Omics Data Management and Annotation

  • Arye Harel
  • Irina Dalah
  • Shmuel Pietrokovski
  • Marilyn Safran
  • Doron LancetEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 719)


Technological Omics breakthroughs, including next generation sequencing, bring avalanches of data which need to undergo effective data management to ensure integrity, security, and maximal knowledge-gleaning. Data management system requirements include flexible input formats, diverse data entry mechanisms and views, user friendliness, attention to standards, hardware and software platform definition, as well as robustness. Relevant solutions elaborated by the scientific community include Laboratory Information Management Systems (LIMS) and standardization protocols facilitating data sharing and managing. In project planning, special consideration has to be made when choosing relevant Omics annotation sources, since many of them overlap and require sophisticated integration heuristics. The data modeling step defines and categorizes the data into objects (e.g., genes, articles, disorders) and creates an application flow. A data storage/warehouse mechanism must be selected, such as file-based systems and relational databases, the latter typically used for larger projects. Omics project life cycle considerations must include the definition and deployment of new versions, incorporating either full or partial updates. Finally, quality assurance (QA) procedures must validate data and feature integrity, as well as system performance expectations. We illustrate these data management principles with examples from the life cycle of the GeneCards Omics project (, a comprehensive, widely used compendium of annotative information about human genes. For example, the GeneCards infrastructure has recently been changed from text files to a relational database, enabling better organization and views of the growing data. Omics data handling benefits from the wealth of Web-based information, the vast amount of public domain software, increasingly affordable hardware, and effective use of data management and annotation principles as outlined in this chapter.

Key words

Data management Omics data integration GeneCards Project life cycle Relational database Heuristics Versioning Quality assurance Annotation Data modeling 



We thank the members of the GeneCards team: Iris Bahir, Tirza Doniger, Tsippi Iny Stein, Hagit Krugh, Noam Nativ, Naomi Rosen, and Gil Stelzer. The GeneCards project is funded by Xennex Inc., the Weizmann Institute of Science Crown Human Genome Center, and the EU SYNLET (FP6 project number 043312) and SysKID (FP7 project number 241544) grants.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Arye Harel
    • 1
  • Irina Dalah
    • 1
  • Shmuel Pietrokovski
    • 1
  • Marilyn Safran
    • 1
  • Doron Lancet
    • 1
    Email author
  1. 1.Department of Molecular GeneticsWeizmann Institute of ScienceRehovotIsrael

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