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.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
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.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
INTRODUCTION OF BIOINFORMATICS
HISTORY
WHAT IS DATABASE
NEED FOR DATABASE
TYPES OF DATABASE
PRIMARY DATABASE
NUCLEIC ACID SEQUENCE DATABASE
GENE BANK
INTRODUCTION
GENE BANK SUBMISSION TOOL
GENE BANK SUBMISSION TYPE
HOW TO RETRIEVE DATA FROM GENEBANK
APPLICATION
CONCLUSION
REFERENCE
In this presentation, I talk about the various tools for the submission of DNA or RNA sequences into various sequence databases. The sequence submission tools talked about in this presentation are BankIt, Sequin and Webin.
As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data.
Protein Sequence, Structure, and Functional Databases: UniProtKB, Swiss-Prot, TrEMBL, PIR, MIPS, PROSITE, PRINTS, BLOCKS, Pfam, NDRB, OWL, PDB, SCOP, CATH, NDB, PQS, SYSTERS, and Motif. Presented at UGC Sponsored National Workshop on Bioinformatics and Sequence Analysis conducted by Nesamony Memorial Christian College, Marthandam on 9th and 10th October, 2017 by Prof. T. Ashok Kumar
Information recovery is the recovery of things (objects, Web pages, archives, and so forth) that fulfill explicit conditions set in an ordinary articulation like query. While IR targets fulfilling a bit of client data need generally communicated in common language, information recovery targets figuring out which records contain the specific terms of the user queries.
INTRODUCTION OF BIOINFORMATICS
HISTORY
WHAT IS DATABASE
NEED FOR DATABASE
TYPES OF DATABASE
PRIMARY DATABASE
NUCLEIC ACID SEQUENCE DATABASE
GENE BANK
INTRODUCTION
GENE BANK SUBMISSION TOOL
GENE BANK SUBMISSION TYPE
HOW TO RETRIEVE DATA FROM GENEBANK
APPLICATION
CONCLUSION
REFERENCE
In this presentation, I talk about the various tools for the submission of DNA or RNA sequences into various sequence databases. The sequence submission tools talked about in this presentation are BankIt, Sequin and Webin.
As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret the biological data.
Protein Sequence, Structure, and Functional Databases: UniProtKB, Swiss-Prot, TrEMBL, PIR, MIPS, PROSITE, PRINTS, BLOCKS, Pfam, NDRB, OWL, PDB, SCOP, CATH, NDB, PQS, SYSTERS, and Motif. Presented at UGC Sponsored National Workshop on Bioinformatics and Sequence Analysis conducted by Nesamony Memorial Christian College, Marthandam on 9th and 10th October, 2017 by Prof. T. Ashok Kumar
Information recovery is the recovery of things (objects, Web pages, archives, and so forth) that fulfill explicit conditions set in an ordinary articulation like query. While IR targets fulfilling a bit of client data need generally communicated in common language, information recovery targets figuring out which records contain the specific terms of the user queries.
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)
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
Data-knowledge transition zones within the biomedical research ecosystemMaryann Martone
Overview of the Neuroscience Information Framework and how it brings together data, in the form of distributed databases, and knowledge, in the form of ontologies to show the mapping of the dataspace and places where there are mismatches between data and knowledge.
This ppt. includes the list of medicinal plants along with their applications which can be easily grown on the terrace. These plants are easy to cultivate and maintain lushed with benefits of their therapeutic values which help to cure ailments.
This topic discusses how microbes or any other living entity could be used as a biological weapon that can cause a threat to humans. This can also be a leading cause of the economical breakdown of a country and can also turn out to be in a form of a pandemic affecting the whole world as happened in the case of novel coronavirus.
This ppt. is about bacteria, its taxonomy, nomenclature, types of bacteria upon oxygen dependency, physiological factors responsible for its growth and development. Cultural characteristics, habitat, and classification on the basis of mode of nutrition.
human settlements/communities increased the possibility of a disease infecting many people at a time in a geographical area i.e known as an epidemic. And with time as communities mingled with each other either for trade, war, etc. they spread the diseases to new location fueling the pandemic.
PubMed provides links to the integrated molecular biology databases maintained by NCBI. These databases contain: DNA and protein sequences, genome mapping data, and 3‑D protein structures, aligned sequences from populations, and the Online Mendelian Inheritance in Man (OMIM). Links between MEDLINE records and sequence records make it easy to find MEDLINE abstracts associated with sequence records and vice versa.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
1. DATA RETRIEVAL SYSTEM
Text-based Database Searching
Submitted By:
Dr. Shikha Thakur
Assistant Professor (Guest Faculty)
TCSC
Mumbai
Maharashtra
2. • The amount of biologically relevant data accessible via the WWW is
increasing at a very rapid rate.
• It is important for scientists to have easy and efficient ways of wading
through the data and finding what is important for their research.
• Knowing how to access and search for information in the database is
essential.
3. Depending on the type of data at hand, there are
two basic ways of searching:
• Using descriptive words to search text databases.
• Using a nucleotide or protein sequence to search sequence
databases.
4. Text- based database Searching
• There are three important data retrieval systems of particular
relevance to molecular biologists:
• Entrez ( at NCBI) (GI(Global Image disk image file) /Accession no.
• Sequence Retreival System, SRS (at EBI)
• DBGET/LinkDB (At Japan)
• The advantage of these retrieval systems is that they not only return
matches to a query, but also provide handy pointers to additional
important information in related databases.
5. Text-based database Searching
• The three systems differ in the databases they search and the links
they provide to other information.
• In using any of these systems, queries can be as simple as entering
the accession number of a newly published sequence or as complex
as searching multiple database fields for specific terms.
6. Text-Based Database Searching
• Basic Search Concepts
• Boolean Search – An advanced query search using two or more terms,
using Boolean operator AND, OR, NOT, default – AND
• Broadening the Search – If the results of a search produce no useful
entries, change or remove terms.
• Narrowing the search – If the results of a search produce no useful entries,
change or remove terms.
• Proximity Searching – To search with multiword terms or phrases, place
quotes around the terms.
• Wild Card – The character prepended or appended to a search term make
a search less specific., e.g., to look for all authors with last name Zav,
search using Zav*.
7. Entrez
• Entrez – is a molecular biology database and retrieval system
developed by the National Center for Biotechnology Information
(NCBI).
• It is an entry point for exploring distinct but integrated databases.
• (http://www.ncbi.nlm.nih.gov/Entrez/)
8. Entrez
• The Entrez system provides access to:
• Nucleotide sequence databases- GenBank/DDBJ/EBI
• Protein sequence databases – Swiss-Prot, PIR, PRF, PDB, and translated
protein sequences from DNA sequence databases.
• Genome and chromosome mapping data
• Molecular Modeling 3-D structures Databases.
• Literature database, PubMed – Provides excellent and easy access to
MEDLINE and pre-MEDLINE articles.
• Taxonomy database – Allows retrieval of DNA and protein sequences for
any taxonomic group.
• Specialized Databases – OMIM, dbSNP, UniSTS, etc.
9.
10.
11.
12.
13.
14.
15.
16.
17. Entrez
• The most valuable feature of Entrez is
• Its exploitation of the concept of ’neighbouring’.
• Which allows related articles indifferent databases to be linked to
each other, whether or not they are cross-referenced directly.
• Neighbours and links are listed in the order of similarity to the query.
• The similarity is based on pre-computed analysis of sequences,
structures and the literature.
18. Entrez
• One particularly useful feature in Entrez is –
• The ability to retrieve large sets of data based on some criterion and
to download them to a local computer- Batch Entrez
• Allowing these sequences to be worked on using analytical tools
available on local computer.
19. Entrez Features
1. Entrez Global Query – Search a subset of Entrez databases.
2. Batch Entrez –Upload a file of GI or accession numbers to retrieve
sequences.
3. Making Links Entrez – Linking to PubMed and Genbank
4.E-Utilities – Entrez programming utilities
5. LinkOut – External links to related resources.
6. Cubby – Provides with a stored search feature to store and update
searches, allows to customize your LinkOut display.
20. SRS.
• The Sequence Retrieval System (SRS) – A network browser for
datbases in molecular biology.
• It is a powerful sequence information indexing, search and retrieval
system (http://srs.ebi.ac.uk/)
21.
22.
23. SRS
• SRS is a homogeneous interface to over 80 biological databases
developed at the European Bioinformatics Institute (EBI) at Hinxton,
UK.
• The types of databases included are sequence and sequence related,
metabolic pathways, transcription factors, application results (e.g.,
BLAST), protein 3D- structure, genome, mapping, mutations, and
locus-specific mutatins.
• One can access and query their contents and navigate among them.
24. SRS
The Web page listing all the databases contains a link to a description
page about the database and includes the date of last update.
One can select one or more datbases to search before entering the
query.
• Over 30 versions of SRS are currently running on the WWW. Each
includes a different subset of databases and associated analytical
tools.
25. SRS
• SRS Features:
• SRS databases are well indexed, thus reducing the search time for the
large number of potential databases.
• SRS allows any flat file database to be indexed to any other. The
advantage being the derived indices may be rapidly searched allowing
users to retrieve link and access entries from all the interconnected
resources.
• The system has the particular strength that it can be readily
customized to use any defined set of databanks.
26. SRS
• Simple SRS queries
• By accession number
• Query on accession number: J00231
• By a simple author or organism: Ausubel and Rhizobium
• Boolean relations between keywords: and, or, but not
27. SRS
• Contd…
• Searching by dates: 01-Jan-1995:31-Dec-1995.
• Searching by size: 400:600
• Using hypertext links in an entry: Medline, Swiss- Prot and PDB
entries can be linked from within the EMBL database.
• Display of molecules via Rasmol plug-in
28.
29. DBGET
• DBGET/LinkDB – Is an integrated bioinformatics database retrieval
system at GenomeNet, developed by the institute for Chemical
Research, Kyoto University, and the Human Genome Center of the
University of Tokyo.
30.
31.
32. DBGET
• DBGET – Is used to search and extract entries from a wide range of
molecular biology databases.
• LinkDB- Is used to compute links between entries in different
databases.
• It is designed to be a network distributed database system with an
open architecture, which is suitable for incorporating local databases
or establishing a server environment.
• http://www.genome.ad.jp/dbget/
33.
34.
35.
36.
37. DBGET
• DBGET/LinkDB is integrated with other search tools, such as BLAST,
FAST and MOTIF to conduct further retreivals instantly.
• DBGET provides access to about 20 databases, which are queried one
at a time. After querying one of these databases, DBGET presents
links to associated information in addition to the list of results.
• A unique feature of DBGET is its connection with the Kyoto
Encyclopedia of Genes and Genomes(KEGG) database – a database of
metabolic and regulatory pathways.
38.
39. DBGET
• DBGET has three basic commands (or three basic modes in the Web
version), bfind, bget, and blink, to search and extract database
entries.
• blink – To search and extract database entries.
• bget – Performs the retrieval of database entries specified by the
combination of dbname:identifier
• bfind – Is used for searching entries by keywords
• Notable feature of DBGET, different from other text search systems, is
that no keyword indexing is performed when a database is installed or
updated.
40. DBGET
• Selected fields are extracted and stored in separate files for bfind
searches.
• An advantage for rapid database updates, but sometimes a
disadvantage for elaborate searching.
• To supplement bfind, the full text search STAG is provided.
• blink – The LinkDB search. Once entries of interest are found, it can
be used to retrieve related entries in a given database or all databases
in GenomeNet.
41. Example
• Let’s consider an example to show how each system can be used to
retrieve the SwissProt entry P04391, an ornithine
carbamoyltransferase protein in Escherichia coli.
• In Entrez, enter the name P04391 in the protein database query
form and view the entry and associated links and neighbours.
42.
43.
44.
45. Example - SRS
• In SRS, first select the SwissProt database, then enter P04391 in the
query form and, once the entry is displayed search for links to other
related databases.
46.
47.
48.
49.
50. Example – LinkDB
• However, the fastest way of gathering the related information for this
entry is to search LinkDB.
• By simply entering swissport:P04391, a list of all links to all the
related databases is displayed.