DNA sequencing and other high-throughput technologies now allow researchers to collect mountains of complex biological data that would be difficult — if not impossible — to analyze manually. Thankfully, researchers have bioinformatics tools, which help them make sense of large, complex datasets such as DNA, RNA and protein sequences.
Top Bioinformatics Tools to Know
- AlphaFold
- BLAST
- FastQC
- Galaxy
- SAMtools
- Bioconductor
- DESeq2
- GATK
- BWA
- PyMOL
By applying principles from molecular biology, computer science and statistics, these tools allow researchers to compare nucleotide or protein sequences, identify gene mutations and predict biomolecular interactions. They’ve already helped researchers make groundbreaking discoveries in recent years, and they could be the key to better understanding diseases and how to treat them.
In this article, we’ll explain some of the most important bioinformatics tools to know and how to choose the right one for your research.
What Are Bioinformatics Tools?
Bioinformatics tools are software applications, algorithms and databases designed to store, process and analyze biological data. They can be used for a wide range of applications:
Sequence alignment and analysis: Compares DNA, RNA, or protein sequences against reference datasets to uncover structural, functional or evolutionary relationships.
Variant calling: Identifying genetic differences, such as single-nucleotide variants and insertions or deletions.
Gene-expression analysis: Determining which genes are expressed differently between biological conditions.
Genome visualization: Displaying sequencing and genomic data in their genomic context.
Workflow management: Automating multi-step analyses and making them easier to reproduce.
Top Bioinformatics Tools
1. AlphaFold
Google DeepMind’s AlphaFold is an artificial intelligence system that transformed the field of bioinformatics in 2020 by accurately predicting protein structures based on their amino acid sequences. The AlphaFold Protein Structure Database provides access to more than 200 million protein structure predictions, which may help researchers with drug discovery, disease modeling and other biological research.
2. BLAST
Basic Local Alignment Search Tool (BLAST) is a foundational tool in the field of bioinformatics. Created by researchers at the National Institute of Health in 1990, it allows researchers to search and compare nucleic acid or protein sequences against a reference database, allowing them to learn more about a novel sequence’s function and its evolutionary relationships across other organisms.
3. FastQC
FastQC is a quality control tool that provides a quick assessment of raw sequencing data. It generates visual reports highlighting metrics that could indicate laboratory artifacts or sequencing errors. Because bad input data skews downstream results, running FastQC is typically the first step in a sequencing workflow.
4. Galaxy
Galaxy is an open-source platform developed to help researchers analyze DNA sequencing data without requiring extensive programming knowledge. Rather than requiring users to install and run individual command-line programs, its graphical interface provides drag-and-drop tools for building and running multi-step analysis workflows.
5. SAMtools
SAMtools is a collection of command-line programs that allow researchers to sort, index, merge, filter and convert file types of sequencing alignment data. The tools work with common high-throughput sequencing formats including SAM, BAM and CRAM. BCFtools, another project in the ecosystem, works with variant data in formats such as VCF and BCF.
6. Bioconductor
Bioconductor, an open-source project built around the R programming language, is an ecosystem of more than 2,000 software packages used for statistical modeling, visualization, annotation and many other forms of genomic analysis. Two of its more popular tools, DESeq2 and edgeR, allow for rigorous statistical modeling of differential gene expression.
7. DESeq2
DESeq2 is a tool within the Bioconductor ecosystem that analyzes count data from high-throughput sequencing experiments. One of its primary applications is differential gene-expression analysis, in which researchers determine whether genes are expressed at significantly different levels between experimental conditions.
8. GATK
The Genome Analysis Toolkit (GATK) is the industry-standard software ecosystem for identifying genetic variants. Developed by the Broad Institute, it specializes in germline and somatic variant discovery — including single nucleotide polymorphisms, insertions and copy number changes — across whole genomes, exomes and targeted panels. Its Best Practices Workflows documentation provides recommendations for pre-processing steps that improve accuracy.
9. BWA
The Burrows-Wheeler Aligner (BWA) is a family of algorithms used to map sequencing reads against a reference genome. The tool excels at processing short reads produced by high-throughput platforms, outputting standard SAM alignment files. It serves as the primary read-mapping engine in many variant analysis workflows.
10. PyMOL
PyMOL is an open-source molecular visualization system designed to create three-dimensional representations of biological molecules. Researchers can use it to examine molecular structures and produce publication-quality images. Its interactive graphical interface and scripting capabilities makes it useful for structural biology, drug discovery and computational biology.
How to Choose the Right Bioinformatics Tool
There are hundreds — if not thousands — of bioinformatics tools. Choosing the right one depends on your data, the question you’re solving and the technical resources available to your team.
Start With Your Data
Determine what kind of data you have before selecting software. The tool you use will depend on whether you’re working with nucleic acids or protein sequences, short-read or long-read sequencing and the data format of the files. Many tools are designed for specific data types and will produce unreliable or meaningless results on other inputs.
Define the Analysis
The tool you use will depend on the specific computational task needed for the job. A researcher trying to identify an unknown DNA sequence, for example, has a fundamentally different problem from one trying to identify mutations in a cancer genome or determine which genes are differentially expressed. Most real-world analyses combine several tools, with each performing a specific step in a larger workflow. The right combination depends on the data and the biological question you’re trying to solve.
Consider Technical Expertise
Evaluate your team’s programming background. Non-coders would probably be more comfortable with Galaxy’s graphic user interface, while computational biologists might prefer the control and flexibility of more technical tools that require a knowledge of programming languages, scriptable libraries and command-line interfaces.
Look for Reproducibility
Bioinformatics results are sensitive to different software versions, reference database versions and parameter settings. A pipeline run with a different version of the same tool can produce a meaningfully different output. These variables may be specified in a workflow management system or locked when a tool is packaged in a container like Docker. When evaluating a tool, check to see that a community-vetted container already exists for it on a repository like BioContainers.
Check Documentation and Community Support
Bioinformatics software can sometimes have complicated installation requirements or parameter choices, or they may grow outdated and unable to support current file formats. Both of these problems can be mitigated by selecting tools with strong documentation, frequent version updates and an active user community on GitHub.
Frequently Asked Questions
What are bioinformatics tools used for?
Bioinformatics tools store, analyze and visualize complex biological data like DNA, RNA and protein sequences. Researchers use these tools to map genomes, identify disease mutations, model molecular structures, trace evolutionary relationships and run automated computational workflows.
Are bioinformatics tools free?
Many core bioinformatics tools, databases and software packages are free and open source, maintained by academic institutions and public initiatives. However, commercial platforms offering specialized enterprise features, dedicated support or clinical pipelines often require paid software licenses or subscription fees.
What programming language is best for bioinformatics?
Python and R are the best languages for bioinformatics. Python excels at general scripting, biological data manipulation and machine learning via libraries like Biopython. R leads in statistical modeling, visualization and expression analysis using Bioconductor. Computational biologists routinely use both languages.
