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Bioinformatics Market Size 2024-2028

The bioinformatics market size is forecast to increase by USD 13.2 billion at a CAGR of 16.59% between 2023 and 2028.

The market is experiencing significant growth, driven by the rapid advancements in genetic sequencing technology and the development of sophisticated bioinformatics tools for Next-Generation Sequencing (NGS). These innovations have led to a dramatic reduction in the cost of genetic sequencing, making it accessible to a wider range of researchers and organizations. However, this market is not without challenges. The shortage of trained laboratory professionals with expertise in bioinformatics and data analysis poses a significant barrier to the effective utilization of these advanced technologies. Companies seeking to capitalize on market opportunities must invest in training and development programs to address this talent gap.
Additionally, the increasing volume and complexity of genomic data require scalable and efficient bioinformatics solutions. Strategic partnerships, collaborations, and acquisitions can help companies overcome these challenges and gain a competitive edge in the market. Overall, the market presents significant opportunities for growth, particularly in areas such as personalized medicine, agricultural biotechnology, and environmental research. Companies that can effectively address the talent shortage and provide scalable, efficient solutions will be well-positioned to succeed in this dynamic and evolving market.

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

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The bioinformatics market, at the intersection of biology and computer science, is undergoing rapid growth, driven by next-generation sequencing (NGS), artificial intelligence (AI), and cloud computing. Clinical laboratories and research institutions leverage NGS for biomarker discovery, genetic testing, and protein sequencing, advancing precision medicine and population genetics. Bioinformatic pipelines and high-throughput screening manage omics data, supporting computational biology, evolutionary biology, and biomedical engineering.
Phylogenetic analysis and biological databases fuel scientific collaboration, while open access and cloud computing enhance data sharing. However, data security and data privacy remain critical challenges amid regulatory complexities. Biotechnology startups and digital health firms invest in single-cell sequencing, sequence alignment, and synthetic biology, developing mobile health applications and wearable devices.
Big data, gene editing, and genome editing drive innovation in biotechnology investment, while wet lab experiments complement in silico analyses. Market dynamics include rising demand for precision medicine, biotechnology startups, and digital health, with AI and NGS transforming clinical laboratories and scientific publications. Staying ahead requires addressing data privacy and embracing computational biology trends.

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 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Molecular phylogenetics
  Proteomics
  Metabolomics


Product

  Platforms
  Tools
  Services
  Genomics
  Chemoinformatics & Drug Design
  Others


Technology

  Sequence Analysis
  Data Warehousing
  Structural Analysis


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil

By Application Insights

The molecular phylogenetics segment is estimated to witness significant growth during the forecast period.

In the realm of bioinformatics, molecular phylogenetics holds a pivotal position, particularly in the global market. This subfield of bioinformatics utilizes molecular data to elucidate the evolutionary relationships among various species. Its applications span numerous research domains, such as drug discovery, disease diagnosis, and conservation biology. One prominent area of application is the study of viral evolution. By deciphering the molecular data of diverse virus strains, researchers can trace their evolutionary history and gain valuable insights into their origins and transmission patterns. Next-generation sequencing technologies have significantly advanced molecular phylogenetics, enabling the analysis of vast amounts of genetic data.

Artificial intelligence and machine learning algorithms further enhance the accuracy and efficiency of these analyses. Clinical laboratories and research institutions employ these tools for genetic testing and biomarker discovery, driving the market's growth. Bioinformatics tools

The dataset consists of 1763 observations, each representing a unique patient, and 12 different attributes associated with heart disease. This dataset is a critical resource for researchers focusing on predictive analytics in cardiovascular diseases.

Variables Overview: 1. Age: A continuous variable indicating the age of the patient. 2. Sex: A categorical variable with two levels ('Male', 'Female'), indicating the gender of the patient. 3. CP (Chest Pain type): A categorical variable describing the type of chest pain experienced by the patient, with categories such as 'Asymptomatic', 'Atypical Angina', 'Typical Angina', and 'Non-Angina'. 4. TRTBPS (Resting Blood Pressure): A continuous variable indicating the resting blood pressure (in mm Hg) on admission to the hospital. 5. Chol (Serum Cholesterol): A continuous variable measuring the serum cholesterol in mg/dl. 6. FBS (Fasting Blood Sugar): A binary variable where 1 represents fasting blood sugar > 120 mg/dl, and 0 otherwise. 7. Rest ECG (Resting Electrocardiographic Results): Categorizes the resting electrocardiographic results of the patient into 'Normal', 'ST Elevation', and other categories. 8. Thalachh (Maximum Heart Rate Achieved): A continuous variable indicating the maximum heart rate achieved by the patient. 9. Exng (Exercise Induced Angina): A binary variable where 1 indicates the presence of exercise-induced angina, and 0 otherwise. 10. Oldpeak (ST Depression Induced by Exercise Relative to Rest): A continuous variable indicating the ST depression induced by exercise relative to rest. 11. Slope (Slope of the Peak Exercise ST Segment): A categorical variable with levels such as 'Flat', 'Up Sloping', representing the slope of the peak exercise ST segment. 14. Target: A binary target variable indicating the presence (1) or absence (0) of heart disease.

Descriptive Statistics: The patients' age ranges from 29 to 77 years, with a mean age of approximately 54 years. The resting blood pressure spans from 94 to 200 mm Hg, and the average cholesterol level is about 246 mg/dl. The maximum heart rate achieved varies widely among patients, from 71 to 202 beats per minute.

Importance for Research: This dataset provides a comprehensive view of various factors that could potentially be linked to heart disease, making it an invaluable resource for developing predictive models. By analyzing relationships and patterns within these variables, researchers can identify key predictors of heart disease and enhance the accuracy of diagnostic tools. This could lead to better preventive measures and treatment strategies, ultimately improving patient outcomes in the realm of cardiovascular health

Background: Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the "citation benefit". Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results: Here, we look at citation rates while controlling for many known citation predictors, and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations th...

Figures and survey data from forthcoming pre-print:

In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principle investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work—including high performance computing (HPC), bioinformatics support, multi-step workflows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the U.S. and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC – acknowledging that data science skills will be required to build a deeper understanding of life.

Keynote presentation by Rebecca Boyles, Senior Manager, Bioinformatics and Data Science from RTI International presented at Better Science through Better Data event. The video recording, slides and scribes are included.

The global biological software market is experiencing robust growth, driven by the increasing adoption of advanced technologies in life sciences research and healthcare. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of approximately 12% from 2025 to 2033, reaching an estimated market value of $7 billion by 2033. This expansion is fueled by several key factors: the escalating demand for high-throughput data analysis in genomics and proteomics, the rising prevalence of chronic diseases necessitating advanced diagnostic tools, and the growing adoption of cloud-based solutions for enhanced collaboration and accessibility. Furthermore, the continuous development of sophisticated algorithms and user-friendly interfaces is making biological software more accessible to a wider range of researchers and clinicians. The segment encompassing experimental design and data analysis software holds a significant market share, reflecting the crucial role of computational tools in optimizing research workflows and extracting meaningful insights from complex biological datasets. North America currently dominates the market, owing to the robust presence of established biotechnology companies and a well-funded research infrastructure. However, Asia-Pacific is expected to witness significant growth in the coming years due to the expanding healthcare sector and increasing government investments in research and development. Market restraints include the high cost of software licenses, the requirement for specialized training to effectively utilize these tools, and the potential challenges associated with data security and integration across different platforms. Nevertheless, the ongoing innovation in software capabilities, coupled with the increasing adoption of subscription-based models and cloud-based solutions, is expected to mitigate these constraints. The competitive landscape is characterized by a mix of established players like Thermo Fisher Scientific and DNASTAR, along with smaller specialized companies offering niche solutions. This dynamic competitive environment fosters innovation and drives the development of advanced biological software solutions tailored to the specific needs of diverse research and clinical applications. Future growth will be influenced by factors such as advancements in artificial intelligence and machine learning within the software, integration with laboratory automation systems, and increasing collaboration between software providers and research institutions.

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.

When we look at the rapid growth of scientific databases on the Internet in the past decade, we tend to take the accessibility and provenance of the data for granted. As we see a future of increased database integration, the licensing of the data may be a hurdle that hampers progress and usability. We have formulated four rules for licensing data for open drug discovery, which we propose as a starting point for consideration by databases and for their ultimate adoption. This work could also be extended to the computational models derived from such data. We suggest that scientists in the future will need to consider data licensing before they embark upon re-using such content in databases they construct themselves.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for bioinformatics, biotechnology, computer science in the U.S.

Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the health data science sandbox. This webpage shows the latest version of the course material.

1. Data.tar.gz Contains the datasets and executable files for some of the softwares
   You can unpack by simply doing
   tar -zxf Data.tar.gz -C ./
   This will create a folder called Data with the uncompressed material inside
2. Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by
   1. creating the folder Course_Env: mkdir Course_Env
   2. untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
   3. Activate the environment: conda activate ./Course_Env
   4. Run the unpacking script (it can take quite some time to get it done): conda-unpack
3. Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
4. environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
   1. conda env create -f environment_with_args.yml -p ./Course_Env
   2. conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

• Identify an experimental platform relevant to a population genomic analysis.
• Apply commonly used population genomic methods.
• Explain the theory behind common population genomic methods.
• Reflect on strengths and limitations of population genomic methods.
• Interpret and analyze results of population genomic inference.
• Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

1. Course intro and overview:
   • Coop chapters 1, 2, 3, Paper: Genome Diversity Project
2. Drift and the coalescent:
   • Coop chapter 4; Paper: Platypus
   • Exercise: Read mapping and base calling
3. Recombination:
   • Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
   • Exercise: Phasing and recombination rate
4. Population strucure and incomplete lineage sorting:
   • Lecture: Coop chapter 6, Review: Incomplete lineage sorting
   • Exercise: Working with VCF files
5. Hidden Markov models:
   • Lecture: Durbin chapter 3, Paper: population structure
   • Exercise: Inference of population structure and admixture
6. Ancestral recombination graphs:
   • Lecture: Paper: Approximating the ARG, Paper: Tree inference
   • Exercise: ARG dashboard exercises + Inference of trees along sequence
7. Past population demography:
   • Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
   • Exercise: Inferring historical populations
8. Direct and linked selection:
   • Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
9. Admixture:
   • Lecture: Review: Admixture, Paper: Admixture inference
   • Exercise: Detecting archaic ancestry in modern humans
10. Genome-wide association study (GWAS):
    • Lecture: Coop lecture notes 99-120
    • Exercise: GWAS quality control
11. Heritability:
    • Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
    • Exercise: Association testing
12. Evolution and disease:
    • Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)
    • Exercise: Estimating heritability

Over the past year, biology educators and staff at the Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free and easily accessible data science platform that integrates many bioinformatics resources into a graphical user interface built upon reproducible analysis notebooks. KBase held conversations with college and high school instructors to understand how KBase could potentially support their educational goals. These conversations morphed into a working group of biological and data science instructors that adapted the KBase platform to their curriculum needs, specifically around concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. The KBase Educators Working Group developed modular, adaptable, and customizable instructional units. Each instructional module contains teaching resources, publicly available data, analysis tools, and markdown capability to tailor instructions and learning goals for each class. The online user interface enables students to conduct hands-on data science research and analyses without requiring programming skills or their own computational resources (these are provided by KBase). Alongside these resources, KBase continues to work with instructors, supporting the development of additional curriculum modules. For anyone new to the platform, KBase, and the growing KBase Educators Organization, provides a community network, accompanied by community-sourced guidelines, instructional templates, and peer support to use KBase within a classroom whether virtual or in-person.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for bioinformatics (computer science program) in the U.S.

Provide a brief description of the dataset, including its purpose, context, and significance.

Dataset Features

List and describe each column or key feature of the dataset.

• Column 1 Name: Description of what this column represents.
• Column 2 Name: Add as needed...

Distribution

Detail the format, size, and structure of the dataset.

• Data Volume: Number of rows/records, number of columns, etc.

Usage

This dataset is ideal for a variety of applications:

• Application: Brief description of the first use case.
• Application: Add more as needed.

Coverage

Explain the scope and coverage of the dataset:

• Geographic Coverage: Region, country, or global.
• Time Range: Start date - End date of data collection.
• Demographics (if applicable): Age groups, gender, industries, etc.

License

CC0

Who Can Use It

List examples of intended users and their use cases:

• Data Scientists: For training machine learning models.
• Researchers: For academic or scientific studies.
• Businesses: For analysis, insights, or AI development.

Include any additional notes or context about the dataset that might be helpful for users.

The Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE) seeks to promote the use of bioinformatics and data science as a way to teach biology.

The bioinformatics market has emerged as a pivotal domain at the intersection of biology and data science, playing an essential role in the analysis and interpretation of complex biological data. As the demand for genomic and proteomic data analysis continues to rise, bioinformatics offers innovative solutions for d

1. SARS-CoV-2SpikeProteinMutations.docx contains data on mutations found in aligned SARS-CoV-2 surface glycoproteins.
2. SARS-CoV-2SpikeProteinVariants.docx contains data on computed SARS-CoV-2 surface glycoprotein variants in Europe.

Supplemental Data 1-S1. Timeline of important events shaping contemporary bioinformatics and comparative genomics. Timeline is not intended to be absolutely comprehensive of each of the observed fields, their respective histories. See footnotes for key review publications, sources in addition to those listed in Reference column. Field of contributions are color-coded accordingly: purple= computer science/engineering, blue= legislation/government action, biology= green, economic/markets= orange, academic institution= pink

The dataset was collected through whole-transcriptome RNA-Sequencing technologies. The processing method was described in the manuscript.

Explore the progression of average salaries for graduates in Science In Bioinformatics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Science In Bioinformatics relative to other fields. This data is essential for students assessing the return on investment of their education in Science In Bioinformatics, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Bioinformatics (Computer Science Program) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Bioinformatics (Computer Science Program) relative to other fields. This data is essential for students assessing the return on investment of their education in Bioinformatics (Computer Science Program), providing a clear picture of financial prospects post-graduation.