The document provides information about a bioinformatics course including the syllabus, topics, and schedule. The course focuses on using relational databases to manage and analyze large biological datasets. Topics include relation databases, web application development, genome browsers, text mining, and systems biology. The schedule lists the topics to be covered each class over 12 weeks from March to May.
The document outlines the usage of the NCBI E-utilities API for programmatically accessing data from NCBI databases like PubMed, Nucleotide, and Gene. It describes the 8 E-utilities programs (ESearch, EPost, ESummary, EFetch, ELink, EInfo, EGQuery, ESpell) and provides examples of building analysis pipelines using combinations of the E-utilities, such as searching PubMed and linking results to Gene records or nucleotide sequences. Sample applications demonstrated include finding related human genes to PubMed articles on osteosarcoma, downloading nucleotide sequences for Burkholderia cepacia complex, and identifying PubMed articles discussing cancer copy number changes that used specific GEO microarray
The document outlines a schedule of bioinformatics lessons taking place from September to December 2011. It includes topics such as biological databases, sequence similarity, sequence alignments, database searching, phylogenetics, protein structure, gene prediction, and bio- and cheminformatics in drug discovery. Dates and times are provided for each lesson.
Bioinformatic Harvester is a software tool that acts as a meta search engine for genes and protein information. It collects and indexes data from 16 major bioinformatics databases and allows users to search across these databases simultaneously. Search results are displayed on a single HTML page and are ranked based on relevance. Users can query the system using terms like gene names, sequences, protein domains, and literature to retrieve integrated information from databases on genes and proteins.
The document introduces various bioinformatics databases and tools that are accessible through the Bioinformatic Harvester II platform, including Harvester, Source, SMART, PSORT, SOSUI, iHOP, GoPubmed, and STRING. Harvester allows users to search genes and proteins across multiple databases. Tools like SMART and PSORT allow for protein sequence analysis and subcellular localization prediction. The summary emphasizes that Harvester provides an integrated access point for experimental data, predicted domains, interactions, and cross-referenced information from sources like UniProt, NCBI, and Ensembl.
This document discusses using semantic web technologies for translational research in life sciences. It provides an overview of semantic web standards and outlines several projects demonstrating applications in healthcare and biomedical research. These include developing an active semantic electronic medical record, semantically annotating experimental glycomics data, and integrating diverse biomedical data sources using ontologies to enable complex querying and knowledge discovery.
The document describes a framework for biological relation extraction using biomedical ontologies and text mining. It discusses introducing biomedical text mining and outlines the problem, motivation, and challenges. It then presents the overall system components and architecture, including searching/browsing, Swanson's algorithm, protein-protein interactions, and gene clustering applications. The framework concept issues, design issues, sequence diagram, and database are also covered at a high level.
Standarization in Proteomics: From raw data to metadata filesYasset Perez-Riverol
The document discusses various mass spectrometry file formats used in proteomics workflows, including the advantages of XML-based formats like mzML and mzIdentML that support metadata and can be read by different software. It also describes challenges with proprietary binary formats and efforts to develop common data standards and APIs through projects like ProteoWizard, PRIDE, and the ms-core-api library. Standard file formats are important for sharing and reusing proteomics data over time as instrumentation and software evolve.
The document discusses views and materialized views in data warehousing and decision support systems. It covers three main points:
1) OLAP queries typically involve aggregate queries, so precomputation is essential for fast response times. Materialized views allow precomputing aggregates across multiple dimensions.
2) Warehouses can be thought of as collections of asynchronously replicated tables and periodically maintained views, renewing interest in efficient view maintenance.
3) Materialized views store the results of views in the database for fast access like a cache, but they require maintenance as underlying tables change. Incremental maintenance algorithms are ideal to efficiently update materialized views.
The document outlines the usage of the NCBI E-utilities API for programmatically accessing data from NCBI databases like PubMed, Nucleotide, and Gene. It describes the 8 E-utilities programs (ESearch, EPost, ESummary, EFetch, ELink, EInfo, EGQuery, ESpell) and provides examples of building analysis pipelines using combinations of the E-utilities, such as searching PubMed and linking results to Gene records or nucleotide sequences. Sample applications demonstrated include finding related human genes to PubMed articles on osteosarcoma, downloading nucleotide sequences for Burkholderia cepacia complex, and identifying PubMed articles discussing cancer copy number changes that used specific GEO microarray
The document outlines a schedule of bioinformatics lessons taking place from September to December 2011. It includes topics such as biological databases, sequence similarity, sequence alignments, database searching, phylogenetics, protein structure, gene prediction, and bio- and cheminformatics in drug discovery. Dates and times are provided for each lesson.
Bioinformatic Harvester is a software tool that acts as a meta search engine for genes and protein information. It collects and indexes data from 16 major bioinformatics databases and allows users to search across these databases simultaneously. Search results are displayed on a single HTML page and are ranked based on relevance. Users can query the system using terms like gene names, sequences, protein domains, and literature to retrieve integrated information from databases on genes and proteins.
The document introduces various bioinformatics databases and tools that are accessible through the Bioinformatic Harvester II platform, including Harvester, Source, SMART, PSORT, SOSUI, iHOP, GoPubmed, and STRING. Harvester allows users to search genes and proteins across multiple databases. Tools like SMART and PSORT allow for protein sequence analysis and subcellular localization prediction. The summary emphasizes that Harvester provides an integrated access point for experimental data, predicted domains, interactions, and cross-referenced information from sources like UniProt, NCBI, and Ensembl.
This document discusses using semantic web technologies for translational research in life sciences. It provides an overview of semantic web standards and outlines several projects demonstrating applications in healthcare and biomedical research. These include developing an active semantic electronic medical record, semantically annotating experimental glycomics data, and integrating diverse biomedical data sources using ontologies to enable complex querying and knowledge discovery.
The document describes a framework for biological relation extraction using biomedical ontologies and text mining. It discusses introducing biomedical text mining and outlines the problem, motivation, and challenges. It then presents the overall system components and architecture, including searching/browsing, Swanson's algorithm, protein-protein interactions, and gene clustering applications. The framework concept issues, design issues, sequence diagram, and database are also covered at a high level.
Standarization in Proteomics: From raw data to metadata filesYasset Perez-Riverol
The document discusses various mass spectrometry file formats used in proteomics workflows, including the advantages of XML-based formats like mzML and mzIdentML that support metadata and can be read by different software. It also describes challenges with proprietary binary formats and efforts to develop common data standards and APIs through projects like ProteoWizard, PRIDE, and the ms-core-api library. Standard file formats are important for sharing and reusing proteomics data over time as instrumentation and software evolve.
The document discusses views and materialized views in data warehousing and decision support systems. It covers three main points:
1) OLAP queries typically involve aggregate queries, so precomputation is essential for fast response times. Materialized views allow precomputing aggregates across multiple dimensions.
2) Warehouses can be thought of as collections of asynchronously replicated tables and periodically maintained views, renewing interest in efficient view maintenance.
3) Materialized views store the results of views in the database for fast access like a cache, but they require maintenance as underlying tables change. Incremental maintenance algorithms are ideal to efficiently update materialized views.
The document provides information about bioinformatics and BLAST (Basic Local Alignment Search Tool). It defines bioinformatics as the application of information technology to molecular biology. It describes what BLAST is and how it works to compare biological sequences and identify similar sequences in databases. It also lists different BLAST programs and databases that can be used depending on the type of sequence being searched.
NCBI has developed a powerful suite of online biomedical and bioinformatics resources, including old friends like PubMed and OMIM and newer resources such as Genome. This collection of databases and tools are widely used by scientists and medical professionals across the world. With such a wealth of information, it is easy to get overwhelmed. Join us for an overview to NCBI resources for the information professional with an emphasis on biodata connectivity. No science degree required!
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).
The NCBI Boot Camp for Beginners was designed to offer an overview of the NCBI suite of resources. In the first half of the presentation, highlighted databases were covered in four main categories: literature, sequences, genes & genomes and expression & structure. The second half of the class used the apolipoprotein A as a query that was explored through many of the NCBI databases, from identifying the reference sequences to a structural analysis of the Cys130Arg variant.
This document discusses next generation DNA sequencing technologies. It begins by describing some of the limitations of traditional Sanger sequencing, such as read lengths of 500-1000 bases and throughput of 57,000 bases per run. It then introduces some key next generation sequencing technologies, such as 454 sequencing which uses emulsion PCR and pyrosequencing to achieve read lengths of 20-100 bases but higher throughput of 20-100 Mb per run. Illumina/Solexa sequencing is also discussed, which uses sequencing by synthesis with reversible terminators and laser-based detection. Finally, third generation sequencing technologies are mentioned, such as Pacific Biosciences' single molecule real time sequencing and nanopore sequencing. In summary, the document provides a high-level
This document discusses various bioinformatics tools and their functions. It provides details on multiple sequence alignment tools like CLUSTAL Omega, CLUSTALW, BLAST, and FASTA. It explains that CLUSTAL Omega can align a large number of sequences quickly and accurately using progressive alignment. CLUSTALW performs multiple sequence alignment in three steps - pairwise alignment, guide tree creation, and multiple alignment using the guide tree. BLAST can identify unknown sequences by comparing them to known sequences. FASTA uses short exact matches to find similar regions between sequences. Expasy provides access to databases for proteomics, genomics, and other areas. MASCOT searches peptide mass fingerprinting and shotgun proteomics datasets.
Keynote presented at the Phenotype Foundation first annual meeting.
Describes data sharing, data annotation and the needs for further tool and ontology and ontology mapping development.
Amsterdam, January 18, 2016
Biological data is widely distributed over the web and can be retrieved using search engines like Google or data retrieval tools. Dedicated data retrieval tools for molecular biologists include Entrez, DBGET, and SRS which allow text searching of linked databases and sequence searching. Entrez, developed by NCBI, integrates information from databases including GenBank, PubMed, and OMIM. DBGET covers databases like GenBank, EMBL, and PDB. SRS, developed by EBI, integrates over 80 molecular biology databases.
WikiPathways: how open source and open data can make omics technology more us...Chris Evelo
This document discusses WikiPathways, an open source pathway database. It began in 2007 with the goals of having an online platform by March 2007 and gaining a first unknown user by January 2008, both of which were successes. WikiPathways has grown significantly since, now containing over 400 human pathways and 6,200 unique human genes. It receives over 1 million pageviews annually. The document advocates for opening up data and code to make omics technology more useful. It describes WikiPathways' various features including its BioPAX format, REST services, and integration with Cytoscape. It also discusses professionalizing open source and collaborating with existing communities and tools rather than trying to change the world alone.
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.
The document provides an overview of several important biomolecular databases. It begins by describing examples of biological databases including nucleic acid sequence databases like GenBank, EMBL, and DDBJ that contain DNA and RNA sequences. It also mentions UniProt, the major database of protein sequences and annotations. Finally, it briefly introduces the Gene Ontology project, which provides controlled vocabularies to describe gene and gene product attributes.
Bioinformatics involves the analysis of biological information using computers and statistical techniques,
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
The sequence alignment is made between a known sequence and unknown sequence or between two unknown sequences. The known sequence is called reference sequence. The unknown sequence is called query sequence .
BLAST stands for Basic Local Alignment Search Tool. It addresses a fundamental problem in bioinformatics research. BLAST tool is used to compare a query sequence with a library or database of sequences.
In Bioinformatics, is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences.
BLAST was developed by stochastic model of Samuel Karlin and Stephen Altschul in 1990. They proposed “a method for estimating similarities between the known DNA sequence of one organism with that of another”.
A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query sequence) with a library or database of sequences and identify database sequences that resemble the query sequence above a certain threshold.
BITs: Genome browsers and interpretation of gene lists.BITS
Module 5 Genome browsers and interpreting gene lists.
Part of training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
Using biological network approaches for dynamic extension of micronutrient re...Chris Evelo
This document discusses using biological network approaches to dynamically extend pathways with regulatory information such as microRNAs (miRNAs). It describes tools like PathVisio that can integrate gene expression, proteomics and metabolomics data onto pathways to identify significantly changed processes. WikiPathways is introduced as a public pathway resource that can be contributed to and curated by researchers. The document outlines approaches for visualizing regulatory interactions on pathways using plugins, exploring pathway interactions through network analysis, and integrating other data types such as SNPs, fluxes and gene annotations to build a more comprehensive understanding of biological systems.
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
The UCSC genome browser: A Neuroscience focused overviewVictoria Perreau
An self guided tutorial based overview of the UCSC genome browser for accessing public neuroscience data, in particular data from the ENCODE project. Including additional transcriptomic resources for the Neurosciences.
These is the second part of the lecture slides of the BITS bioinformatics training session on the UCSC Genome Browser.
See http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203990:orange-genome-browsers-ucsc-training&catid=81:training-pages&Itemid=190
Slides contain information about why bioinformatics appeared,
who bioinformaticians are, what they do, what kind of cool applications and challenges in bioinformatics there are.
Slides were prepared for the Bioinformatics seminar 2016, Institute of Computer Science, University of Tartu.
This document discusses protein sequence databases and their role in storing protein data generated from genome projects and new proteomics technologies. It describes several types of protein databases, including universal repositories like GenPept that store sequences with little annotation, and expertly curated databases like Swiss-Prot that enrich sequence data with additional validation and integration. Specialized databases also exist that focus on specific protein families, organisms, structures like SCOP, or classifications like CATH.
The document discusses various topics in bioinformatics and protein structure. It provides an overview of ongoing thesis topics at Biobix including biomarker prediction, methylation, metabolomics, peptidomics, and more. It also discusses the rationale for understanding protein structure and function, levels of protein structure from primary to quaternary, methods for determining structure like X-ray crystallography, and approaches to secondary structure prediction including Chou-Fasman.
The document provides information about bioinformatics and BLAST (Basic Local Alignment Search Tool). It defines bioinformatics as the application of information technology to molecular biology. It describes what BLAST is and how it works to compare biological sequences and identify similar sequences in databases. It also lists different BLAST programs and databases that can be used depending on the type of sequence being searched.
NCBI has developed a powerful suite of online biomedical and bioinformatics resources, including old friends like PubMed and OMIM and newer resources such as Genome. This collection of databases and tools are widely used by scientists and medical professionals across the world. With such a wealth of information, it is easy to get overwhelmed. Join us for an overview to NCBI resources for the information professional with an emphasis on biodata connectivity. No science degree required!
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).
The NCBI Boot Camp for Beginners was designed to offer an overview of the NCBI suite of resources. In the first half of the presentation, highlighted databases were covered in four main categories: literature, sequences, genes & genomes and expression & structure. The second half of the class used the apolipoprotein A as a query that was explored through many of the NCBI databases, from identifying the reference sequences to a structural analysis of the Cys130Arg variant.
This document discusses next generation DNA sequencing technologies. It begins by describing some of the limitations of traditional Sanger sequencing, such as read lengths of 500-1000 bases and throughput of 57,000 bases per run. It then introduces some key next generation sequencing technologies, such as 454 sequencing which uses emulsion PCR and pyrosequencing to achieve read lengths of 20-100 bases but higher throughput of 20-100 Mb per run. Illumina/Solexa sequencing is also discussed, which uses sequencing by synthesis with reversible terminators and laser-based detection. Finally, third generation sequencing technologies are mentioned, such as Pacific Biosciences' single molecule real time sequencing and nanopore sequencing. In summary, the document provides a high-level
This document discusses various bioinformatics tools and their functions. It provides details on multiple sequence alignment tools like CLUSTAL Omega, CLUSTALW, BLAST, and FASTA. It explains that CLUSTAL Omega can align a large number of sequences quickly and accurately using progressive alignment. CLUSTALW performs multiple sequence alignment in three steps - pairwise alignment, guide tree creation, and multiple alignment using the guide tree. BLAST can identify unknown sequences by comparing them to known sequences. FASTA uses short exact matches to find similar regions between sequences. Expasy provides access to databases for proteomics, genomics, and other areas. MASCOT searches peptide mass fingerprinting and shotgun proteomics datasets.
Keynote presented at the Phenotype Foundation first annual meeting.
Describes data sharing, data annotation and the needs for further tool and ontology and ontology mapping development.
Amsterdam, January 18, 2016
Biological data is widely distributed over the web and can be retrieved using search engines like Google or data retrieval tools. Dedicated data retrieval tools for molecular biologists include Entrez, DBGET, and SRS which allow text searching of linked databases and sequence searching. Entrez, developed by NCBI, integrates information from databases including GenBank, PubMed, and OMIM. DBGET covers databases like GenBank, EMBL, and PDB. SRS, developed by EBI, integrates over 80 molecular biology databases.
WikiPathways: how open source and open data can make omics technology more us...Chris Evelo
This document discusses WikiPathways, an open source pathway database. It began in 2007 with the goals of having an online platform by March 2007 and gaining a first unknown user by January 2008, both of which were successes. WikiPathways has grown significantly since, now containing over 400 human pathways and 6,200 unique human genes. It receives over 1 million pageviews annually. The document advocates for opening up data and code to make omics technology more useful. It describes WikiPathways' various features including its BioPAX format, REST services, and integration with Cytoscape. It also discusses professionalizing open source and collaborating with existing communities and tools rather than trying to change the world alone.
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.
The document provides an overview of several important biomolecular databases. It begins by describing examples of biological databases including nucleic acid sequence databases like GenBank, EMBL, and DDBJ that contain DNA and RNA sequences. It also mentions UniProt, the major database of protein sequences and annotations. Finally, it briefly introduces the Gene Ontology project, which provides controlled vocabularies to describe gene and gene product attributes.
Bioinformatics involves the analysis of biological information using computers and statistical techniques,
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.
The sequence alignment is made between a known sequence and unknown sequence or between two unknown sequences. The known sequence is called reference sequence. The unknown sequence is called query sequence .
BLAST stands for Basic Local Alignment Search Tool. It addresses a fundamental problem in bioinformatics research. BLAST tool is used to compare a query sequence with a library or database of sequences.
In Bioinformatics, is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences.
BLAST was developed by stochastic model of Samuel Karlin and Stephen Altschul in 1990. They proposed “a method for estimating similarities between the known DNA sequence of one organism with that of another”.
A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query sequence) with a library or database of sequences and identify database sequences that resemble the query sequence above a certain threshold.
BITs: Genome browsers and interpretation of gene lists.BITS
Module 5 Genome browsers and interpreting gene lists.
Part of training session "Basic Bioinformatics concepts, databases and tools" - http://www.bits.vib.be/training
Using biological network approaches for dynamic extension of micronutrient re...Chris Evelo
This document discusses using biological network approaches to dynamically extend pathways with regulatory information such as microRNAs (miRNAs). It describes tools like PathVisio that can integrate gene expression, proteomics and metabolomics data onto pathways to identify significantly changed processes. WikiPathways is introduced as a public pathway resource that can be contributed to and curated by researchers. The document outlines approaches for visualizing regulatory interactions on pathways using plugins, exploring pathway interactions through network analysis, and integrating other data types such as SNPs, fluxes and gene annotations to build a more comprehensive understanding of biological systems.
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
The UCSC genome browser: A Neuroscience focused overviewVictoria Perreau
An self guided tutorial based overview of the UCSC genome browser for accessing public neuroscience data, in particular data from the ENCODE project. Including additional transcriptomic resources for the Neurosciences.
These is the second part of the lecture slides of the BITS bioinformatics training session on the UCSC Genome Browser.
See http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203990:orange-genome-browsers-ucsc-training&catid=81:training-pages&Itemid=190
Slides contain information about why bioinformatics appeared,
who bioinformaticians are, what they do, what kind of cool applications and challenges in bioinformatics there are.
Slides were prepared for the Bioinformatics seminar 2016, Institute of Computer Science, University of Tartu.
This document discusses protein sequence databases and their role in storing protein data generated from genome projects and new proteomics technologies. It describes several types of protein databases, including universal repositories like GenPept that store sequences with little annotation, and expertly curated databases like Swiss-Prot that enrich sequence data with additional validation and integration. Specialized databases also exist that focus on specific protein families, organisms, structures like SCOP, or classifications like CATH.
The document discusses various topics in bioinformatics and protein structure. It provides an overview of ongoing thesis topics at Biobix including biomarker prediction, methylation, metabolomics, peptidomics, and more. It also discusses the rationale for understanding protein structure and function, levels of protein structure from primary to quaternary, methods for determining structure like X-ray crystallography, and approaches to secondary structure prediction including Chou-Fasman.
The document discusses the statement of cash flows, which is required under Indian accounting standards. It must be prepared using the direct or indirect method and include cash flows from operating, investing and financing activities. Operating activities relate to main revenue activities, investing activities are the acquisition and disposal of long-term assets, and financing activities alter the capital structure. Cash and cash equivalents include cash, demand deposits and short-term investments that can be readily converted to cash.
This document outlines various demand generation, content generation, and consulting services including email and telemarketing, appointment setting, lead qualification, event planning, content research, market analytics, sales strategy, marketing strategy, sales process management, and CRM consulting. It also mentions services like organization profiling, search engine optimization, social media marketing, and virtual sales office support.
The document discusses a contest involving travel badges and segmentation. Participants can earn badges by making choices that reveal their travel DNA, with the winner to be announced on September 5, 2012. Filling out a form and confirming a 'Like' are required to enter, and Facebook sharing is facilitated.
HotelCom is a SMS customer contact and marketing system developed for the hospitality industry. It imports guest data from property management systems to send targeted messages. Messages can be automated or manual for marketing, reservations, feedback, and more. Using HotelCom can increase customer service and brand awareness through open communication with guests.
The document discusses how politicians use public relations strategies like planned communication and symbolic events to generate media coverage and control their messaging. It provides the example of FDR's famous "Fireside Chats" radio addresses between 1933-1944. It also briefly considers alternative scenarios where governments have no PR and either have no interaction with the press, the press has complete access but no government influence, or the press is directly controlled by the government.
The document provides information about various bioinformatics lessons that will take place on Thursdays, including topics like biological databases, sequence alignments, database searching using FASTA and BLAST, phylogenetics, and protein structure. It also includes details about database searching methods like dynamic programming, FASTA, BLAST, and parameters that can be adjusted for BLAST searches.
The document outlines topics that will be covered in a bioinformatics course, including biological databases, sequence alignments, database searching, phylogenetics, protein structure, gene prediction, gene ontologies, hidden Markov models, and non-coding RNA. It then provides more details on topics like using sequence similarity to gather information from unknown protein sequences, finding conserved patterns in alignments using profiles and hidden Markov models, and challenges around gene prediction from raw genomic sequences.
This document provides information about biological databases including:
- Different types of biological databases such as relational, object-oriented, hierarchical, and hybrid systems.
- Common uses of biological databases including annotation searches, homology searches, pattern searches, predictions, and comparisons.
- Examples of database entries in common formats like GenBank, EMBL, and SwissProt that show the layout and key fields.
This document discusses biological databases and bioinformatics. It begins with an overview of bioinformatics as an interdisciplinary field combining biology, computer science, and information technology. It then discusses different types of biological databases, including those focused on sequences, pathways, protein structures, and gene expression. The document outlines some common uses of biological databases, including searching for annotations, identifying similar sequences through homology, searching for patterns, and making predictions. It also briefly discusses comparing data across databases. The summary provides a high-level overview of the key topics and uses of biological databases covered in the document.
This document provides an overview and introduction to bioinformatics. It discusses the large amounts of biological sequence data that have been generated and how bioinformatics is needed to analyze this data computationally. The document outlines topics that will be covered, including databases, sequence alignment tools like BLAST, gene finding, and protein analysis. Practical workshops are described that will involve database searching, multiple sequence alignments, and interpreting results to understand molecular biology and solve biomedical problems. Questions are welcomed throughout the workshops.
Data mining involves using machine learning and statistical methods to discover patterns in large datasets and is useful in bioinformatics for analyzing biological data. Bioinformatics analyzes data from sequences, molecules, gene expressions, and pathways. Data mining can help understand these rapidly growing biological datasets. Common data mining tools in bioinformatics include BLAST for sequence comparisons, Entrez for integrated database searching, and ORF Finder for identifying open reading frames. Data mining approaches are well-suited to the enormous volumes of data in bioinformatics databases.
Role of bioinformatics in life sciences researchAnshika Bansal
1. The document discusses bioinformatics and summarizes some of its key applications and tools. It describes how bioinformatics merges biology and computer science to solve biological problems by applying computational tools to molecular data.
2. It provides examples of common bioinformatics tasks like retrieving sequences from databases, comparing sequences, analyzing genes and proteins, and viewing 3D structures.
3. The document lists several popular databases for nucleotide sequences, protein sequences, literature, and other biological data. It also introduces common bioinformatics tools for tasks like sequence alignment, translation, and structure analysis.
The document provides information about biological databases and sequence identifiers. It discusses the main objectives of biological databases which include information systems, query systems, storage systems and data. It describes primary databases like GenBank, EMBL, DDBJ, UniProt and PDB as well as secondary curated databases like RefSeq, Taxon and OMIM. It also explains different types of sequence identifiers used in databases like LOCUS, ACCESSION, VERSION, gi numbers and protein identifiers.
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-
This document discusses biological databases and bioinformatics. It begins by listing various related fields including biology, computer science, bioinformatics, statistics, and machine learning. It then describes different types of searches that can be performed in biological databases, including annotation searches, homology searches, pattern searches, and predictions. Finally, it mentions that databases can be used for comparisons, such as gene families and phylogenetic trees.
The document discusses bioinformatics tools used for analyzing biological data. It begins with an introduction to bioinformatics and then describes several categories of tools: biological databases for storing genomic and protein data; homology tools for sequence alignment and comparison; protein function analysis tools; structural analysis tools; and sequence manipulation and analysis tools. Common tools discussed include BLAST, FASTA, ClustalW, and databases like GenBank. The document concludes by covering applications of bioinformatics in areas like molecular modeling, medicine, and computation.
This document provides an overview of bioinformatics and biological databases. It discusses how bioinformatics draws from fields like biology, computer science, statistics, and machine learning. Biological databases are important resources for bioinformatics that can be searched and analyzed to answer questions, find similar sequences, locate patterns, and make predictions. The document also outlines common uses of biological databases, such as annotation searches, homology searches, pattern searches, and predictive analyses.
Data analysis & integration challenges in genomicsmikaelhuss
Presentation given at the Genomics Today and Tomorrow event in Uppsala, Sweden, 19 March 2015. (http://connectuppsala.se/events/genomics-today-and-tomorrow/) Topics include APIs, "querying by data set", machine learning.
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.
The document provides information about various biological sequence databases and bioinformatics tools and resources. It discusses nucleotide sequence databases like GenBank, EMBL, and DDBJ. It also mentions genome-centered databases like NCBI Genomes and Ensembl Genome Browser. Additionally, it covers protein databases like UniProt and PDB. It describes bioinformatics resources at EBI and NCBI like Entrez. Finally, it summarizes tools for sequence retrieval, comparison, and analysis like BLAST, sequence alignment, and genome browsers.
SooryaKiran Bioinformatics is a global bioinformatics solutions provider that focuses on customized bioinformatics services and products. It develops algorithms and software for biological sequence analysis, structure prediction, and other areas. Key products include tools for sequence generation, analysis, and homology identification. The company collaborates with research institutions and has provided solutions for SNP analysis, genome analysis, and mitochondrial DNA analysis to clients around the world.
This document discusses using various online bioinformatics tools and databases to analyze the FXN gene and its potential relationship to pancreatic cancer. It outlines tasks using tools like Ensembl to locate FXN on the human genome and identify pancreatic cancer genes. BLAST will be used to find similar sequences and align sequences related to FXN and pancreatic cancer. Additional tasks involve using metabolic databases like KEGG and Reactome to understand the metabolic pathway and reactions of the frataxin protein. Protein structure databases will also be queried to obtain structural knowledge about frataxin and see if it is connected to pancreatic cancer at the protein level. The overall aim is to gain experience using online biological exploration tools to study FXN and pancreatic cancer through a large-scale
This document discusses the importance of open source software and open data in biomedical research. It notes that biological data is growing exponentially and highlights several open source bioinformatics tools like EMBOSS and web services provided by the EBI that enable researchers to access and analyze data. The document advocates for open standards to facilitate data integration and management across different omics domains.
Performance Improvement of BLAST with Use of MSA Techniques to Search Ancesto...journal ijrtem
process in which instead comparing whole query sequence with database sequence it breaks
query sequence into small words and these words are used to align patterns. it uses heuristic method which
make it faster than earlier smith-waterman algorithm. But due small query sequence used for align in case of
very large database with complex queries it may perform poor. To remove this draw back we suggest by using
MSA tools which can filter database in by removing unnecessary sequences from data. This sorted data set then
applies to BLAST which can then indentify relationship among them i.e. HOMOLOGS, ORTHOLOGS,
PARALOGS. The proposed system can be further use to find relation among two persons or used to create
family tree. Ortholog is interesting for a wide range of bioinformatics analyses, including functional annotation,
phylogenetic inference, or genome evolution. This system describes and motivates the algorithm for predicting
orthologous relationships among complete genomes. The algorithm takes a pairwise approach, thus neither
requiring tree reconstruction nor reconciliation
Performance Improvement of BLAST with Use of MSA Techniques to Search Ancesto...IJRTEMJOURNAL
BLAST is most popular sequence alignment tool used to align bioinformatics patterns. It uses
local alignment process in which instead comparing whole query sequence with database sequence it breaks
query sequence into small words and these words are used to align patterns. it uses heuristic method which
make it faster than earlier smith-waterman algorithm. But due small query sequence used for align in case of
very large database with complex queries it may perform poor. To remove this draw back we suggest by using
MSA tools which can filter database in by removing unnecessary sequences from data. This sorted data set then
applies to BLAST which can then indentify relationship among them i.e. HOMOLOGS, ORTHOLOGS,
PARALOGS. The proposed system can be further use to find relation among two persons or used to create
family tree. Ortholog is interesting for a wide range of bioinformatics analyses, including functional annotation,
phylogenetic inference, or genome evolution. This system describes and motivates the algorithm for predicting
orthologous relationships among complete genomes. The algorithm takes a pairwise approach, thus neither
requiring tree reconstruction nor reconciliation
This document provides an introduction and overview of the field of bioinformatics. It discusses how bioinformatics combines computer science and biology to analyze large amounts of biological data. Specifically, it mentions that bioinformatics uses algorithms and techniques from computer science to solve complex biological problems related to areas like molecular biology, genomics, drug discovery, and more. It also outlines some of the key applications of bioinformatics like sequence analysis, protein structure prediction, genome annotation, and comparative genomics. Finally, it provides brief descriptions of important biological databases and resources that bioinformaticians use to store and analyze genomic and protein sequence data.
Talk by J. Eisen for NZ Computational Genomics meetingJonathan Eisen
This document discusses phylogeny-driven approaches to studying microbial diversity using ribosomal RNA gene sequences. It provides background on how advances in sequencing technology and appreciation of microbial diversity have enabled microbiome research. The document outlines several uses of phylogeny in microbiome studies, including constructing species phylogenies using rRNA sequences and assigning taxonomy to environmental sequences via rRNA phylotyping. It describes challenges with analyzing large rRNA datasets and introduces an automated pipeline called STAP that generates high-quality multiple sequence alignments and phylogenetic trees to classify sequences and analyze species diversity in a manner that scales to large datasets.
The document discusses the Rh blood group system and its clinical significance. It describes the key observations in 1939 that linked adverse reactions in mothers to stillborn fetuses and blood transfusions from fathers, indicating a relationship. This syndrome is now called hemolytic disease of the fetus and newborn. The Rh system was identified in 1940 through experiments immunizing animals with Rhesus macaque monkey red blood cells. The D antigen is the most important RBC antigen in transfusion practice, as those lacking it do not produce anti-D antibody unless exposed to D antigen through transfusion or pregnancy. Testing for D is routinely performed to ensure D-negative patients receive D-negative blood.
The document discusses various database concepts including normalization, which is used to design optimal relation schemas by removing redundant data. It also covers transaction processing, which involves executing logical database operations as transactions to maintain data integrity. Database systems use techniques like logging and concurrency control to prevent transaction anomalies and ensure failures can be recovered from.
This document contains a list of names, emails, and study programs of students. It includes their official student code, last name, first name, email, and educational program. There are 20 students listed with their details.
This document discusses the Biological Databases project being conducted by a group of students. The project involves using the video game Minecraft to visualize protein structures retrieved from the Protein Data Bank (PDB). Python scripts are used to import PDB data files and place blocks in Minecraft to represent atoms, with different block colors used to distinguish atom types. SPARQL queries are also employed to search the RDF version of the PDB for protein entries. The goal is to build 3D protein models inside Minecraft for educational and visualization purposes.
The document discusses various bioinformatics tools and algorithms for analyzing protein sequences, including Biopython for working with biological sequence data, the Kyte-Doolittle algorithm for predicting transmembrane regions, and the Chou-Fasman algorithm for predicting secondary structure from amino acid preferences for alpha helices, beta sheets, and random coils. It also provides examples of analyzing Swiss-Prot data to find properties of human proteins and applying these tools and libraries to extract insights from protein sequences.
The document discusses various topics related to analyzing protein sequences using Python and Biopython. It provides examples of using Biopython to parse sequence data from UniProt, calculate lengths and translations of sequences. It also discusses analyzing properties of sequences like molecular weight, isoelectric point, transmembrane regions, and comparing sequences to find conserved motifs. Finally, it introduces hydropathy indices and tools for predicting properties like transmembrane helices from primary sequences.
This document discusses Python functions. It explains that there are built-in functions provided as part of Python and user-defined functions. User-defined functions are created using the def keyword and can take parameters and return values. The body of a function is indented and runs when the function is called. Functions allow code to be reused and organized in a modular way. Examples are provided to demonstrate defining and calling functions with different parameters and return values.
The document provides a recap of Python programming concepts like conditions and statements, while loops, for loops, break and continue statements, and working with strings. It also introduces regular expressions as a way to match patterns in strings using a formal language that can be interpreted by a regular expression processor.
[SUMMARY
The document provides an overview of the history and evolution of various programming languages. It discusses early languages like FORTRAN, LISP, PASCAL, C, and Java. It also covers scripting languages and their uses. The document explains what Python is as a programming language - that it is interpreted, object-oriented, and high-level. It was named after Monty Python and was created by Guido van Rossum. The document then gives examples of using Python to program Minecraft by importing protein data from PDB files and using coordinates to place blocks to visualize proteins in the game.
This document provides an introduction to bio-ontologies and the semantic web. It discusses what ontologies are and how they are used in the bio domain through initiatives like the OBO Foundry. It introduces key semantic web technologies like RDF, URIs, Turtle syntax, and SPARQL query language. It provides examples of ontologies like the Gene Ontology and how ontologies can be represented and queried using these semantic web standards.
This document provides an overview of NoSQL databases, including:
- Key-value stores store data as maps or hashmaps and are efficient for data access but limited in query capabilities.
- Column-oriented stores group attributes into column families and store data efficiently but are operationally challenging.
- Document databases store loosely structured data like JSON and allow retrieving documents by keys or contents.
- Graph databases are suited for interaction networks and path finding but are less suited for tabular data.
The document discusses creating a multicore database project. It recommends taking the following steps:
1. Define what the project is about, what it aims to achieve, and who it is for.
2. Identify information resources and develop a basic data model.
3. Design a user interface mockup without technical constraints, thinking creatively.
This document discusses biological databases and PHP. It begins with an overview of biological databases and examples using BIOSQL to load genetic data from GenBank into a MySQL database. It then provides examples of building a basic 3-tier model with Apache, PHP, and a MySQL backend database. The document also includes a brief introduction to PHP, covering its history, why it is commonly used, and basic syntax like conditional statements.
This document discusses biological databases and SQL. It provides an overview of primary and derived data in biological research, as well as different data levels. It then discusses direct querying of selected bioinformatics databases using SQL and provides examples of 3-tier database models. The document proceeds to discuss rationale for learning SQL to query biological databases and provides definitions and explanations of key SQL concepts like tables, records, queries, data types, keys, integrity rules and constraints.
The document discusses several topics related to protein structure prediction using Python:
1. It introduces the Chou-Fasman algorithm for predicting protein secondary structure from amino acid sequence. The algorithm calculates preference parameters for each amino acid to be in alpha helices, beta sheets, or other structures.
2. It provides an example of calculating helical propensity.
3. It lists the preference parameters output by the Chou-Fasman algorithm for each amino acid.
4. It outlines the steps of applying the Chou-Fasman algorithm to predict secondary structure elements in a protein sequence.
The document provides information on various Python programming concepts including control structures, lists, dictionaries, regular expressions, exceptions, and biological applications using Biopython. It discusses if/else statements, while and for loops, list operations, dictionary usage, regex patterns, exception handling roles, and gives examples analyzing protein sequences and structures using Biopython.
The document describes a lab for bioinformatics and computational genomics that has over 250 people including 25 "genome hackers" who are mostly engineers and 42 scientists. It discusses using epigenetics and next generation biomarkers for better detecting and understanding cancer. Specifically, it summarizes tests like ConfirmMDx, SelectMDx, and AssureMDx which use epigenetic biomarkers found in urine or blood samples to help determine a patient's risk level for aggressive prostate or bladder cancers and guide decisions about additional testing or biopsies.
The document provides a list of regular expression patterns that could be used to scan protein sequences for prosite patterns. It begins by showing example consensus patterns for protein domains and motifs. It then lists 20 regular expression patterns translated from prosite consensus patterns that could be used to scan protein sequences and look for matches. The document concludes by providing an example Python code snippet to scan sequences for the given prosite patterns using regular expressions.
The document discusses various bioinformatics tools and algorithms for sequence alignment, including:
1. Dynamic programming algorithms like Needleman-Wunsch for global sequence alignment and Smith-Waterman for local sequence alignment.
2. The Burrows-Wheeler Transform (BWT) and how it enables fast, memory-efficient alignment of short reads to reference genomes using tools like BWA. The BWT reorders the characters in a string to group common prefixes together.
3. The SAM format for storing large nucleotide sequence alignments generated by aligners like BWA. SAM files contain the read sequences, positions aligned to the reference, and quality information.
The document discusses regular expressions and provides examples of common regex patterns used for tasks like searching DNA sequences or scanning for protein domains. It then provides a sample of DNA/protein sequences and suggests using translated regex patterns to scan the sequences for specific consensus patterns representing protein domains.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
33. CC -!- FUNCTION: CYTOKINE WITH A WIDE VARIETY OF FUNCTIONS: IT CAN CC CAUSE CYTOLYSIS OF CERTAIN TUMOR CELL LINES, IT IS IMPLICATED CC IN THE INDUCTION OF CACHEXIA, IT IS A POTENT PYROGEN CAUSING CC FEVER BY DIRECT ACTION OR BY STIMULATION OF IL-1 SECRETION, IT CC CAN STIMULATE CELL PROLIFERATION & INDUCE CELL DIFFERENTIATION CC UNDER CERTAIN CONDITIONS. Comments CC -!- SUBUNIT: HOMOTRIMER. CC -!- SUBCELLULAR LOCATION: TYPE II MEMBRANE PROTEIN. ALSO EXISTS AS CC AN EXTRACELLULAR SOLUBLE FORM. CC -!- PTM: THE SOLUBLE FORM DERIVES FROM THE MEMBRANE FORM BY CC PROTEOLYTIC PROCESSING. CC -!- DISEASE: CACHEXIA ACCOMPANIES A VARIETY OF DISEASES, INCLUDING CC CANCER AND INFECTION, AND IS CHARACTERIZED BY GENERAL ILL CC HEALTH AND MALNUTRITION. CC -!- SIMILARITY: BELONGS TO THE TUMOR NECROSIS FACTOR FAMILY. DR EMBL; X02910; G37210; -. Database Cross-references DR EMBL; M16441; G339741; -. DR EMBL; X01394; G37220; -. DR EMBL; M10988; G339738; -. DR EMBL; M26331; G339764; -. DR EMBL; Z15026; G37212; -. DR PIR; B23784; QWHUN. DR PIR; A44189; A44189. DR PDB; 1TNF; 15-JAN-91. DR PDB; 2TUN; 31-JAN-94.
34. KW CYTOKINE; CYTOTOXIN; TRANSMEMBRANE; GLYCOPROTEIN; SIGNAL-ANCHOR; KW MYRISTYLATION; 3D-STRUCTURE. KeyWord FT PROPEP 1 76 Feature Table FT CHAIN 77 233 TUMOR NECROSIS FACTOR. FT TRANSMEM 36 56 SIGNAL-ANCHOR (TYPE-II PROTEIN). FT LIPID 19 19 MYRISTATE. FT LIPID 20 20 MYRISTATE. FT DISULFID 145 177 FT MUTAGEN 105 105 L->S: LOW ACTIVITY. FT MUTAGEN 108 108 R->W: BIOLOGICALLY INACTIVE. FT MUTAGEN 112 112 L->F: BIOLOGICALLY INACTIVE. FT MUTAGEN 162 162 S->F: BIOLOGICALLY INACTIVE. FT MUTAGEN 167 167 V->A,D: BIOLOGICALLY INACTIVE. FT MUTAGEN 222 222 E->K: BIOLOGICALLY INACTIVE. FT CONFLICT 63 63 F -> S (IN REF. 5). FT STRAND 89 93 FT TURN 99 100 FT TURN 109 110 FT STRAND 112 113 FT TURN 115 116 FT STRAND 118 119 FT STRAND 124 125
35. FT STRAND 130 143 FT STRAND 152 159 FT STRAND 166 170 FT STRAND 173 174 FT TURN 183 184 FT STRAND 189 202 FT TURN 204 205 FT STRAND 207 212 FT HELIX 215 217 FT STRAND 218 218 FT STRAND 227 232 SQ SEQUENCE 233 AA; 25644 MW; 666D7069 CRC32; MSTESMIRDV ELAEEALPKK TGGPQGSRRC LFLSLFSFLI VAGATTLFCL LHFGVIGPQR EEFPRDLSLI SPLAQAVRSS SRTPSDKPVA HVVANPQAEG QLQWLNRRAN ALLANGVELR DNQLVVPSEG LYLIYSQVLF KGQGCPSTHV LLTHTISRIA VSYQTKVNLL SAIKSPCQRE TPEGAEAKPW YEPIYLGGVF QLEKGDRLSA EINRPDYLDF AESGQVYFGI IAL //
55. Example 3-tier model in biological database http://www.bioinformatics.be Example of different interface to the same back-end database (MySQL)
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58. What is HTTP? In Summary : HTTP is an acronym for Hypertext Transfer Protocol. HTTP is the set of rules, or protocol, that enables hypertext data to be transferred from one computer to another, and is based on the client/server principle. Hypertext is text that is coded using the Hypertext Markup Language. These codes and HTTP work together to link resources to each other. HTTP enables users to retrieve a wide variety of resources such as text, graphics, sound, animation and other hypertext documents, and allows hypertext access to other Internet protocols.
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60. What is HTML? HTML stands for Hypertext Markup Language. HTML consists of standardized codes, or "tags", that are used to define the structure of information on a web page. HTML is used to prepare documents for the World Wide Web. A web page is single a unit of information, often called a document, that is available on the World Wide Web. HTML defines several aspects of a web page including heading levels, bold, italics, images, paragraph breaks and hypertext links to other resources.
61. What is HTML? HTML is a sub-language of SGML, or Standard Generalized Markup Language. SGML is a system that defines and standardizes the structure of documents. Both SGML and HTML utilize descriptive markup to define the structure of an area of text. In general terms, descriptive markup does not specify a particular font or point size for an area of text. Instead, it describes an area of text as a heading or a caption, for example. Therefore, in HTML, text is marked as a heading, subheading, numbered list, bold, italic, etc.
62. What is a URL? URLs consist of letters, numbers, and punctuation. The basic structure of a URL is hierarchical, and the hierarchy moves from left to right: Examples: http://www.healthyway.com:8080/exercise/mtbike.html gopher://gopher.state.edu/ ftp://ftp.company.com/ protocol://server-name.domain-name.top-level domain:port/directory/filename
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64. What is an IP Address? If you want to connect to another computer, transfer files to or from another computer, or send an e-mail message, you first need to know where the other computer is - you need the computer's "address." An IP (Internet Protocol) address is an identifier for a particular machine on a particular network; it is part of a scheme to identify computers on the Internet. IP addresses are also referred to as IP numbers and Internet addresses. An IP address consists of four sections separated by periods. Each section contains a number ranging from 0 to 255. Example = 198.41.0.52
65. What is an IP Address? The diagram below compares Class A, Class B and Class C IP addresses. The blue numbers represent the network and the red numbers represent hosts on the network. Therefore, a Class A network can support many more hosts than a Class C network.
66. What is Internet Addressing? Most computers on the Internet have a unique domain name. Special computers, called domain name servers, look up the domain name and match it to the corresponding IP address so that data can be properly routed to its destination on the Internet. An example domain name is: healthyway.com Domain names are easier for most people to relate to than a numeric IP address.
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69. What is TCP/IP? TCP/IP stands for Transmission Control Protocol/Internet Protocol. TCP/IP is actually a collection of protocols, or rules, that govern the way data travels from one machine to another across networks. The Internet is based on TCP/IP.
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71. What is TCP/IP? The relationship between data, IP, and networks is often compared to the relationship between a letter, its addressed envelope, and the postal system.
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75. What is a packet? In addition to the actual data, packets also contain header information. The header of a packet contains both the originating and destination IP (Internet Protocol) address. The header also contains coding to handle transmission errors and keep packets flowing. Header information can be compared to addressing an envelope. Like the header of a packet, an envelope contains the addresses of both the sender and the recipient, in order to keep track of who the envelope is from and who it is going to.
76. What is a packet? Header information is used by routers to send packets across a network. Routers are computers that are dedicated to "reading" header information and determining which router to send the packet to next. Packets move from router to router until they reach their final destination, in much the same way that an envelope travels between postal substations before reaching the recipient. The packets that make up data, such as an e-mail message or a web page, will not necessarily all follow the same route to the final destination. The route that a packet travels depends on many variables, including network traffic at that particular moment and the size of the packet being sent.
77. What is a packet? Transmission Control Protocol/Internet Protocol (TCP/IP) is a set of rules that govern how data is transmitted across networks and the Internet. TCP/IP utilizes packets to send information across the Internet. TCP and IP have different functions related to packets.
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80. What is a packet? The following diagram illustrates an e-mail message being sent across a network. 1. Data that makes up an e-mail message is split into packets by the IP portion of TCP/IP. IP also adds header information to each packet. 2. Using header information in the packets, routers determine the best path for each packet to take to its final destination. 3. The TCP portion of TCP/IP reassembles the packets in the correct order and ensures that all packets have arrived undamaged. Message is sent
81. What is Telnet? Telnet is a protocol, or set of rules, that enables one computer to connect to another computer. This process is also referred to as remote login. The user's computer, which initiates the connection, is referred to as the local computer, and the machine being connected to, which accepts the connection, is referred to as the remote, or host, computer. The remote computer can be physically located in the next room, the next town, or in another country.
82. What is Telnet? Once connected, the user's computer emulates the remote computer. When the user types in commands, they are executed on the remote computer. The user's monitor displays what is taking place on the remote computer during the telnet session. The procedure for connecting to a remote computer will depend on how your Internet access is set-up.
83. What is Telnet? Once a connection to a remote computer is made, instructions or menus may appear. Some remote machines may require a user to have an account on the machine, and may prompt users for a username and password. Many resources, such as library catalogs, are available via telnet without an account and password. Here is an example taken from a telnet session to Washington University in St. Louis, MO:
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91. What is FTP? Anonymous FTP Anonymous FTP allows a user to access a wealth of publicly available information. No special account or password is needed. However, an anonymous FTP site will sometimes ask that users login with the name “anonymous” and use their e-mail address as the password.
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93. What is FTP? Files on FTP servers are often compressed. Compression decreases file size. This enables more files to be stored on the server and makes file transfer times shorter. In order to use a compressed file it needs to be decompressed using appropriate software. It is a good idea to have current virus checking software on the computer before files are downloaded to it.
The new curve saturated around 20% for alignments over more than 250 residues --- and for alignments shorter than 11 residues the new equation yielded values above 100%. However, this was acceptable as 100% identity for gragments of 10-11 residues does not imply structural similarity.
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SSH’s first use was as a replacement for rsh, the Unix r emote sh ell application. This tool allowed one to connect to a shell on a remote machine. The tool suffered from two major shortcomings. First, like telnet it sent all traffic in cleartext, meaning that a sniffer tool at any point between the two machines could read all commands sent and replies received. Secondly, the /etc/hosts.equiv and ~/.rhosts files listed trusted machines and users; these could make rsh connections without any further authentication. If an attacker compromised any of these trusted hosts, they would immediately get access to the rsh server with no more effort. Also, if the attacker was successfully able to spoof the IP address of a trusted host, they’d get the same access. SSH encrypts all traffic, including the password or key authentication. It also uses host keys to definitively identify both hosts involved in the communication, getting around man-in-the-middle attacks and IP spoofing.
The licensing issue is rather complex; depending on which release of the ssh1 and ssh2 applications you choose: Source code may or may not be available Use may be free or for cost for educational institutions Use may be free or for cost for companies The O’Reilly SSH book covers this in good detail. The SSH1 protocol has some shortcomings that aren’t easily fixed except by using the newer, but incompatible SSH2 protocol. If possible, you should use SSH clients and servers that support SSH2 and prefer it over SSH1 protocol connections.
The serious problem with the password approach, whether used with telnet or with ssh, is that the password you need to enter at the client end is stored on the server. Even though it’s stored in an encoded form in /etc/passwd or /etc/shadow, this password can be cracked with brute force once one has access to that file. The difference with the public/private key split is that if an attacker gets the public key stored on the server, that public key cannot be used to get back into the server! Only the private key, kept on the client only, can be used to get into a server with the public key.