Bioinformatics Tools Every Lab Should Use

Bioinformatics is the foundation of modern research, translating complex biological data into actionable insights by combining biology, computer science, and data analysis. Selecting the right computational tools is crucial for optimizing data management. This article explores key bioinformatics tools that every lab should integrate into their processes to stay at the forefront of innovative research.

Bioinformatics is essential for managing the rapidly growing life-science data that requires comprehensive databases and tools while ensuring efficient data access and analysis for scientific advancements.1

In biological research, transforming vast amounts of raw data into valuable information mainly requires three interconnected techniques: sequence analysis, structural modeling, and data visualization.

This process is a common starting point where raw biological data (DNA, RNA, or protein sequences) is analyzed for patterns, mutations, or functional elements.2

The results provide meaningful information relating to gene function, evolutionary relationships, biological processes, and disease mechanisms, forming the foundation for understanding molecular biology.3

This builds upon sequence data to predict the 3D structures of molecules encoded by those sequences, offering insights into their functional properties and interactions.

The sequence data is used as input for creating models that represent how the molecules fold and interact. This technique is crucial for drug design, protein function analysis, and understanding the molecular dynamics of biological systems.4

As the final step, this process translates complex biological data into clear, interpretable visual representations, including heatmaps, plots, and pathway diagrams. It enables researchers to analyze large datasets, identify significant patterns, and effectively communicate findings.2,5

Together, these bioinformatics techniques enable the effective extraction, interpretation, and presentation of biological data, forming a powerful toolkit that drives discoveries in genomics, proteomics, drug discovery, and other areas of biomedical research.6,7

Scientific research relies on various bioinformatics tools for diverse applications. Below are a few essential examples commonly used in laboratories:2,3,5-9

BLAST (Basic Local Alignment Search Tool): Helps identify homologous sequences by comparing a query sequence to a database.

Clustal Omega: Performs multiple sequence alignments, essential for evolutionary studies and functional annotation.

Bowtie: Fast alignment of short-read sequencing data to reference genomes.

T-Coffee (Tree-based Consistency Objective Function for Alignment Evaluation): is another multiple sequence alignment tool that combines progressive and consistency-based algorithms for accurate alignment,

These tools allow researchers to decode genetic variations, identify conserved sequences, and infer evolutionary relationships, making them crucial for molecular biology studies.

AlphaFold: An AI-based tool used for the accurate prediction of protein structures.

PyMOL: A molecular visualization tool used for viewing, analyzing, and manipulating 3D structures of biomolecules.

Chimera: A molecular visualization tool used to view, analyze, and manipulate 3D structures of proteins, nucleic acids, and other macromolecules.

MODELLER: A tool primarily used for predicting and constructing 3D protein structures based on known templates, when experimental structures are unavailable.

These tools facilitate the integration of genomic data with functional insights, supporting fields such as structural biology, pharmacology, and personalized medicine.

Cytoscape: For visualizing molecular interaction networks and biological pathways.

IGV (Integrative Genomics Viewer): For visualizing large-scale genomic data such as alignments and mutations.

UCSC Genome Browser: For exploring annotated genomes and visualizing genomic data.

BioVinci: For generating a wide variety of biological and biomedical visualizations.

CLANS (Cluster Analysis of Sequences): A tool used for sequence clustering and graphical representation of sequence relationships.

These tools help researchers detect patterns, highlight significant trends, and make data-driven conclusions that enhance biological research.

In bioinformatics, choosing between free open-source and paid commercial software tools depends on multiple factors, including budget constraints, computational demands, user expertise, technical support availability, and research objectives. Each option has its advantages and limitations, making the decision case-specific.10-13

Free bioinformatics tools, such as BLAST, AlphaFold, MODELLER, and Cytoscape, are widely used because they are accessible and cost-effective and allow users to modify and improve them.

However, they require advanced bioinformatics expertise and strong computational infrastructure and often lack integration and usability, complicating data handling, especially in assembly and downstream analysis.12,13

Paid bioinformatics tools, such as Geneious, GLC Genomics Workbench, and Qlucore Omics Explorer, may address many issues of free alternatives, offering professional-grade software with seamless integration, dedicated support, robust infrastructure, and user-friendly interfaces.

They also tend to be optimized for performance by including built-in pipelines that simplify complex analyses without requiring extensive coding.12,13

However, the high cost of these commercial tools often makes them less accessible to many research labs, particularly independent researchers or smaller institutions with limited budgets. Additionally, the reliance on proprietary software also means users may have less flexibility in modifying algorithms or integrating third-party tools.11,13

While free tools offer flexibility and accessibility, paid solutions ensure seamless integration and support. Where feasible, a hybrid approach can provide the best of both worlds for research environments.

This approach combines open-source and commercial bioinformatics tools to enhance efficiency and cost-effectiveness, a common practice in modern research workflows.

Therefore, selecting the right tools requires balancing multiple factors to support diverse research needs while enabling researchers to extract meaningful biological insights.

The rapid expansion of biological data in modern research has made powerful computational tools indispensable in every lab. Bioinformatics has transformed medicine and science, driving breakthroughs and accelerating discoveries.

By leveraging the right tools, labs can optimize workflows, enhance data interpretation, and improve research efficiency.

As the field of bioinformatics continues to evolve and bridge the gap between biological data and medical insights, its advanced analytical capabilities will play an even greater role in shaping the future of biomedical research innovation.

European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI). (n.d.). Our story. [Online]. Available at: https://www.ebi.ac.uk/about/our-story/ (Accessed on 29 March 2025)

Gabler, F., Nam, S.Z., Till, S., Mirdita, M., Steinegger, M., Söding, J., Lupas, A. N., & Alva, V. (2020). Protein sequence analysis using the MPI bioinformatics toolkit. Current Protocols in Bioinformatics, 72(1), e108. doi: 10.1002/cpbi.108

Bono, K. (2022). Application of Sequence Analysis in Bioinformatics Study. Journal of Proteomics & Bioinformatics, 15(10):610. doi: 10.35248/ 0974-276X.22.15.610

Wenhao, G., Mahajan, S.P., Sulam, J., & Gray, J.J. (2020). Deep Learning in Protein Structural Modeling and Design. Patterns, 1(9). doi: 10.1016/j.patter.2020.100142

Shukla, V., Varghese, V.K., Kabekkodu, S.P., Mallya, S., & Satyamoorthy, K. (2017). A compilation of Web-based research tools for miRNA analysis. Briefings in Functional Genomics, 16(5):249–273, doi: 10.1093/bfgp/elw042

Clark, A.J., & Lillard, J.W. Jr. (2024). A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology. Genes (Basel), 15(8):1036. doi: 10.3390/genes15081036

Zhang, S., Liu, K., Liu, Y., Hu, X., & Gu, X. (2025). The role and application of bioinformatics techniques and tools in drug discovery. Frontiers in Pharmacology, 16, doi: 10.3389/fphar.2025.1547131

Stroe, O. (2023). Case study: AlphaFold uses open data and AI to discover the 3D protein universe. European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI). [Online]. Available at: https://www.ebi.ac.uk/about/news/perspectives/alphafold-using-open-data-and-ai-to-discover-the-3d-protein-universe/ (Accessed on 30 March 2025)

Li, Q., Hu, Z., Wang, Y., Li, L., Fan, Y., King, I., Jia, G., Wang, S., Song, L., & Li, Y. (2024). Progress and opportunities of foundation models in bioinformatics. Briefings in Bioinformatics, 25(6):bbae548. doi: 10.1093/bib/bbae548

Huszar,T.I., Gettings, K.B., & Vallone, P.M. (2021). An Introductory Overview of Open-Source and Commercial Software Options for the Analysis of Forensic Sequencing Data. Genes (Basel), 12(11):1739. doi: 10.3390/genes12111739

Kelliher, J.M., Xu, Y., Flynn, M.C., Babinski, M., Canon, S., Cavanna, E., Clum, A., Corilo, Y.E., Fujimoto, G., Giberson, C., Johnson, L.Y.D., Li, K.J., Li, P.E., Li, V., Lo, C.C., Lynch, W., Piehowski, P., Prime, K., Purvine, S., Rodriguez, F., Roux, S., Shakya, M., Smith, M., Sarrafan, S., Cholia, S., McCue, L.A., Mungall, C., Hu, B., Eloe-Fadrosh, E.A., & Chain, P.S.G. (2024). Standardized and accessible multi-omics bioinformatics workflows through the NMDC EDGE resource. Computational and Structural Biotechnology Journal, 23:3575-3583. doi: 10.1016/j.csbj.2024.09.018

Mangul, S., Martin, L.S., Eskin, E., & Blekhman, R. (2019). Improving the usability and archival stability of bioinformatics software. Genome Biology, 20(47). doi: 10.1186/s13059-019-1649-8

Smith, D.R. (2015). Buying in to bioinformatics: an introduction to commercial sequence analysis software. Briefings in Bioinformatics, 16(4):700–709. doi: 10.1093/bib/bbu030

Last Updated: Apr 4, 2025