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/.

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%.

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.

Toy dataset for running ISOTOPE tutorial.

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.

Bioinformatic analyses and field data associated w. Morris et al. 2016, JPR

Served as links to the Morris contributed package and to GitHub

Interleukin-17F (IL-17F), considered a pro-inflammatory cytokine, has been shown to contribute to skeletal tissue degradation and hence chronic inflammation in rheumatoid arthritis (RA). In this study we utilized bioinformatics tools to analyze the effect of three exonic SNPs (rs2397084, rs11465553, and rs763780) on the structure and function of the IL-17F gene, and evaluated their association with RA in Pakistani patients. The predicted deleterious and damaging effects of identified genetic variants were assessed through the utilization of multiple bioinformatics tools including PROVEAN, SNP&GO, SIFT, and PolyPhen2. Structural and functional effects of these variants on protein structures were evaluated through the use of additional tools such as I-Mutant, MutPred, and ConSurf. Three-dimensional (3D) models of both the wild-type and mutant proteins were constructed through the utilization of I-TASSER software, with subsequent structural comparisons between the models conducted through the use of the TM-align score. A total of 500 individuals, 250 cases and 250 controls, were genotyped through Tri-ARMS-PCR method and the resultant data was statistically analyzed using various inheritance models. Our bioinformatics analysis showed significant structural differences for wild type and mutant protein (TM-scores and RMSD values were 0.85934 and 2.34 for rs2397084 (E126G), 0.87388 and 2.49 for rs11465553 (V155I), and 0.86572 and 0.86572 for rs763780 (H161R) with decrease stability for the later. Overall, these tools enabled us to predict that these variants are crucial in causing disease phenotypes. We further tested each of these single nucleotide variants for their association with RA. Our analysis revealed a strong positive association between the genetic variant rs763780 and the risk of developing rheumatoid arthritis (RA) at both the genotypic and allelic levels. The genotypic association was statistically significant[χ2 = 111.8; P value <0.0001], as was the allelic level [OR 3.444 (2.539–4.672); P value 0.0008]. These findings suggest that the presence of this genetic variant may increase the susceptibility to RA. Similarly, we observed a significant distribution of the genetic variant rs11465553 at the genotypic level [χ2 = 25.24; P value = 0.0001]. However, this variant did not show a significant association with RA at the allelic level [OR = 1.194 (0.930–1.531); P value = 0.183]. However, the distribution of variant rs2397084 was more or less random across our sample with no significant association either at genotypic and or allelic level. Put together, our association study and in silico prediction of decreasing of IL17-F protein stabilty confirmed that two SNPs, rs11465553 and rs763780 are crucial to the suscetibility of and showed that these RA in Pakistani patients.

Pecan scab, caused by the fungal pathogen Venturia effusa, is the most devastating disease of pecan (Carya illinoinensis) in the southeastern United States. Resistance to this pathogen is determined by a complex interaction between host genetics and disease pathotype with even field-susceptible cultivars being resistant to most scab isolates. To understand the underlying molecular mechanisms of scab resistance in pecan, we performed a transcriptome analysis of the pecan cultivar, ‘Desirable’, in response to inoculation with a pathogenic and a non-pathogenic scab isolate at three different time points (24, 48, and 96 hrs. post-inoculation). Differential gene expression and gene ontology enrichment analyses showed contrasting gene expression patterns and pathway enrichment in response to the contrasting isolates with varying pathogenicity. The weighted gene co-expression network analysis of differentially expressed genes detected 11 gene modules. Among them, two modules had significant enrichment of genes involved with defense responses. These genes were particularly upregulated in the resistant reaction at the early stage of fungal infection (24 h) compared to the susceptible reaction. Hub genes in these modules were predominantly related to receptor-like protein kinase activity, signal reception, signal transduction, biosynthesis and transport of plant secondary metabolites, and oxidoreductase activity. Results of this study suggest that the early response of pathogen-related signal transduction and development of cellular barriers against the invading fungus are likely defense mechanisms employed by pecan cultivars against non-virulent scab isolates. The transcriptomic data generated here provide the foundation for identifying candidate resistance genes in pecan against V. effusa and for exploring the molecular mechanisms of disease resistance.

Transpozon SSR

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.

BackgroundDiabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on affected individuals, healthcare systems, and society. However, the specific molecular mechanisms supporting their interrelationship have not been fully defined.MethodsWe identified the differentially expressed genes (DEGs) of COPD and diabetes from multi-center patient cohorts, respectively. Through cross-analysis, we identified the shared DEGs of COPD and diabetes, and investigated alterations of signaling pathways using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). By using weighted gene correlation network analysis (WGCNA), key gene modules for COPD and diabetes were identified, and various machine learning algorithms were employed to identify shared biomarkers. Using xCell, we investigated the relationship between shared biomarkers and immune infiltration in diabetes and COPD. Single-cell sequencing, clinical samples, and animal models were used to confirm the robustness of shared biomarkers.ResultsCross-analysis identified 186 shared DEGs between diabetes and COPD patients. Functional enrichment results demonstrate that metabolic and immune-related pathways are common features altered in both diabetes and COPD patients. WGCNA identified 526 genes from key gene modules in COPD and diabetes. Multiple machine learning algorithms identified 4 shared biomarkers for COPD and diabetes, including CADPS, EDNRB, THBS4 and TMEM27. Finally, the 4 shared biomarkers were validated in single-cell sequencing data, clinical samples, and animal models, and their expression changes were consistent with the results of bioinformatic analysis.ConclusionsThrough comprehensive bioinformatics analysis, we revealed the potential connection between diabetes and COPD, providing a theoretical basis for exploring the common regulatory genes.

The portion of the UniProt protein database that is curated with links to KEGG.

Additional file 1: Supplemental File 1, Orthology predictions of differentially expressed genes.

Genomic start and stop coordinates of structural module encoding genes are derived from GenBank accession number NC_002747. The percentage similarity of TP901-1 protein sequences to solved protein structures using HHpred, including the source of the homologue, is shown. Several TP901-1 proteins had no significant homologue (n.s.h.) detected.Bioinformatic analysis of TP901-1’s predicted structural-encoding proteins.

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.

Bioinformatics analyses of ITPR1 missense variants.

Bioinformatic analysis of AcuK and AcuM DNA binding motif (CCGN7CCG) in T. marneffei genome and their associated functions.

AimsIntegrating bioinformatics and experimental validation to explore the mechanisms of inflammaging in the Brain.MethodAfter dividing the GSE11882 dataset into aged and young groups, we identified co-expressed differentially expressed genes (DEGs) in different brain regions. Enrichment analysis revealed that the co-expressed DEGs were mainly associated with inflammatory responses. Subsequently, we identified 12 DEGs that were related to the inflammatory response and used the DGIdb website for drug prediction. By using both the least absolute shrinkage and selection operator (LASSO) and random forest (RF), four biomarkers were screened and an artificial neural network (ANN) was developed for diagnosis. Subsequently, the biomarkers were validated through animal studies. Then we utilized AgeAnno to investigate the roles of biomarkers at the single cell level. Next, a consensus clustering approach was used to classify the aging samples and perform differential analysis to identify inflammatory response-related genes. After conducting a weighted gene co-expression network analysis (WGCNA), we identified the genes that are correlated with both four brain regions and aging. Wayne diagrams were used to identify seven inflammaging-related genes in different brain regions. Finally, we performed immuno-infiltration analysis and identified macrophage module genes.Key findingsInflammaging may be a major mechanism of brain aging, and the regulation of macrophages by CX3CL1 may play a role in the development of inflammaging.SignificanceIn summary, targeting CX3CL1 can potentially delay inflammaging and immunosenescence in the brain.