Genomics_bioinformatics2

8/14/2019 Genomics_bioinformatics2

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06/10/0906/10/09 URC,AllahabadURC,Allahabad 11

Genomics and Bioinformatics

The "new" biology

Brijesh Singh Yadav

Bioinformatics Research CellUnited Research Center

Allahabad, India.


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Genome sequencing chronology

~3,5001,830,137First free-livingorganism

Haemophilusinfluenzae Rd

1995

~6,00012,086,000First eukaryoteSaccharomycescerevisiae

1996

16,500

5,386

Genome size

(bp)

37First organelleHumanmitochondria1981

11First genomeever!

BacteriophagefX174

1977

Number of

genes

SignificanceOrganismYear
http://www.ncbi.nlm.nih.gov/ICTVdb/Images/Ackerman/Phages/Microvir/238-27_1.jpg


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Genome sequencing chronology

~25,000150,000,000First plantgenome

Arabidopsisthaliana

2000

3,000,000,000

49,000,000

97,000,000

Genome size (bp)

~30,000First humangenome

Human2001

673First humanchromosome

Humanchromosome22

1999

~19,000First multi-cellular organism

Caenorhab-ditis elegans

1998

Number ofgenes

SignificanceOrganismYear
http://shiulab.plantbiology.msu.edu/wiki/images/8/8a/DSCN3107.jpg


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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

Gene

Protein coding genes

RNA genes

Regulatory elements

Gene expression control Chromatin remodeling

Matrix attachment sites

Non-functional elements

Selfish elements

Junk DNA

??


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The central dogma of molecular biology

Central dogma

DNA

RNA

Protein

Transcription

Translation

Replication
http://upload.wikimedia.org/wikipedia/commons/7/74/Rubisco.png


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Expanded central dogma of molecular biology

A more comprehensive view

DNA

RNA

Protein

Transcription

Translation

Replication

Metabolite

Pheno-type
http://upload.wikimedia.org/wikipedia/commons/7/74/Rubisco.png


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New disciplines due to the advance in genomics

Omics

DNA

RNA

Protein

Transcription

Translation

Replication

Metabolite

Pheno-type

Structuralgenomics

Transcriptomics

Proteomics

Metabolomics

Genomic DNAsequences

Transcript seqMicroarray dataCis-elements

TF binding sitesEpigenetic regulation

Shotgun protein seqSubcellular location

Post-translational modProtein interactionProtein structure

Metabolite concnMetabolic flux

Genetic interactionsSystematic KO

Disease information


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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

~1014 cells per individual 2-100x106 species~3x104 genes
http://www.olympusfluoview.com/gallery/cells/hela/helacells.htmlhttp://www.tolweb.org/tree/home.pages/aboutoverview.html


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Further complications

Cell-cell interactions

Cell types

Environmental conditions

Developmentalprogramming

Interactions at theorganismal level

Interactions at thepopulation, ecosystem level


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How to characterize new diseases?

What new treatments can be discovered?

How do we treat individual patients? Tailoring treatments?

Impact of Genomics on Medicine


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Bioinformatics

Conceptualizing biology in terms ofmolecules and then applying

informatics techniques from math,computer science, and statistics to

understand and organize theinformation associated with these

molecules on a large scale


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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

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

MKWVWALLLLAAWAAAERDCRVSSFRVKENFDKARFSGTWYAMAKKDPEGLFLQDNIVAEFSVDETGQMSATAKGRVRL

LNNWDVCADMVGTFTDTEDPAKFKMKYWGVASFLQKGNDDHWIVDTDYDTYAVQYSCRLLNLDGTCADSYSFVFSRDPN

GLPPEAQKIVRQRQEELCLARQYRLIV

>lactoglobulin

MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVEELKPTPEGDLEILLQKWEN

GECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLLFCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKA

LPMHIRLSFNPTQLEEQCHI


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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 genelevel.

They control whether and how vigorously that genes will betranscribed 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

Dhaeseleer (2006) Nature Biotech. 24:4


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Final example: Relationships between sequences

Sanger and colleagues (1950s): 1st sequence

Insulin from various mammals


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The END

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