Bioinformatics uses computer technology to collect, store, and analyze biological data, primarily DNA sequences, protein structures, and other molecular information. It sits at the intersection of biology, computer science, and statistics, and it touches nearly every corner of modern life sciences. The global bioinformatics market is valued at roughly $23 billion in 2025 and is projected to nearly quadruple by 2035, reflecting how central this field has become to medicine, agriculture, forensics, and basic research.
Finding the Right Cancer Treatment
One of the most direct ways bioinformatics affects people is through personalized medicine, particularly in cancer care. When a tumor is sequenced, bioinformatics software sifts through millions of data points to identify the specific mutations driving that cancer. Those mutations determine which therapies are most likely to work. In non-small cell lung cancer, for example, sequencing can reveal mutations in a growth-signaling gene that make the tumor responsive to targeted drugs. In melanoma, a specific mutation called BRAF V600E flags patients who benefit from a different class of therapy entirely.
This approach produces measurable differences in outcomes. In biliary tract cancer, patients who received treatments matched to their tumor’s genetic profile through DNA sequencing had significantly longer progression-free survival and a disease control rate of 61%, compared to 35% for patients on unmatched treatments. In metastatic breast cancer, sequencing has identified mutations that guided patients toward specific drug combinations they would not have received under a one-size-fits-all approach.
Speeding Up Drug Discovery
Developing a new drug traditionally follows a long, expensive path: selecting a biological target, finding a chemical compound that interacts with it, then refining that compound until it works safely in humans. Bioinformatics now plays a role at every stage. Researchers use three-dimensional models of proteins to understand the shape of a drug target’s surface, then virtually screen thousands or millions of candidate molecules to find ones that fit. This dramatically narrows the field before any lab work begins.
Historically, determining a protein’s 3D structure required X-ray crystallography, which was so expensive and slow that most pharmaceutical companies outsourced it to academic labs. Starting in the 1980s, computational modeling began filling the gap. Researchers could build approximate models of a protein based on related proteins whose structures were already known, then use those models to refine drug candidates. Today, structural bioinformatics contributes to target selection, lead discovery, and the optimization stages that follow, compressing timelines and reducing the cost of bringing a drug to market.
Predicting How Proteins Fold
Proteins carry out nearly every function in the body, and their specific three-dimensional shape determines what they do. A misfolded protein can cause disease. Predicting a protein’s shape from its amino acid sequence alone has been one of biology’s hardest problems for decades.
Bioinformatics approaches this from two directions. One uses the laws of physics to simulate how atoms in a protein chain attract and repel each other. The other analyzes evolutionary patterns: if two positions in a protein sequence always change together across thousands of species, those positions are probably close together in the folded structure. The AI system AlphaFold combined both strategies using deep learning trained on known protein structures, achieving accuracy close to experimental methods in many cases. This has practical consequences for disease research and drug design, because knowing a protein’s shape reveals where a drug molecule could bind to it or how a mutation might disrupt its function.
Breeding Better Crops
In agriculture, bioinformatics drives a technique called marker-assisted selection. Instead of crossbreeding plants and waiting an entire growing season to see which offspring inherited a desirable trait, breeders can scan seedlings’ DNA for genetic markers linked to that trait and select the right plants immediately. This is especially valuable for traits that are difficult or slow to measure by observation alone, like resistance to underground pests such as cereal cyst nematodes and root lesion nematodes in wheat.
The technique has been applied across staple crops. In rice, researchers identified a single major gene region responsible for submergence tolerance, the ability to survive flooding, which simplified breeding for flood-prone regions. Breeders have also used genetic markers to stack multiple resistance genes into a single rice variety, combining defenses against bacterial blight, stem borers, and sheath blight. In maize, markers on specific chromosomes have been used to transfer corn borer resistance and to improve yield and earliness. When marker-assisted selection was combined with traditional field screening for Fusarium head blight resistance in wheat, it outperformed field screening alone.
Tracking Evolution and Classifying Species
Bioinformatics is essential to understanding how species are related to one another. By aligning DNA or protein sequences from different organisms, researchers can identify shared ancestry, measure how much two species have diverged, and build phylogenetic trees, the branching diagrams that map evolutionary relationships.
The process works because mutations accumulate at roughly predictable rates. Researchers align sequences, choose a statistical model that best fits the pattern of changes observed, then use algorithms to reconstruct the most likely tree. Different regions of the genome are useful at different scales. Protein-coding regions change slowly enough to compare distantly related species (coffee versus tomato, for instance), while non-coding regions between genes change quickly and are useful for distinguishing closely related species within the same genus. Concatenating sequences from multiple genes, sometimes thousands of base pairs in total, increases the statistical confidence of the resulting tree.
Forensics and Outbreak Investigation
Whole-genome sequencing paired with bioinformatics has transformed both criminal forensics and infectious disease tracking. In microbial forensics, investigators sequence the genome of a pathogen found at a crime scene or in a bioterrorism event, then compare it against databases to identify the organism, trace its origin, and detect signs of genetic engineering or antibiotic resistance.
The same toolkit tracks hospital outbreaks. During a 2011 outbreak of a drug-resistant bacterium at the NIH Clinical Center, whole-genome sequencing revealed exactly how the pathogen spread from patient to patient, information that traditional methods could not provide. A report from the American Academy of Microbiology described bioinformatics as “a fundamental corollary to biodefense research,” noting its importance for understanding genetic diversity, epidemiology, vaccine development, and global health surveillance. The challenge is scale: genomic data from microbial forensics is high-volume, high-variety, and often demands rapid answers, making computational infrastructure as important as the biology itself.
The Core Tools Researchers Use
Most bioinformatics work relies on a handful of publicly available databases and software tools. BLAST (Basic Local Alignment Search Tool) is perhaps the most widely used. It takes a DNA or protein sequence and searches massive databases for similar sequences, helping researchers identify unknown genes, find related proteins in other species, or detect evolutionary conservation. The UniProt database houses protein sequence and function information, offering BLAST searches against its own collection, multiple sequence alignment through a tool called Clustal Omega, and cross-referencing to over 100 external databases including those for 3D structures, gene annotations, and reference sequences. Researchers can filter searches by taxonomy, looking only at bacterial proteins, plant proteins, or mammalian proteins, depending on the question.
How AI Is Changing the Field
Machine learning and deep learning are now woven into nearly every branch of bioinformatics. AI models have significantly improved accuracy in identifying genetic variants, profiling gene expression, and predicting disease risk from genomic data. Deep learning excels at recognizing complex patterns in massive datasets, the kind of patterns that rule-based software and human analysts miss.
One barrier to clinical adoption has been the “black box” problem: a deep learning model might flag a patient as high-risk without explaining why. Explainable AI methods are addressing this by making the model’s reasoning transparent enough for clinicians to trust. Another advancement, federated learning, allows hospitals and research institutions to collaboratively train AI models on patient data without that data ever leaving the institution, preserving privacy while still benefiting from larger, more diverse datasets.