Ncbi basic intro_v_pitt_kent_osu
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The document discusses the National Center for Biotechnology Information (NCBI) and the services and databases it provides. NCBI develops and maintains molecular databases and software tools for searching and analyzing data. It provides a web portal for accessing these data and tools. The databases include those related to biomedical literature, sequences, gene expression, genome maps, structures, pathways and genetic variation. NCBI databases include primary data directly submitted as well as curated data maintained by NCBI. Key databases discussed are GenBank, PubMed, dbSNP, Protein Data Bank and Gene. NCBI provides integrated access to these resources through Entrez and tools for analysis like BLAST, Cn3D and Genome Workbench.
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Web based servers and softwares for genome analysis
There are many characteristics of biological data. All these characteristics make the management of biological information a particularly challenging problem. Here mainly we will focus on characteristics of biological information and multidisciplinary field called bioinformatics. Bioinformatics, now a days has emerged with graduate degree programs in several universities.
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).
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.
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.
Genome assembly
This document discusses different methods for genome sequencing and assembly, including restriction enzyme fingerprinting, marker sequences, and hybridization assays. It focuses on using marker sequences like sequence-tagged sites (STS), expressed sequence tags (ESTs), untranslated regions (UTRs), and single nucleotide polymorphisms (SNPs) to map genomes. Large-insert cloning vectors like BACs and PACs can be used with restriction enzyme fingerprinting and FPC software to assemble contigs and map genomes at a large scale. Marker sequences provide a dense set of physical markers to build accurate physical maps of genomes.
The ensembl database
Ensembl is a genome browser that annotates genes and predicts regulatory functions for vertebrate genomes. It uses raw DNA sequence data to create a tracking database and automatically finds genes and other features. Ensembl incorporates data from other sources and provides web-based access to genomic information through views of genes, transcripts, proteins, DNA homology, and more. It aims to make genome annotation freely accessible to support research.
The European Nucleotide Archive
Event: Plant and Animal Genomes Conference 2012.
Speaker: Bert Overduin
The European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena) provides a comprehensive record of the world's nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. Major components of ENA include the Sequence Read Archive (SRA) for next generation data and EMBL-Bank for assembled and annotated sequences. ENA works closely together with NCBI and DDBJ as partners in the International Nucleotide Sequence Database Collaboration (INSDC). Data arrive at ENA from a variety of sources. These include submissions of raw data, assembled sequences and annotation from small-scale sequencing efforts, data provision from the major European sequencing centres and routine and comprehensive exchange with our INSDC partners. Provision of nucleotide sequence data to ENA or its INSDC partners has become a central and mandatory step in the dissemination of research findings to the scientific community. ENA works with publishers of scientific literature and funding bodies to ensure compliance with these principles and provides a portfolio of interactive and programmatic submission services to ensure the smoothest flow possible of data into the public domain. ENA data can be searched using rapid sequence similarity and text search services provided both within web-based tools and under programmatic interfaces. Data can be retrieved in a variety of appropriate widely adopted formats through a web browser and extensive REST services. This presentation will consist of an introduction to ENA, followed by a short demonstration of the various ways data can be browsed and retrieved.
Tools of bioinforformatics by kk
INTRODUCTION
DEFINITION OF BIOINFORMATICS
HISTORY
OBJECTIVES OF BIOINFORMATICS
TOOLS OF BIOINFORMATICS
BIOLOGICAL DATABASES
HOMOLOGY AND SIMILARITY TOOLS (SEQUENCE ALIGNMENT)
PROTEIN FUNCTION ANALYSIS TOOLS
STRUCTURAL ANALYSIS TOOLS
SEQUENCE MANIPULATION TOOLS
SEQUENCE ANALYSIS TOOLS
APPLICATION
CONCLUSION
REFERENCES
Recommended
Web based servers and softwares for genome analysis
There are many characteristics of biological data. All these characteristics make the management of biological information a particularly challenging problem. Here mainly we will focus on characteristics of biological information and multidisciplinary field called bioinformatics. Bioinformatics, now a days has emerged with graduate degree programs in several universities.
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).
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.
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.
Genome assembly
This document discusses different methods for genome sequencing and assembly, including restriction enzyme fingerprinting, marker sequences, and hybridization assays. It focuses on using marker sequences like sequence-tagged sites (STS), expressed sequence tags (ESTs), untranslated regions (UTRs), and single nucleotide polymorphisms (SNPs) to map genomes. Large-insert cloning vectors like BACs and PACs can be used with restriction enzyme fingerprinting and FPC software to assemble contigs and map genomes at a large scale. Marker sequences provide a dense set of physical markers to build accurate physical maps of genomes.
The ensembl database
Ensembl is a genome browser that annotates genes and predicts regulatory functions for vertebrate genomes. It uses raw DNA sequence data to create a tracking database and automatically finds genes and other features. Ensembl incorporates data from other sources and provides web-based access to genomic information through views of genes, transcripts, proteins, DNA homology, and more. It aims to make genome annotation freely accessible to support research.
The European Nucleotide Archive
Event: Plant and Animal Genomes Conference 2012.
Speaker: Bert Overduin
The European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena) provides a comprehensive record of the world's nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. Major components of ENA include the Sequence Read Archive (SRA) for next generation data and EMBL-Bank for assembled and annotated sequences. ENA works closely together with NCBI and DDBJ as partners in the International Nucleotide Sequence Database Collaboration (INSDC). Data arrive at ENA from a variety of sources. These include submissions of raw data, assembled sequences and annotation from small-scale sequencing efforts, data provision from the major European sequencing centres and routine and comprehensive exchange with our INSDC partners. Provision of nucleotide sequence data to ENA or its INSDC partners has become a central and mandatory step in the dissemination of research findings to the scientific community. ENA works with publishers of scientific literature and funding bodies to ensure compliance with these principles and provides a portfolio of interactive and programmatic submission services to ensure the smoothest flow possible of data into the public domain. ENA data can be searched using rapid sequence similarity and text search services provided both within web-based tools and under programmatic interfaces. Data can be retrieved in a variety of appropriate widely adopted formats through a web browser and extensive REST services. This presentation will consist of an introduction to ENA, followed by a short demonstration of the various ways data can be browsed and retrieved.
Tools of bioinforformatics by kk
INTRODUCTION
DEFINITION OF BIOINFORMATICS
HISTORY
OBJECTIVES OF BIOINFORMATICS
TOOLS OF BIOINFORMATICS
BIOLOGICAL DATABASES
HOMOLOGY AND SIMILARITY TOOLS (SEQUENCE ALIGNMENT)
PROTEIN FUNCTION ANALYSIS TOOLS
STRUCTURAL ANALYSIS TOOLS
SEQUENCE MANIPULATION TOOLS
SEQUENCE ANALYSIS TOOLS
APPLICATION
CONCLUSION
REFERENCES
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.
STRUCTURAL GENOMICS, FUNCTIONAL GENOMICS, COMPARATIVE GENOMICS
Structural genomics is a field that aims to determine the 3D structures of all proteins encoded by a genome. It involves determining structures on a large scale using techniques like X-ray crystallography and NMR. This allows identification of novel protein folds and potential drug targets. Comparative genomics compares genomic features between organisms and provides insights into evolution and conserved sequences and functions. It is a key tool in fields like medicine and agriculture.
Introduction to databases.pptx
This document provides an introduction to biological databases and bioinformatics tools. It defines biological sequences and databases, and describes the types of bioinformatics databases including primary, secondary, and composite databases. Examples of specific biological databases like GenBank, EMBL, and SwissProt are outlined. Common bioinformatics tools for sequence analysis, structural analysis, protein function analysis, and homology/similarity searches are listed, including BLAST, FASTA, EMBOSS, ClustalW, and RasMol. Finally, important bioinformatics resources on the web are highlighted.
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.
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
NCBI
The National Center for Biotechnology Information (NCBI) was created in 1988 as part of the National Library of Medicine at NIH. It establishes public databases for biological research, develops software tools for sequence analysis, and disseminates biomedical information from its location in Bethesda, MD. NCBI houses several integrated databases including PubMed, GenBank, RefSeq, and UniGene that contain literature, sequences, gene information, and more.
Genomic databases
Genomic databases are referred to as online repositories of genomic variants, described for a single (locus-specific) or more (general) genes or specifically for a population or ethnic group (national/ethnic).
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.
Tools and database of NCBI
The document discusses the National Center for Biotechnology Information (NCBI). It provides background that NCBI is part of the National Library of Medicine and houses databases relevant to biotechnology and biomedicine. It describes some of NCBI's major databases, including GenBank for DNA sequences and PubMed for biomedical literature. The document also discusses the BLAST tool and provides examples of some of NCBI's databases, such as the Nucleotide, Protein, and Structural databases.
sequence of file formats in bioinformatics
This document discusses different biological database file formats for storing sequence and molecular data. It describes several common formats including FASTA, Multi-FASTA, GeneBank flat-file format, GCG, EMBL, Clustal, and SWISS PROT. It provides details on the header, feature, and sequence sections of each format and how they represent genetic and protein information.
Sequence homology search and multiple sequence alignment(1)
Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal (or lateral) gene transfer event (xenologs).[1]
Homology among DNA, RNA, or proteins is typically inferred from their nucleotide or amino acid sequence similarity. Significant similarity is strong evidence that two sequences are related by evolutionary changes from a common ancestral sequence. Alignments of multiple sequences are used to indicate which regions of each sequence are homologous.
Biological databases
This document provides an introduction to biological databases. It discusses what databases are and features of an ideal database. It describes the relationships between primary sequence databases like GenBank that contain original submissions, and derived databases like RefSeq that are curated by NCBI. Key databases at NCBI are described, including GenBank, RefSeq, and Entrez, which allows integrated searching across multiple databases. The benefits of data integration through linking related information are highlighted.
NGS: Mapping and de novo assembly
This document provides an introduction and overview of common methods for processing and analyzing next generation sequencing (NGS) data, including mapping NGS reads and de novo assembly of NGS reads. It discusses various NGS applications such as RNA-Seq, epigenetics, structural variation detection, and metagenomics. Key steps in read alignment such as choosing an alignment program and viewing alignments are outlined. Considerations for choosing an alignment program based on library type, read type, and platform are also reviewed. Popular alignment programs including Bowtie, BWA, TopHat, and Novoalign are mentioned.
Est database
ESTs are short sequences of DNA that represent genes expressed in certain tissues or organisms. They provide a quick and inexpensive way for scientists to discover new genes and map their positions in genomes. ESTs represent a snapshot of genes expressed in a tissue at a given time. Sequencing the beginning or end of cDNA clones produces 5' and 3' ESTs, which can help identify genes and study gene expression and regulation.
Gemome annotation
This presentation elaborates the genome annotation. This presentation presented at PMAS-UAAR, Pakistan.
Delivered by: MUHAMMAD TAJAMMAL KHAN
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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Gen bank databases
GenBank is a database that contains annotated nucleotide and protein sequences. It includes genomic DNA, mRNA, and EST sequences. There are three main sections in a GenBank file - the header, features, and sequence. The header provides definition, accession number, organism, and reference information. The features section contains gene and protein annotation. The sequence section displays the actual nucleotide or amino acid sequence. Understanding the GenBank file format helps effectively search and retrieve sequences from this important biological database.
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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.
STRUCTURAL GENOMICS, FUNCTIONAL GENOMICS, COMPARATIVE GENOMICS
Structural genomics is a field that aims to determine the 3D structures of all proteins encoded by a genome. It involves determining structures on a large scale using techniques like X-ray crystallography and NMR. This allows identification of novel protein folds and potential drug targets. Comparative genomics compares genomic features between organisms and provides insights into evolution and conserved sequences and functions. It is a key tool in fields like medicine and agriculture.
Introduction to databases.pptx
This document provides an introduction to biological databases and bioinformatics tools. It defines biological sequences and databases, and describes the types of bioinformatics databases including primary, secondary, and composite databases. Examples of specific biological databases like GenBank, EMBL, and SwissProt are outlined. Common bioinformatics tools for sequence analysis, structural analysis, protein function analysis, and homology/similarity searches are listed, including BLAST, FASTA, EMBOSS, ClustalW, and RasMol. Finally, important bioinformatics resources on the web are highlighted.
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.
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
NCBI
The National Center for Biotechnology Information (NCBI) was created in 1988 as part of the National Library of Medicine at NIH. It establishes public databases for biological research, develops software tools for sequence analysis, and disseminates biomedical information from its location in Bethesda, MD. NCBI houses several integrated databases including PubMed, GenBank, RefSeq, and UniGene that contain literature, sequences, gene information, and more.
Genomic databases
Genomic databases are referred to as online repositories of genomic variants, described for a single (locus-specific) or more (general) genes or specifically for a population or ethnic group (national/ethnic).
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.
Tools and database of NCBI
The document discusses the National Center for Biotechnology Information (NCBI). It provides background that NCBI is part of the National Library of Medicine and houses databases relevant to biotechnology and biomedicine. It describes some of NCBI's major databases, including GenBank for DNA sequences and PubMed for biomedical literature. The document also discusses the BLAST tool and provides examples of some of NCBI's databases, such as the Nucleotide, Protein, and Structural databases.
sequence of file formats in bioinformatics
This document discusses different biological database file formats for storing sequence and molecular data. It describes several common formats including FASTA, Multi-FASTA, GeneBank flat-file format, GCG, EMBL, Clustal, and SWISS PROT. It provides details on the header, feature, and sequence sections of each format and how they represent genetic and protein information.
Sequence homology search and multiple sequence alignment(1)
Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal (or lateral) gene transfer event (xenologs).[1]
Homology among DNA, RNA, or proteins is typically inferred from their nucleotide or amino acid sequence similarity. Significant similarity is strong evidence that two sequences are related by evolutionary changes from a common ancestral sequence. Alignments of multiple sequences are used to indicate which regions of each sequence are homologous.
Biological databases
This document provides an introduction to biological databases. It discusses what databases are and features of an ideal database. It describes the relationships between primary sequence databases like GenBank that contain original submissions, and derived databases like RefSeq that are curated by NCBI. Key databases at NCBI are described, including GenBank, RefSeq, and Entrez, which allows integrated searching across multiple databases. The benefits of data integration through linking related information are highlighted.
NGS: Mapping and de novo assembly
This document provides an introduction and overview of common methods for processing and analyzing next generation sequencing (NGS) data, including mapping NGS reads and de novo assembly of NGS reads. It discusses various NGS applications such as RNA-Seq, epigenetics, structural variation detection, and metagenomics. Key steps in read alignment such as choosing an alignment program and viewing alignments are outlined. Considerations for choosing an alignment program based on library type, read type, and platform are also reviewed. Popular alignment programs including Bowtie, BWA, TopHat, and Novoalign are mentioned.
Est database
ESTs are short sequences of DNA that represent genes expressed in certain tissues or organisms. They provide a quick and inexpensive way for scientists to discover new genes and map their positions in genomes. ESTs represent a snapshot of genes expressed in a tissue at a given time. Sequencing the beginning or end of cDNA clones produces 5' and 3' ESTs, which can help identify genes and study gene expression and regulation.
Gemome annotation
This presentation elaborates the genome annotation. This presentation presented at PMAS-UAAR, Pakistan.
Delivered by: MUHAMMAD TAJAMMAL KHAN
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.
Gene identification using bioinformatic tools.pptx
This document discusses various bioinformatics tools and methods for identifying genes from genomic sequences. It begins by defining genes and genomes, then describes reference databases like RefSeq that are important for gene identification. It outlines the general workflow for gene identification, including obtaining sequences, preprocessing, annotation, prediction, and validation. Specific tools mentioned include GENSCAN, Glimmer, and Augustus for gene prediction, and BLAST for sequence alignment. The document also discusses identifying other genomic features like promoters, repeats, and open reading frames. It emphasizes that accurate gene identification requires both computational and experimental approaches.
prediction methods for ORF
prediction methods for ORF. homology based method, ab initio based . this slide is fully content with prediction methods for ORF.
Introduction to NCBI
This presentation will tells about the Introduction and working of NCBI database and is informative for the beginners.
Gen bank databases
GenBank is a database that contains annotated nucleotide and protein sequences. It includes genomic DNA, mRNA, and EST sequences. There are three main sections in a GenBank file - the header, features, and sequence. The header provides definition, accession number, organism, and reference information. The features section contains gene and protein annotation. The sequence section displays the actual nucleotide or amino acid sequence. Understanding the GenBank file format helps effectively search and retrieve sequences from this important biological database.
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This document summarizes a presentation about providing next-generation sequencing analysis capabilities using Globus Genomics. It outlines challenges with current manual approaches to sequencing data analysis, including difficulties moving large datasets between locations and maintaining complex analysis scripts. The presentation introduces Globus Genomics, which uses Globus data transfer services integrated with Galaxy to provide a workflow-based system for sequencing analysis without requiring local installation or configuration. Key benefits include on-demand access to scalable cloud resources, ability to easily modify and reuse analysis workflows, and integration with data sources. The system aims to accelerate genomic research by automating and simplifying analysis.
Supporting Barack Obama for President
This document summarizes Senator Barack Obama's health policy plan, which focuses on achieving universal health care coverage, health care reform, and strengthening public health. It outlines some of the key problems in the current US healthcare system from the perspectives of providers, purchasers, and consumers. Obama's plan would invest in health information technology and reform reimbursement to align with quality. The plan is estimated to cost $50-65 billion annually but could save $120-200 billion through reduced administrative costs, improved disease management, and health IT savings. If implemented, it could lower family insurance costs by $2,500 and cover 10 million more people.
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Tips for effective use of BLAST and other NCBI tools
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Experiences In Building Globus Genomics Using Galaxy, Globus Online and AWS
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ADAS&ME presentation @ the SCOUT project expert workshop (22-02-2017, Brussels)
ADAS&ME presentation @ the SCOUT project expert workshop (22-02-2017, Brussels)
Similar to Ncbi basic intro_v_pitt_kent_osu
Biological databases
This document discusses biological databases and provides information about GenBank. It defines biological databases as structured collections of biological information consisting of records with predefined fields. It then describes GenBank as the primary database for nucleotide sequences, containing over 100,000 organism sequences submitted from laboratories worldwide. GenBank is one of three major sequence databases that share data daily and is accessible through NCBI's retrieval system Entrez.
Kim Pruitt trainingbiocuration2015
The document discusses curating sequence and literature data for RefSeq and Gene at the National Center for Biotechnology Information. It provides an overview of RefSeq, describing what RefSeq is, how it compares to GenBank, its advantages, and how the RefSeq dataset is built through curated data and sequence analysis. It then discusses the curation process in depth, including examples of curating genes, transcripts, proteins, and literature. It also describes the tools and quality assurance checks used in curation.
DNA Sequence Data in Big Data Perspective
This document discusses opportunities and constraints related to DNA sequencing and analysis. It describes how DNA sequencing is used in academic research, oncology, gene therapy, developing genetically modified organisms, clinical diagnosis, forensics, and pedigree analysis. It also outlines some of the agencies and databases involved and how the capability and cost of sequencing has grown exponentially over time. Finally, it discusses some of the practical constraints in analyzing large DNA sequence data, including reading frames, exons/introns, errors, and the significance of non-coding DNA.
Next-Generation Sequencing and Data Analysis.pptx
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Junior: $60,000 - $90,000
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4. Project Manager: $80,000 - $120,000
5. Academic Faculty: $80,000 - $150,000 (Assistant Professor), $100,000 -
Storing and Accessing Information. Databases and Queries (UEB-UAT Bioinformat...
Storing and Accessing Information. Databases and Queries (UEB-UAT Bioinformat...VHIR Vall d’Hebron Institut de Recerca
Course: Bioinformatics for Biomedical Research (2014).
Session: 1.2- Storing and Accessing Information. Databases and Queries.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona. Use of CEDAR Technology for Ontology-based Submission of Biomedical Data to ...
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This document discusses using the CEDAR technology to improve ontology-based submission of biomedical data to the National Center for Biotechnology Information (NCBI). It describes how CEDAR can link metadata submissions to standard ontologies, helping to address current issues like inconsistent terminology use. Templates were created to submit Adaptive Immune-Receptor Repertoire (AIRR) sequencing data to NCBI repositories like BioProject, BioSample and Sequence Read Archive using controlled vocabularies. This workflow aims to enhance metadata findability, accessibility, interoperability and reusability across NCBI's siloed repositories.Bioinformatics مي.pdf
Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data. It is an interdisciplinary field, which harnesses computer science, mathematics, physics, and biology
Database in bioinformatics
A database is a structured collection of data that can be easily accessed, managed, and updated. It consists of files or tables containing records with fields. Database management systems provide functions like controlling access, maintaining integrity, and allowing non-procedural queries. Major databases include GenBank, EMBL, and DDBJ for nucleotide sequences and UniProt, PDB, and Swiss-Prot for proteins. The NCBI maintains many biological databases and provides tools for analysis.
Karyotype DAS client
The document discusses the Karyotype DAS client, which provides an interactive web client for visualizing and comparing genome annotations from different DAS sources. It summarizes the Distributed Annotation System (DAS) protocol, which allows for distributed genome annotations across multiple independent databases viewed together. The Karyotype DAS client is presented as a solution for exploring genome information and annotations in their full genomic context. Researchers can integrate their own annotations and analysis results by making them accessible via the virtual DAS registry.
Informal presentation on bioinformatics
Here are some suggestions for open online bioinformatics lectures and courses from famous universities:
- MIT OpenCourseWare has free bioinformatics course materials and videos from MIT courses.
- edX has massive open online courses (MOOCs) in bioinformatics from universities like Harvard, Berkeley, MIT. Some are free to audit.
- Coursera has bioinformatics courses from top universities like Johns Hopkins, University of Toronto, Peking University.
- YouTube has full lecture videos from bioinformatics courses at universities like Stanford, UC San Diego, University of Cambridge.
- Khan Academy has introductory bioinformatics lectures on topics like sequence alignment, gene finding, protein structure.
- EMBL-
Building bioinformatics resources for the global community
1. The document evaluates different methods for inferring relationships between Salmonella samples based on whole genome sequencing data from large databases. It compares k-mer based methods and site-based methods using 18,997 Salmonella isolates from public databases.
2. Site-based methods like NUCmer and MLST produced more accurate results, but require more computing resources when dealing with large databases. K-mer based methods are faster but more sensitive to assembly and contamination issues.
3. While k-mer methods may be useful for initial filtering, site-based methods are superior for accuracy, though challenges remain in applying them to databases containing tens of thousands of samples. Quality control and computing resources are important considerations.
Quantitative Medicine Feb 2009
1) Quantitative medicine uses large amounts of medical data and advanced analytics to determine the most effective treatment for individual patients based on their specific clinical profile and biomarkers. This approach can help reduce healthcare costs and improve outcomes compared to the traditional one-size-fits-all model.
2) However, realizing the promise of quantitative personalized medicine is challenging due to the huge quantities of diverse medical data located in dispersed systems, lack of computing capabilities, and barriers to data sharing.
3) Grid and service-oriented computing approaches are helping to address these challenges by enabling federated querying, analysis, and sharing of medical data and services across organizations through virtual integration rather than true consolidation.
Presentation from Code Camp 2017
This document summarizes a project to gather scientific data on medicinal plants from various online resources and load it into an internal database. The project involved extracting taxonomic data like scientific names and IDs for plants from resources like Kew Gardens, ITIS, GRIN, and NCBI Taxonomy. This data was transformed and loaded into spreadsheets with direct URLs to each source record. The KNIME tool was used to develop workflows to retrieve, parse, and transform the plant data from each source into the required output format for the database. This process facilitated regulators' ability to make timely, informed decisions about herbal products.
Jax bio dataworldcongress.ngs.20181128finalwithoutbu
This document discusses next generation sequencing (NGS) data and implications for data stewardship. It notes that NGS allows measuring the full-length transcriptome, including alternatively spliced transcripts specific to samples. This alters gene models and highlights the need to capture gene models and context in data commons for future reuse. The document also recommends that more metadata be captured about samples, experiments, and instruments to provide context and aid in data processing. It emphasizes making data FAIR (findable, accessible, interoperable, and reusable) according to W3C standards to improve data stewardship and enable both human and machine use of data.
Data retriveal ,srg and dbget
The document discusses three main text-based biological databases for data retrieval - Entrez, Sequence Retrieval System (SRS), and DBGET/LinkDB. It provides details on the types of data each database searches, features, and how to perform searches. Entrez searches nucleotide, protein, and literature databases and links related records. SRS is a search interface for over 80 biology databases at EBI. DBGET/LinkDB integrates database searching with tools like BLAST at GenomeNet in Japan.
database retrival.pdf
The document summarizes the various database resources and tools provided by the National Center for Biotechnology Information (NCBI). It describes NCBI's data retrieval systems like Entrez and PubMed, sequence analysis tools like BLAST, and resources for gene sequences, chromosomal sequences, genomes, gene expression, and protein structures. NCBI maintains the GenBank nucleic acid sequence database and provides data retrieval, analysis, and additional biological resources through its website.
Final Acb All Hands 26 11 07.Key
The BioMANTA Network project aims to develop a Semantic Web infrastructure for computational modelling and analysis of large-scale protein-protein interaction and compound activity networks. This will involve creating a Semantic Interactome Model using an OWL-based ontology to represent public interaction data. The goals are to perform network inference and knowledge discovery through network meta-analysis and global network inference. Results will be visualized using COBALT software and high quality ontologies and data sets will be produced.
FAIR as a Working Principle for Cancer Genomic Data
This document discusses making cancer genomic data FAIR (Findable, Accessible, Interoperable, and Reusable) as a working principle. It summarizes a talk given by Ian Fore of the National Cancer Institute on using FAIR data principles for cancer genomic data. The document also briefly describes several other talks from a conference track on FAIR data.
BIOLOGICAL SEQUENCE DATABASES
The document discusses the National Center for Biotechnology Information (NCBI), which maintains biological databases and provides bioinformatics tools. NCBI houses both primary databases directly submitted by researchers and secondary databases compiled from primary sources. Major databases include GenBank (nucleotide sequences), PubMed Central (biomedical literature), and reference sequence databases. Tools like BLAST, Entrez, and ORFfinder allow users to search and analyze sequence data. NCBI aims to make biomedical research data freely accessible worldwide.
Intro to databases
This document provides an introduction to biological databases. It discusses primary databases like GenBank which contain original sequence submissions and secondary databases derived from primary data, maintained by third parties like NCBI. Some key databases mentioned include GenBank, PDB, Swiss-Prot. The document also provides an overview of the NCBI and Entrez retrieval system, which allows integrated searches across literature and sequences.
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Storing and Accessing Information. Databases and Queries (UEB-UAT Bioinformat...
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Building bioinformatics resources for the global community
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Jax bio dataworldcongress.ngs.20181128finalwithoutbu
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FAIR as a Working Principle for Cancer Genomic Data
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This document discusses federating genomic sequencing data across cloud platforms for discovery. It presents four topic areas as examples: 1) virus characterization and discovery through building an index of viral signatures, 2) generating systems to analyze genome graphs to compare individuals to communities, 3) annotating haplotypes and graphs to query complex disease, and 4) indexing data flexibly for federated discovery anywhere through APIs. It emphasizes that metadata is needed to contextualize data and maximize its utility for answering biological questions.
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Ben Busby is the founder of the Department of Bioinformatics and Data Science at the National Institutes of Health. He discusses hosting numerous data hackathons in 2019 focused on topics like RNA sequencing, human pan-genomics, variant datasets, computational medicine, prokaryotic virulence factors, single cell genomics, and structural variants. The hackathons bring researchers together to collaborate on bioinformatics tools and resources. Busby emphasizes the importance of open communication and creating a community to advance genomic and biomedical research.
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Ben Busby is the founder of the Department of Bioinformatics and Data Science at NIH. He discusses several hackathon projects focused on analyzing RNAseq data and developing machine learning applications for genomics. These include tools for polygenic SNP search, genomic robots, and phenogenomics. Upcoming hackathons are planned on topics like RNAseq, human pan-genomics, variant datasets, and virus discovery. The goal is to create an open community around bioinformatics and data science.
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Ben Busby is the founder of the Department of Bioinformatics and Data Science at NIH. He discusses several bioinformatics tools and resources developed during NIH hackathons, including tools for polygenic SNP search, RNAseq analysis, and virus discovery. Upcoming hackathon events focus on RNAseq, human pan-genomics, variant datasets, and more. The goal is to create an open bioinformatics community.
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Sage 2 19_v5_busby
This document contains information about various bioinformatics tools and resources developed by Ben Busby at NCBI, including links to BLAST cloud software, Docker images, and GitHub repositories. It also lists upcoming hackathons focused on data analysis and encourages participation in the bioinformatics community through communication on LinkedIn and contributing to existing events.
Bb health ai_jan26_v2
This document discusses NCBI health resources and hackathons. It provides information about new BLAST features like CloudBLAST and Docker images. It also discusses using BLAST Docker images and magicBLAST. The document describes how to find data when metadata is insufficient by using the taxonomically indexed SRA. It lists several hackathon projects related to virus discovery, gene expression aging, and variant calling. It promotes creating a bioinformatics community and announces upcoming data analysis hackathons.
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This document discusses making the transition from simply sharing genomic data to sharing knowledge and insights gained from the data. It provides information on various tools from NCBI for searching, accessing, and analyzing genomic data including EUtils, EDirect, magicblast, and genomic analysis tools on GitHub. It also encourages creating an open community around sharing knowledge gained from genomic data analysis.
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The document appears to be from a presentation given by Ben Busby on making the transition from sharing data to sharing knowledge. It discusses various tools and resources for accessing genomic data from the National Center for Biotechnology Information including EUtils, EDirect, PubMed FTP, and more. The presentation also highlights several genomic analysis tools and hackathon projects developed by the NCBI community.
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This document summarizes Ben Busby's presentation on making the transition from sharing data to sharing knowledge at NCBI. The presentation discusses NCBI tools and resources like EUtils, EDirect, PubMed FTP, and SRA. It also highlights several genomic analysis tools developed through NCBI hackathons and available on GitHub. The document lists Busby's contact information and encourages attendees to check his SlideShare profile and NCBI's bioinformatics training program for more details.
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Recently uploaded
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.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...Abdul Wali Khan University Mardan,kP,Pakistan
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skillsApplied Science: Thermodynamics, Laws & Methodology.pdf
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
20240520 Planning a Circuit Simulator in JavaScript.pptx
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Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
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https://www.etran.rs/2024/en/home-english/Cytokines and their role in immune regulation.pptx
This presentation covers the content and information on "Cytokines " and their role in immune regulation .
The debris of the ‘last major merger’ is dynamically young
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Equivariant neural networks and representation theory
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Phenomics assisted breeding in crop improvement
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
8.Isolation of pure cultures and preservation of cultures.pdf
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3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Particle-In-Cell (PIC) algorithm,
Plasma expansion in the dipole magnetic field.
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The Petroleum System of the Çukurova Field - the Shallowest Oil Discovery of Türkiye, Adana
What is greenhouse gasses and how many gasses are there to affect the Earth.
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
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ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
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Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
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Deep Software Variability and Frictionless Reproducibility
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Cytokines and their role in immune regulation.pptx
Cytokines and their role in immune regulation.pptx
The debris of the ‘last major merger’ is dynamically young
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The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
8.Isolation of pure cultures and preservation of cultures.pdf
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aziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobel
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
3D Hybrid PIC simulation of the plasma expansion (ISSS-14)
What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
Nucleic Acid-its structural and functional complexity.
Nucleic Acid-its structural and functional complexity.
Ncbi basic intro_v_pitt_kent_osu
- 2. Develop and maintain molecular and bibliographic databases. Develop software for searching, and analysis of these data. Provide Web access point for data and software. 1/24/2017 2
- 3. Sequences Expression Genome Maps 3D Structures Protein Domains Homologous Genes, Proteins, Structures Pathways Genetic Variation 1/24/2017 3
- 4. Biomedical Literature PubMed, PubMed Central, Bookshelf Molecular Databases and Metadatabases Sequences, Structures, Variation, Chemicals etc. Clinical / Medical Genetics GTR, ClinVar, MedGen, OMIM, PubMed Health, dbGaP 1/24/2017 4
- 5. Primary Data /Database Results of a particular technique Submitted to NCBI Submitter has editorial control Curated Data /Database Based on primary database records Third party (NCBI) maintains and updates Often includes additional analyses 1/24/2017 5
- 6. Sequences (DNA) GenBank (International Sequence Database Collaboration) now 2.1 X 1012 bases Sequence Read Archive (SRA), Next-Gen sequence reads now 9.7 X 1015 bases! Other databases with a primary component Expression Gene Expression Omnibus RNA-Seq, Microarray, Other high throughput data Variation dbSNP small scale variants dbVar genomic structural studies Database of Genotype and Phenotype (dbGaP) 1/24/2017 6
- 7. Sequences GenPept translations of CDS regions on INSDC records NCBI Reference Sequences (DNA and Protein) Variation NCBI Reference SNPs (non-redundant set of variants) Structures NCBI’s MMDB based on PDB Conserved Domains NCBI Conserved Domain Database 1/24/2017 7
- 8. Entrez integrated literature and molecular databases Graphical Sequence Viewer annotation viewer and analysis tool BLAST sequence similarity search service VAST structure similarity searches Cn3D 3D structure viewer Genome Workbench standalone sequence analysis annotation platform SRA Utilities SRA Run Browser web access for viewing, searching and downloading next-generation reads SRA toolkit standalone SRA manipulator and client 1/24/2017 8
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- 11. Literature PubMed, PMC, Books Sequences Protein, Nuccore, GSS, SRA, Assembly Expression GEO profiles Variation dbSNP, dbVaR Protein and Nucleic acid structures Structure Small Molecules PubChem Medical Genetics ClinVar, MedGen, GTR 1/24/2017 11
- 12. Central Resources / Databases • Taxonomy • BioProject • Assembly • Gene Follow links to others when needed Nucleotide, Protein, SRA 1/24/2017 12 The Entrez system: 39 (and counting) integrated databases
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- 14. If your question is about data for ... an organism -> Taxonomy a gene name -> Gene (common organisms) a large-scale project -> BioProject a bacterial genome -> Genome a genome sequence -> Assembly 1/24/2017 14
- 15. Organizes gene-centered data Biological role; genomic context; phenotypes; interactions; literature Sequences Genomic Transcript Proteins Best entry point for many biomolecular searches Eukaryotic and Microbial Genomes 17.3 million records for 13,566 taxa 1/24/2017 15
- 16. Provide a reference standard Represent all molecules in the central dogma Selected Eukaryotes Genomic Transcripts Proteins All Prokaryotes and Viruses Genomic and Protein only Maintained by NCBI staff and outside experts Distinct accession series (NC_, AC_, NG_, NM_, NM_, NR_, XM_, XR_) 1/24/2017 16
- 17. Specific gene: XXX[Symbol] AND YYY[Organism] APRT[Symbol] AND human[Organism] apt[Symbol] AND Escherichia coli[Organism] All genes: YYY[Organism] AND current only[Filter] zebrafish[Organism]AND current only[Filter] 1/24/2017 17
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- 20. UniGene GEO Profiles Expression HomoloG ene Homologs PubMed PMC Literature Gene • Genomic Structure • Orthologs via Gpipe Structure Structures SNP ClinVar Variation OMIMdbGaP Nuccore Protein Sequences Homologs via Blink Proteins w Structure via Related Strutures SRA 20 1/24/2017
- 21. Learn: <ncbi>/learn.shtml Factsheets: <ftp>/pub/factsheets/ NCBI YouTube Channel: (www.youtube.com/ncbinlm) NCBI Helpdesk: info@ncbi.nlm.nih.gov 1/24/2017 21