About this Research Topic
Over the past few decades, developments in "omics" approaches, which include functional genomic and transcriptomics, as well as mass-spectrometry based technologies that facilitate in-depth characterization of the proteome, metabolome, glycome, and metallome, have accelerated basic research. Going hand-in-hand with these advances, a high quality of biological sample is a prerequisite for the collection of valuable raw data via sensitive instrumentation.
However, mammalian tissues are of complex chemical composition and contain molecules with different physical and chemical properties. The yield of extraction of molecules of interest for bioanalytical analysis depends on a mutual composition of water, proteins, and lipids in the biological sample. For instance, samples rich in lipids, such as adipose tissue specimens, can compromise the analysis of DNA, RNA, and protein molecules.
This interference observed in the analysis can lead to high sample-to-sample variability along with a high number of missing values, generate batch effects, or introduce a high false discovery rate in feature selection. To compensate, novel software tools and statistical approaches are being developed in order to aid researchers in data manipulation and extraction of accurate biological information.
This collection of articles is dedicated to novel omics approaches to study adipose tissue, or other complex biological specimens, whose extensive and accurate molecular characterization could lead to novel metabolic discoveries.
However, mammalian tissues are of complex chemical composition and contain molecules with different physical and chemical properties. The yield of extraction of molecules of interest for bioanalytical analysis depends on a mutual composition of water, proteins, and lipids in the biological sample. For instance, samples rich in lipids, such as adipose tissue specimens, can compromise the analysis of DNA, RNA, and protein molecules.
This interference observed in the analysis can lead to high sample-to-sample variability along with a high number of missing values, generate batch effects, or introduce a high false discovery rate in feature selection. To compensate, novel software tools and statistical approaches are being developed in order to aid researchers in data manipulation and extraction of accurate biological information.
This collection of articles is dedicated to novel omics approaches to study adipose tissue, or other complex biological specimens, whose extensive and accurate molecular characterization could lead to novel metabolic discoveries.
Keywords: proteomics, metabolomics, genomics, adipose tissue, brain tissue
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