1 Introduction to Bioinformatics
Learn what bioinformatics does, the biological data and databases it uses, how computation supports biological research, and how to carry out and interpret a basic workflow.
What does
applies computation and quantitative reasoning to biological data. It brings together biology, computer science, statistics, and mathematics. Modern experiments can produce more data than researchers can examine manually, so computational methods help manage and analyze those data.
Computation can reveal patterns and generate hypotheses, but biological interpretation and experimental testing remain necessary. is closely related to . The terms often overlap: commonly emphasizes data, databases, and analytical tools, while may also emphasize mathematical models of biological processes.
Biological data types
uses several kinds of biological information, which can be linked together in a research record.
Nucleotide sequences are DNA or RNA represented by letters such as A, C, G, and T, or U in RNA. A sequence may represent a gene, a genomic region, or a complete genome.
Protein sequences are chains of amino acids represented by one-letter codes. Their sequence can provide clues to a protein’s structure and function.
Genomic data include genome assemblies, gene locations, genetic variants, and sequencing reads. Genomics studies the full set of DNA in an organism and how its components relate.
Gene-expression data measure RNA or other molecular activity across cells, tissues, conditions, or time points.
Protein and molecular-structure data include information about protein domains, three-dimensional structures, and molecular interactions.
provide context about a sample or record, such as its organism, tissue, experimental method, gene name, or evidence supporting a functional claim.
These data types can be connected. For example, a gene record may link a genomic location to a DNA sequence, an encoded protein, expression measurements, and scientific references.
Biological databases
A stores and organizes data so researchers can search, retrieve, compare, and reuse it. Some databases focus on particular data types, while others connect records across several kinds of biological information. Records typically include identifiers and annotations; metadata helps users understand where data came from and how it was produced.
Examples include GenBank for nucleotide sequences, NCBI Protein for protein records, SRA for sequencing reads, and UniProt for protein sequence and functional information. Database records are useful starting points, but their annotations can vary in evidence and completeness. Check a record’s source, references, and evidence rather than treating every annotation as equally certain.
What computation contributes
Computational methods support several tasks in biological research:
Managing data: Store, search, retrieve, and connect large collections of records.
Comparing sequences: arranges DNA, RNA, or protein sequences to identify similarities and differences. Similarity can suggest shared ancestry or function, but does not by itself prove either.
Analyzing genomes: Software can help locate genes, map sequencing reads to a reference genome, and identify genetic variation.
Studying proteins: Sequence and structure tools can identify related proteins or conserved domains and support hypotheses about function.
Finding patterns across experiments: Statistical analysis and visualization can reveal differences in gene expression or other measured features.
For example, a researcher may compare an unknown protein sequence against a database. Similarity to well-studied proteins can suggest a possible family or function. That prediction can guide further analysis and experiments, but it is not a substitute for experimental confirmation.
A basic workflow
A basic workflow connects a biological question to data, an appropriate method, and a carefully interpreted result.
Define a biological question. Decide what needs to be learned, such as whether a sequence resembles a known gene.
Find or generate data. Retrieve records from a suitable database or obtain data through an experiment.
Check and prepare the data. Confirm identifiers, formats, sample descriptions, and quality; document relevant metadata.
Choose an appropriate computational method. Match the method to the data and question—for example, use to compare sequences.
Interpret the result in context. Consider data quality, reference choice, method limitations, and the strength of supporting evidence.
Share reproducibly. Record data sources, software or methods, and important parameters so others can assess or repeat the analysis.
Computational results support biological hypotheses. Interpret them in light of the data and method, and test them experimentally where appropriate.