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Bioinformatics

Transcript: the new idea in this website that acceptance multiple problem solve and evaluate for the students that solving this problem based on : the website based on the jquery, php, html, xml, mysql, google analyze tools and python langugue. students : what can the website do ? as Brazilian researchers and professors, we need to have a strong website that arrange the problems, Bioinformatics problems and solve it. so whatever you are and wherever you are, you can add Biodata problem, or solve a given problem in the site, in this way we can share the knowledge between a wide range of persons and we can help researchers to save time in many researches where to publish the students can see and edit their own solution also they can see all questions, question ranking, adding new problem see the ranking for all users the students can get inside the website to solve Bio problems and the researchers to add new problems and see the solutions that we already have Bioinformatics (Problem, solution) website help researchers and students in : professors professors can add, edit and see the problems. also can solve, see the solves for any problems. also the professors have evaluation status bar. the students can learn how to solve problems. the professors can see the students progress while solving the Bio Problem created by : what is the new in this website the website should be published in the universities in Brazil in general medication, informatics and biology colleges to help professors and students to make researches. what else ? done and created by Sameh Alhosni. to professor :Dr. Vinicius Bassaneze.

Bioinformatics

Transcript: Organizes data so that it allows researchers to access existing information and submit new entries. Develops tools and resources to aid in analysis. Use those tools to analyze the data and interpret the results in terms of biology. Example: Protein Data Bank Drawbacks Technological Quality and Quantity Mathematical Numerous variables Translational Difficult to extract data from results My Independent Study What is bioinformatics? Conceptualizing biology in terms of macromolecules and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. (Department of Molecular Biophysics and Biochemistry at Yale University) Schistosomiasis Parasite: Schistosoma haematobium Infects humans and finds home in the bladder. 2nd in the world only to malaria Applications http://www.ncbi.nlm.nih.gov/pmc/articles/PMC102472/table/gkd090tb1/?report=previmg Use biological explanations. Typically use differential equations. Start small. Bioinformatics How does it work? Model of the Cardiovascular System Developed an ordinary differential equation (ODE) model. Works Cited Berman, Helen M. "The Protein Data Bank." Nucleic Acids Research. 28.1 (2000): 235-242. Web. 9 Dec. 2013. <http://www.ncbi.nlm.nih.gov/pmc/articles/PMC102472/>. Debarre, Florence. "SIR models of epidemics." Trans. Array Level 1 module in "Modelling course in population and evolutionary biology". Web. 9 Dec. 2013. <http://www.tb.ethz.ch/education/model/SIR/sir.pdf>. Luscombe, N.M. "What is bioinformatics? An introduction and Overview." Yearbook of Medical Informatics. (2001): 83-99. Web. 9 Dec. 2013. <http://www.ebi.ac.uk/luscombe/docs/imia_review.pdf>. Winslow, Raimond L. "Computational Medicine: Translating Models to Clinical Care." Sci Transl Med. 4.158 (2012): n. page. Print. Zenker, Sven. "From Inverse Problems in Mathematical Physiology to Quantitative Differential Diagnoses." PLoS Computational Biology. 3.11 (2007): n. page. Print. Main Goals ΔP = -rpP + aPSH + bPSHE ΔS = rsS + cSHE ΔH = rhH - dHSP Epidemic Modeling Models pose important questions. Population is constant. Describes the spread of an infection within a certain population as a function of time. SIR Models What the PDB records Katie Bowman

Rust for Bioinformatics

Transcript: Rust for Bioinformatics 07/06/2022 Rory Munro Rust Rust Compiled language Python interpreted Complex Why? Why Most loved language 6 years Fast Statically typed Compiler Checked/Safe Fearless Concurrency Energy Efficient Brilliant memory management (if you are a computer) No garbage collection (borrow checker) Clippy (mean crate) Cargo - think PyPI Reasons to not Why not Not chill Young - lacking utility libraries So not chill (ownership) Slower to develop in - python quick and dirty An example Example Simulating minKNOW Simulating MinKNOW Why Simulate Why simulate 1. Expensive Save time Don't real real experiments Get actual interaction rather than playback 2. Development Hackathon Python based simulator - too slow for promethion scale Works though so rewrite in rust 3. Hackathon Icarust Icarust - A Rust Based simulator Rust is fast Basis An ambitious introductory project Tonic is the fastest GRPC library Fully featured libraries available Ideology Cons A literal nightmare Doesn't work WHAT'S NEXT WHAT'S NEXT Fix rewrite bug Release Document

Bioinformatics

Transcript: 2. Function analysis Large-scale censuses All the applications that analyze various types of sequence information and can compare between similar types of information First subfield of Bioinformatics Considered the heart of modern biological research Term coined by Paulien Hogeweg Bioinformatics = Biology + IT Identification of protein homologues Transferring information between related proteins a. Algorithm and Software Development Protein sequences Data integration Is the systematic development and application of IT solutions to handle biological information by addressing biological data collection and warehousing, data mining, database searches, analyzes and interpretation, modeling and product design Is a management information system for molecular biology References Discovery of potential drug targets Structural differences between proteins may be harnessed to design drug molecules that specifically bind to one structure but not another Refers to all the computational work done so as to develop an application that is aimed to address certain problems in biology macromolecular structures Protein Data Bank(PDB) x-ray crystallography 3 Major databases (proteins, by structure): 1. CATH 2. SCOP 3. FSSP Second subfield of Bioinformatics Protein content and metabolic pathways Interacting proteins Gene products Gene expression levels Bioinformatics Under computational bioinformatics Table 1 lists the types of data that are analyzed in bioinformatics and the range of topics that we consider to fall within the field. Genomics Confirm coding region in newly sequenced genomes and functional data is frequently transferred to annotate individual genes (large scale) Simplifies problem of understanding complex genomes (simple organisms to more complicated ones) Raw DNA sequences Primary over 300,000 sequences vaults: SWISS-PROT, PIR International Secondary information derived from sequences vaults: PROSITE, PRINTS, Interpro Composite combined non-redundant sets vaults: OWL, NRDB Must have first a strategy to tackle the problem; for this, algorithm of the application is a must People with different expertise work together to develop an algorithm. Sources of Information Gene expression data b. Database Construction and Curation Developing algorithms for sequence comparisons methods for producing multiple sequence alignments Searching for functional domains Prediction of protein structures Methods for 3D structural alignments Examining protein geometries Calculations of surface and volume shapes Analysis of protein interactions 1. To store the biological data organized in form of a database. allows the researchers an easy access to existing information and submit new entries 2. To develop tools and resources that aid in the analysis of data 3. The third and the most important aim of bioinformatics is to exploit these computational tools to analyze the biological data interpret the results in a biologically meaningful manner Bioinformatics Nucleotide and genome sequences data Primary databases of DNA sequences 1. GenBank 2. EMBL 3. DDBJ Composite database of DNA sequences 1. Entrez- detailed views of single chromosomes 2. GeneCensus- builds phylogenetic trees 3. COG- predicts function of unclassified proteins Protein sequence databases Level of Organization in Bioinformatics For the amount of protein abundance 1. 2D gel electrophoresis 2. Mass spectrometry Search for similarities between different biomolecules Computational Bioinformatics 3. Structure analysis Finding homologues Aims of Bioinformatics Further applications in the medical sciences Separating coding and non-coding regions Structural databases Rational Drug Design Experimental genomics for individuals are predicted to revolutionalize the future of healthcare Post-natal genotyping: assess susceptibility/immunity from specific diseases and pathogens Application Bioinformatics Identification of genes that are expressed differently in affected cells provides a basis for explaining the causes of illnesses and highlights potential targets. Broad generalizations help identify interesting subject areas for further detailed analysis Address a number of evolutionary, biochemical, and biophysical questions Highlight diversity of metabolic pathways in different organisms Integration of data from multiple sources Pieces are combined, analyzed with respect to the other data Sequence Retrieval System(SRS) allows users to retrieve, access, and link databases of different sources Result: smooth transition of genomes from databases to predict structures of new protein sequences to understand a protein's function The use of structural data Analysis of the function engraved within the sequences and helps predict the functional interaction between various proteins or genes Under computational bioinformatics MLHI gene product (ex.: drug target) Gene encoding a mismatch repair protein (mmr) situated on the short arm of chromosome 3 Nonpolyposis is colorectal cancer Sequence search

PowerPoint Game Templates

Transcript: Example of a Jeopardy Template By: Laken Feeser and Rachel Chapman When creating without a template... http://www.edtechnetwork.com/powerpoint.html https://www.thebalance.com/free-family-feud-powerpoint-templates-1358184 Example of a Deal or No Deal Template PowerPoint Game Templates There are free templates for games such as jeopardy, wheel of fortune, and cash cab that can be downloaded online. However, some templates may cost more money depending on the complexity of the game. Classroom Games that Make Test Review and Memorization Fun! (n.d.). Retrieved February 17, 2017, from http://people.uncw.edu/ertzbergerj/msgames.htm Fisher, S. (n.d.). Customize a PowerPoint Game for Your Class with These Free Templates. Retrieved February 17, 2017, from https://www.thebalance.com/free-powerpoint-games-for-teachers-1358169 1. Users will begin with a lot of slides all with the same basic graphic design. 2. The, decide and create a series of questions that are to be asked during the game. 3. By hyper linking certain answers to different slides, the game jumps from slide to slide while playing the game. 4. This kind of setup is normally seen as a simple quiz show game. Example of a Wheel of Fortune Template https://www.teacherspayteachers.com/Product/Wheel-of-Riches-PowerPoint-Template-Plays-Just-Like-Wheel-of-Fortune-383606 Games can be made in order to make a fun and easy way to learn. Popular game templates include: Family Feud Millionaire Jeopardy and other quiz shows. http://www.free-power-point-templates.com/deal-powerpoint-template/ Quick video on template "Millionaire" PowerPoint Games Some games are easier to make compared to others If users are unsure whether or not downloading certain templates is safe, you can actually make your own game by just simply using PowerPoint. add logo here References Example of a Family Feud Template PowerPoint Games are a great way to introduce new concepts and ideas You can create a fun, competitive atmosphere with the use of different templates You can change and rearrange information to correlate with the topic or idea being discussed. Great with students, workers, family, etc. For example: With games like Jeopardy and Family Feud, players can pick practically any answers. The person who is running the game will have to have all of the answers in order to determine if players are correct or not. However, with a game like Who Wants to be a Millionaire, the players only have a choice between answers, A, B, C, or D. Therefore, when the player decides their answer, the person running the game clicks it, and the game will tell them whether they are right or wrong.

BioInformatics

Transcript: Bioinformatics is a distinct science from biological computation, the latter being a computer science and computer engineering subfield using bioengineering and biology to build biological computers, whereas bioinformatics simply uses computers to better understand biology Areas that Bioinformatics take advantages from : Bioinformatics is an interdisciplinary scientific field that develops methods for storing, retrieving, organizing and analyzing biological data What is Bio informatics ? 1. Introduction 2. Domains 3.Benefits History of Bioinformatics Bioinformatics uses many areas of computer science, statistics, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before and used in decoding the code of life. Databases and information systems are used to store and organize biological data Paulien Hogeweg coined the term "Bioinformatics" in 1970 to refer to the study of information processes in biotic systems. This definition placed bioinformatics as a field parallel to biophysics or biochemistry Developing software tools in order to generate useful biological knowledge Soft wares Used in this Field ? What is the difference between Bioinformatics & Biological Computation ? Goals of Bioinformatics 1- the development and implementation of computer programs that enable efficient access to, use and management of, various types of information. 3- to increase the understanding of biological processes implementing Bioinformatics of Gene Expression Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications BioInformatics Major Activities of Bioinformatics : 2-the development of new algorithms and statistical measures with which to assess relationships among members of large data sets

BioInformatics

Transcript: Perilipin 5 What? E values of 5.1 worst but... Sized sequence Best e Value is close to 0 Sequence BioInformatics 47 Total hits Kind of confident these matches are legit All E values within .66 of 0 NBlast CTGGTGTCTGAAGCCGCTCGCGCCCAGGGTGACCCTGTTTGCAGCACGATGTCTGAAGAA GAGGCGGCTCAGATCCCCAGATCCAGTGTGTGGGAGCAGGACCAGCAGAACGTGGTGCAG CGTGTGGTGGCTCTGCCCCTGGTCAGGGCCACGTGCACCGCGGTCTGCGATGTTTACAGT GCAGCCAAGGACAGGCACCCGCTGCTGGGCTCCGCCTGCCGCCTGGCTGAGAACTGCGTG TGCGGCCTGACCACCCGTGCCCTGGACCACGCCCAGCCGCTGCTCGAGCACCTGCAGCCC CAGCTGGCCACTATGAACAGCCTCGCCTGCAGGGGCCTGGACAAGCTGGAAGAGAAGCTT CCCTTTCTCCAGCAACCTTCGGAGACGGTGGTGACCTCAGCCAAGGACGTGGTGGCCAGC AGTGTCACGGGTGTGGTGGACCTGGCCCGGAGGGGCCGGCGCTGGAGCGTGGAGCTGAAG CGCTCCGTGAGCCATGCTGTGGATGTTGTACTGGAAAAATCAGAGGAGCTGGTGGATCAC TTCCTGCCCATGACGGAGGAAGAGCTCGCGGCACTGGCGGCTGAGGCTGAAGGCCCTGAA GTGGGTTCGGTGGAGGATCAGAGGAGACAGCAGGGCTACTTTGTGCGCCTCGGCTCCCTG TCAGCACGGATCCGCCACCTGGCCTACGAGCACTCTGTGGGGAAACTGAGGCAGAGCAAA CACCGTGCCCAGGACACCCTGGCCCAGCTGCAGGAGACGCTGGAGCTGATAGACCACATG CAGTGTGGGGTGACCCCCACCGCCCCGGCCCGCCCTGGGAAGGTGCACGAGCTGTGGGGG GAATGGGGCCAGCGCCCTCCGGAGAGCCGCCGCCGGAGCCAGGCAGAGCTGGAGACGCTG GTGCTGTCCCGCAGCCTGACCCAGGAGCTGCAGGGCACGGTAGAGGCTCTGGAGTCCAGC GTGTGGGGCCTGCCCGCCGGCGCCCAGGAGAAGGTGGCTGAGGTGCGGCGCAGTGTGGAT GCCCTGCAGACCGCCTTCGCTGATGCCCGCTGCTTCAGGGACGTGCCAGCGGCCGCGCTG GCCGAGGGCCGGGGTCGCGTGGCCCACGCGCACGCCTGCGTGGACGAGCTGCTGGAGCTG GTGGTGCAGGCCGTGCCGCTGCCCTGGCTGGTGGGACCCTTCGCGCCCATCCTTGTGGAG CGACCCGAGCCCCTGCCCGACCTGGCGGACCTGGTGGACGAGGTCATCGGGGGCCCTGAC CCCCGCTGGGCGCACCTGGACTGGCCGGCCCAGCAGAGAGCCTGGGAGGCAGAGCACAGG GACGGGAGTGGGAATGGGGATGGGGACAGGATGGGTGTTGCCGGGGACATCTGCGAGCAG GAACCCGAGACCCCCAGCTGCCCGGTCAAGCACACCCTGATGCCCGAGCTGGACTTCTGA CCCATGGGCCAGTGGAGGCGGGGAGGAAAGGCCACCTGCACACCCCGATCCCTGCTGCCC CCTGGTGGCCACACGTAAGCTCGAGGCCTTGGCCTTGACCCTTCTTTGGAATCAGGCCCA ACTCCGGATCTCTGACCACCTTTTTGGTATTGGACTCTCCCATTTTTTCCTTGAACACAT GGACAAAGAGGCCCGGGGGAGCAGGGCCTCGAACCCTATTCAGGCCAACTTGAGCCACAA GCTGGGTTCTTCACCTATGTCCTGCTCCCTGGCTCCATGAAGCGAATCCAAATCTTTCCA AGAGGCTGGGCACAGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGTCTGAGGCAGGT GGATCATCTGAAGTCAGGAGTTCGGGATCATCCTGGCCAACATGATGAAACCCTGTCTCT ACTTAGAAAGAAAGAAAAAAAA LDP -

Bioinformatics for Beginners

Transcript: A string of processes organised in such a way, that the output of one is the input of another In NGS, we create data that is very fragmented and noisy when compared to the previous standard of Sanger sequencing. As such, data must be processed through a bioinformatics pipeline What we will cover Currently assessing GOS pipeline. This consists of: FastQC step Alignment with BWA Mark duplicates (Picard) Recalibrate quality scores (GATK) Variant Calling (FreeBayes/GATK) Annotation (Alamut Batch) Presentation in GOSH G2P Stages of a pipeline The different ways we can process NGS data What we do at the IoN What we plan to do in the future Two main types of alignment algorithm string matching hash-based What steps are included in a pipeline? GOSH G2P Illumina sequencing produces fragmented, paired-end sequence, usually around 150bp long. In order to make sense of it, we need to identify where in the genome each sequence belongs. We do this using algorithms to match the fragments to a known reference. Bioinformatics for Beginners Also need to include a few more steps for QC etc. Heuristic variant calling Bayesian variant calling Alignment Variant Calling Annotation Data Presentation Alignment The data is then filtered and presented Filtered on: Previous classifications Frequency Prioritised on gene of interest Presented using Excel, Word or a graphical user interface (GUI) What do we do? What is a pipeline? Add additional information, for example which gene, any protein changes, frequency data etc. To assist in the interpretation of any variants identified. To do this we can access live databases or download our own versions. Once the sequence is matched to the known reference we can then look for differences in the sequence. As NGS sequencing is still not exact, we look for differences based on probability Any questions? What will we do? Annotation GOSH G2P Variant Calling QC check using FASTQC Quality Trim Novalign (hash-based) Mark duplicates (Picard Tools) Recalibrate quality scores (GATK) Call variants in batches of 24 (VarScan) Split into variants for individual samples Annotation (VEP & internal databases) Data is presented in Excel - one coverage, one variants Data Presentation

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