Genomics And Bioinformatics: The "new" Biology

Genomics and Bioinformatics The "new" biology Brijesh Singh Yadav Bioinformatics Research Cell United Research Center Allahabad, India.

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What is genomics 

Genome  All the DNA contained in the cell of an organism

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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)

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Genome sequencing chronology 

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Genome size (bp)

Number of genes

Year

Organism

Significance

1977

Bacteriophage fX174

First genome ever!

5,386 11

1981

Human mitochondria

First organelle

16,500 37

1995

Haemophilus influenzae Rd

First free-living organism

1,830,137 ~3,500

1996

Saccharomyces cerevisiae

First eukaryote

12,086,000 ~6,000

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Genome sequencing chronology

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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

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Genome size (bp)

Number of genes

Year

150,000,000 ~25,000

3,000,000,000 ~30,000

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

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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

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Information contents in a genome

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Gene  Protein coding genes  RNA genes

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Regulatory elements  Gene expression control  Chromatin remodeling  Matrix attachment sites

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“Non-functional” elements  Selfish elements  “Junk” DNA  ?? URC,Allahabad

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The “central dogma” of molecular biology 

Central dogma Replication

DNA Transcription

RNA Translation

Protein

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Expanded “central dogma” of molecular biology 

A more comprehensive view Replication

DNA Transcription

RNA Translation

Phenotype

Protein

Metabolite 06/10/09

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New disciplines due to the advance in genomics 

Omics Replication

DNA

Genomic DNA sequences

Structural genomics

Transcript seq Microarray data Cis-elements TF binding sites Epigenetic regulation

Transcriptomics

Transcription

RNA Translation

Phenotype Genetic interactions Systematic KO Disease information

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Protein

Shotgun protein seq Subcellular location Post-translational mod Protein interaction Protein structure

Metabolite

Metabolite concn Metabolic flux URC,Allahabad

Proteomics

Metabolomics 12

Transcription factors, binding sites, and target genes identify transcription  genetic screens factors 

one­hybrid assays sequence motifs/homology

identify binding  motif

find all motifs  in genome

computational searching ChIP­chip

bioinformatics (e.g., Gibbs  sampling on microarray data) molecular biology using purified  protein or protein extracts

identify target  genes computational searching microarrays genetic screens 06/10/09

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Nature omics gateway

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Three perspectives of our biological world 

The cellular level, the individual, the tree of life

~3x104 genes

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~1014 cells per individual

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2-100x106 species

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Further complications

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Cell-cell interactions

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Cell types

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Environmental conditions

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Developmental programming

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Interactions at the organismal level

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Interactions at the population, ecosystem level URC,Allahabad

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Impact of Genomics on Medicine

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How to characterize new diseases? What new treatments can be discovered? How do we treat individual patients? Tailoring treatments?

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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 06/10/09

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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

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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

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137 RLLNLDGTCADSYSFVFSRDPNGLPPEAQKIVRQRQ.EELCLARQYRLIV 185 RBP . | | | : || . | || | 136 QCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI....... 178 lactoglobulin URC,Allahabad

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Microarray data analysis 

A simplified pipeline

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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

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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.

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Motif model: Position Frequency Matrix (PFM) 

fb,i : freuqnecy of a base b occurred at the i-th position

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24 D’haeseleer (2006) Nature Biotech. 24:42

Final example: Relationships between sequences 

Sanger and colleagues (1950s): 1st sequence

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Insulin from various mammals

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The END 

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