Genomics and Bioinformatics in Biological Discovery and Pharmaceutical Development

Wendell Jones ()
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Wendell Jones: EA Genomics, A Business Unit of Q2 Solutions

Chapter Chapter 3 in Quantitative Methods in Pharmaceutical Research and Development, 2020, pp 105-142 from Springer

Abstract: Abstract Bioinformatics is a term coined from the 1970s but, like other adaptive systems, has evolved in its scope and utility. Currently its domain includes most any genomics-related question that involves large amounts of empirical or reference data and information which need to be evaluated for patterns, similarities, and associations or which need to be compared, aligned, or reconstructed. As bioinformatics supports molecular biology and genetics, it is also as diverse as these areas but also requires other disciplines including computer science and statistics. A common theme in bioinformatics is complexity. Although there are Mendelian traits in humans, most diseases and their treatments reflect the enormous complexity of cells and the nucleic acids at their center. Cells have vast intra- and intercellular networks that regulate cell growth, specialization, activity, reaction to stress, defense against infection, repair of DNA damage, etc. The twenty-first century has yielded technologies that allow us to accurately measure these many genomic constituents relatively cheaply and at scale, providing a front row seat as we uncover and understand cellular complexity. These technologies require bioinformatics analysis of the data to enable informed, focused, and rigorous therapy development that frequently goes to the root of many diseases.

Keywords: DNA; RNA; Nucleic acids; Variants; Gene expression; Methylation; Biomarker; Assay; Analyte; Translational; Cancer; Disease; Inheritance; Somatic; Public datasets; PCR; Microarrays; Sequencing; Imaging; Flow cytometry; Graphics; Genome-wide; Transcriptome-wide; Targeted assays; Significance; Dimension reduction; Multiple testing; Molecular subtypes; Copy number; Structural variation; Translocation; Immuno-oncology; Treatment response; Survival analysis (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-48555-9_3

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