Chapter Preview
TopOne way of exploring the interface between molecular bioscience and IT is to track experimental data from its generation, capture and retrieval, to its aggregation and dissemination via international data services, to its subsequent analysis. Here I deconstruct data analysis into data models, algorithms, analytical methods and software, workflows and visualisation. This trajectory is common to most or all experimental data in the sciences, although bioinformatics is notable for its culture of open data, well-established data formats and standards, and large international data repositories and data services.
Key Terms in this Chapter
Transcriptomics: The study of the totality of the RNA molecules arising from the expression of genes under specified conditions.
Omics (or ‘Omics): Genomics, transcriptomics, and/or other fields of biology characterised by the study of the totality of a class of molecule ( e.g. proteomics, metabolomics) or trait ( e.g. phenomics) in a cell or organism.
Epigenetics: The study of heritable changes in gene expression or phenotype that do not arise from changes in the nucleotide sequence of the genome.
Phylogenetics: The study of genetic relationships among molecules, organisms or taxa over time.
Metagenomics: The study of the genetic information in a sample of a mixed community, for example from soil, water, or the intestinal tract of an animal, without prior separation of the constituent organisms.
Gene Expression: The process in which information in a gene is transduced to yield one or more gene products (proteins and/or non-protein-coding RNAs).
Genomics: The study of the totality of genetic information in a cell or organism.
Systems Biology: The study of networks of interactions among the components of a biological system, emphasising a synthetic, non-reductionist approach and emergent properties.
Synthetic Biology: The engineering design and construction of new or modified functions in biological systems.