Computational biology

Computational biology uses algorithms, mathematical models, and computational methods to analyze biological data and systems, test hypotheses, and answer questions about life.

Genome assembly: The reconstruction of genome sequences from overlapping DNA reads produced by sequencing. Assembly turns fragmented sequencing data into genomes that can be analyzed computationally.
Bioinformatics: The development and application of computational tools for storing, organizing, and analyzing biological data. Its emphasis on data and information tools overlaps with computational biology but is not identical.
DNA sequencing: The determination of the order of nucleotides in a DNA molecule. Sequencing produces data that computational analyses use to study genomes.
Margaret Oakley Dayhoff: An American biochemist who pioneered computational analysis of protein sequences and biological data resources. Her protein catalogs and sequence comparisons helped establish computational approaches in biology.
Reproducibility: The ability to obtain consistent results when a study or analysis is repeated using its methods and data. Complex pipelines can make computational findings difficult to reproduce without complete records.
Sequence alignment: The arrangement of DNA, RNA, or protein sequences to reveal corresponding positions and similarities. Alignment supports searches for related genes, proteins, and evolutionary patterns.
Statistical genetics: The use of statistical methods to study genetic variation and its relationship to traits and disease. It shows how models quantify associations between genetic differences and biological outcomes.
Gene expression: The process by which information in a gene is used to produce a functional RNA or protein. Expression measurements let models examine which genes are active and when.
Frederick Sanger: A British biochemist who developed influential methods for determining protein and DNA sequences. Sequencing advances made the large-scale biological data analysis central to this field possible.
Causal inference: The use of evidence and assumptions to determine whether one factor causes changes in another. Predictive associations from biological data do not by themselves establish causal mechanisms.

Linked from 3 pages