Overview

The global AI In Bioinformatics Market is projected to reach USD 136.3 million by 2033, rising from USD 3.8 million in 2023. A CAGR of 42.9% is anticipated from 2024 to 2033. This growth is being supported by the rapid expansion of genomic and biomedical data. Sequencing costs have fallen dramatically since the Human Genome Project, making large-scale sequencing routine. As a result, vast datasets are being created that require automated analysis and advanced algorithms. AI-enabled bioinformatics solutions are being adopted to extract insights, accelerate research, and reduce manual workloads. Increasing reliance on data-driven healthcare further strengthens market fundamentals.

Public health strategies are contributing to steady adoption. International health agencies are promoting genomic surveillance, routine sequencing, and secure data sharing. This shift is stimulating demand for scalable bioinformatics systems built on AI. Government programs are investing in sequencing networks, laboratory infrastructure, and digital health capabilities. These initiatives expand the volume of “AI-ready†data and improve data interoperability. Investments in pathogen genomics programs and national laboratories are enhancing analytical capacity. As a result, AI-based platforms are being embedded into public health workflows to support outbreak detection, surveillance, and decision-making.

Clear regulatory frameworks are improving confidence in AI deployment. Global health authorities have issued guidance on responsible AI use, transparency, and quality standards. Regulatory agencies in the U.S. and Europe have developed review pathways and validation requirements for AI-enabled medical technologies. This clarity reduces uncertainty for industry stakeholders and accelerates product development cycles. Defined evaluation criteria are supporting safer and more reliable model use in healthcare and research settings. As governance frameworks mature, commercial and clinical adoption of AI-driven bioinformatics tools is expected to accelerate.

Data quality improvement remains a critical enabler. Policymakers and research institutions are prioritizing standardized, interoperable, and representative datasets. Initiatives to build FAIR (Findable, Accessible, Interoperable, Reusable) datasets reduce challenges related to data fragmentation and bias. Better-structured data enhances model accuracy and lowers performance risks. Institutional partnerships and cross-border collaboration programs are strengthening data exchange practices. This foundation improves algorithm training and supports reliable analysis across diverse populations. Wider availability of quality datasets improves commercial viability and increases industry participation.

Overall, the market is benefiting from rapid data growth, strong government support, and aligned regulatory frameworks. Declining sequencing costs and expanding public health programs are driving demand for efficient computational platforms. AI-powered bioinformatics tools are expected to transform genomics, disease surveillance, and translational research. Continued investment in data ecosystems, standards, and ethical guidance will further strengthen adoption. The sector is positioned for sustained expansion as healthcare systems prioritize precision medicine and real-time pathogen monitoring.

Bioinformatics Market Outlook

The global bioinformatics market size was projected at $10.4 billion in 2023 and is anticipated to grow to $24.8 billion by 2032, with a compound annual growth rate (CAGR) of 10.2%. This rapid growth is primarily attributed to the increasing demand for bioinformatics tools in genomics and proteomics research, thereby enhancing data interpretation and analysis capabilities. Additionally, the surge in the adoption of cloud-based solutions and the increasing volume of biological data generated through research activities are key factors driving the market growth. Furthermore, the rising emphasis on precision medicine and personalized healthcare approaches plays a significant role in the expansion of this market.

One of the major growth factors driving the bioinformatics market is the vast amount of biological data being generated, necessitating advanced data analysis and management tools. The advent of next-generation sequencing technologies has revolutionized genetic research, leading to exponential data generation. Bioinformatics provides the necessary computational solutions to manage, analyze, and interpret this data efficiently. Moreover, the increasing collaboration between biological scientists and computer experts is further accelerating the development of novel bioinformatics tools, enhancing their application across various domains. This interdisciplinary approach is not only improving research outcomes but also facilitating the discovery of new biological insights.

Another significant growth driver is the rising investment in research and development in the field of genomics and proteomics. Governments and private organizations across the globe are investing heavily in life sciences research to understand complex biological processes and diseases better. These investments are expected to increase the demand for sophisticated bioinformatics tools and services. Additionally, the integration of artificial intelligence and machine learning with bioinformatics is opening new avenues for research, enabling more precise data analysis and prediction models. This technological convergence is expected to provide significant growth opportunities for the bioinformatics market during the forecast period.

The increasing prevalence of chronic diseases and the growing need for personalized medicine are also contributing to the expansion of the bioinformatics market. Personalized medicine, which tailors healthcare to individual patients, relies heavily on bioinformatics to analyze genetic information and develop targeted therapies. As healthcare systems worldwide shift towards more personalized approaches, the demand for bioinformatics solutions is expected to rise significantly. Moreover, bioinformatics plays a crucial role in drug discovery and development processes, providing insights that accelerate the identification of potential drug targets and biomarkers.

The role of Life Sciences Software in the bioinformatics market is becoming increasingly prominent as researchers and healthcare providers seek more sophisticated tools to manage and analyze complex biological data. These software solutions are essential for processing the vast amounts of data generated by modern research techniques, such as next-generation sequencing and mass spectrometry. By providing robust data management and analysis capabilities, Life Sciences Software enables researchers to gain deeper insights into genetic and proteomic information, facilitating the discovery of new therapeutic targets and the development of personalized medicine approaches. As the demand for precision medicine continues to grow, the importance of Life Sciences Software in bioinformatics is expected to rise, driving innovation and market expansion.

Regionally, North America holds the largest share of the bioinformatics market due to the presence of a well-established healthcare infrastructure and significant investments in biotechnological research. The region is home to several leading bioinformatics companies and research institutions, which are at the forefront of innovation and technological advancements. Additionally, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by increasing government funding for genomics research and the growing adoption of bioinformatics in emerging economies like China and India. The expansion of biopharmaceutical industries and a rising focus on precision medicine in these regions are further contributing to market growth.

Pro

Structural Bioinformatics Software Market Outlook

As per our latest research, the global Structural Bioinformatics Software market size reached USD 1.48 billion in 2024, demonstrating robust demand across biopharmaceutical research, drug discovery, and academic sectors. The market is experiencing a healthy compound annual growth rate (CAGR) of 10.2% and is forecasted to attain a value of USD 3.58 billion by 2033. This growth can be attributed to the rapid advancements in computational biology, the increasing adoption of artificial intelligence and machine learning in protein structure prediction, and the surge in drug development activities globally.

One of the primary growth drivers for the Structural Bioinformatics Software market is the intensifying focus on precision medicine and personalized therapeutics. With the global pharmaceutical industry placing increasing emphasis on developing targeted therapies, there is a critical need for advanced software tools that can model, predict, and analyze complex biomolecular structures. These tools are pivotal for understanding protein-ligand interactions, predicting the effects of mutations, and identifying novel druggable targets. The integration of high-throughput sequencing data with structural bioinformatics platforms has further accelerated the pace of discovery, enabling researchers to move from raw data to actionable insights with unprecedented speed and accuracy.

Another significant factor propelling the market is the evolution of computational power and cloud-based infrastructure. The exponential increase in available biological data, coupled with the complexity of protein folding and molecular dynamics simulations, demands scalable and high-performance computing resources. Cloud-based structural bioinformatics solutions have democratized access to sophisticated algorithms and databases, making them available to a broader range of users, including smaller biotech firms and academic labs. This shift has not only reduced the barriers to entry but also fostered greater collaboration and innovation in the field, as researchers can now share data, workflows, and results seamlessly across geographies.

The market is also benefiting from heightened collaboration between academia, research organizations, and industry players. Public-private partnerships, government funding initiatives, and global consortia are fueling the development of next-generation structural bioinformatics platforms. These collaborations are focused on addressing critical challenges such as protein structure prediction, functional annotation, and molecular modeling. The emergence of open-source software and community-driven databases has further enriched the ecosystem, providing researchers with access to a wealth of curated data and cutting-edge analytical tools. As the field continues to evolve, the synergy between computational advancements and experimental validation is expected to drive the adoption of structural bioinformatics software across diverse end-user segments.

Structure-Based Drug Design is an integral component of the drug discovery process, leveraging the detailed knowledge of the three-dimensional structure of biological targets to design more effective therapeutic agents. This approach utilizes advanced computational tools to model the interactions between drug candidates and their targets, allowing researchers to optimize binding affinity and selectivity. By focusing on the structural aspects of drug-target interactions, Structure-Based Drug Design enhances the precision and efficiency of the drug development pipeline, ultimately leading to the creation of more targeted and effective treatments. The integration of this methodology with structural bioinformatics software is revolutionizing the way researchers approach complex biological challenges, offering new avenues for innovation and discovery.

From a regional perspective, North America remains the dominant market for structural bioinformatics software, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific region. The robust presence of leading pharmaceutical and biotechnology companies, coupled with significant investments in research and development, has established North America as a global innovation hub. Meanwhi

The VBRC provides bioinformatics resources to support scientific research directed at viruses belonging to the Arenaviridae, Bunyaviridae, Filoviridae, Flaviviridae, Paramyxoviridae, Poxviridae, and Togaviridae families. The Center consists of a relational database and web application that support the data storage, annotation, analysis, and information exchange goals of this work. Each data release contains the complete genomic sequences for all viral pathogens and related strains that are available for species in the above-named families. In addition to sequence data, the VBRC provides a curation for each virus species, resulting in a searchable, comprehensive mini-review of gene function relating genotype to biological phenotype, with special emphasis on pathogenesis.

Developing student creativity and ability to develop a testable hypothesis represents a significant challenge in most laboratory courses. This lesson demonstrates how students use facets of molecular evolution and bioinformatics approaches involving protein sequence alignments (Clustal Omega, Uniprot) and 3D structure visualization (Pymol, JMol, Chimera), along with an analysis of pertinent background literature, to construct a novel hypothesis and develop a research proposal to explore their hypothesis. We have used this approach in a variety of institutional contexts (community college, research intensive university and primarily undergraduate institutions, PUIs ) as the first component in a protein-centric course-embedded undergraduate research experience (CURE) sequence. Built around the enzyme malate dehydrogenase, the sequence illustrates a variety of foundational concepts from the learning framework for Biochemistry and Molecular Biology. The lesson has three specific learning goals: i) find, use and present relevant primary literature, protein sequences, structures, and analyses resulting from the use of bioinformatics tools, ii) understand the various roles that non-covalent interactions may play in the structure and function of an enzyme. and iii) create/develop a testable and falsifiable hypothesis and propose appropriate experiments to interrogate the hypothesis. For each learning goal, we have developed specific assessment rubrics. Depending on the needs of the course, this approach builds to an in-class student presentation and/or a written research proposal. The module can be extended over several lecture and lab periods. Furthermore, the module lends itself to additional assessments including oral presentation, research proposal writing and the validated pre-post Experimental Design Ability Test (EDAT). Although presented in the context of course-based research on malate dehydrogenase, the approach and materials presented are readily adaptable to any protein of interest.

Primary image: Mind map of the hypothesis development.

BackgroundHealth sciences research is increasingly focusing on big data applications, such as genomic technologies and precision medicine, to address key issues in human health. These approaches rely on biological data repositories and bioinformatic analyses, both of which are growing rapidly in size and scope. Libraries play a key role in supporting researchers in navigating these and other information resources.MethodsWith the goal of supporting bioinformatics research in the health sciences, the University of Arizona Health Sciences Library established a Bioinformation program. To shape the support provided by the library, I developed and administered a needs assessment survey to the University of Arizona Health Sciences campus in Tucson, Arizona. The survey was designed to identify the training topics of interest to health sciences researchers and the preferred modes of training.ResultsSurvey respondents expressed an interest in a broad array of potential training topics, including "traditional" information seeking as well as interest in analytical training. Of particular interest were training in transcriptomic tools and the use of databases linking genotypes and phenotypes. Staff were most interested in bioinformatics training topics, while faculty were the least interested. Hands-on workshops were significantly preferred over any other mode of training. The University of Arizona Health Sciences Library is meeting those needs through internal programming and external partnerships.ConclusionThe results of the survey demonstrate a keen interest in a variety of bioinformatic resources; the challenge to the library is how to address those training needs. The mode of support depends largely on library staff expertise in the numerous subject-specific databases and tools. Librarian-led bioinformatic training sessions provide opportunities for engagement with researchers at multiple points of the research life cycle. When training needs exceed library capacity, partnering with intramural and extramural units will be crucial in library support of health sciences bioinformatic research.

In the last decade, High-Throughput Sequencing (HTS) has revolutionized biology and medicine. This technology allows the sequencing of huge amount of DNA and RNA fragments at a very low price. In medicine, HTS tests for disease diagnostics are already brought into routine practice. However, the adoption in plant health diagnostics is still limited. One of the main bottlenecks is the lack of expertise and consensus on the standardization of the data analysis. The Plant Health Bioinformatic Network (PHBN) is an Euphresco project aiming to build a community network of bioinformaticians/computational biologists working in plant health. One of the main goals of the project is to develop reference datasets that can be used for validation of bioinformatics pipelines and for standardization purposes.

Semi-artificial datasets have been created for this purpose (Datasets 1 to 10). They are composed of a “real†HTS dataset spiked with artificial viral reads. It will allow researchers to adjust ...

Protein Structure Initiative - TargetTrack protein target registration database (795 MB, gzipped tarball)

The Protein Structure Initiative was a high-throughput structural genomics effort from 2000-2015 focused on developing technologies to enable greater coverage of protein structure space. Over its 15-year tenure, over 100 investigators at 35 centers (see ContributingCenters.xls) declared over 350,000 protein sequences (targets) that they would study using state-of-the-art protein production and structure determination methods. Many of these targets were selected through bioinformatics-based methods to serve as representatives for sequence and structure clusters.

From 2003-2010, these selected sequences and some basic identifying metadata were kept in a database called TargetDB, created at the Research Collaboratory for Structural Bioinformatics at Rutgers University. In 2008, a second database named PepcDB was created to track detailed experimental trial history and the standard protocols used by the PSI centers. These two databases became the principal structural genomics target databases, and were rolled into the PSI Structural Biology Knowledgebase in 2008.

As part of the third phase of the PSI, TargetDB and PepcDB were merged into a single resource, TargetTrack, to facilitate one-stop access to the data as well as expanding the schema to include new required data items. Participating centers deposited the latest status on their active targets and the protocols that were used (along with any deviations) on a weekly or quarterly basis. TargetTrack provided a variety of pre-computed data downloads on a weekly basis as well.

In July 2017, the Structural Biology Knowledgebase ceased operations. The files provided in this tarball represent the final datafiles generated by TargetTrack (timestamp June 30, 2017). Please read the README included in this dataset for descriptions of each file.

The entire TargetTrack datafile in XML format can be found in /TargetTrack XML files/tt.xml.gz

Key documentation can be found in the /Documentation folder.
TargetTrack schema: targetTrack-v1.4.1.pdf
Spreadsheet with TargetTrack enumerations for relevant fields: targetTrackEnumeratedDataItems-v1.4.1-1.xls
Image depicted the XML data schema: targetTrack-v1.4.1.jpg

These files are 868 MB in total size, uncompressed.
To open the tarball, use the command 'tar -zxvf TargetTrack-1Jul2017.tar.gz'

-- created by the PSI Structural Biology Knowledgebase, July 5, 2017

Objective: Sepsis is a common disease in internal medicine, with a high incidence and dangerous condition. Due to the limited understanding of its pathogenesis, the prognosis is poor. The goal of this project is to screen potential biomarkers for the diagnosis of sepsis and to identify competitive endogenous RNA (ceRNA) networks associated with sepsis.Methods: The expression profiles of long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs) were derived from the Gene Expression Omnibus (GEO) dataset. The differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) were screened by bioinformatics analysis. DEmRNAs were analyzed by protein-protein interaction (PPI) network analysis, transcription factor enrichment analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Set Enrichment Analysis (GSEA). After the prediction of the relevant database, the competitive ceRNA network is built in Cytoscape. The gene-drug interaction was predicted by DGIgb. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to confirm five lncRNAs from the ceRNA network.Results: Through Venn diagram analysis, we found that 57 DElncRNAs, 6 DEmiRNAs and 317 DEmRNAs expressed abnormally in patients with sepsis. GO analysis and KEGG pathway analysis showed that 789 GO terms and 36 KEGG pathways were enriched. Through intersection analysis and data mining, 5 key KEGG pathways and related core genes were revealed by GSEA. The PPI network consists of 247 nodes and 1,163 edges, and 50 hub genes are screened by the MCODE plug-in. In addition, there are 5 DElncRNAs, 6 DEmiRNAs and 28 DEmRNAs in the ceRNA network. Drug action analysis showed that 7 genes were predicted to be molecular targets of drugs. Five lncRNAs in ceRNA network are verified by qRT-PCR, and the results showed that the relative expression of five lncRNAs was significantly different between sepsis patients and healthy control subjects.Conclusion: A sepsis-specific ceRNA network has been effectively created, which is helpful to understand the interaction between lncRNAs, miRNAs and mRNAs. We discovered prospective sepsis peripheral blood indicators and proposed potential treatment medicines, providing new insights into the progression and development of sepsis.

Objective To identify the core genes linking human parvovirus B19 (B19V) infection and rheumatoid arthritis (RA) using bioinformatics methods, providing new insights into etiology and targeted therapy.Methods The B19V-infected and control dataset (GSE103460) and the RA patient and healthy control dataset (GSE55235) were downloaded from the GEO database. Differentially expressed genes (DEGs) for B19V infection and RA were identified separately using R language. The intersection of DEGs from both diseases was taken to obtain common genes (co-DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the co-DEGs. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized with Cytoscape to screen for hub genes.Results A total of 772 DEGs (411 up-regulated, 361 down-regulated) were identified in the B19V gene expression profile, and 1,413 DEGs (781 up-regulated, 632 down-regulated) were identified in the RA gene expression profile. The intersection revealed 104 key co-DEGs associated with both B19V and RA. Enrichment analysis indicated that these co-DEGs were significantly involved in pathways related to viral infectious diseases, immune cell differentiation (Th17 cell differentiation), inflammatory signaling (TNF, PI3K-Akt), and various cancers. Finally, the top 10 hub genes were identified based on the Maximal Clique Centrality (MCC) algorithm via the CytoHubba plugin: JUN, FOS, EGR1, DUSP1, FOSB, PTGS2, MYC, CDKN1A, ZFP36, and JUNB.Conclusion This bioinformatics study identifies 10 core genes, including JUN, FOS, EGR1, DUSP1, and FOSB, that are commonly associated with both B19V infection and RA. These genes are primarily enriched in inflammatory stress and immune regulation processes related to pathways such as immune cell differentiation and inflammatory signaling, providing new clues and potential therapeutic targets for elucidating the molecular mechanism by which B19V infection contributes to RA pathogenesis.

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

We aimed to identify new targets affecting gastric cancer (GC) prognosis. Six target genes were identified from hub genes based on their relationship with important factors affecting tumor progression, like immune infiltration, purity, tumor mutation burden (TMB), and tumor microenvironment (TME) score. The effect of target genes’ somatic mutations and copy number alteration (CNA) was examined to determine their effect on GC prognosis. Six target genes (FBN1, FN1, HGF, MMP9, THBS1, and VCAN) were identified. Reduced expression of each target gene, except MMP9, indicated better prognosis and lower grade in GC. FBN1, THBS1, and VCAN showed lower expression in stage I GC. Non-silencing mutations of the six genes played a role in significantly higher TMB and TME scores. THBS1 mutation was associated with earlier stage GC, and VCAN mutation was associated with lower grade GC. However, patients with target gene CNA displayed higher tumor purity. MMP9, THBS1, and VCAN CNA was associated with lower grade GC, while FBN1 CNA reflected earlier T stage. Additionally, the target genes may affect GC prognosis by influencing multiple oncogenic signaling pathways. FBN1, FN1, HGF, MMP9, THBS1, and VCAN may be new GC prognostic targets by affecting tumor purity, TMB, TME score, and multiple oncogenic signaling pathways.

Renewable Bioinformatics Chemicals Market Outlook

As per our latest research, the global renewable bioinformatics chemicals market size in 2024 stands at USD 4.82 billion, reflecting robust momentum driven by the convergence of sustainable chemistry and advanced bioinformatics. The market is expanding at a compelling CAGR of 8.1% and is forecasted to reach USD 9.12 billion by 2033. The primary growth factor fueling this surge is the increasing demand for environmentally friendly and sustainable chemicals in life sciences research and healthcare, where bioinformatics tools are pivotal in data-driven discovery and innovation.

A significant growth driver for the renewable bioinformatics chemicals market is the escalating adoption of green chemistry practices across pharmaceutical and biotechnology sectors. As regulatory bodies and global organizations push for reduced environmental footprints, companies are actively transitioning from traditional petrochemical-based reagents and solvents to renewable alternatives. This shift not only aligns with corporate sustainability goals but also reduces hazardous waste generation and improves laboratory safety. Moreover, the integration of bioinformatics in chemical screening, synthesis, and data analysis has greatly enhanced the efficiency and precision of research processes, further accelerating the uptake of renewable chemicals.

The rapid advancements in genomics, proteomics, and metabolomics are also fueling the demand for renewable bioinformatics chemicals. High-throughput sequencing and omics technologies generate vast datasets, necessitating specialized chemicals that are both high-quality and sustainable. Bioinformatics platforms rely on these chemicals for accurate sample preparation, data acquisition, and analysis. The growing number of collaborative research projects, increased funding for life sciences, and a surge in personalized medicine initiatives are collectively propelling the market forward. This trend is particularly evident in academic and research institutions, where adherence to green laboratory standards is becoming a norm.

Another critical factor driving market expansion is the ongoing innovation in renewable chemical production methods. Advances in synthetic biology, enzyme engineering, and fermentation technologies have enabled the scalable and cost-effective production of bio-based reagents, enzymes, and solvents. These innovations are not only reducing the dependency on fossil resources but are also resulting in chemicals with improved purity and performance. The synergy between bioinformatics algorithms and renewable chemical development allows for rapid optimization and customization, meeting the specific needs of drug discovery, diagnostics, and molecular biology applications.

From a regional perspective, North America currently dominates the renewable bioinformatics chemicals market, accounting for over 38% of the global share in 2024. Europe closely follows, driven by stringent environmental regulations and strong government support for green technologies. The Asia Pacific region is emerging as a high-growth market, with a projected CAGR of 10.4% through 2033, fueled by expanding biotechnology sectors in China, India, and Southeast Asia. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as local industries gradually embrace sustainable laboratory practices.

Product Type Analysis

The product type segment of the renewable bioinformatics chemicals market encompasses enzymes, reagents, buffers, solvents, and other specialized chemicals. Enzymes hold a significant share owing to their indispensable role in genomics, proteomics, and molecular diagnostics. The demand for renewable enzymes is particularly high due to their application in DNA amplification, sequencing, and protein analysis. These enzymes, often produced through recombinant technology or microbial fermentation, offer enhanced specificity and reduced contamination risks. The market for renewable enzymes is expected to continue its upward trajectory as researchers seek alternatives to animal-derived or synthetic enzymes, aligning with both ethical and sustainability considerations.

Reagents form the backbone of most laboratory workflows, and the shift toward renewable reagents is reshaping procurement strategies across the life sciences industry. Renewable reagents, synthesized from plant-based or m

This dataset is the accompanying data for the submitted manuscript:"An ANI-2 Enabled Open-Source Protocol To Estimate Ligand Strain After Docking"

The CABANA project (Capacity Building for Bioinformatics in Latin America) was funded by the UK’s Global Challenges Research Fund in 2017 with the aim to strengthen the bioinformatics capacity and extend its applications in Latin America focused on three challenge areas – communicable diseases, sustainable food production and protection of biodiversity. For 5 years, the project executed activities including data analysis workshops, train-the-trainer workshops, secondments, eLearning development, knowledge exchange meetings, and research projects in 10 countries. The project was successful in accomplishing all its goals with a major impact on the region. It became a model by which the research needs determined the training that was delivered. Multiple publications and over 800 trainees are part of the legacy of the project.

The Paired Omics Data Platform is a community-based initiative standardizing links between genomic and metabolomics data in a computer readable format to further the field of natural products discovery. The goals are to link molecules to their producers, find large scale genome-metabolome associations, use genomic data to assist in structural elucidation of molecules, and provide a centralized database for paired datasets. This dataset contains the projects in http://pairedomicsdata.bioinformatics.nl/. The JSON documents adhere to the http://pairedomicsdata.bioinformatics.nl/schema.json JSON schema.

IntroductionMicroalgae constitute a prominent feedstock for producing biofuels and biochemicals by virtue of their prolific reproduction, high bioproduct accumulation, and the ability to grow in brackish and saline water. However, naturally occurring wild type algal strains are rarely optimal for industrial use; therefore, bioengineering of algae is necessary to generate superior performing strains that can address production challenges in industrial settings, particularly the bioenergy and bioproduct sectors. One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. These targets are often orthologs which are defined as genes/proteins originating from a common ancestor in divergent species. Although bioinformatics tools for the identification of protein orthologs already exist, processing the output from such tools is nontrivial, especially for a researcher with little or no bioinformatics experience.MethodsThe present study introduces AlgaeOrtho, a user-friendly tool that builds upon the SonicParanoid orthology inference tool (based on an algorithm that identifies potential protein orthologs based on amino acid sequences) and the PhycoCosm database from JGI (Joint Genome Institute) to help researchers identify orthologs of their proteins of interest in multiple diverse algal species.ResultsThe output of this application includes a table of the putative orthologs of their protein of interest, a heatmap showing sequence similarity (%), and an unrooted tree of the putative protein orthologs. Notably, the tool would be instrumental in identifying novel bioengineering targets in different algal strains, including targets in not-fully annotated algal species, since it does not depend on existing protein annotations. We tested AlgaeOrtho using three case studies, for which orthologs of proteins relevant to bioengineering targets, were identified from diverse algal species, demonstrating its ease of use and utility for bioengineering researchers.DiscussionThis tool is unique in the protein ortholog identification space as it can visualize putative orthologs, as desired by the user, across several algal species.

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.

A database of information on pox viruses. Goals of this project are to acquire and annotate data on poxviruses, and to develop and utilize new tools to facilitate the study of this group of organisms. This basic research is being undertaken with an eye toward the development of novel antiviral therapies, vaccines against human orthopoxvirus infections, new approaches for the environmental detection of virions, and methods to accomplish more rapid diagnosis of disease.

This supplementary data include the full results from our study "Computational Identification of Human Targets of Mitragynine - the Main Active Compound of Mitragyna speciosa" by Nipitpon Jaroenkit and Sirawit Ittisoponpisan