Biological data bioinformatics
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Computational biology
Computational biology involves using computational techniques like data analysis, modeling and simulation to study biological systems. Bioinformatics specifically develops tools to analyze biological data. Other computational biology fields include computational anatomy, genomics, neuroscience, pharmacology, and evolutionary biology which all apply computational methods to study anatomical structures, genomes, the brain, drug effects, and evolution respectively. Cancer computational biology aims to predict cancer mutations by analyzing large biological datasets.
Prosite
The document discusses Prosite, a database of protein family signatures that can be used to determine the function of uncharacterized proteins. It contains patterns and profiles formulated to identify which known protein family a new sequence belongs to. The Prosite database consists of two files - a data file containing information for scanning sequences, and a documentation file describing each pattern and profile. New Prosite entries are mainly profiles developed by collaborators at the SIB Swiss Institute of Bioinformatics to identify distantly related proteins based on conserved residues.
Protein databases
Protein databases can contain either sequence or structure information. Some key protein sequence databases include PIR, Swiss-Prot, and TrEMBL. PIR classifies entries by annotation level, Swiss-Prot aims to provide high annotation levels and interlink information, and TrEMBL contains all coding sequences with some entries eventually incorporated into Swiss-Prot. Important structure databases are PDB, which contains 3D protein structures, and SCOP and CATH, which classify evolutionary and structural relationships between protein domains.
PIR- Protein Information Resource
PIR- Protein Information ResourceThapar Institute of Engineering & Technology, Patiala, Punjab, India
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
EMBL- European Molecular Biology Laboratory
EMBL- European Molecular Biology LaboratoryThapar Institute of Engineering & Technology, Patiala, Punjab, India
Nucleotide Sequence Database maintained by European Bioinformatics Institute (EBI) in Hinxton, Cambridge, UK.
EMBL
The European Molecular Biology Laboratory (EMBL) is a molecular biology research institution supported by 22 member states. EMBL was created in 1974 and operates from five sites, performing basic research in molecular biology and molecular medicine. A key function of EMBL is the EMBL Nucleotide Sequence Database, maintained at the European Bioinformatics Institute, which incorporates and distributes nucleotide sequences from public sources as part of an international collaboration.
protein data bank
The Protein Data Bank (PDB) is a single worldwide database that stores 3D structural data of proteins and nucleic acids. It is operated by Rutgers University, the San Diego Supercomputer Center, and the Research Collaboratory for Structural Bioinformatics. The PDB is freely accessible online and contains over 76,000 biomolecular structure entries as of 2011. It uses a common file format to represent structural data and is updated weekly as new entries are submitted by researchers.
Structural databases
Structural databases like PDB, CSD, and CATH contain 3D structural information of proteins, small molecules, and macromolecules determined through techniques like X-ray crystallography and NMR spectroscopy. These databases provide bibliographic data, atomic coordinates, and other details for each entry. PDB contains protein structures, CSD contains organic and metal-organic structures, and CATH classifies protein domains hierarchically. Structural databases have wide applications in structure prediction, analysis, mining, comparison, classification, structure refinement, and database annotation.
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Computational biology
Computational biology involves using computational techniques like data analysis, modeling and simulation to study biological systems. Bioinformatics specifically develops tools to analyze biological data. Other computational biology fields include computational anatomy, genomics, neuroscience, pharmacology, and evolutionary biology which all apply computational methods to study anatomical structures, genomes, the brain, drug effects, and evolution respectively. Cancer computational biology aims to predict cancer mutations by analyzing large biological datasets.
Prosite
The document discusses Prosite, a database of protein family signatures that can be used to determine the function of uncharacterized proteins. It contains patterns and profiles formulated to identify which known protein family a new sequence belongs to. The Prosite database consists of two files - a data file containing information for scanning sequences, and a documentation file describing each pattern and profile. New Prosite entries are mainly profiles developed by collaborators at the SIB Swiss Institute of Bioinformatics to identify distantly related proteins based on conserved residues.
Protein databases
Protein databases can contain either sequence or structure information. Some key protein sequence databases include PIR, Swiss-Prot, and TrEMBL. PIR classifies entries by annotation level, Swiss-Prot aims to provide high annotation levels and interlink information, and TrEMBL contains all coding sequences with some entries eventually incorporated into Swiss-Prot. Important structure databases are PDB, which contains 3D protein structures, and SCOP and CATH, which classify evolutionary and structural relationships between protein domains.
PIR- Protein Information Resource
PIR- Protein Information ResourceThapar Institute of Engineering & Technology, Patiala, Punjab, India
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
EMBL- European Molecular Biology Laboratory
EMBL- European Molecular Biology LaboratoryThapar Institute of Engineering & Technology, Patiala, Punjab, India
Nucleotide Sequence Database maintained by European Bioinformatics Institute (EBI) in Hinxton, Cambridge, UK.
EMBL
The European Molecular Biology Laboratory (EMBL) is a molecular biology research institution supported by 22 member states. EMBL was created in 1974 and operates from five sites, performing basic research in molecular biology and molecular medicine. A key function of EMBL is the EMBL Nucleotide Sequence Database, maintained at the European Bioinformatics Institute, which incorporates and distributes nucleotide sequences from public sources as part of an international collaboration.
protein data bank
The Protein Data Bank (PDB) is a single worldwide database that stores 3D structural data of proteins and nucleic acids. It is operated by Rutgers University, the San Diego Supercomputer Center, and the Research Collaboratory for Structural Bioinformatics. The PDB is freely accessible online and contains over 76,000 biomolecular structure entries as of 2011. It uses a common file format to represent structural data and is updated weekly as new entries are submitted by researchers.
Structural databases
Structural databases like PDB, CSD, and CATH contain 3D structural information of proteins, small molecules, and macromolecules determined through techniques like X-ray crystallography and NMR spectroscopy. These databases provide bibliographic data, atomic coordinates, and other details for each entry. PDB contains protein structures, CSD contains organic and metal-organic structures, and CATH classifies protein domains hierarchically. Structural databases have wide applications in structure prediction, analysis, mining, comparison, classification, structure refinement, and database annotation.
Protein data bank
The Protein Data Bank (PDB) is an open database that archives 3D structural data of biological macromolecules. It was established in 1971 and currently holds over 150,000 structures determined by X-ray crystallography or NMR spectroscopy. The PDB is overseen by the Worldwide Protein Data Bank and freely accessible online. It serves as a key resource for structural biology and many other databases rely on protein structures deposited in the PDB.
DNA data bank of japan (DDBJ)
The DNA Data Bank of Japan (DDBJ) is a biological database that collects DNA sequences. It is located at the National Institute of Genetics (NIG) in the Shizuoka prefecture of Japan. It is also a member of the International Nucleotide Sequence Database Collaboration or INSDC.
NCBI National Center for Biotechnology Information
NCBI National Center for Biotechnology InformationThapar Institute of Engineering & Technology, Patiala, Punjab, India
The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic informationProtein database
This document provides an overview of several important protein databases:
- SWISS-PROT is an annotated protein sequence database that is maintained collaboratively and contains over 1.29 million entries. TrEMBL is a computer-annotated supplement to SWISS-PROT containing sequences not yet in SWISS-PROT.
- Structural databases like PDB, SCOP, and CATH provide protein structure information. PDB is an international repository for macromolecular structures. SCOP and CATH classify protein domains based on structural similarities and evolutionary relationships.
- Other databases mentioned include InterPro, GOA, Proteome Analysis, and GenBank, which provide functional annotation, gene ontology assignments, proteome analysis
Data Retrieval Systems
The document discusses different text-based database retrieval systems for accessing biological data, including Entrez, SRS, and DBGET/LinkDB. It describes their key features and how each system allows users to search text databases using queries, with Entrez providing linked related data across multiple databases. An example shows how each system can be used to retrieve and view related information for a SwissProt protein entry.
Cath
The CATH database hierarchically classifies protein domains obtained from protein structures deposited in the Protein Data Bank. Domain identification and classification uses both manual and automated procedures. CATH includes domains from structures determined at 4 angstrom resolution or better that are at least 40 residues long with 70% or more residues having defined side chains. Submitted protein chains are divided into domains, which are then classified in CATH.
Entrez databases
The document discusses biological databases and retrieval systems. It provides an overview of Entrez, a retrieval system developed by NCBI that allows integrated searches across multiple biological databases. It also describes how Entrez links related data between databases, and some key features of Entrez like limits, preview/index, and history. Additionally, it summarizes specific NCBI databases accessible through Entrez like PubMed and OMIM, as well as another retrieval system called SRS maintained by EBI.
Protein database
This document outlines the course content for a bioinformatics course covering 4 units:
Unit 1 introduces basic concepts of bioinformatics including proteins, DNA, RNA, and sequence, structure, and function.
Unit 2 covers major bioinformatics databases including those for nucleotide sequences, protein sequences, sequence motifs, protein structures, and other relevant databases.
Unit 3 discusses topics like single and pairwise sequence alignment, scoring matrices, and multiple sequence alignments.
Unit 4 covers the human genome project, gene and genomic databases, genomic data mining, and microarray techniques.
Protein Structure, Databases and Structural Alignment
Protein structures can be aligned and compared using computational methods like structural alignment. Structural alignment finds the optimal rotation and translation that superimposes one protein structure onto another to maximize structural similarity. This is done by treating protein structures as sets of points defined by atom coordinates and finding the transformation that minimizes the root-mean-square deviation between corresponding atoms in the two structures. While useful, structural alignment has limitations like not accounting for differences in amino acid attributes and treating all atoms equally.
Ddbj
The DNA Data Bank of Japan (DDBJ) is a biological database located in Japan that collects and stores nucleotide sequence data. It began operations in 1986 and exchanges data daily with the European Nucleotide Archive and GenBank to form the International Nucleotide Sequence Database Collaboration (INSDC). DDBJ accepts sequence submissions from researchers worldwide and assigns unique identification numbers to published sequences to recognize intellectual property rights. It also provides search and analysis tools and supercomputing resources to support genomic research.
Kegg databse
Rashi Srivastava presented on the KEGG database in biotechnology. KEGG is a database that contains genomic, chemical, and systems information to understand biological functions from the molecular level up. It includes pathways, genes, compounds, diseases, drugs, and organisms. KEGG can be searched through its flat file format using DBGET or through its relational database format for more complex queries. It also contains the KEGG MEDICUS search tool and direct SQL searches of its relational database.
Bioinformatics biological databases
This PDF contains informations about the various types of biological databases, examples, url addresses, etc.
Protein Database
Lecture delivered by T. Ashok Kumar, Head, Department of Bioinformatics, Noorul Islam College of Arts and Science, Kumaracoil, Thuckalay, INDIA. UGC Sponsored National Workshop on BIOINFORMATICS AND GENOME ANALYSIS for College Teachers on August 11 & 12, 2014. Organized by Centre for Bioinformatics, Department of Zoology, NMCC.
Ncbi
The National Center for Biotechnology Information (NCBI) was established in 1988 as part of the National Library of Medicine. NCBI houses numerous biomedical databases including those related to genes, proteins, molecular structures, gene expression, and biomedical literature. Users can utilize various tools on the NCBI site to search databases, perform sequence alignments using BLAST, and submit new sequences. Some key databases include GenBank (nucleotide sequences), PubMed (biomedical literature), and RefSeq (non-redundant reference sequences).
Kegg
The document describes several key databases within the KEGG resource, including:
- The PATHWAY database containing molecular network maps of metabolic and genetic pathways.
- The BRITE database providing hierarchical classifications of biological systems beyond what is shown in pathways.
- The LIGAND database consisting of chemical compounds, carbohydrates, reactions, and enzyme information.
KEGG aims to comprehensively capture biological knowledge through integrated databases covering genomes, pathways, diseases and drugs.
Protein Data Bank (PDB)
Archive of experimentally determined 3D structures of biological macromolecules.
Established in 1971, by Research Collaboratory for Structural Bioinformatics (RCSB), Brookhaven National Laboratories, USA.
Archive contain atomic coordinates, bibliographic citations, primary and secondary structure information, crystallographic structure factors, NMR experimental data.
Protein information resource (PIR)
The Protein Information Resource, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies & contains protein sequences databases
Databases pathways of genomics and proteomics
The document discusses several databases related to human metabolism and pharmacology. It describes the contents and purpose of each database, including the Human Metabolome Database (HMDB), KEGG, MetaCyc, PubChem, ChEBI, DrugBank, the Therapeutic Target Database (TTD), PharmGKB, and Chemical Entities of Biological Interest (ChEBI). These databases contain chemical, clinical, molecular biology, pathway, and genomic data on human metabolites, drugs, and targets.
Biological databases
Biological databases can be categorized as primary, secondary, or specialized. Primary databases contain original sequence data submitted with minimal annotation directly from authors. Examples are GenBank, EMBL, and DDBJ, which closely collaborate and exchange data daily. Secondary databases contain computationally processed or manually curated information based on primary databases, such as SWISS-Prot, which provides detailed functional annotation. Specialized databases focus on a particular organism or data type, like FlyBase or HIV sequence databases.
UniProt
UniProt is a comprehensive database of protein sequences and functional information that is curated by the European Bioinformatics Institute, SIB Swiss Institute of Bioinformatics, and the Protein Information Resource. It contains three main components: UniProtKB for functional information, UniRef for clustered sequences, and UniParc for comprehensive protein sequences. As genome sequencing increases, UniProt provides centralized storage and interconnection of protein data from various sources to further scientific understanding of proteins.
Biological data base
This document discusses biological databases. It defines a biological database as a collection of structured, searchable, and periodically updated biological data like protein sequences, molecular structures, and DNA sequences. It notes that biological data is heterogeneous, high-volume, uncertain, dynamically changing, and integrated from various global sources. The key functions of biological databases are to make biological data available worldwide in a computer-readable format. They are broadly classified into sequence, structure, and pathway databases. Some examples of important biological databases discussed are Swiss-Prot, PDB, GenBank, and COGs.
Biological Database (1)pptxpdfpdfpdf.pdf
Biological databases store and organize large amounts of biological data for research use. There are many types of biological databases that classify data by type, such as nucleotide sequences, protein sequences, genomes, protein structures, gene expression, and metabolic pathways. Databases can also be classified by their data source as primary databases containing experimental results or secondary databases that analyze primary database results. Database availability varies, with some publicly open and others proprietary. Common biological databases discussed include GenBank, UniProt, PDB, KEGG, and FlyBase.
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Protein data bank
The Protein Data Bank (PDB) is an open database that archives 3D structural data of biological macromolecules. It was established in 1971 and currently holds over 150,000 structures determined by X-ray crystallography or NMR spectroscopy. The PDB is overseen by the Worldwide Protein Data Bank and freely accessible online. It serves as a key resource for structural biology and many other databases rely on protein structures deposited in the PDB.
DNA data bank of japan (DDBJ)
The DNA Data Bank of Japan (DDBJ) is a biological database that collects DNA sequences. It is located at the National Institute of Genetics (NIG) in the Shizuoka prefecture of Japan. It is also a member of the International Nucleotide Sequence Database Collaboration or INSDC.
NCBI National Center for Biotechnology Information
NCBI National Center for Biotechnology InformationThapar Institute of Engineering & Technology, Patiala, Punjab, India
The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic informationProtein database
This document provides an overview of several important protein databases:
- SWISS-PROT is an annotated protein sequence database that is maintained collaboratively and contains over 1.29 million entries. TrEMBL is a computer-annotated supplement to SWISS-PROT containing sequences not yet in SWISS-PROT.
- Structural databases like PDB, SCOP, and CATH provide protein structure information. PDB is an international repository for macromolecular structures. SCOP and CATH classify protein domains based on structural similarities and evolutionary relationships.
- Other databases mentioned include InterPro, GOA, Proteome Analysis, and GenBank, which provide functional annotation, gene ontology assignments, proteome analysis
Data Retrieval Systems
The document discusses different text-based database retrieval systems for accessing biological data, including Entrez, SRS, and DBGET/LinkDB. It describes their key features and how each system allows users to search text databases using queries, with Entrez providing linked related data across multiple databases. An example shows how each system can be used to retrieve and view related information for a SwissProt protein entry.
Cath
The CATH database hierarchically classifies protein domains obtained from protein structures deposited in the Protein Data Bank. Domain identification and classification uses both manual and automated procedures. CATH includes domains from structures determined at 4 angstrom resolution or better that are at least 40 residues long with 70% or more residues having defined side chains. Submitted protein chains are divided into domains, which are then classified in CATH.
Entrez databases
The document discusses biological databases and retrieval systems. It provides an overview of Entrez, a retrieval system developed by NCBI that allows integrated searches across multiple biological databases. It also describes how Entrez links related data between databases, and some key features of Entrez like limits, preview/index, and history. Additionally, it summarizes specific NCBI databases accessible through Entrez like PubMed and OMIM, as well as another retrieval system called SRS maintained by EBI.
Protein database
This document outlines the course content for a bioinformatics course covering 4 units:
Unit 1 introduces basic concepts of bioinformatics including proteins, DNA, RNA, and sequence, structure, and function.
Unit 2 covers major bioinformatics databases including those for nucleotide sequences, protein sequences, sequence motifs, protein structures, and other relevant databases.
Unit 3 discusses topics like single and pairwise sequence alignment, scoring matrices, and multiple sequence alignments.
Unit 4 covers the human genome project, gene and genomic databases, genomic data mining, and microarray techniques.
Protein Structure, Databases and Structural Alignment
Protein structures can be aligned and compared using computational methods like structural alignment. Structural alignment finds the optimal rotation and translation that superimposes one protein structure onto another to maximize structural similarity. This is done by treating protein structures as sets of points defined by atom coordinates and finding the transformation that minimizes the root-mean-square deviation between corresponding atoms in the two structures. While useful, structural alignment has limitations like not accounting for differences in amino acid attributes and treating all atoms equally.
Ddbj
The DNA Data Bank of Japan (DDBJ) is a biological database located in Japan that collects and stores nucleotide sequence data. It began operations in 1986 and exchanges data daily with the European Nucleotide Archive and GenBank to form the International Nucleotide Sequence Database Collaboration (INSDC). DDBJ accepts sequence submissions from researchers worldwide and assigns unique identification numbers to published sequences to recognize intellectual property rights. It also provides search and analysis tools and supercomputing resources to support genomic research.
Kegg databse
Rashi Srivastava presented on the KEGG database in biotechnology. KEGG is a database that contains genomic, chemical, and systems information to understand biological functions from the molecular level up. It includes pathways, genes, compounds, diseases, drugs, and organisms. KEGG can be searched through its flat file format using DBGET or through its relational database format for more complex queries. It also contains the KEGG MEDICUS search tool and direct SQL searches of its relational database.
Bioinformatics biological databases
This PDF contains informations about the various types of biological databases, examples, url addresses, etc.
Protein Database
Lecture delivered by T. Ashok Kumar, Head, Department of Bioinformatics, Noorul Islam College of Arts and Science, Kumaracoil, Thuckalay, INDIA. UGC Sponsored National Workshop on BIOINFORMATICS AND GENOME ANALYSIS for College Teachers on August 11 & 12, 2014. Organized by Centre for Bioinformatics, Department of Zoology, NMCC.
Ncbi
The National Center for Biotechnology Information (NCBI) was established in 1988 as part of the National Library of Medicine. NCBI houses numerous biomedical databases including those related to genes, proteins, molecular structures, gene expression, and biomedical literature. Users can utilize various tools on the NCBI site to search databases, perform sequence alignments using BLAST, and submit new sequences. Some key databases include GenBank (nucleotide sequences), PubMed (biomedical literature), and RefSeq (non-redundant reference sequences).
Kegg
The document describes several key databases within the KEGG resource, including:
- The PATHWAY database containing molecular network maps of metabolic and genetic pathways.
- The BRITE database providing hierarchical classifications of biological systems beyond what is shown in pathways.
- The LIGAND database consisting of chemical compounds, carbohydrates, reactions, and enzyme information.
KEGG aims to comprehensively capture biological knowledge through integrated databases covering genomes, pathways, diseases and drugs.
Protein Data Bank (PDB)
Archive of experimentally determined 3D structures of biological macromolecules.
Established in 1971, by Research Collaboratory for Structural Bioinformatics (RCSB), Brookhaven National Laboratories, USA.
Archive contain atomic coordinates, bibliographic citations, primary and secondary structure information, crystallographic structure factors, NMR experimental data.
Protein information resource (PIR)
The Protein Information Resource, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies & contains protein sequences databases
Databases pathways of genomics and proteomics
The document discusses several databases related to human metabolism and pharmacology. It describes the contents and purpose of each database, including the Human Metabolome Database (HMDB), KEGG, MetaCyc, PubChem, ChEBI, DrugBank, the Therapeutic Target Database (TTD), PharmGKB, and Chemical Entities of Biological Interest (ChEBI). These databases contain chemical, clinical, molecular biology, pathway, and genomic data on human metabolites, drugs, and targets.
Biological databases
Biological databases can be categorized as primary, secondary, or specialized. Primary databases contain original sequence data submitted with minimal annotation directly from authors. Examples are GenBank, EMBL, and DDBJ, which closely collaborate and exchange data daily. Secondary databases contain computationally processed or manually curated information based on primary databases, such as SWISS-Prot, which provides detailed functional annotation. Specialized databases focus on a particular organism or data type, like FlyBase or HIV sequence databases.
UniProt
UniProt is a comprehensive database of protein sequences and functional information that is curated by the European Bioinformatics Institute, SIB Swiss Institute of Bioinformatics, and the Protein Information Resource. It contains three main components: UniProtKB for functional information, UniRef for clustered sequences, and UniParc for comprehensive protein sequences. As genome sequencing increases, UniProt provides centralized storage and interconnection of protein data from various sources to further scientific understanding of proteins.
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NCBI National Center for Biotechnology Information
NCBI National Center for Biotechnology Information
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Similar to Biological data bioinformatics
Biological data base
This document discusses biological databases. It defines a biological database as a collection of structured, searchable, and periodically updated biological data like protein sequences, molecular structures, and DNA sequences. It notes that biological data is heterogeneous, high-volume, uncertain, dynamically changing, and integrated from various global sources. The key functions of biological databases are to make biological data available worldwide in a computer-readable format. They are broadly classified into sequence, structure, and pathway databases. Some examples of important biological databases discussed are Swiss-Prot, PDB, GenBank, and COGs.
Biological Database (1)pptxpdfpdfpdf.pdf
Biological databases store and organize large amounts of biological data for research use. There are many types of biological databases that classify data by type, such as nucleotide sequences, protein sequences, genomes, protein structures, gene expression, and metabolic pathways. Databases can also be classified by their data source as primary databases containing experimental results or secondary databases that analyze primary database results. Database availability varies, with some publicly open and others proprietary. Common biological databases discussed include GenBank, UniProt, PDB, KEGG, and FlyBase.
biological databases.pptx
This document discusses biological databases. It notes that biological databases store vast amounts of biological data generated every day, including nucleotide sequences, protein sequences, pathways, and bibliographic information. It describes different types of biological databases, including primary databases that store original data, secondary databases that derive patterns from primary data, and composite databases that amalgamate multiple sources. It provides examples like GenBank, UniProt, KEGG, and PubMed. It also discusses how databases are organized, searched, and tools used like BLAST and FASTA.
BIOINFORMATICS AND DATABASES IN BIOINFORMATICS.pdf
BIOLOGICAL DATABASES are
Collection of files containing records of biological data in machine readable form Can be accessed, added, retrieved, manipulated and modified.
COMPUNATIONAL BIOLOGY AND DATABASES IN BIOINFORMATICS.pptx
BIOLOGICAL DATABASES are
Collection of files containing records of biological data in machine readable form Can be accessed, added, retrieved, manipulated and modified.
Bioinformatics__Lecture_1.ppt
Bioinformatics is the interdisciplinary field that uses computational tools and methods to analyze and interpret biological data such as DNA, RNA, and protein sequences. The goal of bioinformatics is to better understand biological systems at the molecular level. It involves the development of databases to store molecular data and algorithms and software to analyze this data in order to address biological questions and further biological knowledge. Some key applications of bioinformatics include knowledge-based drug design, agricultural biotechnology, and disease research.
Bioinformatics
This document provides an overview of the field of bioinformatics. It defines bioinformatics as the intersection of biology and computer science, using computational tools to analyze and distribute biological information like DNA, RNA, and proteins. The goals of bioinformatics are to better understand cells at the molecular level by analyzing sequence and structure data. Key applications include drug design, DNA analysis, and agricultural biotechnology. The document also describes different types of biological databases like primary databases that contain raw sequence data, and secondary databases that provide additional annotation and analysis of sequences.
Primary Bioinformatics Database.pptx
This file contains- introduction, classification, primary database, nucleic acid database, protein sequence database, protein structure database
Introduction OF BIOLOGICAL DATABASE
Biological databases store and organize biological data and information. There are two main types - primary databases that contain original experimental data that cannot be changed, and secondary databases that contain derived data analyzed from primary sources. Examples of primary databases include GenBank for DNA sequences and SWISS-PROT for protein sequences. Secondary databases include PROSITE for protein families and domains, and Pfam for protein family alignments. Biological databases allow sharing of genomic and protein information worldwide and provide a foundation for research.
Composite protein databases
Information on the following topics is given :-
Database; Biological database - features & needs; Types of databases and it's examples;
Biological Database
This document discusses biological databases. It defines biological databases as structured, searchable collections of biological data that are periodically updated and cross-referenced. It notes that biological databases store data electronically and systematize, make available, and allow analysis of computed biological data. The document then describes some key features of biological databases, including data heterogeneity, high data volumes, uncertainty, data curation, integration, sharing, and dynamic nature. It also provides examples of different types of biological databases classified by data type, maintainer, access, source, design, and organism covered.
EiTESAL eHealth Conference 14&15 May 2017
This document discusses bioinformatics and some of its key concepts and tools. It begins with definitions of bioinformatics as the intersection of biology, computer science, and information technology. It then discusses some of the data formats, tools, and skills used in bioinformatics, including working with nucleotide sequence data, translating sequences into amino acids, and analyzing large datasets. It also summarizes how ontologies are used to represent concepts and how various data types are organized and stored in databases for analysis.
Data retreival system
This document describes several text-based biological databases and how to search them. It discusses Entrez, which searches multiple databases and links related entries. It also describes the Sequence Retrieval System (SRS) which allows searching over 80 biological databases. Additionally, it outlines DBGET/LinkDB, an integrated system that searches about 20 databases and links results to associated information. The document provides an example of using each system to retrieve information on a specific protein entry.
protein databases.ppt
The document discusses several key biological databases and resources for archiving and retrieving genomic and protein information. It describes primary public databases located in Europe, the US, and Japan that house sequence data, analysis tools, and literature. Key databases mentioned include GenBank, EMBL, DDBJ for nucleic acid sequences, Swiss-Prot and PIR for proteins, and PDB for protein structures. NCBI and its Entrez portal provide integrated access to these and additional databases like PubMed, OMIM, and Taxonomy. The document outlines how to submit data to GenBank and search various protein and literature databases.
Primary and secondary database
INTRODUCTION
WHAT IS DATA AND DATABASE?
WHAT IS BIOLOGICAL DATABASE?
TYPES OF BIOLOGICAL DATABASE
PRIMARY DATABASE
Nucleic acid sequence database
Protein sequence database
SECONDARY DATABASE
COMPOSITE DATABASE
TERTIARY DATABASE
WHY NEED?
CONCLUSION
REFRENCES
HKU Data Curation MLIM7350 Class 9
Chris Hunter's slides from Class 9 of the HKU Data Curation course (MLIM7350) giving a biocurators perspective of data curation.
Proteins databases
This document provides an overview of protein databases. It discusses the importance of protein databases for storing and analyzing protein sequence, structure, and functional data generated by modern biology. It summarizes several major public protein databases, including UniProt, NCBI RefSeq, PDB, InterPro, and Pfam, which contain protein sequences, structures, families, domains, and functional annotations. Searching and comparing sequences in these databases is an important first step in studying new proteins.
Designing Biological Databases
A report presented in my BNF 216 (Database Design and Modeling for Bioinformatics) class regarding principles and tips to follow in designing biological databases.
Protein database
Bioinformatics is the application of Information technology to store, organize and analyze the vast amount of biological data which is available in the form of sequences and structures of proteins and nucleic acids. The biological information of nucleic acids is available as sequences while the data of proteins is available as sequences and structures.
A biological database is a collection of data that is organized so that its contents can easily be accessed, managed, and updated. The activity of preparing a database can be divided in to:
Collection of data in a form which can be easily accessed
Making it available to a multi-user system (always available for the user)
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Lipofection is a chemical transfection method that uses liposomes to introduce nucleic acids into cells. Liposomes are lipid vesicles that can fuse with cell membranes and release their contents. In lipofection, nucleic acids bind to cationic liposomes to form lipoplexes, which are taken up by cells via endocytosis. Once inside endosomes, the lipoplexes destabilize the endosomal membranes through their cationic properties, allowing the nucleic acids to enter the cytoplasm and be expressed. Calcium chloride transformation is a common method for transforming competent bacterial cells with plasmid DNA. It involves treating cells with calcium chloride to increase membrane permeability, then exposing the cells to plasmid DNA and subjecting them to a heat shock to facilitate
Medical bt seminar.pptx abc
Sickle cell anemia is a genetic blood disorder caused by a mutation in the beta-globin gene that results in abnormal hemoglobin. The red blood cells take on a sickle shape, which can cause them to block small blood vessels and obstruct blood flow. This document discusses the inheritance pattern, genetics, mechanism of sickling, signs and symptoms, complications, diagnosis, and treatment of sickle cell anemia. Treatment aims to prevent crises and complications through medications, blood transfusions, and potentially a bone marrow transplant to cure the disease.
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Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
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1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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Immersive Learning That Works: Research Grounding and Paths Forward
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
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Biological data bioinformatics
- 1. Biological Database Aakifah Amreen H. E 2nd Semester MSc (BT)
- 2. Content • Bioinformatics • Introduction to database • Objective of biological Database • Features of biological Database • Classification of biological Database • Conclusion • References
- 3. What is Bioinformatics Bioinformatics is an interdisciplinary field mainly involving molecular biology, Genetics, computer science, mathematics, statistics. It has been defined as, As a means for analyzing, comparing, graphically displaying, modeling, storing, searching and ultimately distributing biological information, which includes sequence, structure and function.
- 4. Introduction Data base is a collection of data in an organized manner, which is easily accessible in many ways. Biological Database Is a collection of data that is structured, searchable, updated periodically, and cross referenced. It stores biological data in electronic form
- 6. Objective of biological Database • Recognize various data formats and know what their primary use • Know, understand and utilize all types of sequence identifiers
- 7. Features of biological Databases • Heterogeneity • High volume data • Uncertainty • Data curation • Data integration • Data sharing • Dynamics
- 8. DATA HETEROGENEITY Availability of diverse complex data types. Data types include - sequence, graph, high dimension data, shapes, temporal data, patterns, extracted features data. HIGH VOLUME DATA in addition to highly heterogenous, biological data are voluminous to support comprehensive investigation in various fields & directions. UNCERTAINTY Have great deal of uncertainty as they represent biological phenomenon that are observed and assimed
- 9. DATA INTEGRATION Across different structural scales, data is collected from laboratory worldwide & integrated together through a database and made available for use DATA SHARING For scientific community inspection For cross verification To prevent reputation & validation of data
- 10. DYNAMICS New data is generated everyday in laboratories And sometimes this new data contradicts with old data So, it's necessary to develop new organizational database schemes to incorporate new data
- 11. Classification of biological Database Data types Maintainer status Data access Data source Database design Organism
- 12. Classification
- 13. 1. DATA TYPE • Sequence database a. Nucleotide database - Gene bank, EMBL b. Protein database - Swiss-Prot • Structure database - PDB • Chemical database - Pub chem • Pathway database - KEGG • Literature database - PubMed
- 15. 2.MAINTAINER STATUS • NCBI, EMBL • Academic group of scientists • Commercial company 3. DATA ACCESS • Publicly available • Available with copyright • Browsing only accessible but not download able • Restricted
- 16. 4. DATA SOURCE a. Primary database Original data submission by researchers occurs. Ex. Nucleotide - Gene bank, EMBL ; Protein - UniProt ; Structure - PDB ;Literature - Pub Med b. Secondary database Results of analysis of primary database. Either manually curated or by automated method. Ex. Pfam, PROSITE
- 20. 5.DATABASE DESIGN • Flat files • Relational database (SQL) • Object oriented database 6. ORGANISMS • Bacteria • Virus • Humans • Animals
- 21. Conclusion • A collection of data in systemic manner • Which can be used to check if their is any similarities between present data with old data all ready present • Easily accessible • Doesn't consume much time
- 22. References • Introduction to Bioinformatics, Arthur Less, Edition 5, Publisher Oxford University Press, 2019. • Essential Bioinformatics, Jin Xion, Cambridge University Press, 2006