1. Bioinformatics Basics Cyrus Courtesy from LO Leung Yau’s original presentation

2. Outline Biological Background  Cell  Protein  DNA & RNA  Central Dogma  Gene Expression Bioinformatics  Sequence Analysis  Phylogentic Trees  Data Mining

3. Biological Background – Cell Basic unit of organisms  Prokaryotic  Eukaryotic A bag of chemicals Metabolism controlled by various enzymes Correct working needs  Suitable amounts of various proteins Picture taken from http://en.wikipedia.org/wiki/Cell_(biology)

4. Biological Background – Protein Polymer of 20 types of Amino Acids Folds into 3D structure Shape determines the function Many types  Transcription Factors  Enzymes  Structural Proteins  … Picture taken from http://en.wikipedia.org/wiki/Proteinhttp://en.wikipedia.org/wiki/Protein http://en.wikipedia.org/wiki/Amino_acid

5. Biological Background – DNA & RNA DNA  Double stranded  Adenine, Cytosine, Guanine, Thymine  A-T, G-C  Those parts coding for proteins are called genes RNA  Single stranded  Adenine, Cytosine, Guanine, Uracil Picture taken from http://en.wikipedia.org/wiki/Gene

6. Biological Background – Genes Genes – protein coding regions 3 nucleotides code for one amino acid There are also start and stop codons

7. Biological Background — in a nutshell Abstractions Functional Units: Proteins Templates: RNAs Blueprints: DNAs Templates: RNAs Blueprints: DNAs Not only the information (data), but also the control signals about what and how much data is to be sent Proteins (TFs) so help

8. Biological Background – Sequences Abstractions Sequences acatggccgatcaggctgtttttgtgtgcctgtttttctattttacgtaaatcaccctgaacatgtTTGCATCAacctact ggtgatgcacctttgatcaatacattttagacaaacgtggtttttgagtccaaagatcagggctgggttgacctgaatact ggatacagggcatataaaacaggggcaaggcacagactc FT intron <1..28 FT /gene="CREB" FT /number=3 FT /experiment="experimental evidence … FT recorded" FT exon 29..174 FT /gene="CREB" FT /number=4 FT /experiment="experimental evidence … FT recorded" FT intron 175..>189 FT /gene="CREB" FT /number=4 Annotations Visualizations

9. Biological Background – DNA  RNA  Protein Picture taken from http://en.wikipedia.org/wiki/Gene gene

10. Biological Background – DNA  RNA  Protein Transcriptional Regulatory Network is the complex interaction between genes, transcription factors (TF) and transcription factor binding sites (TFBS). Other functions Transcription Factors Binding sites GenesPromoter regions

11. Complex Interactions between Genes, TFs and TFBSs

12. Biological Background – DNA  RNA  Protein Transcriptional Regulatory Network is the complex interaction between genes, transcription factors (TF) and transcription factor binding sites (TFBS). Other functions Transcription Factors Binding sites GenesPromoter regions

13. Gene Expression Microarray Data High throughput Measures RNA level Relies on A-T, G-C pairing Can monitor expression of many genes Picture taken from http://en.wikipedia.org/wiki/DNA_microarray_experiment

14. Gene Expression Microarray Data Picture taken from http://en.wikipedia.org/wiki/DNA_microarray Genes Time points/Condiditions Colors: Expression (RNA) Levels

15. Bioinformatics — Sequence Analysis Alignments  a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequencesDNARNAproteinstructural evolutionary http://en.wikipedia.org/wiki/Sequence_alignment

16. Bioinformatics — Sequence Analysis Pair-wise alignments  Method: dynamic programming! No penalty for the consecutive ‘-’s before and after the sequence to be aligned \\Pc91106\Old_FYP\Bioinformatics for FYPs\CSC3220 Lectures

17. Bioinformatics — Sequence Analysis Multiple (global) sequence alignment  Also dynamic programming (but can’t scale up!)

18. Bioinformatics — Sequence Analysis Multiple local sequence alignment  i.e. Motif (pattern) discovery >seq1 acatggccgatcagctggtttttgtgtgcctgtttctgaatc >seq2 ttctattttacgtaaatcagcttgaacatgtacctactggtg >seq3 atgcacctttgatcaataccagctagacaaacgtgtgttg >seq4 agtccaaagatcagggctggctgaatactggatcagct >seq5 cagctacagggcatataaaggggcaaggcacagactc Such overrepresented patterns are often important components (e.g. TFBSs if the sequences are promoters of similar genes). TFBSs are the controlling key holes in gene regulation!

19. DNA motifs Similar DNA fragments across individuals and/or species  TFBS Motifs: DNA fragments similar to “TATAA” are common in order to make genes functioning  Expensive and time-consuming to try a large set of candidates in biological experiments Transcription RNA Translation Protein TATAA TFBS (controlling) Gene (functioning) TF Transcription Factor DNA

20. Motif discovery CGATTGA f Similar controlled functions e.g. cancer gene activities Maximized TFBS Motif Discovery SNP (single nucleotide polymorphism) Motif Discovery … DNA from different people Normal Disease! A A A C C C T T T G G G AT CG … … … … f Normal Disease! distinguish Maximized

21. Bioinformatics — Data mining Classification  To predict!  Pre-processing—tidy up your materials!  Feature selection—the key points to go over  Classifier—the thinking style/manner of how to combine the key points and get some answer  Training—your practice of your thinking manner with answers known  Validation—mock quiz to evaluate what you’ve learnt from the training  Testing—your examination! \\Pc91106\Old_FYP\Bioinformatics for FYPs\CSC5180 Data Mining Notes\c3class1.pdf Underfitting & Overfitting

22. TRANSFAC Project TF-Transcription Factors, important regulators TFBS-Transcription Factor Binding Site, major regulatory elements TRANSFAC-The most representative DB for TFs and TFBSs Modeling: statistical models, representations, Markov chains; Discovery: stochastic searching, indexing (suffix trees) 1 Relationship: TF-TFBS; TFBS- Gene… (understanding, prediction) Mining: text mining, approximate matching 2 Annotations: accurate wet-lab candidates (reduced labor and costs); Computation: large scale data processing; parallel computing 3 Representative Publications [1] Gang Li, Tak-Ming Chan, Kwong-Sak Leung and Kin-Hong Lee, A Cluster Refinement Algorithm for Motif Discovery, IEEE/ACM Transaction on Computational Biology and Bioinformatics (accepted) [2] Tak-Ming Chan, Kwong-Sak Leung, Kin-Hong Lee, TFBS identification based on genetic algorithm with combined representations and adaptive post-processing. Bioinformatics, 2008, 24(3), pp. 341-349

23. Bioinformatics — Data mining Evaluation (scores!)  Confusion Matrix  Binary Classification Performance Evaluation Metrics  Accuracy  Sensitivity/Recall/TP Rate  Specificity/TN Rate  Precision/PPV  … \\Pc91106\Old_FYP\Bioinformatics for FYPs\CSC5180 Data Mining Notes\c3class3.pdf

24. Bioinformatics — Data mining Evaluation  ROC (Receiver Operating Characteristics)  Trade-off between positive hits (TP) and false alarms (FP)

25. Not The End Your corresponding tutor will have more project-specific stuff to tell you Thanks Q & A