Abstract Public databases are essential to the development of multi-omics resources. The amount of data created by biological technologies needs a systematic and organized form of storage, that can quickly be accessed, and managed. This is the objective of a biological database. Here, we present an overview of human databases with web applications. The databases and tools allow the search of biological sequences, genes and genomes, gene expression patterns, epigenetic variation, protein-protein interactions, variant frequency, regulatory elements, and comparative analysis between human and model organisms. Our goal is to provide an opportunity for exploring large datasets and analyzing the data for users with little or no programming skills. Public user-friendly web-based databases facilitate data mining and the search for information applicable to healthcare professionals. Besides, biological databases are essential to improve biomedical search sensitivity and efficiency and merge multiple datasets needed to share data and build global initiatives for the diagnosis, prognosis, and discovery of new treatments for genetic diseases. To show the databases at work, we present a a case study using ACE2 as example of a gene to be investigated. The analysis and the complete list of databases is available in the following website .

Bio Resource for array genes is a free online resource for easy access to collective and integrated information from various public biological resources for human, mouse, rat, fly and c. elegans genes. The resource includes information about the genes that are represented in Unigene clusters. This resource provides interactive tools to selectively view, analyze and interpret gene expression patterns against the background of gene and protein functional information. Different query options are provided to mine the biological relationships represented in the underlying database. Search button will take you to the list of query tools available. This Bio resource is a platform designed as an online resource to assist researchers in analyzing results of microarray experiments and developing a biological interpretation of the results. This site is mainly to interpret the unique gene expression patterns found as biological changes that can lead to new diagnostic procedures and drug targets. This interactive site allows users to selectively view a variety of information about gene functions that is stored in an underlying database. Although there are other online resources that provide a comprehensive annotation and summary of genes, this resource differs from these by further enabling researchers to mine biological relationships amongst the genes captured in the database using new query tools. Thus providing a unique way of interpreting the microarray data results based on the knowledge provided for the cellular roles of genes and proteins. A total of six different query tools are provided and each offer different search features, analysis options and different forms of display and visualization of data. The data is collected in relational database from public resources: Unigene, Locus link, OMIM, NCBI dbEST, protein domains from NCBI CDD, Gene Ontology, Pathways (Kegg, Genmapp and Biocarta) and BIND (Protein interactions). Data is dynamically collected and compiled twice a week from public databases. Search options offer capability to organize and cluster genes based on their Interactions in biological pathways, their association with Gene Ontology terms, Tissue/organ specific expression or any other user-chosen functional grouping of genes. A color coding scheme is used to highlight differential gene expression patterns against a background of gene functional information. Concept hierarchies (Anatomy and Diseases) of MESH (Medical Subject Heading) terms are used to organize and display the data related to Tissue specific expression and Diseases. Sponsors: BioRag database is maintained by the Bioinformatics group at Arizona Cancer Center. The material presented here is compiled from different public databases. BioRag is hosted by the Biotechnology Computing Facility of the University of Arizona. 2002,2003 University of Arizona.

This dataset is the result of experiments conducted using Python and rdkit library.

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

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

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

Since 2010, the European Molecular Biology Laboratory's (EMBL) Heidelberg laboratory and the European Bioinformatics Institute (EMBL-EBI) have jointly run bioinformatics training courses developed specifically for secondary school science teachers within Europe and EMBL member states. These courses focus on introducing bioinformatics, databases, and data-intensive biology, allowing participants to explore resources and providing classroom-ready materials to support them in sharing this new knowledge with their students.In this article, we chart our progress made in creating and running three bioinformatics training courses, including how the course resources are received by participants and how these, and bioinformatics in general, are subsequently used in the classroom. We assess the strengths and challenges of our approach, and share what we have learned through our interactions with European science teachers.

PROSITE is a database of protein families and domains. It consists of biologically significant sites, patterns and profiles that help to reliably identify to which known protein family a new sequence belongs. PROSITE is based at the Swiss Institute of Bioinformatics (SIB), Geneva, Switzerland.

jats:titleAbstract/jats:titlejats:pGenetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future./jats:p

The CATH-Gene3D database describes protein families and domain architectures in complete genomes. Protein families are formed using a Markov clustering algorithm, followed by multi-linkage clustering according to sequence identity. Mapping of predicted structure and sequence domains is undertaken using hidden Markov models libraries representing CATH and Pfam domains. CATH-Gene3D is based at University College, London, UK.

MicroRNAs (miRNA) are small endogenous RNA molecules, which regulate target gene expression at post-transcriptional level. Besides, miRNA activity can be controlled by a newly discovered regulatory mechanism called endogenous target mimicry (eTM). In target mimicry, eTMs bind to the corresponding miRNAs to block the binding of specific transcript leading to increase mRNA expression. Thus, miRNA-eTM-target-mRNA regulation modules involving a wide range of biological processes; an increasing need for a comprehensive eTM database arose. Except miRSponge with limited number of Arabidopsis eTM data no available database and/or repository was developed and released for plant eTMs yet. Here, we present an online plant eTM database, called PeTMbase (http://petmbase.org), with a highly efficient search tool. To establish the repository a number of identified eTMs was obtained utilizing from high-throughput RNA-sequencing data of 11 plant species. Each transcriptome libraries is first mapped to corresponding plant genome, then long non-coding RNA (lncRNA) transcripts are characterized. Furthermore, additional lncRNAs retrieved from GREENC and PNRD were incorporated into the lncRNA catalog. Then, utilizing the lncRNA and miRNA sources a total of 2,728 eTMs were successfully predicted. Our regularly updated database, PeTMbase, provides high quality information regarding miRNA:eTM modules and will aid functional genomics studies particularly, on miRNA regulatory networks.

MINT focuses on experimentally verified protein-protein interactions mined from the scientific literature by expert curators

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Bioinformatics Market Size 2025-2029

The bioinformatics market size is forecast to increase by USD 15.98 billion at a CAGR of 17.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the reduction in the cost of genetic sequencing and the development of advanced bioinformatics tools for Next-Generation Sequencing (NGS) technologies. These advancements have led to an increase in the volume and complexity of genomic data, necessitating the need for sophisticated bioinformatics solutions. However, the market faces challenges, primarily the shortage of trained laboratory professionals capable of handling and interpreting the vast amounts of data generated. This skills gap can hinder the effective implementation and utilization of bioinformatics tools, potentially limiting the market's growth potential.
Companies seeking to capitalize on market opportunities must focus on addressing this challenge by investing in training programs and collaborating with academic institutions. Additionally, data security, data privacy, and regulatory compliance are crucial aspects of the market, ensuring the protection and ethical use of sensitive biological data. Partnerships with technology providers and service organizations can help bridge the gap in expertise and resources, enabling organizations to leverage the power of bioinformatics for research and development, diagnostics, and personalized medicine applications.

What will be the Size of the Bioinformatics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is experiencing significant growth, driven by the increasing demand for precision medicine and the exploration of complex biological systems. Structural variation and gene regulation play crucial roles in gene networks and biological networks, necessitating advanced tools for SNP genotyping and statistical analysis. Precision medicine relies on the identification of mutations and biomarkers through mutation analysis and biomarker validation.
Metabolic networks, protein microarrays, CDNA microarrays, and RNA microarrays contribute to the discovery of new insights in evolutionary biology and conservation biology. The integration of these technologies enables a comprehensive understanding of gene regulation, gene networks, and metabolic pathways, ultimately leading to the development of novel therapeutics. Protein-protein interactions and signal transduction pathways are essential in understanding protein networks and metabolic pathways. Ontology mapping and predictive modeling facilitate data warehousing and data analytics in this field.

How is this Bioinformatics Industry segmented?

The bioinformatics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Molecular phylogenetics
  Transcriptomic
  Proteomics
  Metabolomics


Product

  Platforms
  Tools
  Services


End-user

  Pharmaceutical and biotechnology companies
  CROs and research institutes
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Application Insights

The molecular phylogenetics segment is estimated to witness significant growth during the forecast period. In the dynamic and innovative realm of bioinformatics, various technologies and techniques are shaping the future of research and development. Molecular phylogenetics, a significant branch of bioinformatics, employs molecular data to explore the evolutionary connections among species, offering enhanced insights into the intricacies of life. This technique has been instrumental in numerous research domains, such as drug discovery, disease diagnosis, and conservation biology. For instance, it plays a pivotal role in the study of viral evolution. By deciphering the molecular data of distinct virus strains, researchers can trace their evolutionary history and unravel their origins and transmission patterns.

Furthermore, the integration of proteomic technologies, network analysis, data integration, and systems biology is expanding the scope of bioinformatics research and applications. Bioinformatics services, open-source bioinformatics, and commercial bioinformatics software are vital components of the market, catering to the diverse needs of researchers, industries, and institutions. Bioinformatics databases, including sequence databases and bioinformatics algorithms, are indispensable resources for storing, accessing, and analyzing biological data. In the realm of personalized medicine and drug di

File S1. includes Tables S1 to S9 presented in separate tabs of an Excel spreadsheet, representing the input gene product list and the output of the GO term enrichment analyses from GO-Elite and Ontologizer Enrichment tools using the 2009 and 2012 annotation and Gene Ontology datasets for the differentially expressed gene products in the Baelde 2004 study. A description tab entitled ‘Tables S2–S9 Description’, has been included, defining the output from the GO-Elite and Ontologizer GO term enrichment tools presented in Tables S2–S9. Table S1. Input protein list. Mapping of the gene product identifiers from the Baelde 2004 study to UniProtKB accession numbers. Most of the differentially expressed gene products in DN glomeruli were mapped to a UniProtKB accession number and those that could not be mapped were not annotated and are not included in the table. Table S2. GO-Elite analysis on up-regulated proteins in DN glomeruli, using the 2009 Gene Ontology and annotation sets. Results from the GO-Elite enrichment analysis tool on the up-regulated proteins from the Baelde using the Gene Ontology and Annotation files from March 2009; showing significantly enriched GO terms. The boldface terms are referred to in the manuscript text. The “Study” column shows the number of proteins in the input list with an annotation to the given term. The “Population” column shows the number of proteins in the background list with an annotation to the given term. Table S3. Ontologizer analysis on up-regulated proteins in DN glomeruli, using the 2009 Gene Ontology and annotation sets. Results from the Ontologizer enrichment analysis tool on the up-regulated proteins from the Baelde list, using the Gene Ontology and Annotation files from March 2009; showing significantly enriched GO terms. The boldface terms are referred to in the manuscript text. The “Population” column shows the number of proteins in the background list with an annotation to the given term. The “Study” column shows the number of proteins in the input list with an annotation to the given term. Table S4. GO-Elite analysis on down-regulated proteins in DN glomeruli, using the 2009 Gene Ontology and annotation sets. Results from the GO-Elite enrichment analysis tool on the down-regulated proteins from the Baelde list, using the Gene Ontology and Annotation files from March 2009; showing significantly enriched GO terms. The “Study” column shows the number of proteins in the input list with an annotation to the given term. The “Population” column shows the number of proteins in the background list with an annotation to the given term. Table S5. Ontologizer analysis on down-regulated proteins in DN glomeruli, using the 2009 Gene Ontology and annotation sets. Results from the Ontologizer enrichment analysis tool on the down-regulated proteins from the Baelde list, using the Gene Ontology and Annotation files from March 2009; showing significantly enriched GO terms. The boldface terms are referred to in the manuscript text. The “Population” column shows the number of proteins in the background list with an annotation to the given term. The “Study” column shows the number of proteins in the input list with an annotation to the given term. Table S6. GO-Elite analysis on up-regulated proteins in DN glomeruli, using the 2012 Gene Ontology and annotation sets. Results from the GO-Elite enrichment analysis tool on the up-regulated proteins from the Baelde list, using the Gene Ontology and Annotation files from March 2012; showing significantly enriched GO terms. The boldface terms are referred to in the manuscript text and the italicized boldface indicates new terms created during the Renal GO Annotation Initiative. The “Study” column shows the number of proteins in the input list with an annotation to the given term. The “Population” column shows the number of proteins in the background list with an annotation to the given term. Table S7. Ontologizer analysis on up-regulated proteins in DN glomeruli, using the 2012 Gene Ontology and annotation sets. Results from the Ontologizer enrichment analysis tool on the up-regulated proteins from the Baelde list, using the Gene Ontology and Annotation files from March 2012; showing significantly enriched GO terms. The boldface terms are referred to in the manuscript text and the italicized boldface indicates new terms created during the Renal GO Annotation Initiative. The “Population” column shows the number of proteins in the background list with an annotation to the given term. The “Study” column shows the number of proteins in the input list with an annotation to the given term. Table S8. GO-Elite analysis on down-regulated proteins in DN glomeruli, using the 2012 Gene Ontology and annotation sets. Results from the GO-Elite enrichment analysis tool on the down-regulated proteins from the Baelde list, using the Gene Ontology and Annotation files from March 2012; showing significantly enriched GO terms. The boldface terms are referred to in the manuscript text and the italicized boldface indicates new terms created during the Renal GO Annotation Initiative. The “Study” column shows the number of proteins in the input list with an annotation to the given term. The “Population” column shows the number of proteins in the background list with an annotation to the given term. Table S9. Ontologizer analysis on down-regulated proteins in DN glomeruli, using the 2012 Gene Ontology and annotation sets. Results from the Ontologizer enrichment analysis tool on the down-regulated proteins from the Baelde list, using the Gene Ontology and Annotation files from March 2012; showing significantly enriched GO terms. The boldface terms are referred to in the manuscript text and the italicized boldface indicates new terms created during the Renal GO Annotation Initiative. The “Population” column shows the number of proteins in the background list with an annotation to the given term. The “Study” column shows the number of proteins in the input list with an annotation to the given term. (XLSX)

SMART (a Simple Modular Architecture Research Tool) allows the identification and annotation of genetically mobile domains and the analysis of domain architectures. SMART is based at EMBL, Heidelberg, Germany.

This archive contains XSLT transformations from EML v. 2.1.1 and v. 2.1.0 to Pensoft data paper format.

Computational and Structural Biotechnology Journal Impact Factor 2024-2025 - ResearchHelpDesk - Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology The journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence, and enables the rapid publication of papers under the following categories: Research articles Review articles Mini Reviews Highlights Communications Software/Web server articles Methods articles Database articles Book Reviews Meeting Reviews

A summary of the significantly enriched GO terms from the Ontologizer [28] and GO-Elite [27] analyses, which are relevant to kidney development, using the pre-annotation (2009; Tables S2–S5 in File S1) and post-annotation datasets (2012; Tables S6–S9, in File S1). Terms in italics indicate parent terms where the descendants are indicated directly underneath as follows: > descendant of term above in italics. Rank refers to the position of the term in the results of the enrichment analyses (see Tables S2–S9 in File S1) where significance of the enriched term has a p-value of

NCBIfam is a collection of protein families, featuring curated multiple sequence alignments, hidden Markov models (HMMs) and annotation, which provides a tool for identifying functionally related proteins based on sequence homology. NCBIfam is maintained at the National Center for Biotechnology Information (Bethesda, MD). NCBIfam includes models from TIGRFAMs, another database of protein families developed at The Institute for Genomic Research, then at the J. Craig Venter Institute (Rockville, MD, US).

This is a table of results from comparison of the annotated proteins from nine different mycobacterial species. The goal of its creation was to suggest which proteins are likely to have identical functions between species. This table reports only those protein comparisons with greater than 75% amino acid identity. Each row is a different gene used to search for close matches, each column is the genome used for searching. In parentheses next to the name of each match is the percent identity between the sequences (query vs each match).

Supplementary material A.3 for the paper 'IDPredictor: predict database links in biomedical database'. Abstract: Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological knowledge is worldwide represented in a network of databases. These data are spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail, interface, formats and data structure. To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in information retrieval environments an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of providing a comprehensive knowledge excerpt out of the interlinked databases. A prerequisit for supporting the concept of an integrated data view is the to acquiring insights into cross-references among database entities. But only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems. In this work, we investigate to what extend an automated construction of an integrated data network is possible. We propose a method that predict and extracts cross-references from multiple life science databases and thier possible referenced data targets. We study the retrieval quality of our method and the relationship between manually crafted relevance ranking and relevance ranking based on cross-references, and report on first, promising results.

Bioinformatics software: Bioinformatics software is used to manage, analyze, and interpret biological data.Bioinformatics databases: Bioinformatics databases contain a wealth of information on biological sequences, structures, and functions.Bioinformatics services: Bioinformatics services provide expert analysis and interpretation of biological data. Recent developments include: , May 2022: Bruker Corporation and TOFWERK AG established a collaborative relationship for high-speed, ultra-sensitive applied and industrial analytical solutions, together with a Bruker minority investment in TOWERK. The relationship serves as a foundation for technological partnerships to enhance instrument capabilities and for the creation of novel analytical applications where high speed and ultra-sensitivity matter., April 2020: A Next-Generation Sequencing (NGS)-based test called StrandAdvantage500, which was unveiled by Healthcare Global Enterprises and Strand Life Sciences, evaluates cancer-relevant genetic alterations using DNA and RNA extracted from a patient's tumour in a single integrated workflow.. Key drivers for this market are: Growing demand for protein synthesis to propel market growth.