1 / 34

Bioinformatics

2010-2011. Bioinformatics. Lecture 1 Introduction. Dr. Aladdin Hamwieh Khalid Al- shamaa Abdulqader Jighly. Aleppo University Faculty of technical engineering Department of Biotechnology. Main Lines. Definition Bioinformatics areas Bioinformatics data Data types

arella
Télécharger la présentation

Bioinformatics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 2010-2011 Bioinformatics Lecture 1 Introduction Dr. Aladdin Hamwieh Khalid Al-shamaa Abdulqader Jighly Aleppo University Faculty of technical engineering Department of Biotechnology

  2. Main Lines • Definition • Bioinformatics areas • Bioinformatics data • Data types • Applications for these data • Next generation sequencing • Bioinformatics algorithms • Joint international programming initiatives

  3. Definition • Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline. • Bioinformatics is the science of managing and analyzing biological data using advanced computing techniques • Bioinformatics applies principles of information science to make the vast, diverse, and complex life sciences data more understandable and useful.

  4. Definition • There are two extremes in bioinformatics work • Tool users (biologists): know how to press the buttons and the biology but have no clue what happens inside the program • Tool shapers (informaticians): know the algorithms and how the tool works but have no clue about the biology

  5. Bioinformatics areas • Molecular sequenceanalysis • Sequence alignment • Sequence database searching • Motif discovery • Gene and promoter finding • Reconstruction of evolutionary relationships • Genome assembly and comparison

  6. Bioinformatics areas • Molecular structuralanalysis • Protein structure analysis • Nucleic acid structure analysis • Comparison • Classification • prediction

  7. Bioinformatics areas • Molecular functionalanalysis • gene expression profiling • Protein–protein interaction prediction • protein sub-cellular localization prediction • Metabolic pathway reconstruction • simulation

  8. Bioinformatics data There is different data types usually used in bioinformatics The same data may be used in different areas

  9. Data types • DNA sequences • RNA sequences • Expression (microarray) profile • Proteome (x-ray, NMR) profile • Metabolome profile • Haplotype profile • Phenotype profile

  10. 1- DNA Sequences • Simple sequence analysis • Database searching • Pairwise and multiple analysis • Regulatory regions • Gene finding • Whole genome annotation • Comparative genomics

  11. 2- RNAs • Splice variants • Tissue specific expression • 2D structure • 3D structure • Single gene analysis • Microarray

  12. 2D and 3D structure of tRNA

  13. 2D and 3D structure of rRNA

  14. Microarray • 20,000 to 60,000 short DNA probes of specified sequences are orderly tethered on a small slide.Each probe corresponds to a particular short section of a gene.

  15. Microarray • DNA microarrays measure the RNA abundance with either 1 channel (one color) or 2 channels (two colors). • Stanford microarraysmeasure by competitive hybridization the relative expression under a given condition (fluorescent red dye Cy5) compared to its control (labeled with a green fluorescent dye, Cy3) (Two channels) • AffymetrixGeneChip has 1 channel and use eitherfluorescent red dye Cy5 orgreen fluorescent dye, Cy3

  16. 3- Proteins • Protein sequences analysis • Database searching • Pairwise and multiple analysis • 2D structure • 3D structure • Classification of proteins families • Protein arrays

  17. 3D structure

  18. Animation

  19. 4- Metabolome and molecular biology • Metabolic pathways • Regulatory networks Helps to understand systems biology

  20. 5- Haplotype • Molecular Markers • RFLP • RAPD • SSR • ISSR • AFLP • DArT • SNP • ….

  21. SNP

  22. 6- Phenotype • Morphological data • Physiological data • Stresses tolerance • Pathogenic infections • Diseases resistance • Cancers types • …..

  23. Haplotype & Phenotype

  24. Next Generation Sequencing

  25. Short reads assembly problems

  26. Short reads assembly problems

  27. Short reads assembly problems

  28. Algorithms in bioinformatics • String algorithms • Dynamic programming • Machine learning (NN, k-NN, SVM, GA, ..) • Markov chain models • Hidden Markov models • Markov Chain Monte Carlo (MCMC) algorithms • Stochastic context free grammars • EM algorithms • Gibbs sampling • Clustering • Tree algorithms (suffix trees) • Graph algorithms • Text analysis • Hybrid/combinatorial techniques • ….

  29. Joint international programming initiatives • Bioperl http://www.bioperl.org/wiki/Main_Page • Biopython http://www.biopython.org/ • BioTcl http://wiki.tcl.tk/12367 • BioJava www.biojava.org/wiki/Main_Page

  30. Thank You

More Related