Bioinformatics ppt

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Bioinformatics means the person who is using the computer to handle the information related to the biological streams and its field. The real definition or a short introduction about the system is that biology of the molecule related to the computers. Here the computers are used to configure the components of the molecules of the thereby livings. This system includes some extra subtype like the data mining. The detail of the data mining is referred below in detailed.

There are also some of the related principles and the important issues that are explained in the article as well. Data mining the subtype of the bioinformatics is the execution of the process where the hypothesis which is neatly tested are created or constructed by using the functions that are related to the architecture of the system or the amount of interest that are unlimited in the queue of the neatly classified organisms.

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This is the most important and the user device for the user who is in the department of the research. This person can use this system to a program that is available in the market for the detailed knowledge of the stream bioinformatics.

By looking at the system progress of the program execution and the limitations and the drawbacks the developers are at work of finding the solution of the problem issues and turn that limitation and the drawbacks into advantages. This will make us the better and the accurate system to work within the detection devices. Your email address will not be published.

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Leave a Reply Cancel reply Your email address will not be published.Biotech Articles. Publish Your Research Online. Article Summary: Today, bioinformatics is used in large number of fields such as microbial genome applications, biotechnology, waste cleanup, Gene Therapy etc. In this article an effort is made to provide brief information of applications of bioinformatics in the field of Medicine, Microbial Genome Application and Agriculture It was stated as "Study of Informatic processes in biotic systems"[1].

Basically bioinformatics deals with the information in the fields of Information Technology, Computer Science and Biology. Biologist performs research in laboratoty and collects DNA and protein sequences, gene expressions etc. Computer Scientists are involved in developing algorithms, tools, softwares to store and analyze data.

Bioinformaticians study biological questions by analyzing molecular data with various programs and tools. Today, bioinformatics is used in large number of fields such as microbial genome applications, biotechnology, waste cleanup, Gene Therepy etc. In this article an effort is made to provide brief information of applications of bioinformatics in the field of Medicine, Microbial Genome Application and Agriculture.

Applications of Bioinformatics In broad spectrum applications of bioinformatics is mainly used in the field of Medicine, Microbial Genome Applications and Agriculture.

Medicine In the field of Medicine applications of bioinformatics is used for following areas: a. Drug Discovery: The Idea of using X ray Crystallography in drug discovery emerged more than 30 years ago, when the first 3 dimensional structure of protein was determined. Within a decade, a radical change in drug design had begun, incarporating the knowledge of 3 dimensional structures of target protein into design process. Protein structure can influence drug discovery at every stage in design process.

Classicaly, it is used in lead optimization, a process that uses structure to guide the chemical modification of a lead molecule to give an optimised fit in terms of shape, hydrogen bonds and other non -covalent interactions with the target[2]. Personal Medicine: Personalized medicine is a medical model that proposes the customization of healthcare, with all decisions and practices being tailored to the individual patient by use of genetic or other information.

Practical application outside of long established considerations like a patient's family history, social circumstances, environment and behaviors are very limited so far and practically no progress has been made in the last decade.

Personalized medicine research attempts to identify individual solutions based on the susceptibility profile of each individual. It is hoped that these fields will enable new approaches to diagnosis, drug development, and individualized therapy [3]. Preventive Medicine: Preventive medicine or preventive care consists of measures taken to prevent diseases, or injuries rather than curing them or treating their symptoms.

This contrasts in method with curative and palliative medicine, and in scope with public health methods which work at the level of population health rather than individual health [4].

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Simple examples of preventive medicine include hand washing, breastfeeding, and immunizations. Gene Therapy: Gene therapy is a novel form of drug delivery that enlists the synthetic machinery of the patient's cell to produce a therapeutic agent. It involves the efficient introduction of functional gene into the appropriate cells of the patient in order to produce sufficient amount of protein encoded by transferred gene transgene so as to precisely and permanently correct the disorder.

Strategies of Gene Therapy are following [5]: - Gene addition - Removal of harmful gene by antisense nucleotide or ribozymes - Control of gene expression 2. Microbial Genome Applications In the field of Microbial Genome Applications, applications of bioinformatics are used for following areas: a.

Waste Cleanup : In bioinformatics bacteria and microbes are identified which are helpful in cleaning waste. Deinococcus radiodurans Bacterium is listed in the Guinness Book of World Records as "the world's toughest bacterium.

bioinformatics ppt

This is because it has additional copies of its genome. Genes from other bacteria have been inserted into D. It was used to break down organic chemicals, solvents and heavy metals in radioactive waste sites [7]. Climate Change : Climate change is caused by factors that include oceanic processes such as oceanic circulationvariations in solar radiation received by Earth, plate tectonics and volcanic eruptions, and human-induced alterations of the natural world.

By studying microorganisms genome scientists can begin to understand these microbes at a very fundamental level and isolated the genes that give them their unique abilities to survive under extreme conditions [8].

Rhodopseudomonas palustris is a purple non-sulfur phototrophic bacterium commonly found in soils and water.

Molecular Docking: Bioinformatics in Drug Discovery

It converts sunlight to cellular energy by absorbing atmospheric carbon dioxide and converting it to biomass. This microbe can also degrade and recycle a variety of aromatic compounds that comprise lignin, the main constituent of wood and the second most abundant polymer on earth [9].

Not only can it convert carbon dioxide gas into cell material but nitrogen gas into ammonia, and it can produce hydrogen gas. It grows both in the absence and presence of oxygen.Copy embed code:. Automatically changes to Flash or non-Flash embed. WordPress Embed Customize Embed. URL: Copy. Presentation Description This presentation give a brief description about role of bioinformatics in drug designing and development.

Role of Bioinformatics in drug designing and development: Role of Bioinformatics in drug designing and development Division of Biochemistry, Indian Veterinary Research institute, Izatnagar, India introduction: introduction The in silico identification of novel drug targets is now feasible by systematically searching for paralogs related proteins within an organism of known drug targets eg.

Current Opin. Microbiol Using gene expression microarrays and gene chip technologies, a single device can be used to evaluate and compare the expression of up to genes of healthy and diseased individuals at once. Trends Biotechnol Informatics: Informatics The ability to transform raw data into meaningful information by applying computerized techniques for managing, analyzing, and interpreting data. The identification of new biological targets has benefited from the genomics approach: eg.

The sequencing of the human genome. Nature ; Science Blueprint of all proteins Bioinformatics methods are used to transform the raw sequence into meaningful information eg. It has been estimated that up to 10 genes contribute to multifactoral diseases. Many public biological databases are warehousing and providing a great amount of functional information for drug discovery.

Databases to create systematic analysis architecture will be helpful for inferring the underlying interaction of genes and gaining insights about the pathway structures with which drug targets interact List of some relevant databases for drug target identification.

The network-based strategy for drug target identification attempts to reconstruct endogenous metabolic, regulatory and signaling networks with which potential drug targets interact Development of microarray technology, large volume of gene expression or protein expression data have been produced, and there have been considerable models proposed to infer gene networks or protein networks from these data.

In silico characterization can be carried by using approaches such as genetic-network mapping, protein-pathway mapping, protein—protein interactions, disease-locus mapping, and subcellular localization predictions PowerPoint Presentation: Bioinformatics is being increasingly used to support target validation by providing functionally predictive information mined from databases and experimental datasets using a variety of computational tools.

Sequence-based approaches -The most commonly used approach to assign function to proteins is by sequence similarity. Structure-based approaches- homology modelling e. The identification of small molecule modulators of protein function and the process of transforming these into high-content lead series are key activities in modern drug discovery Robert AG Hits can be identified by one or more of several technology-based approaches like high throughput biochemical and cellular assays, assay of natural products, structure-based design High-throughput Screening: High-throughput Screening Used to test large numbers of compounds for their ability to affect the activity of target proteins.

Natural product and synthetic compound libraries with millions of compounds are screened using a test assay. One solution may be to accumulate as much knowledge as possible on biological targets eg.

Free Bioinformatics PowerPoint Template

Virtual screening: Virtual screening It is a computational technique used in drug discovery research.After you enable Flash, refresh this page and the presentation should play. Get the plugin now.

Toggle navigation. Help Preferences Sign up Log in. To view this presentation, you'll need to allow Flash. Click to allow Flash After you enable Flash, refresh this page and the presentation should play. View by Category Toggle navigation. Products Sold on our sister site CrystalGraphics. Title: Bioinformatics. Description: Sequence alignment is the procedure of comparing two pair-wise alignment or Tags: bioinformatics waterman.

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Bioinformatics in Python: Intro

Title: Bioinformatics 1 Lecture 8 Bioinformatics Alignment of pairs of sequence Local and global alignment Methods of alignment Dynamic programming approach Use of scoring matrices and gap penalties PAM and BLOSUM Formal dynamic programming algorithm 2 Definition of sequence alignment Sequence alignment is the procedure of comparing two pair-wise alignment or more multiple sequences by searching for a series of individual characters or patterns that are in the same order in the sequences.

There are two types of alignment local and global. In global alignment, an attempt is made to align the entire sequence. If two sequences have approximately the same length and are quite similar, they are suitable for the global alignment.

bioinformatics ppt

Local alignment concentrates on finding stretches of sequences with high level of matches. Sequences that are very much alike may have similar secondary and 3D structure, similar function and likely a common ancestral sequence. It is extremely unlikely that such sequences obtained similarity by chance.

For DNA molecules with n nucleotides such probability is very low P 4-n. For proteins the probability even much lower P 20 n, where n is a number of amino acid residues Large scale genome studies revealed existence of horizontal transfer of genes and other sequences between species, which may cause similarity between some sequences in very distant species. This is a highly computationally demanding method. However the latest algorithmic improvements and ever increasing computer capacity make possible to align a query sequence against a large DB in a few minutes.

Each alignments has its own score and it is essential to recognise that several different alignments may have nearly identical scores, which is an indication that the dynamic programming methods may produce more than one optimal alignment. However intelligent manipulation of some parameters is important and may discriminate the alignments with similar scores. Global alignment program is based on Needleman-Wunsch algorithm and local alignment on Smith-Waterman.

Both algorithms are derivates from the basic dynamic programming algorithm. The ratio of the first two probabilities is usually provided in an amino acid substitution matrix.

The score for the gap introduction and its extension is also calculated from the matrices and represent a prior knowledge and some assumptions. One of them is quite simple, if negative cost of a gap is too high a reasonable alignment between slightly different sequences will be never achieved but if it is too low an optimal alignment is hardly possible. Other assumptions are based on sophisticated statistical procedures. In order to optimise the alignment gap s may be introduced, which may reflect losses or insertions, which occurred in the past in the sequences.

Introduction of gaps causes penalties. Scores gained by each match are not always the same, for instance two rare amino acids will score more than two common. In this manner, the alignment can be traced back to the first aligned pair that was also an optimal alignment.Bioinformaticsa hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research, including biomedicine.

Bioinformatics is fed by high-throughput data-generating experiments, including genomic sequence determinations and measurements of gene expression patterns. Database projects curate and annotate the data and then distribute it via the World Wide Web. Mining these data leads to scientific discoveries and to the identification of new clinical applications. In the field of medicine in particular, a number of important applications for bioinformatics have been discovered.

For example, it is used to identify correlations between gene sequences and diseases, to predict protein structures from amino acid sequences, to aid in the design of novel drugsand to tailor treatments to individual patients based on their DNA sequences pharmacogenomics. The classic data of bioinformatics include DNA sequences of genes or full genomes; amino acid sequences of proteins; and three-dimensional structures of proteins, nucleic acids and protein—nucleic acid complexes.

In each case there is interest in obtaining comprehensiveaccurate data for particular cell types and in identifying patterns of variation within the data. For example, data may fluctuate depending on cell type, timing of data collection during the cell cycleor diurnal, seasonal, or annual variationsdevelopmental stage, and various external conditions.

Metagenomics and metaproteomics extend these measurements to a comprehensive description of the organisms in an environmental sample, such as in a bucket of ocean water or in a soil sample.

Bioinformatics has been driven by the great acceleration in data-generation processes in biology. Genome sequencing methods show perhaps the most dramatic effects.

In the nucleic acid sequence archives contained a total of 3. The U.

Introduction to Bioinformatics - PowerPoint PPT Presentation

In bioinformatics, data banks are used to store and organize data. Many databases are in the hands of international consortia. To ensure that sequence data are freely available, scientific journals require that new nucleotide sequences be deposited in a publicly accessible database as a condition for publication of an article.

Similar conditions apply to nucleic acid and protein structures. There also exist genome browsers, databases that bring together all the available genomic and molecular information about a particular species. The homepages of the wwPDB partners contain links to the data files themselves, to expository and tutorial material including news itemsto facilities for deposition of new entries, and to specialized search software for retrieving structures.

Other algorithms search data banks to detect similarities between data items. For example, a standard problem is to probe a sequence database with a gene or protein sequence of interest in order to detect entities with similar sequences. Article Media. Info Print Print. Table Of Contents. Submit Feedback.Ever heard of the term bioinformatics? In a world where data is being generated at a faster rate than we can process, bioinformatics is used to analyze massive amounts of data and make sense of it all.

We take a look at how bioinformatics is making a huge impact in drug discovery and design, by focusing on the field of molecular docking in virtual screening of compounds.

As the lesser-known third category of study, in silicois the umbrella term for techniques that make use of computational power. When it comes to drug discoveryit is intuitive to see why in silico methods present good opportunities. The stages that follow the design of a new drug are both costly and time-consuming. The entire process of drug development can take from 12 to 15 years and cost billions of dollars, but in silico studies have been seen to both speed up the discovery rate and reduce although not eliminate!

In this article we will focus on molecular docking since the author has personally worked for some time with the technique — modeling antibodies is tougher than it might seem! Molecular docking a type of bioinformatic modeling, an essential tool in structural molecular biology and in drug design.

Receptors, such as G-protein-coupled receptorsare proteins found inside and on the surface of cells. They are responsible for virtually every single biochemical process inside our bodies. Collectively, molecules that bind to a receptor are called ligands. By binding, ligands can either activate receptors agonists or deactivate them antagonists. Being able to model the binding of receptors and ligands using molecular docking can be beneficial in the discovery of new drug targets or drug candidates.

Computational power can be used to predict — to a certain degree of accuracy — where and how well a given molecule can attach itself to the receptor. The diagram below shows a simplified depiction of how the docking procedure can influence and empower drug design. Computational methods are simply not advanced and robust enough to simulate the exact interactions between large molecules.

The entire process is centered around using software to generate an enormous number of ligand-protein conformations, followed by calculations that predict which ones bind most strongly and are the most stable.

With the aid of sampling algorithms and certain assumptions, they are a key part of molecular docking as we can attempt to reproduce the binding event as accurately as possible 56. It turns out that this is actually way too much computation… even for a computer the irony! In order to refine the search — saving time and costs — these algorithms are put in place in order to remove improbable events. Rather than using precise calculations, these functions estimate the binding energies of many different conformations in a reasonable amount of time, albeit by sacrificing some degree of accuracy.

The different scoring functions used are highlighted below:.

bioinformatics ppt

They assess the binding energy by calculating the sum of non-bonded interactions such as electrostatic interactions and Van der Waals forces.

Some extensions of these calculations include other relevant aspects such as hydrogen bonds or entropy. It is important to take into account the solvent effects in these calculations, as it also plays a role in the free energy of the binding.

Empirical scoring functions use known binding affinities of protein-ligand complexes to perform multiple linear regression analyses. The values generated by this statistical model are then used as coefficients to adjust the equation in general.

These functions use statistical analysis of ligand-protein crystal structures to obtain the measurement of the distance between them; it makes the assumption that an interaction that looks favorable will lead to activity. Its advantage lies in its computational simplicity, with a drawback being some interactions might not be represented in the available database of crystal structures.

This introduction provides a little bit of background behind the whole process of molecular docking; showing how software can calculate how well your drug can fit with the target protein. Of course, there are other factors to take into consideration, such as how to treat individual receptors and ligands — rigid components that fit a certain position an over-simplified version of the reality and because of that, not very popular nowadays or as flexible structures sounds more logical right?

I could keep going on and on with the whole process behind molecular docking, which is a big part of computing sciences, biology and even atomic theorybut I feel like I can stop here. Essentially, molecular docking is a highly versatile tool that provides a nice push to an overly-slow industry, making the development of new drugs a bit less tedious and definitely more interesting in terms of understanding how proteins bind!

March 11, June 28, July 27, She loves archery, science communication, and winning Instagram contests. Structure-Based Design. Table of Contents.This category includes: Comparative genomics, genome assembly, genome and chromosome annotation, identification of genomic features such as genes, splice sites and promoters. We will consider algorithms and applications in any of the above areas.

Small improvements or modifications of existing algorithms will generally not be suitable, unless novel biological results have been predicted and verified. New methods MUST be compared to existing state-of-the-art methods, using real biological data. Improvements in speed of methods may be considered if it is demonstrated that this will significantly widen the application of the method.

This category includes: Multiple sequence alignment, sequence searches and clustering; prediction of function and localisation; novel domains and motifs; prediction of protein, RNA and DNA functional sites and other sequence features. Papers that analyse existing sequence data will only be considered if novel biological insight is obtained. This category includes: novel phylogeny estimation procedures for molecular data including nucleotide sequence data, amino acid data, whole genomes, SNPs, etc.

We will consider algorithms, applications, databases, data repositories, and representation tools in any of the above areas. New methods MUST be compared to existing state-of-the-art methods, using real or simulated biological data with a preference towards the combination of both approaches. This category includes: New methods and tools for structure prediction, analysis and comparison; new methods and tools for model validation and assessment; new methods and tools for docking; models of proteins of biomedical interest; protein design; structure based function prediction.

We will consider papers related to new methods for organizing structural information, and for its representation. Small improvements or modifications of existing algorithms will generally not be suitable. Papers that report three-dimensional models of macromolecules, molecular dynamics simulations and docking results will not be considered.

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Descriptions of repositories of automatically generated models will only be published if it can be demonstrated that they provide significant advantages over existing ones.

This category includes a wide range of applications relevant to the high-throughput analysis of expression of biological quantities, including microarrays nucleic acid, protein, array CGH, genome tiling, and other arraysRNA-seq, proteomics and mass spectrometry. Approaches to data analysis to be considered include statistical analysis of differential gene expression; expression-based classifiers; methods to determine or describe regulatory networks; pathway analysis; integration of expression data; expression-based annotation e.

We will consider novel algorithms and applications in the above areas that constitute significant advances. Applications including databases and web resources will only be considered if significantly innovative.

Measures of significance include anticipated impact on broad community and replacement of heuristics with principled approaches. Development in areas with established approaches, such as normalization or classification, must represent a conceptual advance and show more than marginal improvement over existing methods. This category includes: Segregation analysis, linkage analysis, association analysis, map construction, population simulation, haplotyping, linkage disequilibrium, pedigree drawing, marker discovery, power calculation, genotype calling.

We consider statistical methodology only when there is significant bioinformatics content such as new algorithms or software. We do not consider software that implements methods recently published elsewhere by the same authors.

This category includes whole cell approaches to molecular biology. Any combination of experimentally collected whole cell systems, pathways or signaling cascades on RNA, proteins, genomes or metabolites that advances the understanding of molecular biology or molecular medicine will be considered.

Interactions and binding within or between any of the categories will be considered including protein interaction networks, regulatory networks, metabolic and signaling pathways.