TY - CHAP
T1 - Differential Expression and Functional Analysis of High-Throughput -Omics Data Using Open Source Tools
AU - Kebschull, Moritz
AU - Fittler, Melanie Julia
AU - Demmer, Ryan T
AU - Papapanou, Panos N
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Computational Biology/methods
KW - Databases, Nucleic Acid
KW - Gene Expression Profiling/methods
KW - Gene Expression Regulation
KW - Gene Regulatory Networks
KW - Genomics/methods
KW - High-Throughput Nucleotide Sequencing
KW - Sequence Analysis, RNA/methods
KW - Software
KW - Web Browser
U2 - 10.1007/978-1-4939-6685-1_19
DO - 10.1007/978-1-4939-6685-1_19
M3 - Chapter
C2 - 27924603
SN - 978-1-4939-6683-7
T3 - Methods in molecular biology
SP - 327
EP - 345
BT - Oral Biology
PB - Springer
ER -