiological metadata and with the number of biological and technical replicates matching criteria set by appropriate study design. High-quality, interpreted, and validated data are needed as a base for algorithm development and testing.Currently, the best data coverage of open-data sets is achieved for array-based transcriptomics and sequencing, mostly in humans. Both ArrayExpress and MetaboLights are popular repositories and widely suggested by scientific journals to deposit paper accompanying data sets. The content summary reflects the current landscape of the publicly available functional ‘omics data. ArrayExpress provides to date the most comprehensive overview of molecular assay comprising experiments. As of 29 November 2017, it includes 70,521 mostly transcriptomics experiments (57,588 transcription profiling, including 8134 RNA-seq coding and 1831 RNA-seq noncoding experiments) and a minor fraction of DNA data (205 DNA-seq and 5294 ChIP-seq experiments), proteomics (188 proteomic and 23 translation profiling), and a number of other data types. Complementary ‘omics assays are in the minority and cover mostly sequencing data (1927 paired sequencing assays; e.g. RNA-seq for both coding and noncoding sequences; RNA-seq and ChIP-seq assays), except for four experiments that cover both transcriptomics and proteomic profiling data sets. MetaboLights is a major repository for metabolomics data, but only 313 experiments are publicly shared and none clearly indicated as being accompanied by other data sets. The European Nucleotide Archive and Sequence Read Archive contains a vast space of sequencing data (891.8 sequences deposited ENA), but its lack of functional metadata makes it very difficult to search multiple data types across studies, except finding coding or noncoding sequence but without information about experiment type (transcription profiling vs. genotyping).Therefore, a broad application of FAIR data principles [24] is necessary to ensure date reusability.Despite the necessity of data findability and accessibility, a data sharing process that would provide straightforward compatibility with other data sets and tools has been advocated over the last decade, as not much has been done to practically lower barriers for data reuse. It is partially because data standardization, management, and sharing to appropriately explore ‘omics data proved to be also a big challenge [25], but most importantly because the data are not reaching the public domain where it could be found. For all of this to happen, it is necessary that publishers start demanding more transparency in relation to data generation and analysis and ideally adopt recent developments provided by F1000 that allow users to interact with the data while reading [26], [27].Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.Research funding: None declared.Employment or leadership: None declared.Honorarium: None declared.Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.References[1]Tebani A, Afonso C, Marret S, Bekri S. Omics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations.. Int J Mol Sci. 2016;17(9): E1555.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000385525500175&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f310.3390/ijms17091555TebaniAAfonsoCMarretSBekriSOmics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations.Int J Mol Sci2016179E1555[2]Trivedi DK, Hollywood KA, Goodacre R. Metabolomics for the masses: the future of metabolomics in a personalized world. New Horizons Transl Med [Internet] 2017;3:294–305.TrivediDKHollywoodKAGoodacreRMetabolomics for the masses: the future of metabolomics in a personalized worldNew Horizons Transl Med [Internet]20173294305[3]Chen HH, Kuo MT. Improving radiotherapy in cancer treatment: promises and challenges [Internet]. Oncotarget 2017;8:62742–58.ChenHHKuoMTImproving radiotherapy in cancer treatment: promises and challenges [Internet]Oncotarget201786274258[4]Weinshilboum RM, Wang L. Pharmacogenomics: precision medicine and drug response [Internet]. Mayo Clin Proc 2017;92:1711–22.10.1016/j.mayocp.2017.09.001WeinshilboumRMWangLPharmacogenomics: precision medicine and drug response [Internet]Mayo Clin Proc201792171122[5]Neavin D, Kaddurah-Daouk R, Weinshilboum R. Pharmacometabolomics informs pharmacogenomics [Internet]. Metabolomics 2016;12:121.10.1007/s11306-016-1066-xNeavinDKaddurah-DaoukRWeinshilboumRPharmacometabolomics informs pharmacogenomics [Internet]Metabolomics201612121[6]Senft D, Leiserson MD, Ruppin E, Ronai ZA. Precision oncology: the road ahead [Internet]. Trends Mol Med 2017;23:874–98.10.1016/j.molmed.2017.08.003SenftDLeisersonMDRuppinERonaiZAPrecision oncology: the road ahead [Internet]Trends Mol Med20172387498[7]Kan M, Shumyatcher M, Himes BE. Using omics approaches to understand pulmonary diseases [Internet]. Respir Res 2017;18:149.10.1186/s12931-017-0631-9KanMShumyatcherMHimesBEUsing omics approaches to understand pulmonary diseases [Internet]Respir Res201718149[8]Moran S, Martinez-Cardús A, Boussios S, Esteller M. Precision medicine based on epigenomics: the paradigm of carcinoma of unknown primary [Internet]. Nat Rev Clin Oncol 2017;14:682–94.10.1038/nrclinonc.2017.97MoranSMartinez-CardúsABoussiosSEstellerMPrecision medicine based on epigenomics: the paradigm of carcinoma of unknown primary [Internet]Nat Rev Clin Oncol20171468294[9]Dunkler D, Sánchez-Cabo F, Heinze G. Statistical analysis principles for omics data. Methods Mol Biol 2011;719:113–31.10.1007/978-1-61779-027-0_521370081http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000288923900005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3DunklerDSánchez-CaboFHeinzeGStatistical analysis principles for omics dataMethods Mol Biol201171911331[10]Gottardo R, Pannucci JA, Kuske CR, Brettin T. Statistical analysis of microarray data: a Bayesian approach. Biostatistics 2003;4:597–620.10.1093/biostatistics/4.4.597GottardoRPannucciJAKuskeCRBrettinTStatistical analysis of microarray data: a Bayesian approachBiostatistics20034597620[11]Chadeau-Hyam M, Campanella G, Jombart T, Bottolo L, Portengen L, Vineis P, et al. Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers [Internet]. Environ Mol Mutagen 2013;54:542–57.10.1002/em.21797Chadeau-HyamMCampanellaGJombartTBottoloLPortengenLVineisPDeciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers [Internet]Environ Mol Mutagen20135454257[12]Leipzig J. A review of bioinformatic pipeline frameworks [Internet]. Brief Bioinform 2016;18:bbw020.LeipzigJA review of bioinformatic pipeline frameworks [Internet]Brief Bioinform201618bbw020[13]Merrick BA, London RE, Bushel PR, Grissom SF, Paules RS. Platforms for biomarker analysis using high-throughput approaches in genomics, transcriptomics, proteomics, metabolomics, and bioinformatics. IARC Sci Publ. 2011;(163):121–42.22997859MerrickBALondonREBushelPRGrissomSFPaulesRSPlatforms for biomarker analysis using high-throughput approaches in genomics, transcriptomics, proteomics, metabolomics, and bioinformaticsIARC Sci Publ201116312142[14]Waller T, Gubała T, Sarapata K, Piwowar M, Jurkowski W. DNA microarray integromics analysis platform [Internet]. BioData Min 2015;8:18.10.1186/s13040-015-0052-6WallerTGubałaTSarapataKPiwowarMJurkowskiWDNA microarray integromics analysis platform [Internet]BioData Min2015818[15]Grene R, Klumas C, Suren H, Yang K, Collakova E, Myers E, et al. Mining and visualization of microarray and metabolomic data reveal extensive cell wall remodeling during winter hardening in Sitka spruce (Picea sitchensis). Front Plant Sci 2012;3:241.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000208837900237&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f323112803GreneRKlumasCSurenHYangKCollakovaEMyersEMining and visualization of microarray and metabolomic data reveal extensive cell wall remodeling during winter hardening in Sitka spruce (Picea sitchensis)Front Plant Sci20123241[16]Li S, Todor A, Luo R. Blood transcriptomics and metabolomics for personalized medicine. Comput Struct Biotechnol J 2016;14:1–7.10.1016/j.csbj.2015.10.00526702339http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000392630000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3LiSTodorALuoRBlood transcriptomics and metabolomics for personalized medicineComput Struct Biotechnol J20161417[17]Su G, Burant CF, Beecher CW, Athey BD, Meng F, Ferrara C, et al. Integrated metabolome and transcriptome analysis of the NCI60 dataset. BMC Bioinform 2011;12:S36.10.1186/1471-2105-12-S1-S36SuGBurantCFBeecherCWAtheyBDMengFFerraraCIntegrated metabolome and transcriptome analysis of the NCI60 datasetBMC Bioinform201112S36[18]Kamburov A, Cavill R, Ebbels TM, Herwig R, Keun HC. Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA [Internet]. 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Journal

Bio-Algorithms and Med-Systems – de Gruyter

Published: Feb 21, 2018

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