Database of three-dimensional structures of macromolecules that allows the user to retrieve structures for specific molecule types as well as structures for genes and proteins of interest. Three main databases comprise Structure-The Molecular Modeling Database; Conserved Domains and Protein Classification; and the BioSystems Database. Structure also links to the PubChem databases to connect biological activity data to the macromolecular structures. Users can locate structural templates for proteins and interactively view structures and sequence data to closely examine sequence-structure relationships. * Macromolecular structures: The three-dimensional structures of biomolecules provide a wealth of information on their biological function and evolutionary relationships. The Molecular Modeling Database (MMDB), as part of the Entrez system, facilitates access to structure data by connecting them with associated literature, protein and nucleic acid sequences, chemicals, biomolecular interactions, and more. It is possible, for example, to find 3D structures for homologs of a protein of interest by following the Related Structure link in an Entrez Protein sequence record. * Conserved domains and protein classification: Conserved domains are functional units within a protein that act as building blocks in molecular evolution and recombine in various arrangements to make proteins with different functions. The Conserved Domain Database (CDD) brings together several collections of multiple sequence alignments representing conserved domains, in addition to NCBI-curated domains that use 3D-structure information explicitly to define domain boundaries and provide insights into sequence/structure/function relationships. * Small molecules and their biological activity: The PubChem project provides information on the biological activities of small molecules and is a component of NIH''''s Molecular Libraries Roadmap Initiative. PubChem includes three databases: PCSubstance, PCBioAssay, and PCCompound. The PubChem data are linked to other data types (illustrated example) in the Entrez system, making it possible, for example, to retrieve information about a compound and then Link to its biological activity data, retrieve 3D protein structures bound to the compound and interactively view their active sites, and find biosystems that include the compound as a component. * Biological Systems: A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. The NCBI BioSystems Database provides centralized access to biological pathways from several source databases and connects the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system. BioSystem records list and categorize components (illustrated example), such as the genes, proteins, and small molecules involved in a biological system. The companion FLink icon FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems.

Supplemental tables associated to manuscript: Supplemental Table 1. Text mining of the 525 ranked genes found in PUBMED with symptom keywords: this Table describes the 525 ranked genes obtained by text mining with the ‘Génie’ algorithm against the three MESH terms: biliary inflammation, biliary fibrosis and biliary stasis. The respective ranks, p-values and PMID identifiers collected for each gene and each MESH term are presented. Supplemental Table 2. Symptom-related genes found to be differentially expressed in livers from primary sclerosing cholangitis patients: Differentially expressed gene analysis results (logarithmic Fold Change, Average Expression and Adjusted p-values) are presented with respective machine learning predictive scores for supervised sample categories from the GSE61256 dataset (PSC versus other liver samples). Supplemental Table 3. Pavlidis Template Matching for FXR dependency in the liver transcriptome. Results of Pavlidis template analysis used to describe the FXR regulation dependency of PSC genes in liver samples from the GSE54557 dataset. For each gene, the R-Pearson correlation coefficient and its respective p-value are presented in this Table. Supplemental Table 4. Best one hundred genes found to be significant in the pseudotime trajectory of Abcb4-/- cholangiocytes. Best one hundred ranked genes found to be significant on the pseudotime cell trajectory of Abcb4-/- cholangiocytes based on the alternative expression of Nr0b2 and Sox9 in GSE168758.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 18, 2014. A database for structural and functional information on various protein sites (post-translational modification, catalytic active, organic and inorganic ligand binding, protein-protein, protein-DNA and protein-RNA interactions) in the Protein Data Bank (PDB). It was developed as a daughter database accumulating the data on functional and structural characteristics of functional sites stored in PDB, as well as their spatial surroundings. It consists of functional sites extracted from PDB using the SITE records and of an additional set containing the protein interaction sites inferred from the contact residues in heterocomplexes. The PDBSite was set up by automated processing of the PDB. The PDBSite database can be queried through the functional description and the structural characteristics of the site and its environment. The PDBSite is integrated with the PDBSiteScan tool allowing structural comparisons of a protein against the functional sites. The PDBSite enables the recognition of functional sites in protein tertiary structures, providing annotation of function through structure. The Protein Data Bank (PDB) contains data on the spatial protein structures and their biologically active sites (i.e., ligand binding regions, enzyme catalytic centers, regions subjected to biochemical modifications, etc.). However, neither of the well known systems searching PDB does not provide the user with possibility to make the queries related with the active sites. A database PDBSITE storing the data on biologically active sites contained in the PDB database has been developed. PDBSITE accumulates amino acid content, structure features calculated by spatial protein structures, and physicochemical properties of sites and their spatial surroundings. The data on biologically active protein sites are of extreme importance for solving many problems in molecular biology, biotechnology, and medicine. High specificity of biological activity in proteins is produced by unique structure of active sites that are often organized by a very complicate pattern. In particular, biologically active sites in proteins are often compiled out of remote by primary structure amino acid residues, which form compact clusters in the spatial structure with strictly ordered conformation. Specific structure and conformational parameters of these sites are determined by the structure of their spatial amino acid surroundings. For example, spatial amino acid surroundings of enzyme catalytic centres determine the relief of hollows in catalytic centres of enzymes in a substrate binding regions, whereas the residues of antigen determinants of proteins determine their structure by organizing prominent parts at the protein surface. For many natural and mutant proteins, the relationships were found between protein activity and physico-chemical properties of amino acid residues composing the local surroundings of a functional site. The spatial surroundings of biologically active sites may be detected only if the data on tertiary protein structures are available. The Protein Data Bank (PDB) contains data on the spatial protein structures and their biologically active sites. However, neither of the well-known systems searching PDB does not provide the user with possibility to make the queries related with the active sites. Sponsor: This site is funded by GeneNetWorks.

At the heart of scientific ways of knowing is the systematic collection and analysis of data, which is then used to propose an explanation of how the world works. In this two-day module, students in a large-lecture course are immersed in a biological problem related to the Central Dogma and gene expression. Specifically, students interpret experimental data in small groups, and then use those data to craft a scientific argument to explain how alternative splicing of a transcription factor gene may contribute to human cancer. Prior to the module, students are assigned a reading and provided PowerPoint slides outlining the basics of alternative splicing and refreshing their understanding of gene regulation. Students complete a pre-class assignment designed to reinforce basic terminology and prepare them for interpreting scientific models. Each day of the module, students are presented experimental data or biological models which they interpret in small groups, use to vote for viable hypotheses using clickers, and ultimately leverage in a culminating summary writing task requiring them to craft a data-driven answer to the biological problem. Despite the novelty of the argumentation module, students engage in all aspects (inside and outside of the classroom) of the activity and are connected across data, hypotheses, and course concepts to explain the role of alternative splicing in gene expression and cancer.

Primary image: Splicing it together. Students work together, interpreting primary data and models to investigate the effects alternative splicing may have on gene regulation and cancer.

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.

GlycoSuiteDB is a curated database of carbohydrate (glycan) structures sourced from published material. This entry corresponds to a catologue of structures reported in the publication - Primary structure of the major O-glycosidically linked carbohydrate unit of human von Willebrand factor.

GlycoSuiteDB is a curated database of carbohydrate (glycan) structures sourced from published material. This entry corresponds to a catologue of structures reported in the publication - Primary structure of the low-molecular-weight carbohydrate chains of Helix pomatia alpha-hemocyanin. Xylose as a constituent of N-linked oligosaccharides in an animal glycoprotein.

The primary productivity and biological data in this accession were collected in the Indian Ocean by the Indian Ocean Data Center. Data was collected between February 22, 1976 and April 7, 1991. 1,080 Profiles of Chlorophyll/ Phaeophytin/ Primary Productivity data containing 4,518 records were submitted by J.S. Sarupria of Indian Ocean Biological Center, Cochin, India.

This dataset contains data used to generate figures in the journal article "Improved identification of primary biological aerosol particles using single particle mass spectrometry" published in Atmospheric Chemistry and Physics. Data is provided in .csv format with column headers as variable names.

Measurements of primary productivity (mgC/m^3/d) at specified light levels.

Biological Data Analysis Service Market Outlook

The global biological data analysis service market size was valued at USD 2.5 billion in 2023 and is expected to grow to USD 7.8 billion by 2032, registering a CAGR of 13.5% during the forecast period. This market is driven by factors such as the increasing prevalence of chronic diseases, the rising adoption of precision medicine, and the significant advancements in bioinformatics and computational biology.

One of the primary growth factors of the biological data analysis service market is the increasing demand for precision medicine. Precision medicine seeks to tailor medical treatment to the individual characteristics of each patient, considering genetic, environmental, and lifestyle factors. The necessity for personalized treatment plans has driven the demand for genomic, proteomic, metabolomic, and transcriptomic data analysis to provide precise diagnostics and effective therapies. Moreover, the advancements in high-throughput sequencing technologies have significantly reduced the cost of sequencing, making it more accessible and further fueling market growth.

Another significant growth factor is the surge in chronic diseases and the consequent necessity for advanced diagnostic tools. Chronic diseases such as cancer, diabetes, and cardiovascular diseases require comprehensive data analysis for early detection and effective treatment. Biological data analysis services play a crucial role in understanding the molecular basis of these diseases and developing targeted therapies. The vast amount of data generated through various biological experiments necessitates robust data analysis services, driving market expansion.

Additionally, the continuous advancements in bioinformatics tools and computational biology have considerably enhanced the capabilities of biological data analysis. Artificial intelligence (AI) and machine learning (ML) technologies are being increasingly integrated into bioinformatics tools, enabling more accurate data interpretation and prediction. These technological advancements are propelling the market forward, as they provide researchers and clinicians with more reliable and actionable insights.

Genetic Analysis Services have become increasingly integral to the growth of the biological data analysis service market. As the demand for personalized medicine continues to rise, genetic analysis provides crucial insights into individual genetic variations, enabling more precise diagnostics and tailored treatment plans. The integration of genetic analysis services with advanced bioinformatics tools allows for the comprehensive interpretation of genetic data, facilitating the identification of disease-associated genetic markers. This not only enhances the accuracy of disease diagnosis but also aids in the development of targeted therapies, thereby significantly contributing to market expansion.

Regionally, North America holds the largest share in the biological data analysis service market due to the presence of a well-established healthcare infrastructure, high adoption of advanced technologies, and significant investment in research and development. Europe follows closely, driven by supportive government initiatives and funding for genomic research. The Asia Pacific region is poised for the fastest growth, with increasing healthcare expenditure, growing awareness about precision medicine, and rising investments in biotechnological research.

Service Type Analysis

The biological data analysis service market can be segmented by service type into genomic data analysis, proteomic data analysis, metabolomic data analysis, transcriptomic data analysis, and others. Genomic data analysis constitutes a significant portion of the market owing to the extensive use of genomic data in identifying genetic variants associated with diseases and developing personalized treatment plans. The rise in next-generation sequencing (NGS) technologies has further propelled the demand for genomic data analysis services as it allows for comprehensive genetic profiling.

Proteomic data analysis is another crucial segment, as it involves the large-scale study of proteins, which are vital biomarkers for many diseases. Proteomics provides insights into protein expression, modifications, and interactions, which are essential for understanding disease mechanisms and developing targeted therapies. The advancements in mass spectrometry and bioinformatics tools have significa

One of the major challenges in designing a peptide-based vaccine is the identification of antigenic regions in an antigen that can stimulate B-cell’s response, also called B-cell epitopes. In the past, several methods have been developed for the prediction of conformational and linear (or continuous) B-cell epitopes. However, the existing methods for predicting linear B-cell epitopes are far from perfection. In this study, an attempt has been made to develop an improved method for predicting linear B-cell epitopes. We have retrieved experimentally validated B-cell epitopes as well as non B-cell epitopes from Immune Epitope Database and derived two types of datasets called Lbtope_Variable and Lbtope_Fixed length datasets. The Lbtope_Variable dataset contains 14876 B-cell epitope and 23321 non-epitopes of variable length where as Lbtope_Fixed length dataset contains 12063 B-cell epitopes and 20589 non-epitopes of fixed length. We also evaluated the performance of models on above datasets after removing highly identical peptides from the datasets. In addition, we have derived third dataset Lbtope_Confirm having 1042 epitopes and 1795 non-epitopes where each epitope or non-epitope has been experimentally validated in at least two studies. A number of models have been developed to discriminate epitopes and non-epitopes using different machine-learning techniques like Support Vector Machine, and K-Nearest Neighbor. We achieved accuracy from ∼54% to 86% using diverse s features like binary profile, dipeptide composition, AAP (amino acid pair) profile. In this study, for the first time experimentally validated non B-cell epitopes have been used for developing method for predicting linear B-cell epitopes. In previous studies, random peptides have been used as non B-cell epitopes. In order to provide service to scientific community, a web server LBtope has been developed for predicting and designing B-cell epitopes (http://crdd.osdd.net/raghava/lbtope/).

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.

GlycoSuiteDB is a curated database of carbohydrate (glycan) structures sourced from published material. This entry corresponds to a catologue of structures reported in the publication - Primary structure of N-linked carbohydrate chains of a human chimeric plasminogen activator K2tu-PA expressed in Chinese hamster ovary cells.

Molecular Biology Software Market Outlook

The global molecular biology software market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 4.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.1% during the forecast period. This exponential growth is driven by advancements in bioinformatics, increased funding for genomic research, and the rising prevalence of chronic diseases which require personalized medicine approaches. These factors are collectively propelling the demand for sophisticated molecular biology software that can manage, analyze, and interpret complex biological data.

One of the primary growth factors for the molecular biology software market is the rapid advancements in bioinformatics and genomic technologies. As sequencing technologies become more affordable and accessible, there is a growing need for software solutions that can efficiently process and analyze the vast amounts of data generated. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) algorithms into molecular biology software is enhancing the accuracy and speed of data interpretation, thereby fostering market growth.

Another significant factor contributing to market growth is the increasing investment in genomics research. Governments and private organizations worldwide are investing heavily in genomic projects aimed at understanding the genetic basis of diseases and developing targeted therapies. This surge in research activities is driving the demand for advanced molecular biology software solutions that can facilitate the analysis of large genomic datasets, thereby propelling market expansion.

Bioinformatics Platforms are playing an increasingly pivotal role in the molecular biology software market. These platforms offer comprehensive tools for the management and analysis of biological data, enabling researchers to handle vast datasets with efficiency and precision. As the volume of genomic data continues to grow exponentially, bioinformatics platforms provide the necessary infrastructure to integrate and interpret complex information, facilitating breakthroughs in genomic research and personalized medicine. Their ability to streamline data processing and enhance collaborative research efforts is making them indispensable in the field of molecular biology.

The rising prevalence of chronic diseases such as cancer, diabetes, and cardiovascular disorders is also fueling the demand for molecular biology software. Personalized medicine, which tailors treatment based on an individual's genetic makeup, is becoming increasingly important in the management of these diseases. Molecular biology software plays a crucial role in identifying genetic variations associated with these conditions, enabling the development of targeted therapies and improving patient outcomes.

From a regional perspective, North America is expected to dominate the molecular biology software market, driven by the presence of leading biotechnology firms and extensive research activities. Europe is also anticipated to witness significant growth, supported by favorable government initiatives and increased research funding. The Asia Pacific region presents lucrative opportunities for market players due to the rapidly expanding healthcare infrastructure and growing focus on precision medicine.

Product Type Analysis

The molecular biology software market can be segmented based on product type into sequence analysis software, molecular modeling software, genomics software, proteomics software, and others. Sequence analysis software holds a significant share due to its crucial role in interpreting sequence data from various biological experiments. This software type is vital for genome annotation, variant calling, and aligning sequences, making it indispensable in genomic research and clinical diagnostics.

Molecular modeling software is another critical segment, primarily utilized in drug discovery and molecular simulations. This software helps researchers understand the molecular structure and properties of drugs, thereby aiding in the design of new therapeutic compounds. The increasing focus on drug discovery and development, coupled with the need for accurate molecular simulations, is driving the adoption of molecular modeling software.

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GlycoSuiteDB is a curated database of carbohydrate (glycan) structures sourced from published material. This entry corresponds to a catologue of structures reported in the publication - Primary-structure determination of fourteen neutral oligosaccharides derived from bronchial-mucus glycoproteins of patients suffering from cystic fibrosis, employing 500-MHz 1H-NMR spectroscopy.

The primary objective for developing a CeDAMar database is to generate a map of biological abyssal sampling stations in the World Ocean. These data represent the European coverage. AccConID=21 AccConstrDescription=This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials. AccConstrDisplay=This dataset is licensed under a Creative Commons Attribution 4.0 International License. AccConstrEN=Attribution (CC BY) AccessConstraint=Attribution (CC BY) Acronym=CeDAMar added_date=2009-04-07 18:02:26.243000 BrackishFlag=0 CDate=2009-04-08 cdm_data_type=Other CheckedFlag=0 Citation=Martinez Arbizu, P. Smith, C. R., Keller, S. & Ebbe, B. (Editors). Biogeographic Database of the Census of Abyssal Marine Life. [date accessed]. World Wide Web electronic publication. Available online at http://www.cedamar.org/biogeography Comments=None ContactEmail=None Conventions=COARDS, CF-1.6, ACDD-1.3 CurrencyDate=None DasID=1940 DasOrigin=Research DasType=Data DasTypeID=1 DateLastModified={'date': '2025-04-25 01:33:51.812809', 'timezone_type': 1, 'timezone': '+02:00'} DescrCompFlag=0 DescrTransFlag=0 DOI=10.15468/oc9tsb Easternmost_Easting=180.0 EmbargoDate=None EngAbstract=The primary objective for developing a CeDAMar database is to generate a map of biological abyssal sampling stations in the World Ocean. These data represent the European coverage. EngDescr=The primary objective for developing a CeDAMar database is to generate a map of biological abyssal sampling stations in the World Ocean. Over fifty percent of the World Ocean’s Seafloor is abyssal (4000-6000 m), but there is no comprehensive synthesis of abyssal sampling. A map will show the extent of sampling that has been conducted in abyssal plains, which will provide the information needed to synthesize abyssal biogeography, and for planning future sampling expeditions. This database is an initial effort to gather information to incorporate into a relational database under the auspices of CeDAMar.

The database covers sampling sites from the H.M.S. Challenger Expedition, 1872-1876, to recent expeditions in 2005. FreshFlag=0 GBIF_UUID=96180cd0-f762-11e1-a439-00145eb45e9a geospatial_lat_max=84.3 geospatial_lat_min=-78.5 geospatial_lat_units=degrees_north geospatial_lon_max=180.0 geospatial_lon_min=-180.0 geospatial_lon_units=degrees_east infoUrl=None InputNotes=None institution=DZMB License=https://creativecommons.org/licenses/by/4.0/ Lineage=Prior to publication data undergo quality control checked which are described in https://github.com/EMODnet/EMODnetBiocheck?tab=readme-ov-file#understanding-the-output MarineFlag=1 modified_sync=2021-02-04 00:00:00 Northernmost_Northing=84.3 OrigAbstract=None OrigDescr=None OrigDescrLang=English OrigDescrLangNL=Engels OrigLangCode=en OrigLangCodeExtended=eng OrigLangID=15 OrigTitle=None OrigTitleLang=English OrigTitleLangCode=en OrigTitleLangID=15 OrigTitleLangNL=Engels Progress=Completed PublicFlag=1 ReleaseDate=Apr 7 2009 12:00AM ReleaseDate0=2009-04-07 RevisionDate=None SizeReference=None sourceUrl=(local files) Southernmost_Northing=-78.5 standard_name_vocabulary=CF Standard Name Table v70 StandardTitle=CeDAMar database for benthic biological sampling on the abyssal plains StatusID=1 subsetVariables=ScientificName,BasisOfRecord,aphia_id TerrestrialFlag=0 UDate=2025-03-26 VersionDate=None VersionDay=None VersionMonth=4 VersionName=1 VersionYear=2009 VlizCoreFlag=1 Westernmost_Easting=-180.0

Stress granules (SGs) are cytosolic biomolecular condensates that form in response to cellular stress. Weak, multivalent interactions between their protein and RNA constituents drive their rapid, dynamic assembly through phase separation coupled to percolation. Though a consensus model of SG function has yet to be determined, their perceived implication in cytoprotective processes (e.g., antiviral responses and inhibition of apoptosis) and possible role in the pathogenesis of various neurodegenerative diseases (e.g., amyotrophic lateral sclerosis and frontotemporal dementia) have drawn great interest. Consequently, new studies using numerous cell biological, genetic, and proteomic methods have been performed to unravel the mechanisms underlying SG formation, organization, and function and, with them, a more clearly defined SG proteome. Here, we provide a consensus SG proteome through literature curation and an update of the user-friendly database RNAgranuleDB to version 2.0 (http://rnagranuledb.lunenfeld.ca/). With this updated SG proteome, we use next-generation phase separation prediction tools to assess the predisposition of SG proteins for phase separation and aggregation. Next, we analyze the primary sequence features of intrinsically disordered regions (IDRs) within SG-resident proteins. Finally, we review the protein- and RNA-level determinants, including post-translational modifications (PTMs), that regulate SG composition and assembly/disassembly dynamics.

Responders could provide more than one reason so the numbers do not add to 30.18 responders recorded two advantages, 2 responders recorded three advantages, 1 responder recorded five advantages.28 responders recorded two obstacles, 3 responders recorded three obstacles, 1 responder recorded four obstacles.

Table containing our mapping between authors provided annotations (Author annotations column) and our broad cell type annotations (Class annotations column).