Oral Biology pp 327-345 | Cite as

Differential Expression and Functional Analysis of High-Throughput -Omics Data Using Open Source Tools

  • Moritz KebschullEmail author
  • Melanie Julia Fittler
  • Ryan T. Demmer
  • Panos N. Papapanou
Part of the Methods in Molecular Biology book series (MIMB, volume 1537)


Today, –omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ, or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseases, offering a large advantage over earlier “candidate” gene or pathway analyses. A primary goal in the analysis of these high-throughput assays is the detection of those features among several thousand that differ between different groups of samples. In the context of oral biology, our group has successfully utilized –omics technology to identify key molecules and pathways in different diagnostic entities of periodontal disease.

A major issue when inferring biological information from high-throughput –omics studies is the fact that the sheer volume of high-dimensional data generated by contemporary technology is not appropriately analyzed using common statistical methods employed in the biomedical sciences.

In this chapter, we outline a robust and well-accepted bioinformatics workflow for the initial analysis of –omics data generated using microarrays or next-generation sequencing technology using open-source tools. Starting with quality control measures and necessary preprocessing steps for data originating from different –omics technologies, we next outline a differential expression analysis pipeline that can be used for data from both microarray and sequencing experiments, and offers the possibility to account for random or fixed effects. Finally, we present an overview of the possibilities for a functional analysis of the obtained data.

Key words

Periodontal disease Gene expression Transcriptome microRNA DNA methylation Microarray Next-generation sequencing Gingiva Differential expression analysis Functional groups 



This work was supported by grants from the German Society for Periodontology (DG PARO) and the German Society for Oral and Maxillo-Facial Sciences (DGZMK) to M.K. and by grants from NIH/NIDCR (DE015649, DE021820 and DE024735) and by an unrestricted gift from Colgate-Palmolive Inc. to author P.N.P.


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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Moritz Kebschull
    • 1
    • 2
    Email author
  • Melanie Julia Fittler
    • 3
  • Ryan T. Demmer
    • 4
  • Panos N. Papapanou
    • 2
  1. 1.Department of Periodontology, Operative and Preventive Dentistry, Faculty of MedicineUniversity of BonnBonnGermany
  2. 2.Division of Periodontics, Section of Oral, Diagnostic and Rehabilitation SciencesColumbia University College of Dental MedicineNew YorkUSA
  3. 3.Department of Periodontology, Operative and Preventive DentistryUniversity of BonnBonnGermany
  4. 4.Department of EpidemiologyColumbia University Mailman School of Public HealthNew YorkUSA

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