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

Moritz Kebschull1,2, Melanie Julia Fittler3, Ryan T Demmer4

  • 1Department of Periodontology, Operative and Preventive Dentistry, Faculty of Medicine, University of Bonn, Welschnonnenstr. 17, Bonn, D-53111, Germany. moritz.kebschull@uni-bonn.de.

Methods in Molecular Biology (Clifton, N.J.)
|December 8, 2016
PubMed
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This summary is machine-generated.

High-throughput omics analyses enable comprehensive disease study. This chapter details a bioinformatics workflow for analyzing omics data, crucial for identifying disease-related molecular differences.

Area of Science:

  • Bioinformatics
  • Genomics
  • Oral Biology

Background:

  • Omics analyses (RNA, microRNA, DNA methylation) provide unbiased, genome-level insights into complex diseases.
  • Traditional statistical methods struggle with the high-dimensional data from omics studies.
  • Omics technology has been successfully applied to periodontal disease research.

Purpose of the Study:

  • To outline a robust bioinformatics workflow for analyzing omics data.
  • To provide a guide for initial analysis of microarray and next-generation sequencing data.
  • To facilitate the identification of differentially expressed features between sample groups.

Main Methods:

  • Quality control and preprocessing of omics data.
  • Differential expression analysis pipeline adaptable for microarrays and sequencing.
Keywords:
DNA methylationDifferential expression analysisFunctional groupsGene expressionGingivaMicroarrayNext-generation sequencingPeriodontal diseaseTranscriptomemicroRNA

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  • Functional analysis of omics data.

    • Main Results:

    • A comprehensive bioinformatics workflow is presented.
    • The workflow addresses challenges in analyzing high-dimensional omics data.
    • Methods for identifying key molecules and pathways in periodontal disease are discussed.

    Conclusions:

    • Bioinformatics workflows are essential for effective omics data analysis.
    • The outlined methods support the identification of disease-specific molecular signatures.
    • This approach enhances understanding of complex diseases like periodontal disease.