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.

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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/.

Global Bioinformatics market size was USD 12.76 Billion in 2022 and it is forecasted to reach USD 29.32 Billion by 2030. Bioinformatics Industry's Compound Annual Growth Rate will be 10.4% from 2023 to 2030. What are the driving factors for the Bioinformatics market?

The primary factors propelling the global bioinformatics industry are advances in genomics, rising demand for protein sequencing, and rising public-private sector investment in bioinformatics. Large volumes of data are being produced by the expanding use of next-generation sequencing (NGS) and other genomic technologies; these data must be analyzed using advanced bioinformatics tools. Furthermore, the global bioinformatics industry may benefit from the development of emerging advanced technologies. However, the bioinformatics discipline contains intricate algorithms and massive amounts of data, which can be difficult for researchers and demand a lot of processing power. What is Bioinformatics?

Bioinformatics is related to genetics and genomics, which involves the use of computer technology to store, collect, analyze, and disseminate biological information, and data, such as DNA and amino acid sequences or annotations about these sequences. Researchers and medical professionals use databases that organize and index this biological data to better understand health and disease, and in some circumstances, as a component of patient care. Through the creation of software and algorithms, bioinformatics is primarily used to extract knowledge from biological data. Bioinformatics is frequently used in the analysis of genomics, proteomics, 3D protein structure modeling, image analysis, drug creation, and many other fields.

Edge Bioinformatics for Sequencing Market Outlook

According to our latest research, the global Edge Bioinformatics for Sequencing market size reached USD 1.56 billion in 2024, reflecting robust adoption across clinical diagnostics, genomics research, and pharmaceutical sectors. The market is forecasted to grow at a CAGR of 15.2% from 2025 to 2033, with the total market size projected to reach approximately USD 5.01 billion by 2033. This accelerated growth is primarily driven by the increasing demand for rapid, decentralized data analysis, and the proliferation of next-generation sequencing platforms that require real-time, edge-based computational solutions.

The primary growth factor for the Edge Bioinformatics for Sequencing market is the exponential increase in sequencing data generated from advanced genomics platforms. With the widespread adoption of next-generation sequencing (NGS) technologies, the volume of genomic data has surged, necessitating immediate, on-site analysis to accelerate clinical and research workflows. Edge bioinformatics solutions enable localized data processing, reducing the latency and bandwidth requirements associated with cloud-based models. This capability is particularly critical in time-sensitive environments such as clinical diagnostics, where rapid turnaround times can significantly impact patient outcomes. Furthermore, the integration of artificial intelligence and machine learning algorithms into edge bioinformatics platforms is enhancing the accuracy and speed of sequence data interpretation, further propelling market growth.

Another key driver is the growing application of sequencing technologies across diverse sectors, including agriculture, animal research, and personalized medicine. In agricultural genomics, edge bioinformatics facilitates real-time analysis of crop and livestock genomes, enabling faster breeding decisions and disease management. The pharmaceutical and biotechnology industries are leveraging these solutions to streamline drug discovery and development processes by enabling immediate analysis of genetic variations and biomarker identification at the point of data generation. Additionally, the increasing focus on decentralized healthcare and the expansion of mobile sequencing devices are creating new opportunities for edge bioinformatics, particularly in remote or resource-limited settings where cloud connectivity may be unreliable or infeasible.

The Edge Bioinformatics for Sequencing market is also benefiting from significant investments in infrastructure and technological innovation. Leading vendors are developing advanced hardware and software solutions tailored to the unique requirements of edge-based sequencing analysis, such as optimized algorithms for low-latency computation and secure data handling. The emergence of hybrid deployment models, which integrate on-premises, cloud, and edge resources, is further enhancing flexibility and scalability for end-users. Regulatory support for genomics research and precision medicine initiatives, particularly in North America and Europe, is fostering an environment conducive to market expansion. However, the complexity of integrating edge bioinformatics into existing workflows and the need for skilled personnel remain challenges that must be addressed for sustained growth.

From a regional perspective, North America currently dominates the Edge Bioinformatics for Sequencing market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the presence of major sequencing technology providers, advanced healthcare infrastructure, and substantial funding for genomics research. Europe follows closely, driven by government-backed initiatives in precision medicine and agricultural genomics. The Asia Pacific region is expected to exhibit the fastest growth over the forecast period, fueled by increasing investments in biotechnology, expanding healthcare access, and rising adoption of digital health solutions. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by improving research capabilities and growing awareness of the benefits of edge bioinformatics in sequencing applications.

Translational Bioinformatics Market Outlook

According to our latest research, the global translational bioinformatics market size reached USD 4.12 billion in 2024, with a robust year-on-year growth trajectory. The market is exhibiting a strong compound annual growth rate (CAGR) of 13.7% during the forecast period. By 2033, the translational bioinformatics market is projected to attain a value of USD 12.05 billion. This growth is being propelled by the increasing integration of bioinformatics in clinical settings, the rising demand for precision medicine, and significant advancements in genomic and proteomic technologies.

One of the primary growth drivers for the translational bioinformatics market is the accelerating adoption of bioinformatics tools in drug discovery and development processes. Pharmaceutical and biotechnology companies are leveraging advanced computational platforms to analyze biological data, identify novel drug targets, and streamline the drug development pipeline. The ability of translational bioinformatics to bridge the gap between laboratory research and clinical application is significantly enhancing the efficiency of new drug discovery, reducing time-to-market, and minimizing costs. Furthermore, the rise of next-generation sequencing technologies and the resulting surge in omics data generation have created an urgent need for sophisticated bioinformatics solutions capable of managing, analyzing, and interpreting vast datasets. This trend is expected to continue fueling demand for translational bioinformatics solutions across the globe.

Another significant factor contributing to market expansion is the growing emphasis on personalized medicine. Healthcare providers are increasingly adopting translational bioinformatics to tailor medical treatments to individual patient profiles based on genetic, proteomic, and metabolic information. By integrating clinical data with multi-omics datasets, bioinformatics platforms enable the development of personalized therapeutic strategies, improving patient outcomes and reducing adverse drug reactions. The integration of artificial intelligence and machine learning algorithms into bioinformatics software further enhances predictive analytics capabilities, supporting more accurate disease diagnosis, prognosis, and treatment planning. As the healthcare industry continues to shift toward personalized and precision medicine, the demand for translational bioinformatics solutions is anticipated to surge.

The expanding application of translational bioinformatics in clinical diagnostics is also driving market growth. The increasing prevalence of chronic diseases, cancer, and rare genetic disorders has intensified the need for advanced diagnostic tools that can provide actionable insights from complex biological data. Translational bioinformatics enables the identification of biomarkers, facilitates early disease detection, and supports the development of targeted diagnostic assays. Moreover, the integration of bioinformatics with electronic health records and clinical decision support systems is streamlining diagnostic workflows and enhancing the quality of patient care. This convergence of clinical and bioinformatics data is expected to play a pivotal role in shaping the future of healthcare delivery.

From a regional perspective, North America currently dominates the translational bioinformatics market, accounting for the largest share in 2024. This leadership position is attributed to the presence of a well-established healthcare infrastructure, significant investments in biomedical research, and a high adoption rate of advanced technologies. Europe follows closely, driven by government initiatives to promote genomics research and personalized medicine. The Asia Pacific region is emerging as a high-growth market, fueled by rapidly expanding healthcare sectors, increasing research funding, and rising awareness of the benefits of translational bioinformatics. Latin America and the Middle East & Africa are also witnessing gradual market growth, supported by improving healthcare systems and growing investments in life sciences research.

Solution Analysis

The solution segment of the translational bioinformatics market is broadly categorized into software, services, and platforms. Software solutions remain the cornerstone of this market, accounting for the largest revenue share in 2024. These solutions encompass a wide array of bioinformatics tools designed for data

IntroductionSkin cutaneous melanoma (SKCM) is a common malignant skin cancer with high mortality and recurrence rates. Although the mRNA vaccine is a promising strategy for cancer treatment, its application against SKCM remains confusing. In this study, we employed computational bioinformatics analysis to explore SKCM-associated antigens for an mRNA vaccine and suitable populations for vaccination.MethodsGene expression and clinical data were retrieved from GEO and TCGA. The differential expression levels and prognostic index of selected antigens were computed via GEPIA2,while genetic alterations were analyzed using cBioPortal. TIMER was utilized to assess the correlation between antigen-presenting cell infiltration and antigen. Consensus clustering identified immune subtypes, and immune characteristics were evaluated across subtypes. Weighted gene co-expression network analysis was performed to identify modules of immune-related genes.ResultsWe discovered five tumor antigens (P2RY6, PLA2G2D, RBM47, SEL1L3, and SPIB) that are significantly increased and mutated, which correlate with the survival of patients and the presence of immune cells that present these antigens. Our analysis revealed two distinct immune subtypes among the SKCM samples. Immune subtype 1 was associated with poorer clinical outcomes and exhibited low levels of immune activity, characterized by fewer mutations and lower immune cell infiltration. In contrast, immune subtype 2 showed higher immune activity and better patient outcomes. Subsequently, the immune landscape of SKCM exhibited immune heterogeneity among patients, and a key gene module that is enriched in immune-related pathways was identified.ConclusionsOur findings suggest that the identified tumor antigens could serve as valuable targets for developing mRNA vaccines against SKCM, particularly for patients in immune subtype 1. This research provides valuable insights into personalized immunotherapy approaches for this challenging cancer and highlights the advantages of bioinformatics in identifying immune targets and optimizing treatment approaches.

Veterinary Bioinformatics Solutions Market Outlook

The global veterinary bioinformatics solutions market size reached USD 1.48 billion in 2024, as per our latest research, and is projected to grow at a robust CAGR of 12.6% from 2025 to 2033, reaching a forecasted value of USD 4.34 billion by 2033. The market's expansion is primarily driven by the increasing prevalence of zoonotic diseases, rising adoption of precision medicine in veterinary care, and growing investments in animal health research. The integration of advanced bioinformatics tools in veterinary diagnostics and research is enabling more accurate disease detection, more efficient drug discovery, and improved animal health management, fueling the growth trajectory of the veterinary bioinformatics solutions market worldwide.

One of the most significant growth factors for the veterinary bioinformatics solutions market is the rising incidence of zoonotic diseases and the growing awareness of their impact on public health. As outbreaks such as avian influenza, rabies, and other animal-borne illnesses continue to pose threats to both animal and human populations, the need for advanced bioinformatics tools to analyze and interpret complex biological data has never been greater. Governments and regulatory bodies across the globe are increasingly mandating surveillance and diagnostic programs for early detection and control of these diseases, which necessitates the adoption of sophisticated bioinformatics platforms. Furthermore, the integration of next-generation sequencing (NGS) and high-throughput data analytics is allowing veterinary professionals to identify pathogens with greater precision, supporting proactive measures and timely interventions.

Another driving force behind the market's growth is the expanding application of genomics and precision medicine in veterinary sciences. The ability to analyze genetic information at a granular level is transforming the way veterinarians approach disease management, breeding, and drug development. Bioinformatics solutions are pivotal in processing and interpreting vast datasets generated from genomic, proteomic, and metabolomic studies, enabling tailored healthcare strategies for both companion and livestock animals. This personalized approach not only enhances treatment outcomes but also contributes to improved animal welfare and productivity. The increasing use of bioinformatics in veterinary research is also accelerating the development of novel therapeutics and vaccines, further propelling market demand.

The market is also benefitting from growing investments in animal health research and the rising adoption of digital technologies in veterinary practices. Pharmaceutical companies, research institutes, and academic organizations are allocating substantial resources to develop innovative bioinformatics tools and platforms tailored for veterinary applications. The surge in collaborations between veterinary clinics, universities, and technology providers is fostering the development of integrated solutions that streamline data management, facilitate real-time diagnostics, and enable evidence-based decision-making. The ongoing digital transformation in the veterinary sector, coupled with increasing awareness about the benefits of bioinformatics, is expected to sustain the market's upward momentum over the coming years.

From a regional perspective, North America currently dominates the veterinary bioinformatics solutions market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of advanced veterinary healthcare infrastructure, high adoption of digital solutions, and a strong focus on research and development are key factors contributing to North America's leadership position. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by the increasing livestock population, rising pet ownership, and growing investments in animal health technologies. The market in Latin America and the Middle East & Africa is also witnessing steady growth, supported by improving veterinary services and government initiatives to enhance animal health surveillance and disease management.

Component Analysis

The veterinary bioinformatics solutions market by component is segmented into software and services, each playing a critical role in shaping the industry's landscape. The software segment comprises various platforms and tools that faci

Bioinformatics Cloud Platform Market Outlook

According to our latest research, the Global Bioinformatics Cloud Platform market size was valued at $2.1 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 15.2% during the forecast period of 2025–2033. The primary factor fueling this impressive growth trajectory is the increasing demand for scalable, cost-effective, and secure solutions to manage the ever-growing volumes of biological and genomic data. As life sciences and healthcare organizations intensify their focus on precision medicine, drug discovery, and translational research, the adoption of cloud-based bioinformatics platforms is accelerating globally. This shift is underpinned by the pressing need for real-time data analysis, collaborative research, and seamless integration of multi-omics datasets, all of which are optimally supported by cloud computing paradigms.

Regional Outlook

North America currently dominates the Bioinformatics Cloud Platform market, accounting for the largest market share in 2024, with an estimated value exceeding $900 million. This region’s leadership is attributed to its mature healthcare infrastructure, high adoption of advanced technologies, and the presence of leading pharmaceutical and biotechnology companies. The United States, in particular, benefits from robust government funding for genomics and biomedical research, as well as a strong ecosystem of academic and research institutes. Furthermore, the region’s favorable regulatory environment and proactive data privacy policies have fostered confidence in cloud-based solutions, allowing for rapid integration and deployment across various end-user segments. The vast network of cloud service providers and ongoing collaborations between technology firms and life sciences companies further consolidate North America’s market position.

The Asia Pacific region is projected to be the fastest-growing market, registering a remarkable CAGR of 18.7% from 2025 to 2033. This growth is propelled by significant investments in healthcare infrastructure, expanding genomic research initiatives, and increasing government support for digital health transformation. Countries such as China, India, and Japan are witnessing a surge in demand for bioinformatics cloud platforms as they seek to modernize their research capabilities and address rising healthcare challenges. The region’s burgeoning population, coupled with a growing prevalence of chronic diseases and genetic disorders, is driving the need for advanced data analytics and personalized medicine solutions. Additionally, the proliferation of local cloud service providers and strategic partnerships with global technology leaders are accelerating market penetration and adoption in Asia Pacific.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing bioinformatics cloud platforms, albeit at a slower pace due to infrastructural constraints and limited digital literacy. However, localized demand for precision medicine, increasing research collaborations, and policy reforms aimed at boosting healthcare innovation are beginning to stimulate market growth. Challenges such as data security concerns, fragmented regulatory frameworks, and limited access to high-speed internet persist, but ongoing investments in digital health and capacity building are expected to bridge these gaps over the forecast period. These regions represent untapped opportunities for market players willing to invest in education, training, and localized solutions tailored to regional healthcare needs.

Report Scope

k-word matches, the number of words of length k shared between two sequences, also known as the D2 statistic, are used in alignment-free sequence comparison statistic. The advantages of the use of this statistic over alignment-based methods for nucleotide and amino-acid sequence comparisons are firstly that it does not assume that homologous segments are contiguous, and secondly that the algorithm is computationally extremely fast, the runtime being proportional to the size of the sequence under scrutiny. We summarise our results to date on determing the distributional properties of the D2 statistic for a range of biologically relevant parameters and outline the directions in which the research will proceed. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

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

Bioinformatics skills have become essential for many research areas; however, the availability of qualified researchers is usually lower than the demand and training to increase the number of able bioinformaticians is an important task for the bioinformatics community. When conducting training or hands-on tutorials, the lack of control over the analysis tools and repositories often results in undesirable situations during training, as unavailable online tools or version conflicts may delay, complicate, or even prevent the successful completion of a training event. The eBioKit is a stand-alone educational platform that hosts numerous tools and databases for bioinformatics research and allows training to take place in a controlled environment. A key advantage of the eBioKit over other existing teaching solutions is that all the required software and databases are locally installed on the system, significantly reducing the dependence on the internet. Furthermore, the architecture of the eBioKit has demonstrated itself to be an excellent balance between portability and performance, not only making the eBioKit an exceptional educational tool but also providing small research groups with a platform to incorporate bioinformatics analysis in their research. As a result, the eBioKit has formed an integral part of training and research performed by a wide variety of universities and organizations such as the Pan African Bioinformatics Network (H3ABioNet) as part of the initiative Human Heredity and Health in Africa (H3Africa), the Southern Africa Network for Biosciences (SAnBio) initiative, the Biosciences eastern and central Africa (BecA) hub, and the International Glossina Genome Initiative.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Exploring new insights on genetic factors may help reveal SLE etiology and improve the survival of SLE patients. The current study is designed to identify key genes involved in SLE and develop potential diagnostic biomarkers for SLE in clinical practice. Expression data of all genes of SLE and control samples in GSE65391 and GSE72509 datasets were downloaded from the Gene Expression Omnibus (GEO) database. A total of 11 accurate differentially expressed genes (DEGs) were identified by the “limma” and “RobustRankAggreg” R package. All these genes were functionally associated with several immune-related biological processes and a single KEGG (Kyoto Encyclopedia of Genes and Genome) pathway of necroptosis. The PPI analysis showed that IFI44, IFI44L, EIF2AK2, IFIT3, IFITM3, ZBP1, TRIM22, PRIC285, XAF1, and PARP9 could interact with each other. In addition, the expression patterns of these DEGs were found to be consistent in GSE39088. Moreover, Receiver operating characteristic (ROC) curves analysis indicated that all these DEGs could serve as potential diagnostic biomarkers according to the area under the ROC curve (AUC) values. Furthermore, we constructed the transcription factor (TF)-diagnostic biomarker-microRNA (miRNA) network composed of 278 nodes and 405 edges, and a drug-diagnostic biomarker network consisting of 218 nodes and 459 edges. To investigate the relationship between diagnostic biomarkers and the immune system, we evaluated the immune infiltration landscape of SLE and control samples from GSE6539. Finally, using a variety of machine learning methods, IFI44 was determined to be the optimal diagnostic biomarker of SLE and then verified by quantitative real-time PCR (qRT-PCR) in an independent cohort. Our findings may benefit the diagnosis of patients with SLE and guide in developing novel targeted therapy in treating SLE patients.

Xylan is the main component of hemicellulose which is present in nature in large amounts and can be degraded by either acid or enzymic catalysis with the advantages of a highly efficient conversion rate and non-corrosive and environmentally friendly conditions. Although, the complete breakdown of xylan requires the action of several different enzymes, the depolymerizing endo-1,4,β-xylanase (EC 3.2.1.8) is the key enzyme with possible applications in waste treatment, fuel and chemical production and paper manufacture. In consequence, the importance of finding or making thermostable xylanases has been highlighted. Therefore, it is inevitable to understand the features involving in xylanase thermostability. Here, we looked at more than seventy attributes of 30 xylanase proteins (active in different temperatures) by applying a feature selection algorithm which assigns a p value to each attribute based on the asymptotic distribution of a transformation on the Pearson correlation coefficient, and then, sorts them according to their p values in order to find the most contributing ones regarding the xylanase proteins thermostability. The results showed that the count of oxygen, nitrogen, Glu, Lys, Cys, Phe, Trp, the count of positively and negatively charged residues as well as the count of other residues were the most important features with respect to xylanase thermostability, and 12 more properties were recognized to have a marginal effect on this aspect, while the rest were revealed to be unimportant. The importance of "important" and "marginal" features in xylanase thermostability has been discussed in this paper. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

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

The clinical genomics software market is experiencing robust growth, projected to reach $219.2 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 6.9% from 2025 to 2033. This expansion is fueled by several key factors. The increasing prevalence of genetic disorders and the rising demand for personalized medicine are driving the adoption of these sophisticated software solutions. Hospitals and health systems are at the forefront of adoption, leveraging clinical genomics software for improved diagnostics, treatment planning, and patient management. Research organizations are also significantly contributing to market growth, utilizing the software for genomic research and data analysis. The shift towards cloud-based solutions offers advantages like scalability, accessibility, and reduced infrastructure costs, further propelling market expansion. Technological advancements, such as improved algorithms for data analysis and interpretation, are enhancing the accuracy and efficiency of genomic testing, contributing to market growth. Furthermore, the increasing availability of genomic data and the growing collaborations between technology providers and healthcare institutions are creating a favorable environment for market expansion. The market segmentation reveals significant opportunities across different application areas and software types. Web-based solutions are currently more prevalent, but cloud-based solutions are rapidly gaining traction due to their enhanced flexibility and cost-effectiveness. Geographical analysis indicates that North America currently holds a substantial market share, driven by advanced healthcare infrastructure and significant investments in genomic research. However, Asia-Pacific is poised for rapid growth in the coming years, fueled by increasing healthcare expenditure and expanding genomic testing capabilities in countries like China and India. Competitive landscape analysis reveals a mix of established players and emerging companies, indicating a dynamic and innovative market environment characterized by ongoing technological advancements and strategic partnerships.

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

The Single Molecule Real-Time (SMRT) Sequencing market is poised for significant expansion, projected to reach an estimated market size of approximately $1.8 billion by 2025, with a robust Compound Annual Growth Rate (CAGR) of around 22% anticipated to sustain this momentum through 2033. This upward trajectory is primarily fueled by the increasing adoption of SMRT sequencing in academic and research institutes, driven by its unparalleled accuracy and ability to detect epigenetic modifications and structural variations that are crucial for groundbreaking biological discoveries. The pharmaceutical and biotechnology sectors are also heavily invested, leveraging SMRT sequencing for advanced drug discovery, development, and personalized medicine initiatives, particularly in areas like oncology and infectious diseases. Furthermore, hospitals and clinics are increasingly integrating SMRT sequencing into diagnostic workflows, especially for complex genetic disorders and pathogen identification, underscoring its growing clinical utility. The market's growth is further bolstered by continuous technological advancements that enhance sequencing speed, reduce costs, and improve data analysis capabilities. Trends such as the rise of long-read sequencing for comprehensive genome assembly, the integration of SMRT sequencing with other omics technologies, and the development of sophisticated bioinformatics tools are collectively driving deeper insights into complex biological systems. However, the market faces certain restraints, including the initial capital investment required for SMRT sequencing platforms and the need for specialized bioinformatics expertise for data interpretation. Despite these challenges, the inherent advantages of SMRT sequencing in providing high-resolution, error-free data are expected to outweigh these limitations, driving widespread adoption across diverse applications and ensuring sustained market vitality in the coming years.

Hybrid Cloud Bioinformatics Orchestration Market Outlook

As per our latest research, the Hybrid Cloud Bioinformatics Orchestration market size reached USD 2.18 billion in 2024, marking a significant milestone in the global bioinformatics industry. The market is experiencing robust momentum, driven by the growing integration of cloud computing in life sciences. With a compelling CAGR of 19.6% projected over the forecast period, the market is anticipated to reach USD 8.91 billion by 2033. This growth is primarily fueled by the increasing demand for scalable, cost-effective, and flexible bioinformatics solutions that can handle complex and voluminous biological datasets, especially in genomics and personalized medicine.

The surge in demand for advanced bioinformatics infrastructure is one of the primary growth factors propelling the hybrid cloud bioinformatics orchestration market. As the volume and complexity of biological data continue to escalate, particularly with the proliferation of next-generation sequencing (NGS) technologies, organizations are compelled to adopt hybrid cloud solutions. These platforms offer the agility to scale computational resources dynamically, ensuring efficient data processing and storage. Moreover, the hybrid cloud model enables seamless integration of on-premises and cloud-based resources, allowing research institutes, pharmaceutical companies, and healthcare providers to optimize their workflows while maintaining data security and regulatory compliance. The growing emphasis on precision medicine, drug discovery, and genomics research further accelerates the adoption of hybrid cloud bioinformatics orchestration, as these applications demand high-performance computing and collaborative data environments.

Another critical driver for the market’s expansion is the increasing collaboration between public and private sectors in biomedical research. Governments and research organizations are investing heavily in cloud-enabled bioinformatics platforms to support large-scale projects such as population genomics, epidemiological studies, and disease surveillance. The hybrid cloud approach is particularly advantageous in these scenarios, as it allows for the secure sharing of sensitive data across distributed teams while leveraging the computational power of public clouds for intensive analytics. Additionally, the hybrid model addresses concerns related to data sovereignty and compliance with regional data protection regulations, making it an attractive choice for multinational research initiatives. The rising trend of cloud-native bioinformatics applications and the integration of artificial intelligence and machine learning further contribute to the market’s rapid growth trajectory.

The evolution of cloud infrastructure and bioinformatics tools is also fostering innovation in the hybrid cloud bioinformatics orchestration market. Leading technology vendors are continuously enhancing their offerings with advanced features such as automated workflow orchestration, containerization, and seamless integration with laboratory information management systems (LIMS). These advancements enable end-users to accelerate research timelines, reduce operational costs, and improve data reproducibility. Furthermore, the growing adoption of multi-cloud strategies and the emergence of specialized bioinformatics cloud platforms are creating new opportunities for market players. The increasing awareness of the benefits of hybrid cloud solutions—such as enhanced scalability, disaster recovery, and cost optimization—continues to drive their adoption across diverse end-user segments, from pharmaceutical giants to academic institutions.

From a regional perspective, North America continues to dominate the hybrid cloud bioinformatics orchestration market, owing to its strong presence of leading biotechnology firms, advanced healthcare infrastructure, and significant investments in genomics research. Europe follows closely, driven by government initiatives to promote digital health and collaborative research. The Asia Pacific region is witnessing the fastest growth, fueled by expanding healthcare expenditure, increasing adoption of cloud technologies, and the rise of genomics research initiatives in countries like China, India, and Japan. Latin America and the Middle East & Africa are gradually catching up, supported by improving IT infrastructure and growing awareness of bioinformatics in healthcare. Overall, the global market landscape is characterized by dynamic growth, te

Hepatocellular carcinoma (HCC) accounts for approximately 85–90% of all liver cancer cases and has poor relapse-free survival. There are many gene expression studies that have been performed to elucidate the genetic landscape and driver pathways leading to HCC. However, existing studies have been limited by the sample size and thus the pathogenesis of HCC is still unclear. In this study, we performed an integrated characterization using four independent datasets including 320 HCC samples and 270 normal liver tissues to identify the candidate genes and pathways in the progression of HCC. A total of 89 consistent differentially expression genes (DEGs) were identified. Gene-set enrichment analysis revealed that these genes were significantly enriched for cellular response to zinc ion in biological process group, collagen trimer in the cellular component group, extracellular matrix (ECM) structural constituent conferring tensile strength in the molecular function group, protein digestion and absorption, mineral absorption and ECM-receptor interaction. Network system biology based on the protein–protein interaction (PPI) network was also performed to identify the most connected and important genes based on our DEGs. The top five hub genes including osteopontin (SPP1), Collagen alpha-2(I) chain (COL1A2), Insulin-like growth factor I (IGF1), lipoprotein A (LPA), and Galectin-3 (LGALS3) were identified. Western blot and immunohistochemistry analysis were employed to verify the differential protein expression of hub genes in HCC patients. More importantly, we identified that these five hub genes were significantly associated with poor disease-free survival and overall survival. In summary, we have identified a potential clinical significance of these genes as prognostic biomarkers for HCC patients who would benefit from experimental approaches to obtain optimal outcome.

Community composition data are essential for conservation management, facilitating identification of rare native and invasive species, along with abundant ones. However, traditional capture-based morphological surveys require considerable taxonomic expertise, are time consuming and expensive, can kill rare taxa and damage habitats, and often are prone to false negatives. Alternatively, metabarcode assays can be used to assess the genetic identity and compositions of entire communities from environmental samples, comprising a more sensitive, less damaging, and relatively time- and cost-efficient approach. However, there is a trade-off between the stringency of bioinformatic filtering needed to remove false positives and the potential for false negatives. The present investigation thus evaluated use of four mitochondrial (mt) DNA metabarcode assays and a customized bioinformatic pipeline to increase confidence in species identifications by removing false positives, while achieving high detection probability. Positive controls were used to calculate sequencing error, and results that fell below those cutoff values were removed, unless found with multiple assays. The performance of this approach was tested to discern and identify North American freshwater fishes using lab experiments (mock communities and aquarium experiments) and processing of a bulk ichthyoplankton sample. The method then was applied to field environmental (e)DNA water samples taken concomitant with electrofishing surveys and morphological identifications. This protocol detected 100% of species present in concomitant electrofishing surveys in the Wabash River and an additional 21 that were absent from traditional sampling. Using single 1 L water samples collected from just four locations, the metabarcoding assays discerned 73% of the total fish species that were discerned in comparison to four months of an extensive electrofishing river survey in the Maumee River, along with an additional nine species. In both rivers, total fish species diversity was best resolved when all four metabarcode assays were used together, which identified 35 additional species missed by electrofishing. Ecological distinction and diversity levels among the fish communities also were better resolved with the metabarcode assays than with morphological sampling and identifications, especially with the combined assays. At the population-level, metabarcode analyses targeting the invasive round goby Neogobius melanostomus and the silver carp Hypophthalmichthys molitrix identified all population haplotype variants found using Sanger sequencing of morphologically sampled fish, along with additional intra-specific diversity, meriting further investigation. Overall findings demonstrated that the use of multiple metabarcode assays and custom bioinformatics that filter potential error from true positive detections improves confidence in evaluating biodiversity.

Methods These scripts were written and databases curated by Matthew Snyder during his PhD Dissertation research in Dr. Carol Stepien's Genetics and Genomics Group at the Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle, WA.

The estimation of three-dimension neural active sources from the magnetoencephalography (MEG) record is a very critical issue for both clinical neurology and brain functions research. Nowadays multiple signal classification (MUSIC) algorithm and recursive MUSIC algorithm are widely used to locate dipolar sources from MEG data. The drawback of these algorithms is that they need excessive calculation and is quite time-consuming when scanning a three-dimensional space. In order to solve this problem, we propose a MEG sources localization scheme based on an improved Particle Swarm Optimization (PSO). This scheme uses the advantage of global searching ability of PSO to estimate the rough source location. Then combining with grids search in small area, the accurate dipolar source localization is performed. In addition, we compare the results of our method with those based on Genetic Algorithm (GA). Computer simulation results show that our PSO strategy is an effective and precise approach to dipole localization which can improve the speed greatly and localize the sources accurately. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

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