Britannica
Sources used in this answer
How do high-throughput experiments produce data for bioinformatics?
Created from Britannica articles
using AI.
AI can make mistakes, so verify using Britannica articles.
bioinformatics
This computerized image of anthrax shows the various structural relationships of seven units within the protein and demonstrates the interaction of a drug (shown in yellow) bound to the protein to block the so-called lethal factor unit. Bioinformatics plays an important role in enabling scientists to predict where a drug molecule will bind within a protein, given the individual structures of the molecules.
High-throughput experiments are essential to bioinformatics, providing the massive datasets required to analyze biological systems. These experiments generate data through several key processes:
- Massively Parallel Sequencing: Next-generation sequencing technologies have largely replaced older methods by allowing millions of DNA fragments to be sequenced simultaneously. This high-throughput capacity enables rapid whole genome sequencing and provides the raw data for structural genomics.12
- Automated Processing: In structural genomics, robots physically manipulate libraries of clones, which are then sequenced by automated machines. Computers subsequently assemble these sequences into complete chromosomal maps.2
- Microarray Analysis: To study functional genomics, researchers use microarrays—slides imprinted with thousands of DNA samples. By bathing these in fluorescently labeled mRNA, scientists can identify which genes are active, defining the "transcriptome".2
- Omics Streams: Beyond genomics, high-throughput experiments measure patterns in proteomics, metabolomics, and interactomics, capturing comprehensive data on cellular activity and molecular interactions.3
These data-generating processes allow bioinformatics to identify patterns of variation across different cell types, developmental stages, and environmental conditions.3