snapshot-tab-pane Bioinformatics Market Size 2025-2029The 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 InsightsNorth 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 2023By Product - Platforms segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 309.88 million Market Future Opportunities 2024: USD 15978.00 millionCAGR from 2024 to 2029 : 17.4%Market SummaryThe 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 LandscapeBioinformatics, 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 fun

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.

This study developed a computational tool with a graphical interface and a web-service that allows the identification of phage regions through homology search and gene clustering. It uses G+C content variation evaluation and tRNA prediction sites as evidence to reinforce the presence of prophages in indeterminate regions. Also, it performs the functional characterization of the prophages regions through data integration of biological databases. The performance of PhageWeb was compared to other available tools (PHASTER, Prophinder, and PhiSpy) using Sensitivity (Sn) and Positive Predictive Value (PPV) tests. As a reference for the tests, more than 80 manually annotated genomes were used. In the PhageWeb analysis, the Sn index was 86.1% and the PPV was approximately 87%, while the second best tool presented Sn and PPV values of 83.3 and 86.5%, respectively. These numbers allowed us to observe a greater precision in the regions identified by PhageWeb while compared to other prediction tools submitted to the same tests. Additionally, PhageWeb was much faster than the other computational alternatives, decreasing the processing time to approximately one-ninth of the time required by the second best software. PhageWeb is freely available at http://computationalbiology.ufpa.br/phageweb.

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).

Raw data of ‘Mitochondrial glutamine import sustains electron transport chain integrity independent of glutaminolysis in cancer’

An information extracting and processing package for biological literature that can be used online or installed locally via a downloadable software package, http://www.textpresso.org/downloads.html Textpresso's two major elements are (1) access to full text, so that entire articles can be searched, and (2) introduction of categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., methods, etc). A search engine enables the user to search for one or a combination of these categories and/or keywords within an entire literature. The Textpresso project serves the biological and biomedical research community by providing: * Full text literature searches of model organism research and subject-specific articles at individual sites. Major elements of these search engines are (1) access to full text, so that the entire content of articles can be searched, and (2) search capabilities using categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or identify one (e.g., cell, gene, allele, etc). The search engines are flexible, enabling users to query the entire literature using keywords, one or more categories or a combination of keywords and categories. * Text classification and mining of biomedical literature for database curation. They help database curators to identify and extract biological entities and facts from the full text of research articles. Examples of entity identification and extraction include new allele and gene names and human disease gene orthologs; examples of fact identification and extraction include sentence retrieval for curating gene-gene regulation, Gene Ontology (GO) cellular components and GO molecular function annotations. In addition they classify papers according to curation needs. They employ a variety of methods such as hidden Markov models, support vector machines, conditional random fields and pattern matches. Our collaborators include WormBase, FlyBase, SGD, TAIR, dictyBase and the Neuroscience Information Framework. They are looking forward to collaborating with more model organism databases and projects. * Linking biological entities in PDF and online journal articles to online databases. They have established a journal article mark-up pipeline that links select content of Genetics journal articles to model organism databases such as WormBase and SGD. The entity markup pipeline links over nine classes of objects including genes, proteins, alleles, phenotypes, and anatomical terms to the appropriate page at each database. The first article published with online and PDF-embedded hyperlinks to WormBase appeared in the September 2009 issue of Genetics. As of January 2011, we have processed around 70 articles, to be continued indefinitely. Extension of this pipeline to other journals and model organism databases is planned. Textpresso is useful as a search engine for researchers as well as a curation tool. It was developed as a part of WormBase and is used extensively by C. elegans curators. Textpresso has currently been implemented for 24 different literatures, among them Neuroscience, and can readily be extended to other corpora of text.

Of interest to pharmaceutical, nutritional, and biomedical researchers, as well as individuals and companies involved with alternative therapies and and herbal products, this database is one of the world's leading repositories of ethnobotanical data, evolving out of the extensive compilations by the former Chief of USDA's Economic Botany Laboratory in the Agricultural Research Service in Beltsville, Maryland, in particular his popular Handbook of phytochemical constituents of GRAS herbs and other economic plants (CRC Press, Boca Raton, FL, 1992). In addition to Duke's own publications, the database documents phytochemical information and quantitative data collected over many years through research results presented at meetings and symposia, and findings from the published scientific literature. The current Phytochemical and Ethnobotanical databases facilitate plant, chemical, bioactivity, and ethnobotany searches. A large number of plants and their chemical profiles are covered, and data are structured to support browsing and searching in several user-focused ways. For example, users can get a list of chemicals and activities for a specific plant of interest, using either its scientific or common name download a list of chemicals and their known activities in PDF or spreadsheet form find plants with chemicals known for a specific biological activity display a list of chemicals with their LD toxicity data find plants with potential cancer-preventing activity display a list of plants for a given ethnobotanical use find out which plants have the highest levels of a specific chemical References to the supporting scientific publications are provided for each specific result. Resources in this dataset: Resource Title: Duke-Source-CSV.zip. File Name: Duke-Source-CSV.zipResource Description: Dr. Duke's Phytochemistry and Ethnobotany - raw database tables for archival purposes. Visit https://phytochem.nal.usda.gov/phytochem/search for the interactive web version of the database. Resource Title: Data Dictionary (preliminary). File Name: DrDukesDatabaseDataDictionary-prelim.csvResource Description: This Data Dictionary describes the columns for each table. [Note that this is in progress and some variables are yet to be defined or are unused in the current implementation. Please send comments/suggestions to nal-adc-curator@ars.usda.gov ]

Abstract:

These Phytochemical and Ethnobotanical databases offer convenient search functionalities for plants, chemicals, bioactivity, and ethnobotany. They cover a wide range of plants and their chemical profiles, allowing users to browse and search in various user-oriented ways. This is a resource that caters to pharmaceutical, biomedical, and nutritional researchers, looking to improve the treatment of diseases in a natural way. The data originates from extensive compilations by a former Chief of USDA's Economic Botany Laboratory, specifically their Handbook of phytochemical constituents of GRAS herbs and other economic plants. Users can download a PDF or spreadsheet format containing chemical lists and their known activities.

Instruction:

Data was cleaned and duplicates were removed.

Inspiration:

The dataset was uploaded to UBRITE for "DGR_DEPOT” summer 2023 team project.

Acknowledgements:

Duke, J. A. (1992). Database of Biologically Active Phytochemicals and Their Activity. Boca Raton, Fla: CRC Press. ISBN 9780849336713. 183 pp. [Available on diskette with manual. https://www.crcpress.com/Database-of-Biologically-Active-Phytochemicals-... ]

U-BRITE Last Updated July 5, 2023

Context

PubMed Knowledge Graph Datasets http://er.tacc.utexas.edu/datasets/ped

Content

Dataset Name : PKG2020S4 (1781-Dec. 2020), Version 4 The new version PKG, PKG2020S4 (1781-Dec. 2020), updated the previous PKG version with PubMed 2021 baseline files, PubMed daily updates files (up to Jan. 4th 2021), and extracted bio-entities, author disambiguation results, extended author information, Scimago that containing journal information, and WOS citations which contains reference relations between PMID and reference PMID and extracted from WOS.

Database Features: 1-PKG2020S4 (1781-Dec. 2020) Features.pdf (https://web.corral.tacc.utexas.edu/dive_datasets/PKG2020S4/PKG2020S4_MySQL/1-PKG2020S4%20(1781-Dec.%202020)%20Features.pdf) Database Description: 2-PKG2020S4 (1781-Dec. 2020) Database Description.pdf (https://web.corral.tacc.utexas.edu/dive_datasets/PKG2020S4/PKG2020S4_MySQL/2-PKG2020S4%20(1781-Dec.%202020)%20Database%20Description.pdf)

Acknowledgements

http://er.tacc.utexas.edu/datasets/ped

Inspiration

http://er.tacc.utexas.edu/datasets/ped

Location of RYR2 Associated CPVT Variants Dataset Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a rare inherited arrhythmia caused by pathogenic RYR2 variants. CPVT is characterized by exercise/stress-induced syncope and cardiac arrest in the absence of resting ECG and structural cardiac abnormalities. Here, we present a database collected from 225 clinical papers, published from 2001-October 2020, about CPVT associated RYR2 variants. 1355 patients, both with and without CPVT, with RYR2 variants are in the database. There are a total of 968 CPVT patients or suspected CPVT patients in the database. The database includes information regarding genetic diagnosis, location of the RYR2 variant(s), clinical history and presentation, and treatment strategies for each patient. Patients will have a varying depth of information in each of the provided fields. Database website: https://cpvtdb.port5000.com/ Dataset Information This dataset includes: eTable2.xlsx Tabular version of the database Most relevant tables in the PostgreSQL database regarding patient sex, conditions, treatments, family history, and variant information were joined to create this database Views calculating the affected RYR2 exons, domains and subdomains have been joined to patient information m-n tables for patient's conditions and treatments have been converted to pivot tables - every condition and treatment that has at least 1 person with that condition or treatment is a column. NOTE: This was created using a LEFT JOIN of individuals and individual_variants tables. Individuals with more than 1 recorded variant will be listed on multiple rows. There is only 1 person in this database as of the current version with multiple recorded variants _.gz.sql PostgreSQL database dump Expands to about 4.1 GB after loading the database dump The database includes two schemas: public: Includes all information in patients and variants Also includes all RYR2 variants in ClinVar uta: Contains the biocommons/uta database required to make the hgvs Python package to work locally See https://github.com/biocommons/uta for more information NOTE: It is recommended to use this version of the database only for development or analysis purposes database_tables.pdf Contains information on most of the database tables in the public schema 00_globals.sql Required to load the PostgreSQL database dump Creates a user named anonymous for the uta schema How To Load Database Using Docker First, download the 00_globals.sql and _.gz.sql file and move it into a directory. The default postgres image will load files from the /docker-entrypoint-initdb.d directory if the database is empty. See Docker Hub for more information. Example using docker compose with pgadmin and a volume to persist the data. # Use postgres/example user/password credentials version: '3.9' volumes: mydatabasevolume: null services: db: image: postgres:16 restart: always environment: POSTGRES_PASSWORD: mysecretpassword POSTGRES_USER: postgres volumes: - ':/docker-entrypoint-initdb.d/' - 'mydatabasevolume:/var/lib/postgresql/data' pgadmin: image: dpage/pgadmin4 environment: PGADMIN_DEFAULT_EMAIL: user@domain.com PGADMIN_DEFAULT_PASSWORD: SuperSecret Creating the Database from Scratch See https://github.com/alexdaiii/cpvt-database-loader for source code to create the database from scratch.

Additional tables, graphs and details. Tables, graphs and details about the used dataset, experiment design and complete results are provided in this document. (PDF)

Pursuant to the provisions of Minnesota Statutes, section 103G.223, this database contains points that represent calcareous fens as defined in Minnesota Rules, part 8420.0935, subpart 2. These calcareous fens have been identified by the commissioner by written order published in the State Register on June 2, 2008 (32 SR 2148-2154), August 31, 2009 (34 SR 278) and December 7, 2009 (34 SR 823-824). The current list of fens (DNR List of Known Calcareous Fens) is posted on the DNR's web site at: http://files.dnr.state.mn.us/eco/wetlands/calcareous_fen_list.pdf

This data set is a GIS point shapefile derived from the Natural Heritage "Biotics" Database. Data in the Biotics Database are maintained according to established Natural Heritage Methodology developed by NatureServe and The Nature Conservancy. The core of the Biotics Database is made up of Element Occurrence (EO) records of rare plant and animal species, animal aggregations, native plant communities, and geologic features. An Element is a unit of biological diversity, such as a species, subspecies, or a native plant community. An EO is an area of land and/or water in which an Element is, or was, present, and which has practical conservation value for the Element (e.g. species or community) as evidenced by potential continued (or historical) presence and/or regular recurrence at a given location. Source Features are the mapped representation of observations of rare features. Source Features are then evaluated using biological standards, and grouped into EOs as appropriate.

This data set contains a point for each Calcareous Fen (DNR List of Known Calcareous Fens) Source Feature in the Biotics database, and selected attributes from the Source Feature record and it’s linked Element Occurrence (EO) record.

snapshot-tab-pane AI In Genomics Market Size 2025-2029The ai in genomics market size is valued to increase by USD 1.73 billion, at a CAGR of 32.6% from 2024 to 2029. Precipitous decline in sequencing costs and subsequent genomic data will drive the ai in genomics market.Market InsightsEurope dominated the market and accounted for a 32% growth during the 2025-2029.By Component - Software segment was valued at USD 87.00 billion in 2023By Technology - Machine learning segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 1.00 million Market Future Opportunities 2024: USD 1729.20 millionCAGR from 2024 to 2029 : 32.6%Market SummaryThe market is experiencing significant growth due to the precipitous decline in sequencing costs and subsequent genomic data proliferation. This data deluge is driving the need for advanced analytical tools to make sense of the complex genetic information. Enter generative AI and foundation models, which are increasingly being adopted in the biological domain to analyze and interpret genomic data. These models can identify patterns, make predictions, and even generate new sequences, revolutionizing research and development in genomics. However, the implementation of AI in genomics is not without challenges. The labyrinth of data privacy, security, and complex regulatory frameworks presents significant hurdles.For instance, in a pharmaceutical company, AI is used to optimize the supply chain by predicting demand for specific genetic therapies. This involves analyzing vast amounts of patient data, raising concerns around data security and privacy. Additionally, regulatory compliance adds another layer of complexity, requiring stringent data handling protocols. Despite these challenges, the potential benefits of AI in genomics are immense, from accelerating drug discovery to improving patient outcomes. The future of genomics lies in harnessing the power of AI to unlock the secrets of the human genome.What will be the size of the AI In Genomics Market during the forecast period?Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, revolutionizing various sectors such as comparative genomics, population genetics studies, and infectious disease genomics. Big data analytics plays a pivotal role in processing vast genomic data, enabling faster and more accurate discoveries. Microbial genomics, cancer genomics, and structural genomics are among the fields benefiting from advanced algorithm optimization and high-performance computing. In the realm of human genomics, data mining methods and statistical genetics methods uncover hidden patterns and correlations, while explainable AI methods ensure transparency and interpretability. Parallel computing and predictive modeling enable real-time analysis and model validation techniques ensure accuracy.Variant annotation databases facilitate quicker identification of genetic mutations, contributing to personalized medicine and diagnostics. Cloud computing platforms provide scalable and cost-effective genomic data storage solutions, ensuring easy access to data for researchers and clinicians. Synthetic biology and plant genomics also gain from AI, with applications ranging from gene editing to crop improvement. Data sharing initiatives foster collaboration and accelerate research progress. In the boardroom, AI in Genomics translates to significant improvements in research efficiency and accuracy. For instance, companies have reported a substantial reduction in processing time, enabling them to bring products to market faster and stay competitive.The integration of AI in genomics is a strategic investment, offering potential cost savings, increased productivity, and improved patient outcomes.Unpacking the AI In Genomics Market LandscapeIn the dynamic realm of genomics, Artificial Intelligence (AI) is revolutionizing various applications, including genotype-phenotype association and therapeutic target validation. AI-driven solutions enable a 30% increase in efficiency compared to traditional methods, resulting in accelerated research and development. CRISPR gene editing benefits from AI integration, achieving a 25% improvement in precision and accuracy. Data security measures are reinforced through AI's ability to monitor and analyze access patterns, reducing potential breaches by 40%. Bioinformatics pipelines, diagnostics test development, and machine learning algorithms leverage AI for enhanced performance and accuracy. Protein-protein interactions, epigenetic modifications, and systems biology modeling gain new insights through AI-powered analysis. Personalized medicine approaches, gene expression profiling, and protein structure prediction are transformed by AI, leading to improved

Bacteriophages are the most prevalent biological entities in the biosphere. However, limitations in both medical relevance and sequencing technologies have led to a systematic underestimation of the genetic diversity within phages. This underrepresentation not only creates a significant gap in our understanding of phage roles across diverse biosystems but also introduces biases in computational models reliant on these data for training and testing. In this study, we focused on publicly available genomes of bacteriophages infecting high-priority ESKAPE pathogens to show the extent and impact of this underrepresentation. First, we demonstrate a stark underrepresentation of ESKAPE phage genomes within the public genome and protein databases. Next, a pangenome analysis of these ESKAPE phages reveals extensive sharing of core genes among phages infecting the same host. Furthermore, genome analyses and clustering highlight close nucleotide-level relationships among the ESKAPE phages, raising concerns about the limited diversity within current public databases. Lastly, we uncover a scarcity of unique lytic phages and phage proteins with antimicrobial activities against ESKAPE pathogens. This comprehensive analysis of the ESKAPE phages underscores the severity of underrepresentation and its potential implications. This lack of diversity in phage genomes may restrict the resurgence of phage therapy and cause biased outcomes in data-driven computational models due to incomplete and unbalanced biological datasets.

The current and future consequences of anthropogenic impacts such as climate change and habitat loss on ecosystems will be better understood and therefore addressed if diverse ecological data from multiple environmental contexts are more effectively shared. Re-use requires that data are readily available to the scientific scrutiny of the research community. A number of repositories to store shared data have emerged in different ecological domains and developments are underway to define common data and metadata standards. Nevertheless, the goal is far from being achieved and many challenges still need to be addressed. The definition of best practices for data sharing and re-use can benefit from the experience accumulated by pilot collaborative projects. The Euromammals bottom-up initiative has pioneered collaborative science in spatial animal ecology since 2007. It involves more than 150 institutes to address scientific, management and conservation questions regarding terrestrial mammal species in Europe using data stored in a shared database. In this manuscript we present some key lessons that we have learnt from the process of making shared data and knowledge accessible to researchers and we stress the importance of data management for data quality assurance. We suggest putting in place a pro-active data review before data are made available in shared repositories via robust technical support and users’ training in data management and standards. We recommend pursuing the definition of common data collection protocols, data and metadata standards, and shared vocabularies with direct involvement of the community to boost their implementation. We stress the importance of knowledge sharing, in addition to data sharing. We show the crucial relevance of collaborative networking with pro-active involvement of data providers in all stages of the scientific process. Our main message is that for data-sharing collaborative efforts to obtain substantial and durable scientific returns, the goals should not only consist in the creation of e-infrastructures and software tools but primarily in the establishment of a network and community trust. This requires moderate investment, but over long-term horizons.

A growing body of evidence indicates that the immune system plays a central role in sepsis. By analyzing immune genes, we sought to establish a robust gene signature and develop a nomogram that could predict mortality in patients with sepsis. Herein, data were extracted from the Gene Expression Omnibus and Biological Information Database of Sepsis (BIDOS) databases. We enrolled 479 participants with complete survival data using the GSE65682 dataset, and grouped them randomly into training (n = 240) and internal validation (n = 239) sets based on a 1:1 proportion. GSE95233 was set as the external validation dataset (n=51). We validated the expression and prognostic value of the immune genes using the BIDOS database. We established a prognostic immune genes signature (including ADRB2, CTSG, CX3CR1, CXCR6, IL4R, LTB, and TMSB10) via LASSO and Cox regression analyses in the training set. Based on the training and validation sets, the Receiver Operating Characteristic curves and Kaplan-Meier analysis revealed that the immune risk signature has good predictive power in predicting sepsis mortality risk. The external validation cases also showed that mortality rates in the high-risk group were higher than those in the low-risk group. Subsequently, a nomogram integrating the combined immune risk score and other clinical features was developed. Finally, a web-based calculator was built to facilitate a convenient clinical application of the nomogram. In summary, the signature based on the immune gene holds potential as a novel prognostic predictor for sepsis.

BackgroundHepatocellular carcinoma (HCC) is an extremely malignant cancer with poor survival. H2AFY gene encodes for a variant of H2A histone, and it has been found to be dysregulated in various tumors. However, the clinical value, biological functions and correlations with immune infiltration of H2AFY in HCC remain unclear.MethodsWe analyzed the expression and clinical significance of H2AFY in HCC using multiple databases, including Oncomine, HCCDB, TCGA, ICGC, and so on. The genetic alterations of H2AFY were analyzed by cBioPortal and COSMIC databases. Co-expression networks of H2AFY and its regulators were investigated by LinkedOmics. The correlations between H2AFY and tumor immune infiltration were explored using TIMER, TISIDB databases, and CIBERSORT method. Finally, H2AFY was knocked down with shRNA lentiviruses in HCC cell lines for functional assays in vitro.ResultsH2AFY expression was upregulated in the HCC tissues and cells. Kaplan–Meier and Cox regression analyses revealed that high H2AFY expression was an independent prognostic factor for poor survival in HCC patients. Functional network analysis indicated that H2AFY and its co-expressed genes regulates cell cycle, mitosis, spliceosome and chromatin assembly through pathways involving many cancer-related kinases and E2F family. Furthermore, we observed significant correlations between H2AFY expression and immune infiltration in HCC. H2AFY knockdown suppressed the cell proliferation and migration, promoted cycle arrest, and apoptosis of HCC cells in vitro.ConclusionOur study revealed that H2AFY is a potential biomarker for unfavorable prognosis and correlates with immune infiltration in HCC.

Malignant glioma is the most common solid tumor of the adult brain, with high lethality and poor prognosis. Hence, identifying novel and reliable biomarkers can be advantageous for diagnosing and treating glioma. Several galectins encoded by LGALS genes have recently been reported to participate in the development and progression of various tumors; however, their detailed role in glioma progression remains unclear. Herein, we analyzed the expression and survival curves of all LGALS across 2,217 patients with glioma using The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Rembrandt databases. By performing multivariate Cox analysis, we built a survival model containing LGALS1, LGALS3, LGALS3BP, LGALS8, and LGALS9 using TCGA database. The prognostic power of this panel was assessed using CGGA and Rembrandt datasets. ESTIMATE and CIBERSORT algorithms confirmed that patients in high-risk groups exhibited significant stromal and immune cell infiltration, immunosuppression, mesenchymal subtype, and isocitrate dehydrogenase 1 (IDH1) wild type. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), CancerSEA, and Gene Set Enrichment Analysis (GSEA) showed that pathways related to hypoxia, epithelial-to-mesenchymal transition (EMT), stemness, and inflammation were enriched in the high-risk group. To further elucidate the function of LGALS in glioma, we performed immunohistochemical staining of tissue microarrays (TMAs), Western blotting, and cell viability, sphere formation, and limiting dilution assays following lentiviral short hairpin RNA (shRNA)-mediated LGALS knockdown. We observed that LGALS expression was upregulated in gliomas at both protein and mRNA levels. LGALS could promote the stemness maintenance of glioma stem cells (GSCs) and positively correlate with M2-tumor-associated macrophages (TAMs) infiltration. In conclusion, we established a reliable survival model for patients with glioma based on LGALS expression and revealed the essential roles of LGALS genes in tumor growth, immunosuppression, stemness maintenance, pro-neural to mesenchymal transition, and hypoxia in glioma.

BackgroundDERL3 has been implicated as an essential element in the degradation of misfolded lumenal glycoproteins induced by endoplasmic reticulum (ER) stress. However, the correlation of DERL3 expression with the malignant phenotype of lung adenocarcinoma (LUAD) cells is unclear and remains to be elucidated. Herein, we investigated the interaction between the DERL3 and LUAD pathological process.MethodsThe Cancer Genome Atlas (TCGA) database was utilized to determine the genetic alteration of DERL3 in stage I LUAD. Clinical LUAD samples including carcinoma and adjacent tissues were obtained and were further extracted to detect DERL3 mRNA expression via RT-qPCR. Immunohistochemistry was performed to evaluate the protein expression of DERL3 in LUAD tissues. The GEPIA and TIMER website were used to evaluate the correlation between DERL3 and immune cell infiltration. We further used the t-SNE map to visualize the distribution of DERL3 in various clusters at the single-cell level via TISCH database. The potential mechanisms of the biological process mediated by DERL3 in LUAD were conducted via KEGG and GSEA.ResultsIt was indicated that DERL3 was predominantly elevated in carcinoma compared with adjacent tissues in multiple kinds of tumors from the TCGA database, especially in LUAD. Immunohistochemistry validated that DERL3 was also upregulated in LUAD tissues compared with adjacent tissues from individuals. DERL3 was preliminarily found to be associated with immune infiltration via the TIMER database. Further, the t-SNE map revealed that DERL3 was predominantly enriched in plasma cells of the B cell population. It was demonstrated that DERL3 high-expressed patients presented significantly worse response to chemotherapy and immunotherapy. GSEA and KEGG results indicated that DERL3 was positively correlated with B cell activation and unfolded protein response (UPR).ConclusionOur findings indicated that DERL3 might play an essential role in the endoplasmic reticulum-associated degradation (ERAD) process in LUAD. Moreover, DERL3 may act as a promising immune biomarker, which could predict the efficacy of immunotherapy in LUAD.