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bioinformatics simple
This document provides an overview of bioinformatics. It defines bioinformatics as the science of collecting, analyzing and conceptualizing biological data through computational techniques. It discusses that bioinformatics involves managing, organizing and processing biological information from databases, as well as analyzing, visualizing and sharing biological data over the internet. It also outlines some of the goals of bioinformatics like organizing the human and mouse genomes, as well as some applications like genomic and protein sequence analysis, protein structure prediction, and characterizing genomes.
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Introduction to bioinformatics, which combines biological data with informatics techniques for analysis and conceptualization.
Discusses biological data quantities, including human genome size (3 billion base pairs), and goals of bioinformatics such as data management and visualization.
Bioinformatics defined through molecular biology aspects and central dogma, highlighting data processing and statistical analysis in biological contexts.
Details computational methods in bioinformatics, including algorithm development and the human genome project, emphasizing data integration and accessibility.
Information analysis processes in bioinformatics, focusing on data management, hypothesis derivation, and the significance of quick analysis.
Different domains of bioinformatics applications including computational biology, medical informatics, and drug development.
Main goals in the post-genomics era, including gene annotation and prediction of gene functions from DNA sequences.
Assesses methods for gene identification within genomes and comparative genomics for evolutionary studies.
Focus on structural genomics, emphasizing protein structures, functionality, and evolutionary relationships derived from 3D structures.
Different biological databases including sequence, structure, and literature databases, and their interrelation for data accessibility.
Applications in genomics, including gene finding, characterization of genomic elements, and comparative analyses across species.
Applications in protein sequence analysis including alignment methods, prediction of secondary and tertiary structures, and evolutionary implications.
Overall characterization of genomes, including expression analysis, comparisons among organisms, and statistical evaluations.
Closure and thanks, likely summarizing the significance of bioinformatics in modern biological research.
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bioinformatics simple
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- 2. Science ofcollecting, analyzing and conceptualizing biological data by implication of informatics techniques. 2 Bioinformatics Biology Informa- tics Bioinformatics
- 3. Biological Data Computer Analysis+ Mouse Genome: 2.5billion base pairs Human Genome: 3 billion base pairs 3
- 4. Manage biologicalinformation organize biological information using databases Process, analyze, and visualize biological data Share biological information to the public using the Internet. 4 Goals of Bioinformatics
- 5. Bio –informatics Bioinformatics is conceptualizing biology in terms of molecules (in the sense of physical-chemistry) applying “informatics” techniques (derived from disciplines such as applied math, CS, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Bioinformatics is a practical discipline with many applications. 5 Definition
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- 7. 7 Biological Information CentralDogma of Molecular Biology DNA -> RNA -> Protein -> Phenotype -> DNA Molecules Sequence, Structure, Function, Interaction Processes Mechanism, Specificity, Regulation Central Paradigm for Bioinformatics Genomic Sequence Information -> mRNA (level) -> Protein Sequence -> Protein Structure -> Protein Function -> Protein Interaction -> Phenotype Large Amounts of Information Statistical Computer Processing
- 8. Systems Analysis Information Theory GraphTheory Robotics Algorithms Artificial IntelligenceStatistics 8
- 9. 9 Domains of bioinformatics Bio-informatist Development ofnew software Algorithms Bio-informaticians. Using different algorithms and computer software
- 10. Could nothave been achieved without bioinformatics Goals 3 billion DNA subunits Discover all the human genes Make them accessible for further biological study then ? Need to bring together and store vast amounts of information from Lab equipment and experiments Computer Analysis Human Analysis Make visible to the world’s scientists 10 Human genome project
- 11. 11 How to analyze information Data –Management. –Analysis. –Derive Hypothesis. –Design and Implement an in silico experiment. –Confirm in the wet lab.
- 12. Find ananswer quickly Most in silico biology is faster than in vitro 2. Massive amounts of data to analyze Need to make use of all information Not possible to do analysis by hand Can’t organize and store information only using lab note books• Automation is key However! Verification ? 12 Why bioinformatics
- 13. 1. Computational biology- Computing methods for classical biology Primarily concerned ----> Evolutionary, population and theoretical biology, Cellular/Molecular biology ? 2. Medical informatics- Computing methods to improve communication, understanding, and management of medical data Data Manipulation Applications
- 14. 3. Chemo -informatics Chemical and biological technology, for drug design and development 4. Genomics Analysis and comparison of the entire genome of a single species or of multiple species Genomics existed before any genomes were completely sequenced, but in a very primitive state Continued…
- 15. 5. Proteomics Studyof how the genome is expressed in proteins, and of how these proteins function and interact Concerned with the actual states of specific cells, rather than the potential states described by the genome 6. Pharmacogenomics The application of genomic methods to identify drug targets For example, searching entire genomes for potential drug receptors, or by studying gene expression patterns in tumors Continued….
- 16. 7. Pharmacogenetics : The use of genomic methods to determine what causes variations in individual response to drug treatments The goal is to identify drugs that may be only be effective for subsets of patients, or to tailor drugs for specific individuals or groups
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- 18. 18 Annotation Identify the geneswithin a given sequence of DNA Identify the sites Which regulate the gene Predict the function
- 19. A geneis characterized by several features (promoter, ORF…) some are easier and some harder to detect… 19 How do we identify a gene in a genome?
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- 21. 21 Comparison between thefull drafts of the human and chimp genomes revealed that they differ only by 1.23% How humans are chimps? Perhaps not surprising!!!
- 22. So where arewe different ?? 22 Human ATAGCGGGGGGATGCGGGCCCTATACCC Chimp ATAGGGG - - GGATGCGGGCCCTATACCC Mouse ATAGCG - - - GGATGCGGCGC -TATACCA
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- 24. 24 The protein threedimensional structure can tell much more than the sequence alone Protein-ligand complexes Functional sites fold Evolutionary relationship Shape and electrostatics Active sites protein complexes Biologic processes
- 25. The different typesof data are collected in database Sequence databases Structural databases Databases of Experimental Results All databases are connected 25 Resources and Databases
- 26. Gene database Genome database Diseaserelated mutation database 26 Sequence databases
- 27. 3-dimensional structuresof proteins, nucleic acids, molecular complexes etc 3-d data is available due to techniques such as NMR and X-Ray crystallography 27 Structure Databases
- 28. Data suchas experimental microarray images- gene expression data Proteomic data- protein expression data Metabolic pathways, protein-protein interaction data, regulatory networks 28 Databases of Experimental Results
- 29. 29 PubMed Service of theNational Library of Medicine http://www.ncbi.nlm.nih.gov/pubmed/ Literature Databases
- 30. Each Databasecontains specific information Like other biological systems also these databases are interrelated 30 Putting it all Together
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- 32. Applications I-- Genomics Finding Genes in Genomic DNA introns exons Promotors Characterizing Repeats in Genomic DNA Statistics Patterns Expression Analysis Time Course Clustering Identifying regulatory Regions Measuring Differences • Genome Comparisons Ortholog Families Genome annotation Evolutionary Phylogenetic trees • Characterizing Intergenic Regions Finding Pseudo genes Patterns • Duplications in the Genome Large scale genomic alignment
- 33. Application II- Protein Sequence SequenceAlignment non-exact string matching, gaps How to align two strings optimally via Dynamic Programming Local vs Global Alignment Suboptimal Alignment Hashing to increase speed (BLAST, FASTA) Amino acid substitution scoring matrices Multiple Alignment and Consensus Patterns How to align more than one sequence and then fuse the result in a consensus representation Transitive Comparisons HMMs, Profiles Motifs Scoring schemes and Matching statistics How to tell if a given alignment or match is statistically significant A P-value (or an e-value)? Score Distributions (extreme val. dist.) Low Complexity Sequences Evolutionary Issues Rates of mutation and change
- 34. Application III-- Protein Structure SecondaryStructure “Prediction” via Propensities Neural Networks, Genetic Algorithm. Simple Statistics Trans Membrane Regions Assessing Secondary Structure Prediction Tertiary Structure Prediction Fold Recognition Threading Ab initio Function Prediction Active site identification Relation of Sequence Similarity to Structural Similarity
- 35. Example Application IV:Finding Homologs Core
- 36. Overall Occurrenceof a Certain Feature in the Genome e.g. how many kinases in Yeast Compare Organisms and Tissues Expression levels in Cancerous vs Normal Tissues Databases, Statistics Example Application IV: Overall Genome Characterization
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