Bioinformatics is an interdisciplinary field that develops and applies methods and software tools for understanding biological data, particularly when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combines biology, chemistry, physics, computer science, information engineering, mathematics, and statistics to analyze and interpret biological data. It plays a crucial role in managing, analyzing, and interpreting data from various "-omics" fields, including genomics (the study of genomes), proteomics (the study of proteins), and transcriptomics (the study of RNA molecules). The ultimate goal is to extract meaningful insights from biological data to answer questions in biology and medicine.
The term "bioinformatics" was coined by Paulien Hogeweg and Ben Hesper in 1970 to describe "the study of informatic processes in biotic systems." The field began to grow rapidly in the 1980s and 1990s, driven by advancements in molecular biology and the advent of high-throughput sequencing technologies. The need to manage and analyze the massive amounts of data generated by genome sequencing projects, especially the Human Genome Project (launched in 1990 and completed in 2003), was a major catalyst for the development of bioinformatics. Early work focused on creating databases for protein sequences (like the Protein Information Resource) and developing algorithms for sequence alignment, such as the Needleman-Wunsch algorithm.
Bioinformatics is now an essential tool in modern biology and medicine. Its applications are wide-ranging and continue to expand. In genomics, it's used to identify genes, regulatory elements, and variations like single nucleotide polymorphisms (SNPs), which helps in understanding the genetic basis of diseases and unique traits. In personalized medicine, bioinformatics helps tailor treatments to an individual's genetic makeup. It is critical for drug discovery and development, enabling the identification of new drug targets and the design of new molecules. Furthermore, it's used in evolutionary biology to construct phylogenetic trees and understand the relationships between species, in agriculture to improve crop yields and resistance, and in environmental science for metagenomics studies of microbial communities.