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Genomics and Bioinformatics The "new" biology

Genomics and Bioinformatics The "new" biology

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Genomics and Bioinformatics The "new" biology

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  1. Genomics and BioinformaticsThe "new" biology

  2. What is genomics • Genome • All the DNA contained in the cell of an organism • Genomics • The comprehensive study of the interactions and functional dynamics of whole sets of genes and their products. (NIAAA, NIH) • A "scaled-up" version of genetics research in which scientists can look at all of the genes in a living creature at the same time. (NIGMS, NIH) • Which organism’s genome was sequenced first?

  3. Genome sequencing chronology http://www.ncbi.nlm.nih.gov/ICTVdb/Images/Ackerman/Phages/Microvir/238-27_1.jpg http://www.alsa.org/research/article.cfm?id=822 http://www.waterscan.co.yu/images/virusi-bakterije/Haemophilus%20influenzae.jpg http://www.biochem.wisc.edu/yeastclub/buddingyeast(color).jpg

  4. Genome sequencing chronology http://www.sih.m.u-tokyo.ac.jp/chem1.gif http://lter.kbs.msu.edu/Biocollections/Herbarium/Images/ARBTH3H.jpg

  5. Genome sequencing projects (as of 1/26,2007)

  6. Sequencing strategies: Hierarchical shotgun sequencing http://www.bio.davidson.edu/courses/GENOMICS/method/shotgun.html

  7. plasmids viruses bacteria fungi plants algae insects mollusks bony fish amphibians reptiles birds mammals 104 105 106 107 108 109 1010 1011 Genome size range • What’re there in the genomes? Why are there such a big difference?

  8. Information contents in a genome • Gene • Protein coding genes • RNA genes • Regulatory elements • Gene expression control • Chromatin remodeling • Matrix attachment sites • “Non-functional” elements • Selfish elements • “Junk” DNA • ??

  9. The “central dogma” of molecular biology • Central dogma Replication DNA Transcription RNA Translation Protein

  10. Expanded “central dogma” of molecular biology • A more comprehensive view Replication DNA Transcription RNA Translation Pheno- type Protein Metabolite

  11. New disciplines due to the advance in genomics • Omics Genomic DNA sequences Replication Structural genomics DNA Transcription Transcript seq Microarray data Cis-elements TF binding sites Epigenetic regulation Transcriptomics RNA Translation Shotgun protein seq Subcellular location Post-translational mod Protein interaction Protein structure Pheno- type Proteomics Protein Genetic interactions Systematic KO Disease information Metabolite concn Metabolic flux Metabolomics Metabolite

  12. Nature omics gateway http://www.nature.com/omics/subjects/index.html

  13. 2-100x106 species ~1014 cells per individual ~3x104 genes Three perspectives of our biological world • The cellular level, the individual, the tree of life Rosenzweig et al., 2002. Conservation Biol. Image: htto://www.tolweb.org/tree/ Image: http://www.olympusfluoview.com/gallery/cells/hela/helacells.html

  14. Further complications • Cell-cell interactions • Cell types • Environmental conditions • Developmental programming • Interactions at the organismal level • Interactions at the population, ecosystem level

  15. Definition of bioinformatics • Bioinformatics • Research, development, or application of • Computational tools and approaches for expanding the use of • Biological, medical, behavioral or health data, including those to • Acquire, store, organize, archive, analyze, or visualize such data. • Computational biology • The development and application of • Data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to • The study of biological, behavioral, and social systems • Q: What kinds of data are we taking about? http://www.bisti.nih.gov/

  16. Example: Sequence assembly • Cut into ~150kb pieces • Clone into Bacterial Artificial Chromosome (BAC) • Mapped to determine order of the BAC clones (golden/tiling path) • Shear a BAC clone randomly • Sequencing • Assembie sequence reads http://www.bio.davidson.edu/courses/GENOMICS/method/shotgun.html

  17. Sequence assembly • Challenges • The presence of gaps • Due to incomplete coverage • Sequencing error and quality issue: worse at the end of reactions • So can’t rely on perfectly identical sequences all the time • Sequences derived from one strand of DNA • Need to take orientations of reads into account • Non-random sequencing of DNA • Presence of repeats Correct layout Mis-assembly http://www.cbcb.umd.edu/research/assembly_primer.shtml

  18. Overlap-layout consensus • The relationships between reads can be represented as a graph • Nodes (vertices): reads • Edges (lines): connecting “overlapping reads” • Goal: identifying a path through that graph that visits each node exactly once Genome 2 1 2 3 4 1 4 3 http://en.wikipedia.org/wiki/Image:Hamilton_path.gif

  19. Example: Gene prediction • How can we identify functional elements in the genomes? • How can we assign functions to these elements? • How can we determine/predict the structures of these elements? • How can we reconstruct networks describing the relationships and dynamics between these elements? • How can we link genotypes to phenotypes?

  20. Characteristic of protein coding genes • Similarity to other genes • Assuming there is some level of conservation. • Substitutions that change amino acids vs. those that won’t. http://www.mun.ca/biology/scarr/MGA2_03-20.html

  21. Hidden Markov Model and gene finding • Goal: • Choose a path that maximize the probability that you will enjoy the trip (or the other way around if you wish) • How is the probability determined? p = p(EL-CHI)*p(CHI-MAD) = 0.5*0.4 = 0.2

  22. Example: Sequence alignment • Align retinol-binding protein and b-lactoglobulin >RBP MKWVWALLLLAAWAAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPNGLPPEAQKIVRQRQEELCLARQYRLIV >lactoglobulin MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKWENGECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLLFCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI 1 MKWVWALLLLAAWAAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDPEG 50 RBP . ||| | . |. . . | : .||||.:| : 1 ...MKCLLLALALTCGAQALIVT..QTMKGLDIQKVAGTWYSLAMAASD. 44 lactoglobulin 51 LFLQDNIVAEFSVDETGQMSATAKGRVR.LLNNWD..VCADMVGTFTDTE 97 RBP : | | | | :: | .| . || |: || |. 45 ISLLDAQSAPLRV.YVEELKPTPEGDLEILLQKWENGECAQKKIIAEKTK 93 lactoglobulin 98 DPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAV...........QYSC 136 RBP || ||. | :.|||| | . .| 94 IPAVFKIDALNENKVL........VLDTDYKKYLLFCMENSAEPEQSLAC 135 lactoglobulin 137 RLLNLDGTCADSYSFVFSRDPNGLPPEAQKIVRQRQ.EELCLARQYRLIV 185 RBP . | | | : || . | || | 136 QCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI....... 178 lactoglobulin

  23. Goal of PSA • Find an alignment between 2 sequences with the maximum score

  24. Extreme value distribution • Normal vs. extreme value distribution 0.40 normal distribution 0.35 0.30 extreme value distribution 0.25 0.20 probability 0.15 0.10 0.05 0 -5 -4 -3 -2 -1 0 1 2 3 4 5 x

  25. Example: Microarray • A solid support (e.g. a membrane or glass slide) on which DNA of known sequence is deposited in a grid-like fashion http://shadygrove.umbi.umd.edu/microarray/Microarray.gif

  26. Microarray data analysis • A simplified pipeline http://www.microarray.lu/images/overview_1.jpg

  27. What’s in the cel files • Intensities of perfect and mismatch probes #### Dimension of the data matrix nrow(M); ncol(M) ### Perfect match pm <- pm(M) # perfect match intensities dim(pm) # dimension of the pm matrix pm[1:5,] # the first five columns summary(pm) # summary stat for the pm matrix GSM131151.CEL GSM131152.CEL GSM131153.CEL GSM131160.CEL GSM131161.CEL GSM131162.CEL [1,] 252.5 267.0 349.0 424.8 213.5 237.8 [2,] 138.0 129.8 147.5 335.5 215.3 142.3 [3,] 172.3 155.5 174.8 411.8 241.0 128.3 [4,] 163.3 142.8 155.5 494.3 225.5 119.5 [5,] 259.5 257.3 245.3 505.5 308.8 217.0 GSM131151.CEL GSM131152.CEL GSM131153.CEL GSM131160.CEL Min. : 56.3 Min. : 67.5 Min. : 69.5 Min. : 96.0 1st Qu.: 144.3 1st Qu.: 143.3 1st Qu.: 157.3 1st Qu.: 303.6 Median : 212.5 Median : 215.0 Median : 234.8 Median : 414.5 Mean : 423.1 Mean : 437.5 Mean : 458.4 Mean : 648.2 3rd Qu.: 383.5 3rd Qu.: 397.8 3rd Qu.: 426.0 3rd Qu.: 637.0 Max. :39818.5 Max. :39268.0 Max. :28628.0 Max. :24854.5

  28. Probe intensity behaviors between arrays • Distributions vary widely between experiments ### Summarize the intensity par(mfrow=c(1,2)) # get a plotting region with 1 row, 2 col hist(M) # generate log2 histograms boxplot(M) # generate log2 boxplots log intensity

  29. Example: Identification of cis-elements • The on-off switches and rheostats of a cell operating at the gene level. • They control whether and how vigorously that genes will be transcribed into RNAs. http://genomicsgtl.energy.gov/science/generegulatorynetwork.shtml

  30. Motif model: Position Frequency Matrix (PFM) • fb,i: freuqnecy of a base b occurred at the i-th position D’haeseleer (2006) Nature Biotech. 24:423

  31. Motif model: Position Weight Matrix (PWM) • Suppose pA,T = 0.32 and pG,C = 0.18 (Arabidopsis thaliana) Position Frequency Matrix Position Wight Matrix

  32. Example: Cis-regulatory logic • Based on a high confidence set of binding sites: • 3,353 interactions between • 116 regulators and • 1,296 promoters Harbison et al. (2004) Nature 43:99

  33. Identification of putative cis elements • Pearson's correlation coefficient as the similarity measure. • k-mean clustering to identify co-regulated genes. • Motifs identified only with AlignACE Beer and Tavazoie (2004) Cell 117:185

  34. Bayesian network • Bayes' theorem • Bayesian network Charniak (1991) Bayesian networks without tears

  35. Final example: Relationships between sequences • Sanger and colleagues (1950s): 1st sequence • Insulin from various mammals

  36. External branch Operational taxonomic unit A Ancestral taxonomic units 2 2 1 1 1 1 B 2 C 2 2 2 2 1 D 1 6 6 Internal branch E Trees • An acyclic, un-directed graph with nodes and edges A F B G C I H D E time one unit Li 1997. Molecular Evolution. p101

  37. Enumerating trees • Suppose there are n OTUs (n ≥ 3) • Bifurcating rooted trees: • Unrooted trees: • For 10 OTUs • 3.4x107 possible rooted trees • 2.0x106 possible unrooted trees http://w3.uniroma1.it/cogfil/philotrees.jpg

  38. Impacts of genomics and bioinformatics • New ways to ask and answer question? • Hypothesis driven vs. data driven • A matter of scale • A matter of integration • Quantitative emphasis • Multi-displinary approaches • How is genomics different from genetics? • Whole genome approach versus a few genes • Investigations into the structure and function of very large numbers of genes undertaken in a simultaneous fashion. • Genetics looks at single genes, one at a time, as a snapshot. • Genomics is trying to look at all the genes as a dynamic system, over time, and determine how they interact and influence biological pathways and physiology, in a much more global sense

  39. The END • ...