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Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, [1] including both DNA and RNA sequence as well as other "post-genomic" data (i.e., experimental data obtained with technologies that require the genome sequence, such as genomic DNA microarrays). These ...
Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.
Sequence alignment is useful in a number of bioinformatics applications, such as computing the longest common subsequence of two genes or comparing variants of certain diseases. [citation needed] An untouched project in computational genomics is the analysis of intergenic regions, which comprise roughly 97% of the human genome. [19]
Genome informatics dealing with [6] microbial and metagenomics, sequencing algorithms, variant discovery and genome assembly, evolution, complex traits and phylogenetics, personal and medical genomics, transcriptomics, genome structure and function. [6]
Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, [1] including genomics, proteomics, microarrays, systems biology, evolution, and text mining. [ 2 ] [ 3 ]
Genomics – applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble, and analyze the function and structure of genomes (the complete set of DNA within a single cell of an organism). [35] [36] The field includes efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping.
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