List of bioinformatics tools and databases students used.

The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

List of bioinformatics tools and databases used for sequence based function annotation.

Database of curated links to molecular resources, tools and databases selected on the basis of recommendations from bioinformatics experts in the field. This resource relies on input from its community of bioinformatics users for suggestions. Starting in 2003, it has also started listing all links contained in the NAR Webserver issue. The different types of information available in this portal: * Computer Related: This category contains links to resources relating to programming languages often used in bioinformatics. Other tools of the trade, such as web development and database resources, are also included here. * Sequence Comparison: Tools and resources for the comparison of sequences including sequence similarity searching, alignment tools, and general comparative genomics resources. * DNA: This category contains links to useful resources for DNA sequence analyses such as tools for comparative sequence analysis and sequence assembly. Links to programs for sequence manipulation, primer design, and sequence retrieval and submission are also listed here. * Education: Links to information about the techniques, materials, people, places, and events of the greater bioinformatics community. Included are current news headlines, literature sources, educational material and links to bioinformatics courses and workshops. * Expression: Links to tools for predicting the expression, alternative splicing, and regulation of a gene sequence are found here. This section also contains links to databases, methods, and analysis tools for protein expression, SAGE, EST, and microarray data. * Human Genome: This section contains links to draft annotations of the human genome in addition to resources for sequence polymorphisms and genomics. Also included are links related to ethical discussions surrounding the study of the human genome. * Literature: Links to resources related to published literature, including tools to search for articles and through literature abstracts. Additional text mining resources, open access resources, and literature goldmines are also listed. * Model Organisms: Included in this category are links to resources for various model organisms ranging from mammals to microbes. These include databases and tools for genome scale analyses. * Other Molecules: Bioinformatics tools related to molecules other than DNA, RNA, and protein. This category will include resources for the bioinformatics of small molecules as well as for other biopolymers including carbohydrates and metabolites. * Protein: This category contains links to useful resources for protein sequence and structure analyses. Resources for phylogenetic analyses, prediction of protein features, and analyses of interactions are also found here. * RNA: Resources include links to sequence retrieval programs, structure prediction and visualization tools, motif search programs, and information on various functional RNAs.

Abstract Public databases are essential to the development of multi-omics resources. The amount of data created by biological technologies needs a systematic and organized form of storage, that can quickly be accessed, and managed. This is the objective of a biological database. Here, we present an overview of human databases with web applications. The databases and tools allow the search of biological sequences, genes and genomes, gene expression patterns, epigenetic variation, protein-protein interactions, variant frequency, regulatory elements, and comparative analysis between human and model organisms. Our goal is to provide an opportunity for exploring large datasets and analyzing the data for users with little or no programming skills. Public user-friendly web-based databases facilitate data mining and the search for information applicable to healthcare professionals. Besides, biological databases are essential to improve biomedical search sensitivity and efficiency and merge multiple datasets needed to share data and build global initiatives for the diagnosis, prognosis, and discovery of new treatments for genetic diseases. To show the databases at work, we present a a case study using ACE2 as example of a gene to be investigated. The analysis and the complete list of databases is available in the following website .

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Bioinformatics Market Size 2025-2029

The bioinformatics market size is valued to increase by USD 15.98 billion, at a CAGR of 17.4% from 2024 to 2029. Reduction in cost of genetic sequencing will drive the bioinformatics market.

Market Insights

North America dominated the market and accounted for a 43% growth during the 2025-2029.
By Application - Molecular phylogenetics segment was valued at USD 4.48 billion in 2023
By Product - Platforms segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 309.88 million 
Market Future Opportunities 2024: USD 15978.00 million
CAGR from 2024 to 2029 : 17.4%

Market Summary

The market is a dynamic and evolving field that plays a pivotal role in advancing scientific research and innovation in various industries, including healthcare, agriculture, and academia. One of the primary drivers of this market's growth is the rapid reduction in the cost of genetic sequencing, making it increasingly accessible to researchers and organizations worldwide. This affordability has led to an influx of large-scale genomic data, necessitating the development of sophisticated bioinformatics tools for Next-Generation Sequencing (NGS) data analysis. Another significant trend in the market is the shortage of trained laboratory professionals capable of handling and interpreting complex genomic data. This skills gap creates a demand for user-friendly bioinformatics software and services that can streamline data analysis and interpretation, enabling researchers to focus on scientific discovery rather than data processing. For instance, a leading pharmaceutical company could leverage bioinformatics tools to optimize its drug discovery pipeline by analyzing large genomic datasets to identify potential drug targets and predict their efficacy. By integrating these tools into its workflow, the company can reduce the time and cost associated with traditional drug discovery methods, ultimately bringing new therapies to market more efficiently. Despite its numerous benefits, the market faces challenges such as data security and privacy concerns, data standardization, and the need for interoperability between different software platforms. Addressing these challenges will require collaboration between industry stakeholders, regulatory bodies, and academic institutions to establish best practices and develop standardized protocols for data sharing and analysis.

What will be the size of the Bioinformatics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleBioinformatics, a dynamic and evolving market, is witnessing significant growth as businesses increasingly rely on high-performance computing, gene annotation, and bioinformatics software to decipher regulatory elements, gene expression regulation, and genomic variation. Machine learning algorithms, phylogenetic trees, and ontology development are integral tools for disease modeling and protein interactions. cloud computing platforms facilitate the storage and analysis of vast biological databases and sequence datas, enabling data mining techniques and statistical modeling for sequence assembly and drug discovery pipelines. Proteomic analysis, protein folding, and computational biology are crucial components of this domain, with biomedical ontologies and data integration platforms enhancing research efficiency. The integration of gene annotation and machine learning algorithms, for instance, has led to a 25% increase in accurate disease diagnosis within leading healthcare organizations. This trend underscores the importance of investing in advanced bioinformatics solutions for improved regulatory compliance, budgeting, and product strategy.

Unpacking the Bioinformatics Market Landscape

Bioinformatics, an essential discipline at the intersection of biology and computer science, continues to revolutionize the scientific landscape. Evolutionary bioinformatics, with its molecular dynamics simulation and systems biology approaches, enables a deeper understanding of biological processes, leading to improved ROI in research and development. For instance, next-generation sequencing technologies have reduced sequencing costs by a factor of ten, enabling genome-wide association studies and transcriptome sequencing on a previously unimaginable scale. In clinical bioinformatics, homology modeling techniques and protein-protein interaction analysis facilitate drug target identification, enhancing compliance with regulatory requirements. Phylogenetic analysis tools and comparative genomics studies contribute to the discovery of novel biomarkers and the development of personalized treatments. Bioimage informatics and proteomic data integration employ advanced sequence alignment algorithms and functional genomics tools to unlock new insights from complex

jats:titleAbstract/jats:titlejats:pGenetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future./jats:p

Structural Bioinformatics Software Market Outlook

As per our latest research, the global Structural Bioinformatics Software market size reached USD 1.48 billion in 2024, demonstrating robust demand across biopharmaceutical research, drug discovery, and academic sectors. The market is experiencing a healthy compound annual growth rate (CAGR) of 10.2% and is forecasted to attain a value of USD 3.58 billion by 2033. This growth can be attributed to the rapid advancements in computational biology, the increasing adoption of artificial intelligence and machine learning in protein structure prediction, and the surge in drug development activities globally.

One of the primary growth drivers for the Structural Bioinformatics Software market is the intensifying focus on precision medicine and personalized therapeutics. With the global pharmaceutical industry placing increasing emphasis on developing targeted therapies, there is a critical need for advanced software tools that can model, predict, and analyze complex biomolecular structures. These tools are pivotal for understanding protein-ligand interactions, predicting the effects of mutations, and identifying novel druggable targets. The integration of high-throughput sequencing data with structural bioinformatics platforms has further accelerated the pace of discovery, enabling researchers to move from raw data to actionable insights with unprecedented speed and accuracy.

Another significant factor propelling the market is the evolution of computational power and cloud-based infrastructure. The exponential increase in available biological data, coupled with the complexity of protein folding and molecular dynamics simulations, demands scalable and high-performance computing resources. Cloud-based structural bioinformatics solutions have democratized access to sophisticated algorithms and databases, making them available to a broader range of users, including smaller biotech firms and academic labs. This shift has not only reduced the barriers to entry but also fostered greater collaboration and innovation in the field, as researchers can now share data, workflows, and results seamlessly across geographies.

The market is also benefiting from heightened collaboration between academia, research organizations, and industry players. Public-private partnerships, government funding initiatives, and global consortia are fueling the development of next-generation structural bioinformatics platforms. These collaborations are focused on addressing critical challenges such as protein structure prediction, functional annotation, and molecular modeling. The emergence of open-source software and community-driven databases has further enriched the ecosystem, providing researchers with access to a wealth of curated data and cutting-edge analytical tools. As the field continues to evolve, the synergy between computational advancements and experimental validation is expected to drive the adoption of structural bioinformatics software across diverse end-user segments.

Structure-Based Drug Design is an integral component of the drug discovery process, leveraging the detailed knowledge of the three-dimensional structure of biological targets to design more effective therapeutic agents. This approach utilizes advanced computational tools to model the interactions between drug candidates and their targets, allowing researchers to optimize binding affinity and selectivity. By focusing on the structural aspects of drug-target interactions, Structure-Based Drug Design enhances the precision and efficiency of the drug development pipeline, ultimately leading to the creation of more targeted and effective treatments. The integration of this methodology with structural bioinformatics software is revolutionizing the way researchers approach complex biological challenges, offering new avenues for innovation and discovery.

From a regional perspective, North America remains the dominant market for structural bioinformatics software, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific region. The robust presence of leading pharmaceutical and biotechnology companies, coupled with significant investments in research and development, has established North America as a global innovation hub. Meanwhi

Explore the booming Molecular Biology Software market, projected to reach $3.7 billion by 2033. Discover key drivers, trends in bioinformatics, DNA analysis, and drug discovery.

Global Bioinformatics market size was USD 12.76 Billion in 2022 and it is forecasted to reach USD 29.32 Billion by 2030. Bioinformatics Industry's Compound Annual Growth Rate will be 10.4% from 2023 to 2030. What are the driving factors for the Bioinformatics market?

The primary factors propelling the global bioinformatics industry are advances in genomics, rising demand for protein sequencing, and rising public-private sector investment in bioinformatics. Large volumes of data are being produced by the expanding use of next-generation sequencing (NGS) and other genomic technologies; these data must be analyzed using advanced bioinformatics tools. Furthermore, the global bioinformatics industry may benefit from the development of emerging advanced technologies. However, the bioinformatics discipline contains intricate algorithms and massive amounts of data, which can be difficult for researchers and demand a lot of processing power. What is Bioinformatics?

Bioinformatics is related to genetics and genomics, which involves the use of computer technology to store, collect, analyze, and disseminate biological information, and data, such as DNA and amino acid sequences or annotations about these sequences. Researchers and medical professionals use databases that organize and index this biological data to better understand health and disease, and in some circumstances, as a component of patient care. Through the creation of software and algorithms, bioinformatics is primarily used to extract knowledge from biological data. Bioinformatics is frequently used in the analysis of genomics, proteomics, 3D protein structure modeling, image analysis, drug creation, and many other fields.

A protein database which connects multiple disparate bioinformatics tools and systems text mining, data mining, analysis and visualization tools, and databases and ontologies.

List of tools, databases and software used in this study.

Databases used for MyCodentifier a Nextflow pipeline to identify Mycobacterium tuberculosis complex (MTBC) and Nontuberculous mycobacteria (NTM) species from Next-generation sequencing (NGS) data.

Short description:
The pipeline is constructed using nextflow as workflow manager running in a docker container. It is able to identify species of MTBC/NTM from positive Mycobacterial Growth Indicator Tube (MGIT) cultures. To do so it uses an hsp65 database for fast identification coupled with a Metagenomic method using centrifuge to identify on genome level. For TB it also is able to identify subspecies. Results are presented in automated pdf and html reports.

MyCodentifier Github:

https://jordycoolen.github.io/MyCodentifier/

Harvester is a Web-based tool that bulk-collects bioinformatic data on human proteins from various databases and prediction servers. It is a meta search engine for gene and protein information. It searches 16 major databases and prediction servers and combines the results on pregenerated HTML pages. In this way Harvester can provide comprehensive gene-protein information from different servers in a convenient and fast manner. As full text meta search engine, similar to Google trade mark, Harvester allows screening of the whole genome proteome for current protein functions and predictions in a few seconds. With Harvester it is now possible to compare and check the quality of different database entries and prediction algorithms on a single page. Sponsors: This work has been supported by the BMBF with grants 01GR0101 and 01KW0013.

This exercise is an adaptation of the Annotation Lesson by Rosenwald et al. It introduces the use of bioinformatics tools to extract information from genome databases. It is a basic lesson on genome annotation databases.

The Sol Genomics Network (SGN) is a clade-oriented database dedicated to the biology of the Solanaceae family which includes a large number of closely related and many agronomically important species such as tomato, potato, tobacco, eggplant, pepper, and the ornamental Petunia hybrida. SGN is part of the International Solanaceae Initiative (SOL), which has the long-term goal of creating a network of resources and information to address key questions in plant adaptation and diversification. A key problem of the post-genomic era is the linking of the phenome to the genome, and SGN allows to track and help discover new such linkages. Data: Solanaceae and other Genomes SGN is a home for Solanaceae and closely related genomes, such as selected Rubiaceae genomes (e.g., Coffea). The tomato, potato, pepper, and eggplant genome are examples of genomes that are currently available. If you would like to include a Solanaceae genome that you sequenced in SGN, please contact us. ESTs SGN houses EST collections for tomato, potato, pepper, eggplant and petunia and corresponding unigene builds. EST sequence data and cDNA clone resources greatly facilitate cloning strategies based on sequence similarity, the study of syntenic relationships between species in comparative mapping projects, and are essential for microarray technology. Unigenes SGN assembles and publishes unigene builds from these EST sequences. For more information, see Unigene Methods. Maps and Markers SGN has genetic maps and a searchable catalog of markers for tomato, potato, pepper, and eggplant. Tools SGN makes available a wide range of web-based bioinformatics tools for use by anyone, listed here. Some of our most popular tools include BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an Alignment Analyzer and browser for phylogenetic trees. The VIGS tool can help predict the properties of VIGS (Viral Induced Gene Silencing) constructs. The data in SGN have been submitted by many different research groups around the world. A web form is available to submit data for display on SGN. SGN community-driven gene and phenotype database: Simple web interfaces have been developed for the SGN user-community to submit, annotate, and curate the Solanaceae locus and phenotype databases. The goal is to share biological information, and have the experts in their field review existing data and submit information about their favorite genes and phenotypes. Resources in this dataset:Resource Title: Website Pointer to Sol Genomics Network. File Name: Web Page, url: https://solgenomics.net/ Specialized Search interfaces are provided for: Organisms/Taxon; Genes and Loci; Genomic sequences and annotations; QTLs, Mutants & Accessions, Traits; Transcripts: Unigenes, ESTs, & Libraries; Unigene families; Markers; Genomic clones; Images; Expression: Templates, Experiments, Platforms; Traits.

expam reference database used for benchmarking and comparison against metagenome profilers.

This FASTA file is the NCBI Nt (Nucleotide) database (public domain) used for holistic metagenomic screening of ancient DNA data at the Department of Archaeogenetics at the Max Planck Institute for the Science of Human History. We offer here the FASTA file used to construct MALT databases (https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/algorithms-in-bioinformatics/software/malt/), which are generally too large for uploading. Please see each relevent publications that use the database for MALT database construction commands.

NCBI does not retain older versions of this database which is why this has been uploaded here. It was downloaded on 2017-10-26 12:39 from: ftp://ftp-trace.ncbi.nih.gov/blast/db/FASTA/nt.gz. The NCBI Nt database is released into the public domain as per https://www.ncbi.nlm.nih.gov/home/about/policies/.

Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

ASA³P is an automatic and highly scalable assembly, annotation and higher-level analyses pipeline for closely related bacterial isolates. https://github.com/oschwengers/asap

ASA³P is a fully automatic, locally executable and scalable assembly, annotation and higher-level analysis pipeline creating results in standard bioinformatics file formats as well as sophisticated HTML5 documents. Its main purpose is the automatic processing of NGS WGS data of multiple closely related isolates, thus transforming raw reads into assembled and annotated genomes and finally gathering as much information on every single bacterial genome as possible. Per-isolate analyses are complemented by comparative insights. Therefore, the pipeline incorporates many best-in-class open source bioinformatics tools and thus minimizes the burden of ever-repeating tasks. Envisaged as a preprocessing tool it provides comprehensive insights as well as a general overview and comparison of analysed genomes along with all necessary result files for subsequent deeper analyses. All results are presented via modern HTML5 documents comprising interactive visualizations.

Schwengers et al, 2020 PLOS Comp Bio DOI:10.1371/journal.pcbi.1007134