Data for sequence comparison of commamox genomes and genes identified. This dataset is associated with the following publication: Camejo, P., J. Santodomingo, K. McMahon, and D. Noguera. Genome-enabled insights into the ecophysiology of the comammox bacterium Ca. Nitrospira nitrosa. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 2(5): 1-16, (2017).

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.

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

(A) Bioinformatics Summary statistics and (B) Sequence identity matrix between strains. (XLSX)

Snapshot img

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

Arabidopsis_2029_Maf001_NoFilter

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.

The bioinformatics market, valued at USD 15,135.48 million in 2023, is expected to grow at a steady CAGR of 10.2%, reaching USD 32,663.77 million by 2031. Asia-Pacific is forecasted to grow at the fastest CAGR of 10.9%.

The size of the Bioinformatics Platforms Market market was valued at USD 16.36 Million in 2023 and is projected to reach USD 27.93 Million by 2032, with an expected CAGR of 7.94% during the forecast period. Recent developments include: In June 2022, California's biotechnology research startup LatchBio launched an end-to-end bioinformatics platform for handling big biotech data to accelerate scientific discovery., In March 2022, ARUP launched Rio, a bioinformatics pipeline and analytics platform for better, faster next-generation sequencing test results.. Key drivers for this market are: Increasing Demand for Nucleic Acid and Protein Sequencing, Increasing Initiatives from Governments and Private Organizations; Accelerating Growth of Proteomics and Genomics; Increasing Research on Molecular Biology and Drug Discovery. Potential restraints include: Lack of Well-defined Standards and Common Data Formats for Integration of Data, Data Complexity Concerns and Lack of User-friendly Tools. Notable trends are: Sequence Analysis Platform Segment is Expected Hold a Significant Share Over the Forecast Period.

Financial overview and grant giving statistics of International Society of Big Data and Bioinformatics Inc.

Subjective data models dataset

This dataset is comprised of data collected from study participants, for a study into how people working with biological data perceive data, and whether or not this perception of data aligns with a person's experiential and educational background. We call the concept of what data looks like to an individual a "subjective data model".

Todo: link paper/preprint once published.

Computational python analysis code: https://doi.org/10.5281/zenodo.7022789 and https://github.com/yochannah/subjective-data-models-analysis

Files

Transcripts of the recorded sessions are attached and have been verified by a second researcher. These files are all in plain text .txt format. Note that participant 3 did not agree to sharing the transcript of their interview. Interview paper files This folder has digital and photographed versions of the files shown to the participants for the file mapping task. Note that the original files are from the NCBI and from FlyBase. Videos and stills from the recordings have been deleted in line with the Data Management Plan and Ethical Review. anonymous_participant_list.csv shows which files have transcripts associated (not all participants agreed to share transcripts), what the order of Tasks A and B were, the date of interview, and what entities participants added to the set provided (if any). See the paper methods for more info about why entities were added to the set. cards.txt is a full list of the cards presented in the tasks. background survey and background manual annotations are the select survey data about participant background and manual additions to this where necessary, e.g. to interpret free text. codes.csv shows the qualitative codes used within the transcripts. entry_point.csv is a record of participants' identified entry points into the data. file_mapping_responses shows a record of responses to the file mapping task.

Subtitle

"Synthetic protein dataset with sequences, physical properties, and functional classification for machine learning tasks."

Description

Introduction

This synthetic dataset was created to explore and develop machine learning models in bioinformatics. It contains 20,000 synthetic proteins, each with an amino acid sequence, calculated physicochemical properties, and a functional classification.

Columns Included

• ID_Protein: Unique identifier for each protein.
• Sequence: String of amino acids.
• Molecular_Weight: Molecular weight calculated from the sequence.
• Isoelectric_Point: Estimated isoelectric point based on the sequence composition.
• Hydrophobicity: Average hydrophobicity calculated from the sequence.
• Total_Charge: Sum of the charges of the amino acids in the sequence.
• Polar_Proportion: Percentage of polar amino acids in the sequence.
• Nonpolar_Proportion: Percentage of nonpolar amino acids in the sequence.
• Sequence_Length: Total number of amino acids in the sequence.
• Class: The functional class of the protein, one of five categories: Enzyme, Transport, Structural, Receptor, Other.

Inspiration and Sources

While this is a simulated dataset, it was inspired by patterns observed in real protein datasets, such as: - UniProt: A comprehensive database of protein sequences and annotations. - Kyte-Doolittle Scale: Calculations of hydrophobicity. - Biopython: A tool for analyzing biological sequences.

Proposed Uses

This dataset is ideal for: - Training classification models for proteins. - Exploratory analysis of physicochemical properties of proteins. - Building machine learning pipelines in bioinformatics.

How This Dataset Was Created

1. Sequence Generation: Amino acid chains were randomly generated with lengths between 50 and 300 residues.
2. Property Calculation: Physicochemical properties were calculated using the Biopython library.
3. Class Assignment: Classes were randomly assigned for classification purposes.

Limitations

• The sequences and properties do not represent real proteins but follow patterns observed in natural proteins.
• The functional classes are simulated and do not correspond to actual biological characteristics.

Data Split

The dataset is divided into two subsets: - Training: 16,000 samples (proteinas_train.csv). - Testing: 4,000 samples (proteinas_test.csv).

Acknowledgment

This dataset was inspired by real bioinformatics challenges and designed to help researchers and developers explore machine learning applications in protein analysis.

This dataset is about book subjects. It has 1 row and is filtered where the books is Statistical methods in bioinformatics : an introduction. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The global bioinformatics services market size is projected to grow from USD 4.21 billion in 2025 to USD 18.41 billion by 2035, recording a CAGR of 15.9%. Companies leading innovation in the industry are Illumina, Thermo Fisher, QIAGEN, BGI, Eurofins Scientific, contributing to the sector’s development and expansion.

The global Biological Data Analysis Services market is booming, driven by personalized medicine and advancements in bioinformatics. Explore market size, growth trends, key players (Profacgen, CD ComputaBio, Eurofins Scientific), and regional analysis (North America, Europe, Asia-Pacific) in this comprehensive report covering biomarker identification, biological modeling, and more. Discover future projections and investment opportunities in this rapidly evolving field.

Financial overview and grant giving statistics of Phoenix Bioinformatics Corporation

The global Bioinformatics Data Analysis Service market is estimated to be valued at USD XXX million in 2025 and is projected to grow at a compound annual growth rate (CAGR) of XX% during the forecast period from 2025 to 2033. The market growth is attributed to the increasing adoption of bioinformatics in various research fields, such as genomics, transcriptomics, and proteomics. The availability of large-scale genomic and transcriptomic data has led to the development of sophisticated bioinformatics tools and techniques for data analysis, interpretation, and visualization. Furthermore, the growing awareness of personalized medicine and the need for precision medicine are driving the demand for bioinformatics data analysis services. Key market trends include the increasing adoption of cloud-based platforms for bioinformatics analysis, the development of artificial intelligence (AI) and machine learning (ML) algorithms for data analysis, and the emergence of new bioinformatics software and tools. These trends are expected to continue to drive the growth of the Bioinformatics Data Analysis Service market in the coming years. Major players in the market include Illumina, Thermo Fisher Scientific, QIAGEN, Seven Bridges, DNAnexus, SOPHiA GENETICS, Geneious, Macrogen, BGI Genomics, and Biomatters, among others. These companies offer a wide range of bioinformatics data analysis services, including data management, analysis, interpretation, and visualization. The market is expected to be highly competitive in the coming years, with major players focusing on innovation and strategic partnerships to gain market share.

Presentation on teaching introductory bioinformatics with Jupyter notebook-based active learning at the 2019 Great Lakes Bioinformatics Conference

The global Genetic Data Analysis Software market is experiencing robust growth, projected to reach a market size of $348.5 million in 2025. While the provided CAGR (Compound Annual Growth Rate) is missing, considering the rapid advancements in genomics and the increasing adoption of precision medicine, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 15%. This growth is fueled by several key drivers. The rising prevalence of genetic disorders necessitates sophisticated software for analysis and interpretation. Furthermore, the decreasing cost of genomic sequencing is making large-scale genetic studies more feasible, leading to a greater demand for robust and efficient analysis tools. The market is segmented by deployment (web-based and cloud-based) and application (hospitals and health systems, research organizations, and others). Cloud-based solutions are gaining traction due to their scalability and accessibility, while hospitals and health systems represent a significant portion of the market share due to their increasing focus on personalized medicine. Major players like Agilent Technologies, Illumina, and QIAGEN Digital Insights are driving innovation through continuous product development and strategic partnerships. Technological advancements such as artificial intelligence and machine learning are enhancing the capabilities of these software solutions, leading to improved accuracy and faster analysis times. The integration of these advanced analytics with electronic health records (EHRs) is another significant trend further propelling market expansion. The market's growth trajectory is influenced by several factors. The increasing availability of high-throughput sequencing technologies continues to generate massive amounts of genomic data, further stimulating demand for advanced analytics. However, the complexity of genomic data analysis and the need for skilled professionals can act as a restraint, alongside data privacy and security concerns. Despite these challenges, the long-term outlook for the Genetic Data Analysis Software market remains highly positive, driven by the continuous advancements in genomics research, the expanding applications of genomic information in healthcare, and the increasing investments in precision medicine initiatives globally. The market is expected to witness considerable expansion across all geographical regions, with North America and Europe maintaining a significant market share due to their well-established healthcare infrastructure and advanced research capabilities.

Bioinformatics Software Market Outlook

The global bioinformatics software market size was valued at approximately USD 10 billion in 2023, and it is projected to reach around USD 25 billion by 2032, growing at a robust CAGR of 11% during the forecast period. This remarkable growth is fueled by the increased application of bioinformatics in drug discovery and development, the rising demand for personalized medicine, and the ongoing advancements in sequencing technologies. The convergence of biology and information technology has led to the optimization of biological data management, propelling the market's expansion as it transforms the landscape of biotechnology and pharmaceutical research. The rapid integration of artificial intelligence and machine learning techniques to process complex biological data further accentuates the growth trajectory of this market.

An essential growth factor for the bioinformatics software market is the burgeoning demand for sequencing technologies. The decreasing cost of sequencing has led to a massive increase in the volume of genomic data generated, necessitating advanced software solutions to manage and interpret this data efficiently. This demand is particularly evident in genomics and proteomics, where bioinformatics software plays a critical role in analyzing and visualizing large datasets. Additionally, the adoption of cloud computing in bioinformatics offers scalable resources and cost-effective solutions for data storage and processing, further fueling market growth. The increasing collaboration between research institutions and software companies to develop innovative bioinformatics tools is also contributing positively to market expansion.

Another significant driver is the growth of personalized medicine, which relies heavily on bioinformatics for the analysis of individual genetic information to tailor therapeutic strategies. As healthcare systems worldwide move towards precision medicine, the demand for bioinformatics software that can integrate genetic, phenotypic, and environmental data becomes more pronounced. This trend is not only transforming patient care but also significantly impacting drug development processes, as pharmaceutical companies aim to create more effective and targeted therapies. The strategic partnerships and collaborations between biotech firms and bioinformatics software providers are critical in advancing personalized medicine and enhancing patient outcomes.

The increasing prevalence of complex diseases such as cancer and neurological disorders necessitates comprehensive research efforts, driving the need for robust bioinformatics software. These diseases require multi-omics approaches for better understanding, diagnosis, and treatment, where bioinformatics tools are indispensable. The ongoing research and development activities in this area, supported by government funding and private investments, are fostering innovation in bioinformatics solutions. Furthermore, the development of user-friendly and intuitive software interfaces is expanding the market beyond specialized research labs to include clinical settings and hospitals, broadening the potential user base and enhancing market penetration.

From a regional perspective, North America currently leads the bioinformatics software market, thanks to its advanced technological infrastructure, significant investment in healthcare R&D, and the presence of numerous key market players. The region accounted for the largest market share in 2023 and is expected to maintain its dominance throughout the forecast period. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by increasing investments in biotechnology and pharmaceutical research, expanding healthcare infrastructure, and the rising adoption of bioinformatics in emerging economies like China and India. Europe's market growth is also significant, supported by substantial funding for genomic research and a strong focus on precision medicine initiatives.

Lifesciences Data Mining and Visualization are becoming increasingly vital in the bioinformatics software market. As the volume of biological data continues to grow exponentially, the need for sophisticated tools to mine and visualize this data is paramount. These tools enable researchers to uncover hidden patterns and insights from complex datasets, facilitating breakthroughs in genomics, proteomics, and other life sciences fields. The integration of advanced data mining techniques with visualization capabilities allows for a more intuitive