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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

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Bioinformatics

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  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

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