Exercises for STRING and STITCH, given as part of the EMBO Practical Course 'Computational aspects of protein structure determination and analysis: from data to structure to function' at the EBI in Hinxton (Sept. 10, 2010)
Talk about STRING and STITCH, given as part of the EMBO Practical Course 'Computational aspects of protein structure determination and analysis: from data to structure to function' at the EBI in Hinxton (Sept. 10, 2010)
Talk about STRING and STITCH, given as part of the EMBO Practical Course 'Computational aspects of protein structure determination and analysis: from data to structure to function' at the EBI in Hinxton (Sept. 10, 2010)
Proteins play a key role in molecular recognition and are at the core of all biological processes. They can interact with other components of the cell, such as small molecular metabolites, nucleic acids, membranes and other proteins to build supramolecular components and carefully design molecular machines that perform various functions, from chemical catalysis, mechanical work to signal transmission And adjustment. So far, large-scale protein-protein interactions have been identified, and all the generated data is collected in a special database, which can create large-scale protein interaction networks. Like metabolism or genetic/epigenetic networks, the study of PPIs can help us understand the mechanisms of signal transduction, transmembrane transport, cell metabolism and other biological processes through stable or transient, covalent or non-covalent interactions. https://www.creative-proteomics.com/services/protein-protein-interaction-networks.htm
Comparing Genomes to Determine Evolutionary RelationshipsUsing BLynellBull52
Comparing Genomes to Determine Evolutionary Relationships
Using Bioinformatics (NCBI Database)
In this activity, you will be analyzing the genomes and evolutionary relationships of a variety of species. By utilizing The NCBI (National Center for Biotechnology Information) database, you will have access to thousands of genomes. The amount of information available (as you will soon see) is absolutely astounding!! (TYPE RESPONSES IN RED or BLUE)
Directions
I. Visit the NCBI Homepage https://www.ncbi.nlm.nih.gov/ and click on “Genomes and Maps”. Next click on “Genome” (blue link in the center of the page/also found in right side bar). Next click on “Browse by Organism”.
1. What types of genomes are currently available for making comparisons?
2. How many genomes are currently available to view?
II. Next, click on “Eukaryotes” at the top. Click on a species name that looks interesting. If there is a common name for this species, it will be shown in parenthesis after the species name. Keep searching until you find a species that displays a common name and record both of the following below:
Species Name: _____________ ________________ Common Name: ____________________
III. Next, return to the “Genome” Page and click on “Genome Data Viewer”. Here you will see a phylogenetic tree that encompasses over 660 eukaryotic genomes. If you hover over the names/pictures of the organisms, it will provide the species name for each. If you click on the “+” symbols, you will be shown more detailed phylogeny(common ancestry).
3. Click on the “+” symbol to the left of Humans. Then click the single arrow symbol two more times until you see the phylogeny for all primates. Which primate has a closer common ancestor to humans, a monkey or a chimpanzee?
4. Which species has a closer common ancestor to gorillas, a monkey or a human?
5. Which species shown on the diagram has the closest common ancestor to a chimpanzee?
IV. Click on the “<<” symbol to return to the original phylogenetic tree. Hover over the “Open” circle on the very left side of the tree (common ancestor to the eukaryotes). It will indicate how many eukaryotes are classified in this phylogenetic tree. Record that number below:
Number of eukaryotes in the Phylogenetic Tree: __________
If you click and hold on this open circle, you will see that the eukaryotes are further subdivided. Continue to click and hold on the open circles displaying the GREATEST number of species (so the next one you should click & hold on is Opisthonkonta (and so on). Toward the end of the phylogeny you will see that both Whales and Pecora still have several remaining species. Click and hold on “Whales” and continue on.
6. What are the two major groups of whales displayed on the phylogenetic tree?
7. Click on a toothed or baleen whale. What is the species name? What is the common name? (You may need to hover over the blue or green circle to find the common or species name).
8. There are several genera (groups ...
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge ...ijcsa
The correlation and interactions among different biological entities comprise the biological system. Although already revealed interactions contribute to the understanding of different existing systems, researchers face many questions everyday regarding inter-relationships among entities. Their queries have potential role in exploring new relations which may open up a new area of investigation. In this paper, we introduce a text mining based method for answering the biological queries in terms of statistical computation such that researchers can come up with new knowledge discovery. It facilitates user to submit their query in natural linguistic form which can be treated as hypothesis. Our proposed approach analyzes the hypothesis and measures the p-value of the hypothesis with respect to the existing literature. Based on the measured value, the system either accepts or rejects the hypothesis from statistical point of view. Moreover, even it does not find any direct relationship among the entities of the hypothesis, it presents a network to give an integral overview of all the entities through which the entities might be related. This is also congenial for the researchers to widen their view and thus think of new hypothesis for further investigation. It assists researcher to get a quantitative evaluation of their assumptions such that they can reach a logical conclusion and thus aids in relevant re-searches of biological knowledge discovery. The system also provides the researchers a graphical interactive interface to submit their hypothesis for assessment in a more convenient way.
Drug discovery and development is a long and expensive process over time has notoriously bucked Moore's law that it now has its own law called Eroom's Law named after it (the opposite of Moore). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of drug failures. Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible becomes all the more important to accelerate drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains. Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories: 1. Classification 2. Regression 3. Read-across. The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them. We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.
PowerMV is a software environment for statistical analysis, molecular viewing, descriptor generation, and similarity search.
In this presentation we will study about two modules nearest neighbor search an molecular descriptor generation.
What's in a name? Better vocabularies = better bioinformatics?Keith Bradnam
Most of the pain and suffering that occurs in bioinformatics happens when database identifier 'A' in file 1, doesn't quite match database identifier 'B' in file 2...even when they are supposed to be the same identifier.
Things don't always match up for a number of reasons, most of which *should* be under our control. This talk covers a few points relating to this and briefly discusses how we should all be using curated ontologies to describe our data.
Short tutorials on how to use the web-based tool DAVID - Database for Annotation, Visualization and Integrated Discovery) - http://david.abcc.ncifcrf.gov/
DAVID provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes.
Proteins play a key role in molecular recognition and are at the core of all biological processes. They can interact with other components of the cell, such as small molecular metabolites, nucleic acids, membranes and other proteins to build supramolecular components and carefully design molecular machines that perform various functions, from chemical catalysis, mechanical work to signal transmission And adjustment. So far, large-scale protein-protein interactions have been identified, and all the generated data is collected in a special database, which can create large-scale protein interaction networks. Like metabolism or genetic/epigenetic networks, the study of PPIs can help us understand the mechanisms of signal transduction, transmembrane transport, cell metabolism and other biological processes through stable or transient, covalent or non-covalent interactions. https://www.creative-proteomics.com/services/protein-protein-interaction-networks.htm
Comparing Genomes to Determine Evolutionary RelationshipsUsing BLynellBull52
Comparing Genomes to Determine Evolutionary Relationships
Using Bioinformatics (NCBI Database)
In this activity, you will be analyzing the genomes and evolutionary relationships of a variety of species. By utilizing The NCBI (National Center for Biotechnology Information) database, you will have access to thousands of genomes. The amount of information available (as you will soon see) is absolutely astounding!! (TYPE RESPONSES IN RED or BLUE)
Directions
I. Visit the NCBI Homepage https://www.ncbi.nlm.nih.gov/ and click on “Genomes and Maps”. Next click on “Genome” (blue link in the center of the page/also found in right side bar). Next click on “Browse by Organism”.
1. What types of genomes are currently available for making comparisons?
2. How many genomes are currently available to view?
II. Next, click on “Eukaryotes” at the top. Click on a species name that looks interesting. If there is a common name for this species, it will be shown in parenthesis after the species name. Keep searching until you find a species that displays a common name and record both of the following below:
Species Name: _____________ ________________ Common Name: ____________________
III. Next, return to the “Genome” Page and click on “Genome Data Viewer”. Here you will see a phylogenetic tree that encompasses over 660 eukaryotic genomes. If you hover over the names/pictures of the organisms, it will provide the species name for each. If you click on the “+” symbols, you will be shown more detailed phylogeny(common ancestry).
3. Click on the “+” symbol to the left of Humans. Then click the single arrow symbol two more times until you see the phylogeny for all primates. Which primate has a closer common ancestor to humans, a monkey or a chimpanzee?
4. Which species has a closer common ancestor to gorillas, a monkey or a human?
5. Which species shown on the diagram has the closest common ancestor to a chimpanzee?
IV. Click on the “<<” symbol to return to the original phylogenetic tree. Hover over the “Open” circle on the very left side of the tree (common ancestor to the eukaryotes). It will indicate how many eukaryotes are classified in this phylogenetic tree. Record that number below:
Number of eukaryotes in the Phylogenetic Tree: __________
If you click and hold on this open circle, you will see that the eukaryotes are further subdivided. Continue to click and hold on the open circles displaying the GREATEST number of species (so the next one you should click & hold on is Opisthonkonta (and so on). Toward the end of the phylogeny you will see that both Whales and Pecora still have several remaining species. Click and hold on “Whales” and continue on.
6. What are the two major groups of whales displayed on the phylogenetic tree?
7. Click on a toothed or baleen whale. What is the species name? What is the common name? (You may need to hover over the blue or green circle to find the common or species name).
8. There are several genera (groups ...
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge ...ijcsa
The correlation and interactions among different biological entities comprise the biological system. Although already revealed interactions contribute to the understanding of different existing systems, researchers face many questions everyday regarding inter-relationships among entities. Their queries have potential role in exploring new relations which may open up a new area of investigation. In this paper, we introduce a text mining based method for answering the biological queries in terms of statistical computation such that researchers can come up with new knowledge discovery. It facilitates user to submit their query in natural linguistic form which can be treated as hypothesis. Our proposed approach analyzes the hypothesis and measures the p-value of the hypothesis with respect to the existing literature. Based on the measured value, the system either accepts or rejects the hypothesis from statistical point of view. Moreover, even it does not find any direct relationship among the entities of the hypothesis, it presents a network to give an integral overview of all the entities through which the entities might be related. This is also congenial for the researchers to widen their view and thus think of new hypothesis for further investigation. It assists researcher to get a quantitative evaluation of their assumptions such that they can reach a logical conclusion and thus aids in relevant re-searches of biological knowledge discovery. The system also provides the researchers a graphical interactive interface to submit their hypothesis for assessment in a more convenient way.
Drug discovery and development is a long and expensive process over time has notoriously bucked Moore's law that it now has its own law called Eroom's Law named after it (the opposite of Moore). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of drug failures. Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible becomes all the more important to accelerate drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains. Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories: 1. Classification 2. Regression 3. Read-across. The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them. We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.
PowerMV is a software environment for statistical analysis, molecular viewing, descriptor generation, and similarity search.
In this presentation we will study about two modules nearest neighbor search an molecular descriptor generation.
What's in a name? Better vocabularies = better bioinformatics?Keith Bradnam
Most of the pain and suffering that occurs in bioinformatics happens when database identifier 'A' in file 1, doesn't quite match database identifier 'B' in file 2...even when they are supposed to be the same identifier.
Things don't always match up for a number of reasons, most of which *should* be under our control. This talk covers a few points relating to this and briefly discusses how we should all be using curated ontologies to describe our data.
Short tutorials on how to use the web-based tool DAVID - Database for Annotation, Visualization and Integrated Discovery) - http://david.abcc.ncifcrf.gov/
DAVID provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Unit 8 - Information and Communication Technology (Paper I).pdf
Excercises for STRING and STITCH
1. http://string-db.org/ http://stitch-db.org/ Tutorial: STRING and STITCH<br />Michael Kuhn, Biotec, TU Dresdenmichael.kuhn@biotec.tu-dresden.de<br />These two databases are tools for exploring the interactions of proteins and chemicals. STRING concentrates on protein–protein interactions and integrates genomic evidence with data from experiments, manually curated databases and the literature. STITCH additionally collects information on associations between chemicals and proteins.<br />You can do several things with STRING and STITCH. If you quickly want to get information about a protein or chemical, you can enter them and look at the interaction partners. It’s also possible to submit multiple items; for example when you find that several proteins cause the same phenotype in a screen, you can check if they have common interaction partners. Lastly, you can also download the whole database of interactions and use it in computational screens.<br />Both databases work with snapshots of genomes and source databases, so you will not find very recently published data. Nonetheless, you will get a good idea on what is known for particular molecules.<br />Discovering protein–protein interactions<br />Imagine that you have just solved the structure of the Archaeoglobus fulgidus protein AF2331, and you have found that is has an unusual fold. Now, you would like to find potential interaction partners for a follow-up studies. Go to the STRING homepage http://string-db.org/ and search for AF2331. Select the Archaeoglobus protein. Look at the network and at the table of interaction partners below. What are the associations based upon? (Compare the colors of the edges to the table header, or click on edges to find out.)<br />Click on the “Neighborhood” button below the table. It will show you that the proteins are part of the same operon. Going back to the network, click the “more” button. An additional interacting protein appears. You can click on the green edge in the network to get more information on the source of the association.<br />Go back to the network again and, after clicking on AF_0211, select “add this node to input nodes.” This will expand the network, and some characterized proteins appear. Now, you see other types of edges. Click on the edge connecting fen and pcn and explore the sources of the interaction. (Note that few proteins have been studied in Archaeoglobus and therefore most of the information comes from similar proteins from other species.)<br />Context for chemicals<br />Suppose you would like to design inhibitors for the enzyme enoyl reductase from Toxoplasma gondii. How could you quickly get an overview of the existing data on this enzyme? Go to the STITCH homepage: http://stitch-db.org/ . Enter the protein name “enoyl reductase.” If you begin to enter the organism name “Toxoplasma,” you will notice that it is not yet in the database of genomes. However, you can search for related organisms by using the clade “apicomplexa” as organism name. Click “go” and see which species is found. <br />You will see a network of interactions with proteins (spheres) and chemicals (capsule-shaped). Among the chemicals, you will notice both metabolites and drugs, although the database does not distinguish between them. Click on the different items to see what they are. In interactive mode you can also drag the items around to better see the edges. <br />Change to the actions view. Now, the edge colors symbolize different types of interactions, as you can see from the table header. Look at the edge between triclosan and FabI: How do they interact? How do you explain the apparently contradicting information? <br />Let’s try to find as much information as possible by increasing the number of shown interaction partners. You can does this with the “interactors shown” menu in the box at the bottom of the page. Select “no more than 50 interactors” and then “update parameters.” Now, you will on one side see a cluster of metabolites, and on the other side various drugs. We can get rid of most metabolites by disabling the “database” and “text-mining” checkboxes at the bottom of the page. (Why?) If you change to confidence view, you will notice both strong and weak edges between PDB ligands and FabI. What is the difference? <br />When you inspect edges details, you can click on the “show” button to find more details. If there’s only one PDB structure, you will directly see information about it, otherwise you can click “info” for any of them. Notice the link to the PDB on the top right. <br />Your protein or chemical of interest<br />If there’s still time left, look for your protein or small molecule of interest and see if you find something you didn’t know before. <br />