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Genomics and Bioinformatics The "new" biology 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? Genome sequencing chronology Genome size (bp) Number of genes Year Organism Significance 1977 Bacteriophage fX174 First genome ever! 1981 Human mitochondria First organelle 1995 Haemophilus influenzae Rd First freeliving organism 1,830,137 ~3,500 1996 Saccharomyces cerevisiae First eukaryote 12,086,000 ~6,000 5,386 11 16,500 37 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 Genome sequencing chronology Genome size (bp) Number of genes Year Organism Significance 1998 Caenorhabditis elegans First multicellular organism 97,000,000 ~19,000 1999 Human chromosome 22 First human chromosome 49,000,000 673 2000 Arabidopsis thaliana First plant genome 2001 Human First human genome 150,000,000 ~25,000 3,000,000,000 ~30,000 http://www.sih.m.u-tokyo.ac.jp/chem1.gif http://lter.kbs.msu.edu/Biocollections/Herbarium/Images/ARBTH3H.jpg Genome sequencing projects (as of 1/26,2007) Sequencing strategies: Hierarchical shotgun sequencing http://www.bio.davidson.edu/courses/GENOMICS/method/shotgun.html Genome size range What’re there in the genomes? Why are there such a big difference? plasmids viruses bacteria fungi plants algae insects mollusks bony fish amphibians reptiles birds mammals 104 105 106 107 108 109 1010 1011 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 ?? The “central dogma” of molecular biology Central dogma Replication DNA Transcription RNA Translation Protein Expanded “central dogma” of molecular biology A more comprehensive view Replication DNA Transcription RNA Translation Phenotype Protein Metabolite New disciplines due to the advance in genomics Omics Replication DNA Genomic DNA sequences Structural genomics Transcription RNA Translation Phenotype Genetic interactions Systematic KO Disease information Transcript seq Microarray data Cis-elements TF binding sites Epigenetic regulation Protein Shotgun protein seq Subcellular location Post-translational mod Protein interaction Protein structure Metabolite Metabolite concn Metabolic flux Transcriptomics Proteomics Metabolomics Nature omics gateway http://www.nature.com/omics/subjects/index.html Three perspectives of our biological world The cellular level, the individual, the tree of life ~3x104 genes ~1014 cells per individual 2-100x106 species Rosenzweig et al., 2002. Conservation Biol. Image: htto://www.tolweb.org/tree/ Image: http://www.olympusfluoview.com/gallery/cells/hela/helacells.html Further complications Cell-cell interactions Cell types Environmental conditions Developmental programming Interactions at the organismal level Interactions at the population, ecosystem level 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/ 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 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 Overlap-layout consensus The relationships between reads can be represented as a graph Nodes (vertices): reads Edges (lines): connecting “overlapping reads” Genome 1 2 3 4 2 1 4 3 Goal: identifying a path through that graph that visits each node exactly once http://en.wikipedia.org/wiki/Image:Hamilton_path.gif 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? 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 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 Example: Sequence alignment Align retinol-binding protein and b-lactoglobulin >RBP MKWVWALLLLAAWAAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRL LNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPN GLPPEAQKIVRQRQEELCLARQYRLIV >lactoglobulin MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKWEN GECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLLFCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKA LPMHIRLSFNPTQLEEQCHI 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 Goal of PSA Find an alignment between 2 sequences with the maximum score Extreme value distribution Normal vs. extreme value distribution 0.40 normal distribution 0.35 0.30 probability extreme value distribution 0.25 0.20 0.15 0.10 0.05 0 -5 -4 -3 -2 -1 0 x 1 2 3 4 5 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 Microarray data analysis A simplified pipeline http://www.microarray.lu/images/overview_1.jpg 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) dim(pm) pm[1:5,] summary(pm) [1,] [2,] [3,] [4,] [5,] # # # # perfect match intensities dimension of the pm matrix the first five columns summary stat for the pm matrix GSM131151.CEL GSM131152.CEL GSM131153.CEL GSM131160.CEL GSM131161.CEL GSM131162.CEL 252.5 267.0 349.0 424.8 213.5 237.8 138.0 129.8 147.5 335.5 215.3 142.3 172.3 155.5 174.8 411.8 241.0 128.3 163.3 142.8 155.5 494.3 225.5 119.5 259.5 257.3 245.3 505.5 308.8 217.0 GSM131151.CEL Min. : 56.3 1st Qu.: 144.3 Median : 212.5 Mean : 423.1 3rd Qu.: 383.5 Max. :39818.5 GSM131152.CEL Min. : 67.5 1st Qu.: 143.3 Median : 215.0 Mean : 437.5 3rd Qu.: 397.8 Max. :39268.0 GSM131153.CEL Min. : 69.5 1st Qu.: 157.3 Median : 234.8 Mean : 458.4 3rd Qu.: 426.0 Max. :28628.0 GSM131160.CEL Min. : 96.0 1st Qu.: 303.6 Median : 414.5 Mean : 648.2 3rd Qu.: 637.0 Max. :24854.5 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 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 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 Motif model: Position Weight Matrix (PWM) Suppose pA,T = 0.32 and pG,C = 0.18 (Arabidopsis thaliana) Wb,i ln n b ,i pb /( N 1) pb Position Frequency Matrix Position Wight Matrix 1 2 3 4 5 1 2 3 4 5 A 8 0 4 4 2 A 1.1 -2.2 0.4 0.4 -0.2 T 0 0 0 2 2 T -2.2 -2.2 -2.2 -0.2 -0.2 G 0 8 4 2 2 G -2.2 1.6 1.0 0.3 0.3 C 0 0 0 0 2 C -2.2 -2.2 -2.2 -2.2 0.3 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 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 Bayesian network Bayes' theorem P( A | B) P( B | A) P( A) P( B) n Bayesian network P X 1 ,..., X n P X i | parents X i i 1 Charniak (1991) Bayesian networks without tears Final example: Relationships between sequences Sanger and colleagues (1950s): 1st sequence Insulin from various mammals Trees An acyclic, un-directed graph with nodes and edges External branch Operational taxonomic unit Ancestral taxonomic units 1 2 1 1 B G I Internal branch F 2 H 6 1 C D A 2 2 A 1 2 2 C 2 1 D 6 E time B E one unit Li 1997. Molecular Evolution. p101 Enumerating trees Suppose there are n OTUs (n ≥ 3) Bifurcating rooted trees: NR Unrooted trees: NU (2n 3)! 2n3 (n 3)! (2n 5)! 2n 3 (n 3)! For 10 OTUs 3.4x107 possible rooted trees 2.0x106 possible unrooted trees http://w3.uniroma1.it/cogfil/philotrees.jpg 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 The END ...