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

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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

Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/.

Anterior chamber depth (ACD) is a quantitative trait associated with primary angle closure glaucoma (PACG). Although ACD is highly heritable, known genetic variations explain a small fraction of the phenotypic variability. The purpose of this study was to identify additional ACD-influencing loci using strains of mice. Cohorts of 86 N2 and 111 F2 mice were generated from crosses between recombinant inbred BXD24/TyJ and wild-derived CAST/EiJ mice. Using anterior chamber optical coherence tomography, mice were phenotyped at 10–12 weeks of age, genotyped based on 93 genome-wide SNPs, and subjected to quantitative trait locus (QTL) analysis. In an analysis of ACD among all mice, six loci passed the significance threshold of p = 0.05 and persisted after multiple regression analysis. These were on chromosomes 6, 7, 11, 12, 15 and 17 (named Acdq6, Acdq7, Acdq11, Acdq12, Acdq15, and Acdq17, respectively). Our findings demonstrate a quantitative multi-genic pattern of ACD inheritance in mice and identify six previously unrecognized ACD-influencing loci. We have taken a unique approach to studying the anterior chamber depth phenotype by using mice as genetic tool to examine this continuously distributed trait.

Bioinformatics Services Market will grow from USD 4,399.58 Million to USD 16,297.10 Million by 2034, showing an impressive CAGR of 15.7%.

The Bioinformatics Services Market is Segmented by Service Type (Data Analysis, Database Management, and More), Application (Drug Design & Discovery, Genomics & Proteomics, and More), End User (Pharmaceutical & Biotechnology Companies, and More), Deployment Model (On-Premise and Cloud-Based), and Geography (North America, Europe, Asia-Pacific, and More). The Market Sizes and Forecasts are Provided in Terms of Value (USD).

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.

Ameloblastoma is a highly aggressive odontogenic tumor, and its pathogenesis is associated with multiple participating genes. Objective: Our aim was to identify and validate new critical genes of conventional ameloblastoma using microarray and bioinformatics analysis. Methods: Gene expression microarray and bioinformatic analysis were performed to use CHIP H10KA and DAVID software for enrichment. Protein-protein interactions (PPI) were visualized using STRING-Cytoscape with MCODE plugin, followed by Kaplan-Meier and GEPIA analysis that were employed for the candidate's postulation. RT-qPCR and IHC assays were performed to validate the bioinformatic approach. Results: 376 upregulated genes were identified. PPI analysis revealed 14 genes that were validated by Kaplan-Meier and GEPIA resulting in PDGFA and IL2RA as candidate genes. The RT-qPCR analysis confirmed their intense expression. Immunohistochemistry analysis showed that PDGFA expression is parenchyma located. Conclusion: With bioinformatics methods, we can identify upregulated genes in conventional ameloblastoma, and with RT-qPCR and immunoexpression analysis validate that PDGFA could be a more specific and localized therapeutic target.

Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases worldwide. Advances in genomics have provided new ideas for the development of novel molecular biomarkers of potential clinical value for AMI.MethodsBased on microarray data from a public database, differential analysis and functional enrichment analysis were performed to identify aberrantly expressed genes in AMI and their potential functions. CIBERSORT was used for immune landscape analysis. We also obtained whole blood samples of 3 patients with AMI and performed second-generation sequencing (SGS) analysis. Weighted gene co-expression network analysis (WGCNA) and cross-tabulation analysis identified AMI-related key genes. Receiver operating characteristic (ROC) curves were used to assess the diagnostic power of key genes. Single-gene gene set enrichment analysis (GSEA) revealed the molecular mechanisms of diagnostic indicators.ResultsA total of 53 AMI-related DEGs from a public database were obtained and found to be involved in immune cell activation, immune response regulation, and cardiac developmental processes. CIBERSORT confirmed that the immune microenvironment was altered between AMI and normal samples. A total of 77 hub genes were identified by WGCNA, and 754 DEGs were obtained from own SGS data. Seven diagnostic indicators of AMI were obtained, namely GZMA, NKG7, TBX21, TGFBR3, SMAD7, KLRC4, and KLRD1. The single-gene GSEA suggested that the diagnostic indicators seemed to be closely implicated in cell cycle, immune response, cardiac developmental, and functional regulatory processes.ConclusionThe present study provides new diagnostic indicators for AMI and further confirms the feasibility of the results of genome-wide gene expression analysis.

The global bioinformatics services market is expected to exhibit robust growth over the forecast period, with a CAGR of XX% from 2025 to 2033. The market is currently valued at XXX million and is projected to reach XXX million by 2033. The increasing demand for personalized medicine, rising prevalence of chronic diseases, and advancements in sequencing technologies are driving the market growth. Moreover, the growing adoption of cloud-based bioinformatics platforms and the increasing number of public-private partnerships for bioinformatics research are further contributing to the market expansion. Key trends shaping the market include the increasing adoption of artificial intelligence (AI) and machine learning (ML) for bioinformatics analysis, the growing integration of bioinformatics with other disciplines such as clinical genomics and molecular diagnostics, and the increasing focus on personalized and precision medicine. Key players in the market include Illumina, Thermo Fisher Scientific, Eurofins Scientific, BGI, NeoGenomics, PerkinElmer, CD Genomics, Macrogen, QIAGEN, GENEWIZ, Source BioScience, Microsynth, MedGenome, Fios Genomics, and BaseClear. The market is expected to witness significant growth in regions such as North America, Europe, and Asia-Pacific, due to increasing healthcare expenditure, rising prevalence of chronic diseases, and government initiatives to promote personalized medicine.

The Personalized Medicine Bioinformatics Market is Segmented by Technology (Gene Sequencing, Pharmacogenomics, and More), Application (Genomics, Proteomics, and More), End User (Biotechnology & Pharmaceutical Companies, Clinical Diagnostics Laboratories, Hospitals & Academic Medical Centers, and More), and Geography (North America, Europe, Asia-Pacific, and More). The Market Sizes and Forecasts are Provided in Terms of Value (USD).

Accompanying dataset to the protocol published in Methods in Molecular Biology. Click on the PURL link below in the "External Files" section to download the dataset.

Access to Data WebLab is a multifunctional bioinformatics analysis platform integrating diversified tools with unified, user-friendly web interface. However, WebLab is not a mere bioinformatics toolbox, we also offer powerful data management function, group strategy and knowledge sharing mechanism, which will bring considerable advance of efficiency for both wet bench and in silico scientists working in biomedicine community.

Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.

The global bioinformatics market is expected to rise from USD 18.7 billion in 2025 to around USD 58.1 billion by 2035, reflecting a CAGR of 12% during the forecast period. The market is undergoing substantial transformation driven by advancements in next-generation sequencing, AI-powered analytics, and rapid data generation from genomics and proteomics.

Exploring Top Countries Driving Innovation, Adoption, and Delivery of Bioinformatics Solutions

The project contains raw and result files from a comparative proteomic analysis of malignant [primary breast tumor (PT) and axillary metastatic lymph nodes (LN)] and non-tumor [contralateral (NCT) and adjacent breast (ANT)] tissues of patients diagnosed with invasive ductal carcinoma. A label-free mass spectrometry was conducted using nano-liquid chromatography coupled to electrospray ionization–mass spectrometry (LC-ESI-MS/MS) followed by functional annotation to reveal differentially expressed proteins and their predicted impacts on pathways and cellular functions in breast cancer. A total of 462 proteins was observed as differentially expressed (DEPs) among the groups of samples analyzed. Ingenuity Pathway Analysis software version 2.3 (QIAGEN Inc.) was employed to identify the most relevant signaling and metabolic pathways, diseases, biological functions and interaction networks affected by the deregulated proteins. Upstream regulator and biomarker analyses were also performed by IPA’s tools. Altogether, our findings revealed differential proteomic profiles that affected the associated and interconnected cancer signaling processes.

Predefined workflows in the ZBIT Bioinformatics Toolbox.

Additional file 4: Table S4. KEGG pathways of the hub genes in two groups.

The horizontal transfer of mobile gene elements between bacteria plays a crucial role in their evolutionary dynamics. Additionally, it enables the accumulation and dissemination of genes conferring antibiotic and heavy-metal resistance, and thus contributes to the worldwide emergence and spread of drug-resistance pathogens. This process is instrumental in maintaining genetic diversity within bacterial populations and facilitates their adaptation to novel environments. It allows bacteria to acquire genes responsible for the synthesis of enzymes that utilize alternative energy sources and substrates. Furthermore, bacteria can acquire genes associated with toxin production and increased virulence. Horizontal gene transfer serves as a pivotal mechanism in bacterial evolution, enabling the acquisition of novel genetic information and enhancing their capabilities. However, the proper detection and identification of horizontally transferred genes at the microbiome scale is challenging, whether using wet-lab experiments or bioinformatics approaches. In the paper, we summarize current bioinformatics tools for detecting Horizontal Gene Transfer and present the results of our bioinformatic analysis on a collection of genomes originating from chicken gut microbiota.

DAVID analysis and DEG from the meta-analysis