This dataset is related to the manuscript "An empirical meta-analysis of the life sciences linked open data on the web" published at Nature Scientific Data. If you use the dataset, please cite the manuscript as follows:Kamdar, M.R., Musen, M.A. An empirical meta-analysis of the life sciences linked open data on the web. Sci Data 8, 24 (2021). https://doi.org/10.1038/s41597-021-00797-yWe have extracted schemas from more than 80 publicly available biomedical linked data graphs in the Life Sciences Linked Open Data (LSLOD) cloud into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. The dataset published here contains the following files:- The set of Linked Data Graphs from the LSLOD cloud from which schemas are extracted.- Refined Sets of extracted classes, object properties, data properties, and datatypes, shared across the Linked Data Graphs on LSLOD cloud. Where the schema element is reused from a Linked Open Vocabulary or an ontology, it is explicitly indicated.- The LSLOD Schema Graph, which contains all the above extracted schema elements interlinked with each other based on the underlying content. Sample instances and sample assertions are also provided along with broad level characteristics of the modeled content. The LSLOD Schema Graph is saved as a JSON Pickle File. To read the JSON object in this Pickle file use the Python command as follows:with open('LSLOD-Schema-Graph.json.pickle' , 'rb') as infile: x = pickle.load(infile, encoding='iso-8859-1')Check the Referenced Link for more details on this research, raw data files, and code references.

Database of curated links to molecular resources, tools and databases selected on the basis of recommendations from bioinformatics experts in the field. This resource relies on input from its community of bioinformatics users for suggestions. Starting in 2003, it has also started listing all links contained in the NAR Webserver issue. The different types of information available in this portal: * Computer Related: This category contains links to resources relating to programming languages often used in bioinformatics. Other tools of the trade, such as web development and database resources, are also included here. * Sequence Comparison: Tools and resources for the comparison of sequences including sequence similarity searching, alignment tools, and general comparative genomics resources. * DNA: This category contains links to useful resources for DNA sequence analyses such as tools for comparative sequence analysis and sequence assembly. Links to programs for sequence manipulation, primer design, and sequence retrieval and submission are also listed here. * Education: Links to information about the techniques, materials, people, places, and events of the greater bioinformatics community. Included are current news headlines, literature sources, educational material and links to bioinformatics courses and workshops. * Expression: Links to tools for predicting the expression, alternative splicing, and regulation of a gene sequence are found here. This section also contains links to databases, methods, and analysis tools for protein expression, SAGE, EST, and microarray data. * Human Genome: This section contains links to draft annotations of the human genome in addition to resources for sequence polymorphisms and genomics. Also included are links related to ethical discussions surrounding the study of the human genome. * Literature: Links to resources related to published literature, including tools to search for articles and through literature abstracts. Additional text mining resources, open access resources, and literature goldmines are also listed. * Model Organisms: Included in this category are links to resources for various model organisms ranging from mammals to microbes. These include databases and tools for genome scale analyses. * Other Molecules: Bioinformatics tools related to molecules other than DNA, RNA, and protein. This category will include resources for the bioinformatics of small molecules as well as for other biopolymers including carbohydrates and metabolites. * Protein: This category contains links to useful resources for protein sequence and structure analyses. Resources for phylogenetic analyses, prediction of protein features, and analyses of interactions are also found here. * RNA: Resources include links to sequence retrieval programs, structure prediction and visualization tools, motif search programs, and information on various functional RNAs.

To enable the identification of mutated peptide sequences in complex biological samples, in this work, a cancer protein database with mutation information collected from several public resources such as COSMIC, IARC P53, OMIM and UniProtKB, was developed. In-house developed Perl-scripts were used to search and process the data, and to translate each gene-level mutation into a mutated peptide sequence. The cancer mutation database comprises a total of 872,125 peptide entries from 25,642 protein IDs. A description line for each entry provides the parent protein ID and name, the cDNA- and protein-level mutation site and type, the originating database, and the cancer tissue type and corresponding hits. The database is FASTA formatted to enable data retrieval by commonly used tandem MS search engines.

Supplemental Data S1. All keywords accumulated for DUF34 protein family during the comprehensive published data capture process.Supplemental Data S2. Curated sets of tools organized by: (a) orthology-/homology-limited and phyletic patterning tools; (b) gene neighborhood; and (c) synteny tools. Supplemental Data S3. All curated yields of specialized corpus search tools: PubMed, EuropePMC, PubTator, Scinapse.Supplemental Data S4. Simple Comparison of Text-based vs. Sequence-based Publication Retrieval.Supplemental Data S5. Publication Retrieval Method Comparison — Single-sequence vs. HMM-based PaperBLAST Methods.Supplemental Data S6. Venn Diagram Data for the Comparison of the Two Major PaperBLAST Retrieval Methods.Supplemental Data S7. Publication Retrieval Method Comparison — Single-sequence vs. HMM-based vs. Idealized "QCC" Cycle Method.Supplemental Data S8. Venn Diagram Data Comparing PaperBLAST Retrieval Methods to "QCC Cycle" Approach.Supplemental Data S9. Comparison of the PaperBLAST Results of Several, Disparately Related DUF34 Homolog Sequences.Supplemental Data S10. Final Summary Venn Diagram of all Methods' Results Relevant to DUF34.

Bioinformatics Market Outlook

According to our latest research, the global bioinformatics market size reached USD 16.2 billion in 2024, exhibiting robust expansion driven by growing demand across various life science applications. The market is anticipated to maintain a strong momentum, registering a CAGR of 12.6% during the forecast period, and is projected to achieve a value of USD 47.3 billion by 2033. This significant growth is primarily fueled by advancements in genomics and proteomics, the proliferation of high-throughput sequencing technologies, and the rising integration of artificial intelligence and machine learning in biological data analysis. As per our latest research, the increasing need for efficient data management and analysis in drug discovery, personalized medicine, and agricultural biotechnology continues to propel the global bioinformatics market forward.

One of the core growth drivers for the bioinformatics market is the exponential rise in biological data generation, particularly from next-generation sequencing (NGS) platforms. As sequencing costs have plummeted and throughput has soared, researchers and organizations across academia, healthcare, and agriculture are generating vast amounts of genomic, proteomic, and metabolomic data. This deluge of information necessitates robust bioinformatics tools and platforms for storage, retrieval, analysis, and interpretation. The capability to translate raw biological data into actionable insights for disease research, crop improvement, and environmental monitoring has made bioinformatics indispensable. Furthermore, collaborations between biotechnology companies, academic institutions, and IT firms are fostering innovation in software and algorithm development, amplifying the market’s growth trajectory.

Another significant growth factor is the integration of artificial intelligence (AI) and machine learning (ML) within bioinformatics platforms. AI-driven analytics are revolutionizing the way researchers interpret complex biological datasets, enabling more accurate predictions in genomics, drug discovery, and personalized medicine. The ability of ML algorithms to identify patterns, predict molecular interactions, and automate data processing is enhancing the efficiency and reliability of bioinformatics workflows. Moreover, the increasing adoption of cloud-based bioinformatics solutions is democratizing access to powerful computational resources, allowing small and medium enterprises (SMEs) and academic labs to leverage advanced analytics without heavy infrastructure investments. These technological advancements are expected to further accelerate market expansion over the coming years.

The growing focus on personalized medicine and precision healthcare is also catalyzing the demand for bioinformatics. Healthcare providers and pharmaceutical companies are increasingly utilizing bioinformatics tools to tailor treatments based on individual genetic profiles, leading to improved patient outcomes and reduced adverse effects. In drug discovery, bioinformatics accelerates target identification, biomarker discovery, and candidate screening, shortening development timelines and reducing costs. Furthermore, bioinformatics is playing a pivotal role in agricultural biotechnology, helping researchers develop genetically modified crops with enhanced traits, improved yield, and resistance to diseases. The convergence of these diverse applications underscores the strategic importance of bioinformatics across multiple sectors.

From a regional perspective, North America continues to lead the global bioinformatics market, supported by a well-established biotechnology industry, significant R&D investments, and favorable government initiatives. The United States, in particular, is home to several leading bioinformatics companies and research institutions, driving innovation and adoption. Europe follows closely, with strong contributions from countries like Germany, the UK, and France, where collaborative research projects and public-private partnerships are prevalent. Meanwhile, the Asia Pacific region is witnessing the fastest growth, propelled by expanding genomics research, increasing healthcare expenditures, and a surge in government funding for life science initiatives, particularly in China, India, and Japan.

Product & Service Analysis

The product & service segment of the bioinformatics market is broadly categorized into software, hardware, and

Chemical substance resources on the Web are often made accessible to researchers through public APIs (Application Programming Interfaces). A significant problem of missing provenance information arises when extracting and integrating data in such APIs. Even when provenance is stated, it is usually not done with any prescribed templates or terminology. This creates a burden on data producers and makes it challenging for API developers to automatically extract and analyse this information. Downstream, these consequences hinder efforts to automatically determine the veracity and quality of extracted data, critical for proving the integrity of associated research findings. In this paper, we propose a model for capturing provenance of assertions about chemical substances by systematically analyzing three sources: (i) Nanopublications, (ii) Wikidata and (iii) selected Minimal Information Standards (MISTS) for reporting biomedical studies\footnote{Reported in FAIRsharing.org \url{https://fairsharing.org}}. We analyse provenance terms used in these sources along with their frequency of use and synthesize our findings into a preliminary model for capturing provenance.

The EBI is a centre for research and services in bioinformatics. The Institute manages databases of biological data including nucleic acid, protein sequences and macromolecular structures. As we move towards understanding biology at the systems level, access to large data sets of many different types has become crucial. Technologies such as genome-sequencing, microarrays, proteomics and structural genomics have provided 'parts lists' for many living organisms, and researchers are now focusing on how the individual components fit together to build systems. The hope is that scientists will be able to translate their new insights into improving the quality of life for everyone. However, the high-throughput revolution also threatens to drown us in data. There is an ongoing, and growing, need to collect, store and curate all this information in ways that allow its efficient retrieval and exploitation. The European Bioinformatics Institute is one of the few places in the world that has the resources and expertise to fulfil this important task.

We have applied 3D shape-based retrieval to various disciplines such as computer vision, CAD/CAM, computer graphics, molecular biology and 3D anthropometry. We have organized two workshops on 3D shape retrieval and two shape retrieval contests. We also have developed 3D shape benchmarks, performance evaluation software and prototype 3D retrieval systems. We have developed a robotic map quality assessment tool in collaboration with MEL) We also have developed different shape descriptors to represent 3D human bodies and heads efficiently and other work related to 3D anthropometry. Finally, we also have done some in a Structural Bioinformatics, Bio-Image analysis and retrieval.

Venn Diagram Data for the Comparison of the Two Major PaperBLAST Retrieval Methods.

To enable the identification of mutated peptide sequences in complex biological samples, in this work, a disease protein database with mutation information collected from several public resources such as OMIM and UniProtKB, was developed. In-house developed Perl-scripts were used to search and process the data, and to translate each gene-level mutation into a mutated peptide sequence. The disease mutation database comprises a total of 27,148 peptide entries from 2913 protein IDs. A description line for each entry provides the parent protein ID and name, the cDNA- and protein-level mutation site and type, the originating database, and the tissue type and corresponding hits. The database is FASTA formatted to enable data retrieval by commonly used tandem MS search engines.

BackgroundCoronary heart disease (CHD) is the most common progressive disease that is difficult to diagnose and predict in the young asymptomatic period. Our study explored a mechanistic understanding of the genetic effects of premature CHD (PCHD) and provided potential biomarkers and treatment targets for further research through high throughput sequencing and integrated bioinformatics analysis.MethodsHigh throughput sequencing was performed among recruited patients with PCHD and young healthy individuals, and CHD-related microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by using R software. Enrichment analysis and CIBERSORT were performed to explore the enriched pathways of DEGs and the characteristics of infiltrating immune cells. Hub genes identified by protein–protein interaction (PPI) networks were used to construct the competitive endogenous RNA (ceRNA) networks. Potential drugs were predicted by using the Drug Gene Interaction Database (DGIdb).ResultsA total of 35 DEGs were identified from the sequencing dataset and GEO database by the Venn Diagram. Enrichment analysis indicated that DEGs are mostly enriched in excessive immune activation pathways and signal transduction. CIBERSORT exhibited that resting memory CD4 T cells and neutrophils were more abundant, and M2 macrophages, CD8 T cells, and naïve CD4 T cells were relatively scarce in patients with PCHD. After the identification of 10 hub gens, three ceRNA networks of CD83, CXCL8, and NR4A2 were constructed by data retrieval and validation. In addition, CXCL8 might interact most with multiple chemical compounds mainly consisting of anti-inflammatory drugs.ConclusionsThe immune dysfunction mainly contributes to the pathogenesis of PCHD, and three ceRNA networks of CD83, CXCL8, and NR4A2 may be potential candidate biomarkers for early diagnosis and treatment targets of PCHD.

Bgee is a database for retrieval and comparison of gene expression patterns across multiple animal species. It provides an intuitive answer to the question -where is a gene expressed?- and supports research in cancer and agriculture, as well as evolutionary biology.

Metagenomic approaches became increasingly popular in the past decades due to decreasing costs of DNA sequencing and bioinformatics development. So far, however, the recovery of long genes coding for secondary metabolites still represents a big challenge. Often, the quality of metagenome assemblies is poor, especially in environments with a high microbial diversity where sequence coverage is low and complexity of natural communities high. Recently, new and improved algorithms for binning environmental reads and contigs have been developed to overcome such limitations. Some of these algorithms use a similarity detection approach to classify the obtained reads into taxonomical units and to assemble draft genomes. This approach, however, is quite limited since it can classify exclusively sequences similar to those available (and well classified) in the databases. In this work, we used draft genomes from Lake Stechlin, north-eastern Germany, recovered by MetaBat, an efficient binning tool that integrates empirical probabilistic distances of genome abundance, and tetranucleotide frequency for accurate metagenome binning. These genomes were screened for secondary metabolism genes, such as polyketide synthases (PKS) and non-ribosomal peptide synthases (NRPS), using the Anti-SMASH and NAPDOS workflows. With this approach we were able to identify 243 secondary metabolite clusters from 121 genomes recovered from our lake samples. A total of 18 NRPS, 19 PKS, and 3 hybrid PKS/NRPS clusters were found. In addition, it was possible to predict the partial structure of several secondary metabolite clusters allowing for taxonomical classifications and phylogenetic inferences. Our approach revealed a high potential to recover and study secondary metabolites genes from any aquatic ecosystem.

Despite continued advances in sequencing technologies, there is a need for methods that can efficiently sequence large numbers of genes from diverse species. One approach to accomplish this is targeted capture (hybrid enrichment). While these methods are well established for genome resequencing projects, cross-species capture strategies are still being developed and generally focus on the capture of conserved regions, rather than complete coding regions from specific genes of interest. The resulting data is thus useful for phylogenetic studies, but the wealth of comparative data that could be used for evolutionary and functional studies is lost. Here we design and implement a targeted capture method that enables recovery of complete coding regions across broad taxonomic scales. Capture probes were designed from multiple reference species and extensively tiled in order to facilitate cross-species capture. Using novel bioinformatics pipelines we were able to recover nearly all of the targeted genes with high completeness from species that were up to 200 myr divergent. Increased probe diversity and tiling for a subset of genes had a large positive effect on both recovery and completeness. The resulting data produced an accurate species tree, but importantly this same data can also be applied to studies of molecular evolution and function that will allow researchers to ask larger questions in broader phylogenetic contexts. Our method demonstrates the utility of cross-species approaches for the capture of full length coding sequences, and will substantially improve the ability for researchers to conduct large-scale comparative studies of molecular evolution and function.

An integrated genomic and functional genomic database for the parasite Cryptosporidium. CryptoDB integrates whole genome sequence and annotation along with experimental data and environmental isolate sequences provided by community researchers. The database includes supplemental bioinformatics analyses and a web interface for data-mining. Organisms included in CryptoDB are Cryptosporidium parvum, Cryptosporidium hominis, Cryptosporidium muris and environmental isolate sequences from numerous species. CryptoDB is allied with the databases PlasmoDB and ToxoDB via ApiDB, an NIH/NIAID-funded Bioinformatics Resource Center. Tools include: * BLAST: Identify Sequence Similarities * Sequence Retrieval: Retrieve Specific Sequences using IDs and coordinates * PubMed and Entrez: View the Latest Cryptosporidium Pubmed and Entrez Results * Genome Browser: View Sequences and Features in the genome browser * CryptoCyc: Explore Automatically Defined Metabolic Pathways * Searches via Web Services: Web service access to our data

Metagenomics Market Outlook

The global metagenomics market size was valued at approximately $1.7 billion in 2023 and is anticipated to reach $4.2 billion by 2032, growing at a robust CAGR of around 10.6% during the forecast period. This market is experiencing remarkable growth due to advancements in sequencing technologies and the increasing application of metagenomics in various fields such as healthcare, agriculture, and environmental science. A key growth factor is the burgeoning demand for comprehensive genomic analysis to understand complex microbial communities, which play a crucial role in health, disease, and ecosystems.

One of the primary growth drivers of the metagenomics market is the rapid evolution of sequencing technologies. As sequencing capabilities have expanded, the cost associated with these technologies has decreased, making them more accessible to a broader range of researchers and industries. This price reduction has been pivotal in democratizing access to metagenomics tools, allowing for widespread adoption across various sectors. The ability to analyze complex microbial communities accurately has opened new avenues for research and development, particularly in healthcare and environmental science, driving the market's expansion.

Another significant factor propelling the growth of the metagenomics market is its rising application in clinical diagnostics and personalized medicine. With the growing recognition of the role of the microbiome in human health, there is an increasing interest in understanding the microbial contributions to diseases. Metagenomic approaches have proven critical in identifying novel biomarkers for disease diagnosis and prognosis, enabling more targeted and effective therapeutic strategies. This has spurred investment in metagenomics research and development, further accelerating market growth.

The agricultural and environmental sectors are also contributing significantly to the growth of the metagenomics market. The application of metagenomics in agriculture for soil health assessment, pest control, and crop yield improvement is gaining momentum. Similarly, environmental metagenomics is crucial for monitoring biodiversity and ecosystem dynamics, offering valuable insights into the impact of human activities on the environment. These applications are not only enhancing the understanding of ecological balance but also informing policies and practices aimed at sustainable development, thereby supporting market expansion.

Regionally, North America continues to dominate the metagenomics market, driven by substantial investments in research and development, a robust healthcare infrastructure, and the presence of key market players. However, the Asia Pacific region is poised to exhibit the fastest growth rate, buoyed by increasing research activities, government funding, and a burgeoning biotechnology sector. As the awareness and adoption of genomic technologies grow in emerging economies, the regional outlook for the metagenomics market remains optimistic, contributing to its overall global expansion.

Technology Analysis

The technology segment of the metagenomics market is a cornerstone that propels the advancement of the field. Sequencing technology, in particular, plays a pivotal role in the analysis of complex microbial communities. The continuous advancements in next-generation sequencing (NGS) have revolutionized the way genetic material is analyzed, allowing for high-throughput sequencing at unprecedented speeds and accuracy. This has made it possible to decode vast amounts of genomic data from diverse environments, facilitating a deeper understanding of microbial diversity and function. The reduction in sequencing costs has been a significant enabler, allowing more institutions and companies to partake in metagenomic research, further expanding the market.

Bioinformatics, another critical component of the technology segment, is indispensable in managing and interpreting the enormous volumes of data generated by sequencing technologies. Advanced bioinformatics tools and platforms are essential for data analysis, offering functionalities such as data storage, retrieval, and computational analysis to derive meaningful insights from raw genomic data. The integration of artificial intelligence and machine learning into bioinformatics solutions is further enhancing the capability to predict microbial interactions and functions, which is crucial for applications in drug discovery and personalized medicine. This synergy between sequencing and bioinformatics is a driving force behind the metagenomics market's growth

Publication Retrieval Method Comparison — Single-sequence vs. HMM-based PaperBLAST Methods.

Livestock Genomic Dataplaces Market Outlook

According to our latest research, the global livestock genomic dataplaces market size reached USD 1.51 billion in 2024, demonstrating a robust expansion driven by advancements in genomics and precision agriculture. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, culminating in a forecasted market value of USD 4.25 billion by 2033. This rapid growth is underpinned by the increasing adoption of genomic technologies for livestock improvement, disease management, and data-driven breeding strategies, as well as heightened investments in animal biotechnology.

One of the primary growth factors for the livestock genomic dataplaces market is the escalating demand for high-quality animal protein and the need to enhance livestock productivity. As populations grow and dietary patterns shift toward increased consumption of meat, milk, and eggs, farmers and producers are turning to genomics to optimize breeding programs, improve disease resistance, and increase yield efficiency. The integration of genomic data into livestock management allows for the identification of desirable traits at an early stage, thereby reducing production costs and improving overall herd health. This technological shift is also supported by government initiatives and funding for agricultural biotechnology research, further catalyzing market growth.

Another significant driver is the advent of advanced bioinformatics tools and big data analytics, which have revolutionized the way genomic information is collected, stored, and interpreted. The proliferation of next-generation sequencing (NGS) technologies has enabled the generation of vast amounts of genomic data, necessitating sophisticated dataplaces for data management and analysis. These platforms facilitate real-time data sharing, collaboration among stakeholders, and the application of machine learning algorithms to uncover genetic markers associated with economically important traits. The resulting insights are invaluable for breeding management, disease surveillance, and the development of resilient livestock breeds, fostering a data-centric approach to animal agriculture.

Furthermore, the growing awareness regarding animal health and biosecurity, especially in the wake of global zoonotic outbreaks, has amplified the importance of comprehensive genomic databases. Livestock genomic dataplaces empower researchers and producers to monitor genetic diversity, track disease outbreaks, and implement targeted interventions for disease control. The integration of these platforms with IoT devices and cloud computing further enhances their capabilities, enabling remote access, scalability, and interoperability across different regions and organizations. As a result, the market is witnessing increased adoption not only among large-scale commercial producers but also among small and medium enterprises seeking to leverage genomics for competitive advantage.

Regionally, North America holds a dominant share of the livestock genomic dataplaces market, driven by the presence of established biotechnology companies, advanced research infrastructure, and a strong focus on agricultural innovation. Europe follows closely, benefiting from supportive regulatory frameworks and robust investments in animal genetics. The Asia Pacific region is emerging as a high-growth market, propelled by rising livestock populations, increasing government support, and the rapid expansion of agritech startups. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as they gradually adopt genomic technologies to address productivity and sustainability challenges in livestock farming.

Component Analysis

The livestock genomic dataplaces market is segmented by component into software, services, and hardware, each playing a crucial role in the overall ecosystem. The software segment dominates the market, accounting for the largest share in 2024, as robust data management and analytical tools are essential for handling the massive volumes of genomic data generated in livestock research. These software solutions offer functionalities such as data storage, retrieval, visualization, and advanced analytics, enabling users to derive actionable insights from complex datasets. The integration of artificial intelligence and machine learning algorithms within these platforms further enhances their predictive capabilities, supporting informed decision-m

Raw Transcriptome and Metabolome Datasets, manuscript related supplemental table including standardized expression count tables, differential expression tables and co-expression network tables generated by WGCNA and consensus analysis, and bioinformatics analysis workflow.