Genomics and BioinformaticsThe "new" biology :6/10/2009 URC,Allahabad 1 Genomics and BioinformaticsThe "new" biology Brijesh Singh Yadav Bioinformatics Research Cell United Research Center Allahabad, India.


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What is genomics :6/10/2009 URC,Allahabad 3 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)


Genome sequencing chronology :6/10/2009 URC,Allahabad 4 Genome sequencing chronology


Genome sequencing chronology :6/10/2009 URC,Allahabad 5 Genome sequencing chronology


Genome sequencing projects (as of 1/26,2007) :6/10/2009 URC,Allahabad 6 Genome sequencing projects (as of 1/26,2007)


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Slide 8:6/10/2009 URC,Allahabad 8 Genome sequencing helps in: Identifying new genes (“gene discovery”) Looking at chromosome organization and structure Finding gene regulatory sequences Comparative genomics These in turn lead to advances in: Medicine Agriculture Biotechnology Understanding evolution and other basic science questions


Information contents in a genome :6/10/2009 URC,Allahabad 9 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 :6/10/2009 URC,Allahabad 10 The “central dogma” of molecular biology Central dogma DNA RNA Protein Transcription Translation Replication


Expanded “central dogma” of molecular biology :6/10/2009 URC,Allahabad 11 Expanded “central dogma” of molecular biology A more comprehensive view DNA RNA Protein Transcription Translation Replication Metabolite Pheno- type


New disciplines due to the advance in genomics :6/10/2009 URC,Allahabad 12 New disciplines due to the advance in genomics Omics DNA RNA Protein Transcription Translation Replication Metabolite Pheno- type Structural genomics Transcriptomics Proteomics Metabolomics Genomic DNA sequences Transcript seq Microarray data Cis-elements TF binding sites Epigenetic regulation Shotgun protein seq Subcellular location Post-translational mod Protein interaction Protein structure Metabolite concn Metabolic flux Genetic interactions Systematic KO Disease information


Slide 13:6/10/2009 URC,Allahabad 13 find all motifs in genome identify transcription factors identify binding motif identify target genes Transcription factors, binding sites, and target genes computational searching ChIP-chip computational searching microarrays genetic screens bioinformatics (e.g., Gibbs sampling on microarray data) molecular biology using purified protein or protein extracts genetic screens one-hybrid assays sequence motifs/homology


Nature omics gateway :6/10/2009 URC,Allahabad 14 Nature omics gateway


Three perspectives of our biological world :6/10/2009 URC,Allahabad 15 Three perspectives of our biological world The cellular level, the individual, the tree of life


Further complications :6/10/2009 URC,Allahabad 16 Further complications Cell-cell interactions Cell types Environmental conditions Developmental programming Interactions at the organismal level Interactions at the population, ecosystem level


Impact of Genomics on Medicine :6/10/2009 URC,Allahabad 17 How to characterize new diseases? What new treatments can be discovered? How do we treat individual patients? Tailoring treatments? Impact of Genomics on Medicine


Bioinformatics :6/10/2009 URC,Allahabad 18 Bioinformatics Conceptualizing biology in terms of molecules and then applying “informatics” techniques from math, computer science, and statistics to understand and organize the information associated with these molecules on a large scale


How do we use Bioinformatics? :6/10/2009 URC,Allahabad 19 How do we use Bioinformatics? Store/retrieve biological information (databases) Retrieve/compare gene sequences Predict function of unknown genes/proteins Search for previously known functions of a gene Compare data with other researchers Compile/distribute data for other researchers


Example: Sequence alignment :6/10/2009 URC,Allahabad 20 Example: Sequence alignment Align retinol-binding protein and b-lactoglobulin 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 >RBP MKWVWALLLLAAWAAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRLLNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPNGLPPEAQKIVRQRQEELCLARQYRLIV >lactoglobulin MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKWENGECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLLFCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI


Microarray data analysis :6/10/2009 URC,Allahabad 21 Microarray data analysis A simplified pipeline


Example: Microarray :6/10/2009 URC,Allahabad 22 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


Example: Identification of cis-elements :6/10/2009 URC,Allahabad 23 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.


Motif model: Position Frequency Matrix (PFM) :6/10/2009 URC,Allahabad 24 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


Final example: Relationships between sequences :6/10/2009 URC,Allahabad 25 Final example: Relationships between sequences Sanger and colleagues (1950s): 1st sequence Insulin from various mammals


The END :6/10/2009 URC,Allahabad 26 The END ...