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Advanced Data Analytics in Health pp 201–216Cite as

Challenges and Cases of Genomic Data Integration Across Technologies and Biological Scales

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 93))

Abstract

Current technological advancements have facilitated novel experimental methods that measure a diverse assortment of biological processes, creating a data deluge in biology and medicine. This proliferation of data sources, from large repositories and data warehouses to specialist databases that store a variety of different data types, contributing to a multitude of different file formats, have necessitated minimal data standards that describe both data and annotation. In addition to integrating at the data resource level, development of integrative computational or statistical methods that explore two or more data types or biological layers to understand their joint influence can lead to a better understanding of both normal and pathological processes. Combination of these different data-layers, in turn enables us to glean a more integrative understanding of complex biological systems. Development of integrative methods that bridge both biology and technology can provide insight into different scales of gene and genome regulation. Some of these integrative approaches and their application are explored in this chapter in the context of modern genomics.

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Notes

  1. 1.

    Batch effects are sources of technical variation that have been added to samples during handling and processing, such as when samples belonging to the same experiment are processed at different times, produced with different reagent batches, on different machines or by different people.

  2. 2.

    The epigenome consists of a collection of chemical compounds that tell the DNA what to do. These can attach to DNA or proteins associated with DNA and regulate gene activity without changing the DNA sequence.

  3. 3.

    Chromatin consists of DNA, the disk like nucleosomes that DNA spools around for efficient packaging, non-coding RNA and other DNA associated accessory proteins. When epigenomic compounds attach to chromatin, they are said to have “marked” the genome. These modifications do not change the sequence of the DNA, they change how cells use the information encoded by DNA.

References

  1. Bader GD, Cary MP, Sander C (2006) Pathguide: a pathway resource list. Nucleic Acids Res 34:D504–D506. https://doi.org/10.1093/nar/gkj126

    Article  Google Scholar 

  2. Bednar J, Horowitz RA, Grigoryev SA et al (1998) Nucleosomes, linker DNA, and linker histone form a unique structural motif that directs the higher-order folding and compaction of chromatin. Proc Natl Acad Sci U S A 95:14173–14178. https://doi.org/10.1073/pnas.95.24.14173

    Article  Google Scholar 

  3. Berners-Lee T. (2006) Linked Data Design Issues. http://www.w3.org/DesignIssues/LinkedData.html. Accessed 30 June 2017

  4. Benson DA, Cavanaugh M, Clark K et al (2017) GenBank. Nucleic Acids Res 45:D37–D42. https://doi.org/10.1093/nar/gkw1070

    Article  Google Scholar 

  5. BioMart (2009) https://www.biomart.org. Accessed 30 June 2017

  6. Biosharing (2016) https://biosharing.org. Accessed 30 June 2017

  7. Brazma A (2009) Minimum information about a microarray experiment (MIAME)–successes, failures, challenges. SciWorld J 9:420–423. https://doi.org/10.1100/tsw.2009.57

    Article  Google Scholar 

  8. Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33–37. https://doi.org/10.1038/4462

    Article  Google Scholar 

  9. Cairns J (2012) Rcade: a tool for integrating a count-based ChIP-seq analysis with differential expression summary data. R package version 1.16.0

    Google Scholar 

  10. Casper J, Zweig AS, Villarreal C et al (2017) The UCSC Genome browser database: 2018 update. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx1020

    Article  Google Scholar 

  11. Chen H, Yu T, Chen JY (2013) Semantic web meets integrative biology: a survey. Brief Bioinform 14:109–125. https://doi.org/10.1093/bib/bbs014

    Article  Google Scholar 

  12. Ching T, Huang S, Garmire LX (2014) Power analysis and sample size estimation for RNA-Seq differential expression. RNA 20:1684–1696. https://doi.org/10.1261/rna.046011.114

    Article  Google Scholar 

  13. Cremer T, Cremer C (2001) Chromosome territories, nuclear architecture and gene regulation in mammalian cells. Nat Rev Genet 2:292–301. https://doi.org/10.1038/35066075

    Article  Google Scholar 

  14. Crowdflower (2016) Crowdflower Data Science Report 2016. http://visit.crowdflower.com/rs/416-ZBE-142/images/CrowdFlower_DataScienceReport_2016.pdf. Accessed 30 June 2017

  15. Dekker J, Mirny L (2016) The 3D genome as moderator of chromosomal communication. Cell 164:1110–1121. https://doi.org/10.1016/j.cell.2016.02.007

    Article  Google Scholar 

  16. Durinck S, Spellman PT, Birney E, Huber W (2009) Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 4:1184–1191. https://doi.org/10.1038/nprot.2009.97

    Article  Google Scholar 

  17. Ernst J, Kellis M (2012) ChromHMM: automating chromatin-state discovery and characterization. Nat Methods 9:215–216. https://doi.org/10.1038/nmeth.1906

    Article  Google Scholar 

  18. Fillbrunn A, Dietz C, Pfeuffer J et al (2017) KNIME for reproducible cross-domain analysis of life science data. J Biotechnol 261:149–156. https://doi.org/10.1016/j.jbiotec.2017.07.028

    Article  Google Scholar 

  19. Flavahan WA, Drier Y, Liau BB et al (2016) Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529:110–114. https://doi.org/10.1038/nature16490

    Article  Google Scholar 

  20. Functional Genomics Data Society (2010) http://fged.org. Accessed 30 June 2017

  21. Galperin MY, Fernández-Suárez XM, Rigden DJ (2017) The 24th annual nucleic acids research database issue: a look back and upcoming changes. Nucleic Acids Res 45:5627. https://doi.org/10.1093/nar/gkx021

    Article  Google Scholar 

  22. Giardine B, Riemer C, Hardison RC et al (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15:1451–1455. https://doi.org/10.1101/gr.4086505

    Article  Google Scholar 

  23. Giorgetti L, Lajoie BR, Carter AC et al (2016) Structural organization of the inactive X chromosome in the mouse. Nature 535:575–579. https://doi.org/10.1038/nature18589

    Article  Google Scholar 

  24. Gligorijević V, Malod-Dognin N, Pržulj N (2016) Integrative methods for analyzing big data in precision medicine. Proteomics 16:741–758. https://doi.org/10.1002/pmic.201500396

    Article  Google Scholar 

  25. Goble C, Stevens R (2008) State of the nation in data integration for bioinformatics. J Biomed Inform 41:687–693. https://doi.org/10.1016/j.jbi.2008.01.008

    Article  Google Scholar 

  26. Henry VJ, Bandrowski AE, Pepin A-S et al (2014) OMICtools: an informative directory for multi-omic data analysis. Database (Oxford). https://doi.org/10.1093/database/bau069

    Article  Google Scholar 

  27. Hoffman MM, Buske OJ, Wang J et al (2012) Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods 9:473–476. https://doi.org/10.1038/nmeth.1937

    Article  Google Scholar 

  28. Hood L, Rowen L (2013) The Human Genome Project: big science transforms biology and medicine. Genome Med 5:79. https://doi.org/10.1186/gm483

    Article  Google Scholar 

  29. Horbach SPJM, Halffman W (2017) The ghosts of HeLa: how cell line misidentification contaminates the scientific literature. PLoS ONE 12:e0186281. https://doi.org/10.1371/journal.pone.0186281

    Article  Google Scholar 

  30. Hull D, Wolstencroft K, Stevens R et al (2006) Taverna: a tool for building and running workflows of services. Nucleic Acids Res 34:W729–W732. https://doi.org/10.1093/nar/gkl320

    Article  Google Scholar 

  31. Illumina Press Release (2017) https://www.illumina.com/company/news-center/press-releases/press-release-details.html%3Fnewsid%3D2236383

  32. Jenkinson AM, Albrecht M, Birney E et al (2008) Integrating biological data–the distributed annotation system. BMC Bioinform 9(Suppl 8):S3. https://doi.org/10.1186/1471-2105-9-S8-S3

    Article  Google Scholar 

  33. Kalderimis A, Lyne R, Butano D et al (2014) InterMine: extensive web services for modern biology. Nucleic Acids Res 42:W468–W472. https://doi.org/10.1093/nar/gku301

    Article  Google Scholar 

  34. Kirschner K, Samarajiwa SA, Cairns JM et al (2015) Phenotype specific analyses reveal distinct regulatory mechanism for chronically activated p53. PLoS Genet 11:e1005053. https://doi.org/10.1371/journal.pgen.1005053

    Article  Google Scholar 

  35. Landfors M, Philip P, Rydén P, Stenberg P (2011) Normalization of high dimensional genomics data where the distribution of the altered variables is skewed. PLoS ONE 6:e27942. https://doi.org/10.1371/journal.pone.0027942

    Article  Google Scholar 

  36. Leek JT (2014) svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. https://doi.org/10.1093/nar/gku864

    Article  Google Scholar 

  37. Lieberman-Aiden E, van Berkum NL, Williams L et al (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326:289–293. https://doi.org/10.1126/science.1181369

    Article  Google Scholar 

  38. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8

    Article  Google Scholar 

  39. Luger K, Dechassa ML, Tremethick DJ (2012) New insights into nucleosome and chromatin structure: an ordered state or a disordered affair? Nat Rev Mol Cell Biol 13:436–447. https://doi.org/10.1038/nrm3382

    Article  Google Scholar 

  40. Mammana A, Chung H-R (2015) Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome. Genome Biol 16:151. https://doi.org/10.1186/s13059-015-0708-z

    Article  Google Scholar 

  41. Martínez-Bartolomé S, Binz P-A, Albar JP (2014) The minimal information about a proteomics experiment (MIAPE) from the proteomics standards initiative. Methods Mol Biol 1072:765–780. https://doi.org/10.1007/978-1-62703-631-3_53

    Article  Google Scholar 

  42. McQuilton P, Gonzalez-Beltran A, Rocca-Serra P et al (2016) BioSharing: curated and crowd-sourced metadata standards, databases and data policies in the life sciences. Database (Oxford). https://doi.org/10.1093/database/baw075

    Article  Google Scholar 

  43. Merali Z, Giles J (2005) Databases in peril. Nature 435:1010–1011. https://doi.org/10.1038/4351010a

    Article  Google Scholar 

  44. Morgan M, Carlson M, Tenenbaum D and Arora S (2017). AnnotationHub: Client to access AnnotationHub resources. R package version 2.6.5

    Google Scholar 

  45. National Centre for Biotechnology Information (1988) Bethesda (MD): National Library of Medicine (US), https://www.ncbi.nlm.nih.gov/NLM. Accessed 30 June 2017 (NCBI)

  46. OmicTools (2014), https://omictools.com/. Accessed 30 June 2017

  47. Pasquier C (2008) Biological data integration using semantic web technologies. Biochimie 90:584–594. https://doi.org/10.1016/j.biochi.2008.02.007

    Article  Google Scholar 

  48. Pearson H (2001) Biology’s name game. Nature 411:631–632. https://doi.org/10.1038/35079694

    Article  Google Scholar 

  49. Pepke S, Wold B, Mortazavi A (2009) Computation for ChIP-seq and RNA-seq studies. Nat Methods 6:S22–S32. https://doi.org/10.1038/nmeth.1371

    Article  Google Scholar 

  50. Pathguide: The pathway resource list (2006) TP53 knowledge based network models. http://www.pathguide.org. Accessed 30 June 2017

  51. Robertson G, Hirst M, Bainbridge M et al (2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4:651–657. https://doi.org/10.1038/nmeth1068

    Article  Google Scholar 

  52. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp616

    Article  Google Scholar 

  53. Samarajiwa SA (2015) TP53 knowledge-based network models. http://australian-systemsbiology.org/tp53/. Accessed 30 June 2017

  54. Samarajiwa SA, Forster S, Auchettl K, Hertzog PJ (2009) INTERFEROME: the database of interferon regulated genes. Nucleic Acids Res 37:D852–D857. https://doi.org/10.1093/nar/gkn732

    Article  Google Scholar 

  55. Sawyer IA, Dundr M (2017) Chromatin loops and causality loops: the influence of RNA upon spatial nuclear architecture. Chromosoma 1–17. https://doi.org/10.1007/s00412-017-0632-y

  56. Schadt EE, Linderman MD, Sorenson J et al (2010) Computational solutions to large-scale data management and analysis. Nat Rev Genet 11:647–657. https://doi.org/10.1038/nrg2857

    Article  Google Scholar 

  57. Smedley D, Haider S, Durinck S et al (2015) The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res 43:W589–W598. https://doi.org/10.1093/nar/gkv350

    Article  Google Scholar 

  58. Stein L (2002) Creating a bioinformatics nation. Nature 417:119–120. https://doi.org/10.1038/417119a

    Article  Google Scholar 

  59. Stephens ZD, Lee SY, Faghri F et al (2015) Big data: astronomical or genomical? PLoS Biol 13:e1002195. https://doi.org/10.1371/journal.pbio.1002195

    Article  Google Scholar 

  60. Taylor CF, Field D, Sansone S-A et al (2008) Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project. Nat Biotechnol 26:889–896. https://doi.org/10.1038/nbt.1411

    Article  Google Scholar 

  61. Wang S, Sun H, Ma J et al (2013) Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc 8:2502–2515. https://doi.org/10.1038/nprot.2013.150

    Article  Google Scholar 

  62. Yates B, Braschi B, Gray KA et al (2017) Genenames.org: the HGNC and VGNC resources in 2017. Nucleic Acids Res 45:D619–D625. https://doi.org/10.1093/nar/gkw1033

    Article  Google Scholar 

  63. Yu L, Fernandez S, Brock G (2017) Power analysis for RNA-Seq differential expression studies. BMC Bioinformatics 18:234. https://doi.org/10.1186/s12859-017-1648-2

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. MRC Cancer Unit, University of Cambridge, Cambridge, CB2 0XZ, UK

    Shamith A. Samarajiwa & Dóra Bihary

  2. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK

    Ioana Olan

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  1. Shamith A. Samarajiwa
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  2. Ioana Olan
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Correspondence to Shamith A. Samarajiwa .

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Editors and Affiliations

  1. Computer Science Department, Furman University, Greenville, South Carolina, USA

    Philippe J. Giabbanelli

  2. Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada

    Vijay K. Mago

  3. Department of Computer Engineering, Technological Educational Institute, Lamia, Greece

    Elpiniki I. Papageorgiou

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Samarajiwa, S.A., Olan, I., Bihary, D. (2018). Challenges and Cases of Genomic Data Integration Across Technologies and Biological Scales. In: Giabbanelli, P., Mago, V., Papageorgiou, E. (eds) Advanced Data Analytics in Health. Smart Innovation, Systems and Technologies, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-319-77911-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-77911-9_12

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