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Bioinformatics Market Size 2025-2029

The bioinformatics market size is forecast to increase by USD 15.98 billion at a CAGR of 17.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the reduction in the cost of genetic sequencing and the development of advanced bioinformatics tools for Next-Generation Sequencing (NGS) technologies. These advancements have led to an increase in the volume and complexity of genomic data, necessitating the need for sophisticated bioinformatics solutions. However, the market faces challenges, primarily the shortage of trained laboratory professionals capable of handling and interpreting the vast amounts of data generated. This skills gap can hinder the effective implementation and utilization of bioinformatics tools, potentially limiting the market's growth potential.
Companies seeking to capitalize on market opportunities must focus on addressing this challenge by investing in training programs and collaborating with academic institutions. Additionally, data security, data privacy, and regulatory compliance are crucial aspects of the market, ensuring the protection and ethical use of sensitive biological data. Partnerships with technology providers and service organizations can help bridge the gap in expertise and resources, enabling organizations to leverage the power of bioinformatics for research and development, diagnostics, and personalized medicine applications.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is experiencing significant growth, driven by the increasing demand for precision medicine and the exploration of complex biological systems. Structural variation and gene regulation play crucial roles in gene networks and biological networks, necessitating advanced tools for SNP genotyping and statistical analysis. Precision medicine relies on the identification of mutations and biomarkers through mutation analysis and biomarker validation.
Metabolic networks, protein microarrays, CDNA microarrays, and RNA microarrays contribute to the discovery of new insights in evolutionary biology and conservation biology. The integration of these technologies enables a comprehensive understanding of gene regulation, gene networks, and metabolic pathways, ultimately leading to the development of novel therapeutics. Protein-protein interactions and signal transduction pathways are essential in understanding protein networks and metabolic pathways. Ontology mapping and predictive modeling facilitate data warehousing and data analytics in this field.

How is this Bioinformatics Industry segmented?

The bioinformatics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Molecular phylogenetics
  Transcriptomic
  Proteomics
  Metabolomics


Product

  Platforms
  Tools
  Services


End-user

  Pharmaceutical and biotechnology companies
  CROs and research institutes
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Application Insights

The molecular phylogenetics segment is estimated to witness significant growth during the forecast period. In the dynamic and innovative realm of bioinformatics, various technologies and techniques are shaping the future of research and development. Molecular phylogenetics, a significant branch of bioinformatics, employs molecular data to explore the evolutionary connections among species, offering enhanced insights into the intricacies of life. This technique has been instrumental in numerous research domains, such as drug discovery, disease diagnosis, and conservation biology. For instance, it plays a pivotal role in the study of viral evolution. By deciphering the molecular data of distinct virus strains, researchers can trace their evolutionary history and unravel their origins and transmission patterns.

Furthermore, the integration of proteomic technologies, network analysis, data integration, and systems biology is expanding the scope of bioinformatics research and applications. Bioinformatics services, open-source bioinformatics, and commercial bioinformatics software are vital components of the market, catering to the diverse needs of researchers, industries, and institutions. Bioinformatics databases, including sequence databases and bioinformatics algorithms, are indispensable resources for storing, accessing, and analyzing biological data. In the realm of personalized medicine and drug di

Examples of bioinformatics training programs in China.

Example files to test URL handling

This dataset contains INSDC sequences associated with environmental sample identifiers. The dataset is prepared periodically using the public ENA API (https://www.ebi.ac.uk/ena/portal/api/) by querying data with the search parameters: `environmental_sample=True & host=""`

EMBL-EBI also publishes other records in separate datasets (https://www.gbif.org/publisher/ada9d123-ddb4-467d-8891-806ea8d94230).

The data was then processed as follows:

1. Human sequences were excluded.

2. For non-CONTIG records, the sample accession number (when available) along with the scientific name were used to identify sequence records corresponding to the same individuals (or group of organism of the same species in the same sample). Only one record was kept for each scientific name/sample accession number.

3. Contigs and whole genome shotgun (WGS) records were added individually.

4. The records that were missing some information were excluded. Only records associated with a specimen voucher or records containing both a location AND a date were kept.

5. The records associated with the same vouchers are aggregated together.

6. A lot of records left corresponded to individual sequences or reads corresponding to the same organisms. In practise, these were "duplicate" occurrence records that weren't filtered out in STEP 2 because the sample accession sample was missing. To identify those potential duplicates, we grouped all the remaining records by `scientific_name`, `collection_date`, `location`, `country`, `identified_by`, `collected_by` and `sample_accession` (when available). Then we excluded the groups that contained more than 50 records. The rationale behind the choice of threshold is explained here: https://github.com/gbif/embl-adapter/issues/10#issuecomment-855757978

7. To improve the matching of the EBI scientific name to the GBIF backbone taxonomy, we incorporated the ENA taxonomic information. The kingdom, Phylum, Class, Order, Family, and genus were obtained from the ENA taxonomy checklist available here: http://ftp.ebi.ac.uk/pub/databases/ena/taxonomy/sdwca.zip

More information available here: https://github.com/gbif/embl-adapter#readme

You can find the mapping used to format the EMBL data to Darwin Core Archive here: https://github.com/gbif/embl-adapter/blob/master/DATAMAPPING.md

This collection contains an example MINUTE-ChIP dataset to run minute pipeline on, provided as supporting material to help users understand the results of a MINUTE-ChIP experiment from raw data to a primary analysis that yields the relevant files for downstream analysis along with summarized QC indicators. Example primary non-demultiplexed FASTQ files provided here were used to generate GSM5493452-GSM5493463 (H3K27m3) and GSM5823907-GSM5823918 (Input), deposited on GEO with the minute pipeline all together under series GSE181241. For more information about MINUTE-ChIP, you can check the publication relevant to this dataset: Kumar, Banushree, et al. "Polycomb repressive complex 2 shields naïve human pluripotent cells from trophectoderm differentiation." Nature Cell Biology 24.6 (2022): 845-857. If you want more information about the minute pipeline, there is a public biorXiv and a GitHub repository and official documentation.

This dataset includes all raw Miseq high-throughput sequencing data, bioinformatic pipeline and R codes that were used in the publication "Liu M, Baker SC, Burridge CP, Jordan GJ, Clarke LJ (2020) DNA metabarcoding captures subtle differences in forest beetle communities following disturbance. Restoration Ecology. 28:1475-1484. DOI:10.1111/rec.13236."

Miseq_16S.zip - Miseq sequencing dataset for gene marker 16S, including 48 fastq files for 24 beetle bulk samples; Miseq_CO1.zip -Miseq sequencing dataset for gene marker CO1, including 46 fastq files for 23 beetle bulk samples (one sample failed to be sequenced); nfp4MBC.nf - A nextflow bioinformatic script to process Miseq datasets; nextflow.config - A configuratioin file needed when using nfp4MBC.nf; adapters_16S.zip - Adapters used to tag each of 24 beetle bulk samples for 16S, also used to process 16S Miseq dataset when using nfp4MBC.nf; adapters_CO1.zip - Adapters used to tag each of 24 beetle bulk samples for CO1, also used to process CO1 Miseq dataset when using nfp4MBC.nf; rMBC.Rmd - R markdown codes for community analyses; rMBC.zip - Datasets used in rMBC.Rmd. COI_ZOTUs_176.fasta - DNA sequences of 176 COI ZOTUs. 16S_ZOTUs_156 -DNA sequences of 156 16S ZOTUs.

Modern research is increasingly data-driven and reliant on bioinformatics software. Publication is a common way of introducing new software, but not all bioinformatics tools get published. Giving there are competing tools, it is important not merely to find the appropriate software, but have a metric for judging its usefulness. Journal's impact factor has been shown to be a poor predictor of software popularity; consequently, focusing on publications in high-impact journals limits user's choices in finding useful bioinformatics tools. Free and open source software repositories on popular code sharing platforms such as GitHub provide another venue to follow the latest bioinformatics trends. The open source component of GitHub allows users to bookmark and copy repositories that are most useful to them. This Perspective aims to demonstrate the utility of GitHub “stars,” “watchers,” and “forks” (GitHub statistics) as a measure of software impact. We compiled lists of impactful bioinformatics software and analyzed commonly used impact metrics and GitHub statistics of 50 genomics-oriented bioinformatics tools. We present examples of community-selected best bioinformatics resources and show that GitHub statistics are distinct from the journal's impact factor (JIF), citation counts, and alternative metrics (Altmetrics, CiteScore) in capturing the level of community attention. We suggest the use of GitHub statistics as an unbiased measure of the usability of bioinformatics software complementing the traditional impact metrics.

Bioinformatics Market Outlook

According to our latest research, the global bioinformatics market size reached USD 16.2 billion in 2024, reflecting robust industry momentum. The market is exhibiting a healthy compound annual growth rate (CAGR) of 13.1% and is projected to attain a value of USD 42.7 billion by 2033. This vigorous expansion is driven by the rapid integration of computational tools in life sciences, accelerating advancements in genomics, proteomics, and drug discovery. The increasing demand for personalized medicine and the surge in big data analytics within biological research are pivotal growth factors shaping the bioinformatics landscape.

One of the principal growth factors fueling the bioinformatics market is the explosive rise in genomics research, particularly in the context of next-generation sequencing (NGS) technologies. The cost of sequencing has plummeted over the past decade, making large-scale genomic projects more accessible to both public and private sector entities. This democratization of sequencing technology has led to a significant influx of biological data, necessitating sophisticated bioinformatics tools for analysis, interpretation, and storage. The development of cloud-based bioinformatics platforms further enables researchers to manage and analyze vast datasets efficiently, fostering greater collaboration and innovation in genomics-driven healthcare, agriculture, and environmental sciences.

Another critical driver is the increasing adoption of bioinformatics in drug discovery and development. Pharmaceutical and biotechnology companies are leveraging bioinformatics solutions to accelerate target identification, drug candidate screening, and biomarker discovery. The integration of artificial intelligence (AI) and machine learning algorithms within bioinformatics workflows is enhancing the predictive accuracy of drug response models and facilitating the identification of novel therapeutic targets. This not only shortens the drug development lifecycle but also reduces costs and improves the likelihood of clinical success. As precision medicine gains traction, bioinformatics is becoming indispensable in tailoring treatments based on individual genetic profiles, further propelling market growth across the healthcare sector.

The expanding application of bioinformatics beyond human health is another significant growth factor. In agriculture, bioinformatics is instrumental in crop improvement, pest resistance, and livestock management through the analysis of genomic and phenotypic data. Environmental biotechnology also benefits from bioinformatics in monitoring biodiversity, tracking pathogen outbreaks, and assessing ecosystem health. Moreover, forensic biotechnology utilizes bioinformatics for DNA profiling and criminal investigations. These diverse applications underscore the versatility and critical importance of bioinformatics across multiple sectors, driving sustained investment and innovation in the market.

From a regional perspective, North America continues to dominate the global bioinformatics market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major industry players, significant government funding for genomics research, and a well-established healthcare infrastructure. Europe follows closely, supported by strong academic research and collaborative initiatives such as the European Bioinformatics Institute. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rising investments in life sciences, expanding biotechnology industries, and increasing adoption of digital health solutions. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a comparatively nascent stage, driven by growing awareness and infrastructural improvements.

Product & Service Analysis

The bioinformatics market by product & service is segmented into software, hardware, and services, each playing a pivotal role in driving the

Dataset

This dataset was created by Sreshta Putchala

Contents

It contains the following files:

Small test dataset for MAPP workflow:

• 5mln reads sampled from two paired-end RNA-seq samples of the GEO record: GSE69656
• 100 arbitrarily chosen Position Weight Matrices for RNA binding proteins and their sequence logos
• configuration template for the workflow (YAML)
• experiment design table (TSV)
• Genomic annotation for human: chr21 (GTF)
• Genomic sequence for human: chr21 (FASTA)
• PolyAsite Atlas 2.0 annotation: chr21 (BED)

This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets - DA and DB - as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The genetic algorithm is guided by an objective function that takes into account the accuracy of classification in both datasets, the number of biomarkers that support the partition, and the distribution of the samples across the classes for each dataset. To illustrate the method, two whole-genome breast cancer instances from dfferent sources were used. In this application, the results indicate that the method could be used to find unknown subtypes of diseases supported by biomarkers presenting similar gene expression profiles across platforms. Moreover, even though this initial study was restricted to two datasets and two classes, the method can be easily extended to consider both more datasets and classes. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

RMQS: The French Soil Quality Monitoring Network (RMQS) is a national program for the assessment and long-term monitoring of the quality of French soils. This network is based on the monitoring of 2240 sites representative of French soils and their land use. These sites are spread over the whole French territory (metropolitan and overseas) along a systematic square grid of 16 km x 16 km cells. The network covers a broad spectrum of climatic, soil and land-use conditions (croplands, permanent grasslands, woodlands, orchards and vineyards, natural or scarcely anthropogenic land and urban parkland). The first sampling campaign in metropolitan France took place from 2000 to 2009. Dataset: This dataset contains config files used to run the bioinformatic pipeline and the control sample data that were not published before Reference environmental DNA samples named “G4” in internal laboratory processes were added for each molecular analysis. They were used for technical validation, but not necessarily published alongside the datasets. The taxonomy and OTU abundance files for these control samples were built like the taxonomy and abundance file of the main dataset. As these internal control samples were clustered against the RMQS dataset in an open reference fashion, they contained new OTUs (noted as “OUT”) that corresponded to sequences that did not match any of 188,030 RMQS reference sequences. The sample bank association file links each sample to its sequencing library. The G4 metadata file links each G4 to its library, molecular tag and sequence repository information. File structure: Taxonomy files rmqs1_control_taxonomy_: Taxonomy is splitted across five files with one line per site and one column per taxa. Each line sums to 10k (rarefaction threshold). Three supplementary columns are present: Unknown: not matching any reference. Unclassified: missing taxa between genus and phylum. Environmental: matched to sample from environmental study, generally with only a phylum name. rmqs1_16S_otu_abundance.tsv: OTU abundance per site (one column per OTUs, “DB” + number for OTUs from RMQS reference set, “OUT” for OTUs not matching any “DB” ones). Each line sums to 10k (rarefaction threshold). rmqs1_16S_bank_association.tsv: two columns file with bank name for each sample rmqs1_16S_bank_metadata.tsv: library_name: library name used in labs study_accession, sample_accession, experiment_accession, run_accession: SRA EBI identifier library_name_genoscope: library name used in the Genoscope sequence center MID: multiplex identifier sequence run_alias: Genoscope internal alias ftp_link: FTP link to download library Input_G4.txt: Tabulated file containing the parameters and the bioinformatic steps done by the BIOCOM-PIPE pipeline to extract, treat and analyze controls from raw librairies detailed in the rmqs1_16S_bank_metadata.tsv. project_G4.tab: Comma separated file containing the needed information to generate the Input.txt file with the BIOCOM-PIPE pipeline for controls only: PROJECT: Project name chosen by the user LIBRARY_NAME: Library name chosen by the user LIBRARY_NAME_RECEIVED: Library name chosen by the sequencing partner and used by BIOCOM-PIPE SAMPLE_NAME: Sample name chosen by the user MID_F: MID name or MID sequence associated to the Forward primer MID_R: MID name or MID sequence associated to the Reverse primer TARGET: Target gene (16S, 18S, or 23S) PRIMER_F: Forward primer name used for amplification PRIMER_R: Reverse primer name used for amplification SEQUENCE_PRIMER_F: Forward primer sequence used for amplification SEQUENCE_PRIMER_R: Reverse primer sequence used for amplification Input_GLOBAL.txt: Tabulated file containing the parameters and the bioinformatic steps done by the BIOCOM-PIPE pipeline to extract, treat and analyze controls and samples from raw librairies detailed in the rmqs1_16S_bank_metadata.tsv. project_GLOBAL.tab: Comma separated file containing the needed information to generate the Input.txt file for controls and samples with the BIOCOM-PIPE pipeline: PROJECT: Project name chosen by the user LIBRARY_NAME: Library name chosen by the user LIBRARY_NAME_RECEIVED: Library name chosen by the sequencing partner and used by BIOCOM-PIPE SAMPLE_NAME: Sample name chosen by the user MID_F: MID name or MID sequence associated to the Forward primer MID_R: MID name or MID sequence associated to the Reverse primer TARGET: Target gene (16S, 18S, or 23S) PRIMER_F: Forward primer name used for amplification PRIMER_R: Reverse primer name used for amplification SEQUENCE_PRIMER_F: Forward primer sequence used for amplification SEQUENCE_PRIMER_R: Reverse primer sequence used for amplification Details: Three libraries (58,59 and 69) data were re-sequenced and are not detailed in files. Some samples can be present in several libraries. We kept only the one with the highest number of sequences.

Example files to run DrugSimDB interface

Dataset

This dataset was created by Sreshta Putchala

Contents

Three examples dataset to perform bioinformatics analysis.

Example files to run DrugSimDB interface

The dataset contains reference samples that will be useful for benchmarking and comparing bioinformatics tools for genome analysis. Examples include: NA12878 (HG001) and NA24385 (HG002) sequenced on an Oxford Nanopore Technologies (ONT) PromethION using the latest R10.4.1 flowcells; and, UHR RNA (direct-RNA) on an ONT PromethION using the latest RNA004 flowcells. Raw signal data output by the sequencer is provided for these datasets in BLOW5 format, and can be rebasecalled when basecalling software updates bring accuracy and feature improvements over the years. Raw signal data is not only for rebasecalling, but also can be used for emerging bioinformatics tools that directly analyse raw signal data. We also provide the basecalled data alongside the raw signal data.

Biological data is increasing at a high speed, creating a vast amount of knowledge, while updating knowledge in teaching is limited, along with the unchanged time in the classroom. Therefore, integrating bioinformatics into teaching will be effective in teaching biology today. However, the big challenge is that pedagogical university students have yet to learn the basic knowledge and skills of bioinformatics, so they have difficulty and confusion when using it. However, the big challenge is that pedagogical university students have yet to learn the basic knowledge and skills of bioinformatics, so they have difficulty and confusion when using it in biology teaching. This dataset includes survey results on high school teachers, teacher training curriculums and pedagogical students in Vietnam. The highlights of this dataset are six basic principles and four steps of bioinformatics integration in teaching biology at high schools, with illustrative examples. The principles and approaches of integrating Bioinformatics into biology teaching improve the quality of biology teaching and promote STEM education in Vietnam and developing countries.

Dataset

This dataset entry is meant to be downloaded programmatically while rendering the txtools_useCases.Rmd notebooks, to facilitate their replication using the provided genomic references. Processed data is also provided to show ready-to-use examples of data processed by txtools.

Abstract

We present txtools, an R package that enables the processing, analysis, and visualization of RNA-seq data at the nucleotide-level resolution, seamlessly integrating alignments to the genome with transcriptomic representation. txtools’ main inputs are BAM files and a transcriptome annotation, and the main output is a table, capturing mismatches, deletions, and the number of reads beginning and ending at each nucleotide in the transcriptomic space. txtools further facilitates downstream visualization and analyses. We showcase, using examples from the epitranscriptomic field, how a few calls to txtools functions can yield insightful and ready-to-publish results. txtools is of broad utility also in the context of structural mapping and RNA:protein interaction mapping. By providing a simple and intuitive framework, we believe that txtools will be a useful and convenient tool and pave the path for future discovery. txtools is available for installation from its GitHub repository at https://github.com/AngelCampos/txtools.

This data repository provides exemplary bacterial genome annotations conducted with Bakta of a broad taxonomical range of genomes comprising many pathogens (all ESKAPE), commensals and environmental species.

Bakta is a tool for the rapid & standardized local annotation of bacterial genomes & plasmids. It provides dbxref-rich and sORF-including annotations in machine-readble JSON & bioinformatics standard file formats for automatic downstream analysis: https://github.com/oschwengers/bakta