gene identification[1]

8/4/2019 Gene Identification[1]

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Gene Identification - I

Shivani Chandra

Birla Institute of Scientific Research


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Gene Identification

Goals :

Find genes

Map their position

Identify function(s)


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Gene Identification

Approaches :

Classical

Computational


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Classical Approaches

The three big Ms :

Mendel (1822-1884)

Morgan (1866-1945)

McClintock (1902-1992)


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Mendels Genetics

Dominant / Recessive

Genotype / Phenotype

Monohybrid / Dihybrid Crosses

Laws :

Segregation

Independent assortment


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Morgans Genetics

Won noble prize in 1933 for white eyed

fruit-fly mutation.

Linkage and crossing- over (recombination)

Genetic and chromosome mapping.


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McClintocks Genetics

Won a noble prize in 1983 for

transposons.

Ability of genes to change position on a

chromosomegenetic transposition.

Transposons :

Cause mutations.

Increase/decrease amount of DNA.


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Classical Approaches

Study F1 and F2 to Fn generations.

Test cross, back cross.

Complementation tests.

Chromosome mapping.

Transgenics. Gene Knock-outs.


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The Genomics Era


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What is Computational Gene

IdentificationGiven an uncharacterized DNA sequence, find out:

Where does the gene starts and ends?

Which DNA strand is used to encode the gene?

Which reading frame is used in that strand?

Which region codes for a protein?


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Computational Approaches

Computational methods to identify genes

have been an active field of research for

past 15 - 20 years.

Fast and accurate.


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Computational Gene

Identification Classes : Intrinsic and Extrinsic

Intrinsic, or ab initio, gene findersmake no

explicit use of information about DNAs or

proteins outsidethe sequence being studied.

Extrinsic gene finders utilize sequence

similarity search methods to identify thelocations of protein-codingregions.


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Software Tools

GeneMark

Glimmer

Grail

GenScan

Combined


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GeneMark

First tool for finding prokaryotic genes.(1993).

Access the protein coding potential of aDNA sequence by using Markov models ofcoding and non coding regions.

Relies on organism specific recognitionparameters to separate coding and non-coding regions.


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GeneMark

Exists in separate variants of gene

prediction in prokaryotic, eukaryotic, and

viral DNA sequences.

Requires a sufficiently large training set of

known genes.


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GeneMark

Input File :

DNA sequence in fasta format.

The file can contain multiple fasta records.

Each fasta record should be less than 5Mbp.

The total sequence size should be in the

range of 10 Mbp to 50 Mbp


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GeneMark

Open the GeneMarkserver:http://opal.biology.gatech.edu/GeneMark/

genemark24.cgi

Hit Browse to select your inputfile. Select the closest species of organism or host as

the model (M. tuberculosis for the

Mycobacteriophages)

Under graphics export options, select everything

except generate postscript & mark putative

exons.


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GeneMark

In the second column choose only list open

reading frames and list regions of

interest. Run Genemark (Start)10.

You should see a text output of your

GeneMark results.


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GeneMark Output

GeneMark can be instructed to generate

reports on open reading frames (ORFs),

regions of interest, and estimated exonboundaries.


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GeneMark Output

GENEMARK PREDICTIONS

Sequence file: cya

Sequence length: 2100 GC Content: 51.65%

Threshold value: 0.500


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GeneMark Output

Open Reading Frames ListLeft Right DNA Coding Avg Start RBS RBS RBS

end end Strand Frame Prob Prob Prob Site Seq

----- ---- ---------- ----- ---- ---- ---- --- ------

3 308 direct fr 3 0.82 .... 0 0 ....

195 308 direct fr 3 0.6 0.04 0.74 177 CCGCAG

348 668 complement fr 2 0.9 0.96 0.98 680 CAGGAT

1368 2102 direct fr 3 0.9 0.98 0.96 1359 TTGGAG

1371 2102 direct fr 3 0.91 0.96 0.96 1359 TTGGAG

1386 2102 direct fr 3 0.93 0.63 0.91 1367 AATGAT1410 2102 direct fr 3 0.96 0.9 0.76 1401 AACGAT

1509 2102 direct fr 3 0.98 0.27 0.51 1490 AGGGTT

1578 2102 direct fr 3 0.97 0.11 0.73 1567 ATGGCA

1620 2102 direct fr 3 0.97 0.11 0.16 1601 GCGCTG


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GeneMark Output

LEnd REnd Strand Frame

3 308 direct fr 3

348 686 complement fr 2

1092 1334 direct fr 31365 2102 direct fr 3

Regions of Interest


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Genemark Ouput

Frame Frame At base Strand

2 1 31152 +/-11bp complement

2 1 63372 +/-11bp direct

3 2 75528 +/-11bp complement

Possible Frameshifts


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GeneMark Output

Approx. Exon LocationLeft Right

End End Strand Frame Prob

50 300 direct fr 3 0.8566

63 247 0.9998

365 666 complement fr 2 0.9415

378 657 0.978

1201 1277 direct fr 3 0.8722

1225 1254 0.9986

1377 2042 direct fr 3 0.9085

1434 2042 0.978


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GenScan

Analyzes the DNA sequences : by using complex probabilistic structure

of gene based on research on the level

of transcriptional, translational, andsplicing signals.

Statistical properties of coding and non-

coding regions. GC contents.


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GenScan

The model treats the most general case

in which the sequence may contain no

genes, one gene, or multiple genes oneither or both DNA strands and partial

genes as well as complete genes are

considered.


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GenScan

Important Restrictions:

Only protein coding regions are considered

(no tRNA or rRNA genes).

Transcription units are assumed to be non

overlapping.


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GenScan

Input file :

The sequence file may be in either Fasta or

minimal GenBank format.

For minimal Genbank format,Locus and

Origin lines must be present.


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Minimal GenBank FormatL O C U S H U M R A S H 6 4 5 3 b p d s - D N A P R I 1 5 - M A R - 1 9 8 8

D E F I N I T I O N H u m a n c - H a - ra s 1 p r o t o - o n c o g e n e , c o m p l e te c o d i n g s e q u e n c e .

A C C E S S I O N J 0 0 2 7 7 J 0 0 2 0 6 J 0 0 2 7 6 K 0 0 9 5 4

F E A T U R E S L o c a t i o n / Q u a l i f i e r s

p r im _ t r a n s c r i p t < 1 6 6 4 . .3 7 4 4 / n o t e = " c - H a - r a s 1 m R N A "

C D S j o in ( 1 6 6 4 . .1 7 7 4 , 2 0 4 2 . . 2 2 2 0 , 2 3 7 4 . .2 5 3 3 , 3 2 3 1 . .3 3 5 0 )

/ n o t e = " c - H a - r a s 1 p 2 1 p r o t e i n ; N C B I g i : 1 9 0 8 9 1 . "

/ c o d o n _ s t a r t = 1

/ t r a n s l a t i o n = " M T E Y K L V V V G A G G V G K S A L T I Q L I Q N H F V D E Y D P T I E D S Y R K Q VV I D G E T C L L D I L D T A G Q E E Y S A M R D Q Y M R T G E G F L C V F A I N N T K S F E D I H Q Y R E Q I K RV K D S D D V P M V L V G N K C D L A A R T V E S R Q A Q D L A R S Y G I P Y I E T S A K T R Q G V E D A F Y T L V R E I R Q H K L R K L N P P D E S G P G C M S C K C V L S "

s o u r c e 1 . . 6 4 5 3 /

o r g a n i s m = " H o m o s a p i e n s "

B A S E C O U N T 9 4 6 a 2 2 8 7 c 2 1 1 3 g 1 1 0 7 t

O R I G I N 1 b p u p s t re a m o f B a m H I s i te .

1 g g a t c c c a g c c t t tc c c c a g c c c g t a g c c c c g g g a c c t c c g c g g t g g g c g g c g c c g c g c t 6 1 g c c g g c g c a g g g a g g g c c t c t g g t g c a c c g g c a c c g c t g a g t c g g g t tc t c t c g c c g g c c 1 2 1t g t tc c c g g g a g a g c c c g g g g c c c t g c t c g g a g a t g c c g c c c c g g g c c c c c a g a c a c c g g . .. .. .. .. .. ..


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GenScan Output

Predicted Genes/ExonsGn.Ex Type S Begin End Len Fr Ph I/Ac Do/T CodRg P. Tscr..

1.01 Intr + 739 851 113 0 2 49 66 74 0.287 0.98

1.02 Intr + 1748 1860 113 2 2 53 110 80 0.866 7.231.03 Intr + 1976 2055 80 0 2 97 94 10 0.999 2.271.04 Intr + 2132 2194 63 1 0 84 80 87 0.99 6.911.05 Intr + 2434 2631 198 0 0 88 -9 263 0.895 16.671.06 Intr + 2749 2910 162 0 0 107 109 97 0.965 14.391.07 Intr + 3279 3416 138 2 0 52 77 126 0.812 9.071.08 Intr + 3576 3676 101 2 2 87 119 113 0.996 13.711.09 Intr + 3780 3846 67 0 1 63 77 46 0.998 0.41.1 Term + 4179 4340 162 2 0 75 47 276 0.979 20.451.11 PlyA + 4397 4402 6 1.05


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GenScan Output

Predicted peptide sequence(s)

HS307871|GENSCAN_predicted_peptide_1|398_aa

VQAIVWTWLDKTVGIIVGTCAKLRIPRLSDENKFLMSPPQGFPELKNDTFLRAAWGEETDYTPVWCMRQAGRYLPEFRETRAAQDFFSTCRSPEACCELTLQPLRRFLLDAAIIFSDILVVPQALGMEVTMVPGKGPSFPEPLREEQDLERLRDPEVVASELGYVFQAITLTRQRLAGRVPLIGFAGAPWTLM

TYMVEGGGSSTMAQAKRWLYQRPQASHQLLRILTDALVPYLVGQVVAGAQALQLFESHAGHLGPQLFNKFALPYIRDVAKQVKARLREAGLAPVPMIIFAKDGHFALEELAQAGYEVVGLDWTVAPKKARECVGKTVTLQGNLDPCALYASEEEIGQLVKQMLDDFGPHRYIANLGHGLYPDMDPEHVGAFVDAVHKHSRLLRQN


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GRAIL

Gene Recognition and Assembly Internet

Link.

Identifies exons, polyA sites, promoters,repeats and frameshift errors in DNA

sequence by comparing them to database of

known mouse and human sequenceelements.


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GRAIL

Incorporates BLAST searches and

Glimmer.

It supports the protocols and file formatscommonly found on the World-Wide Web,

such as HTTP, FTP, and HTML.


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GRAIL

GrailExp is a software package developed

specifically for gene finding using pattern

recognition and expressed sequence tags.

Grail is an algorithm for inferring gene

structures from predicted exon candidates,

based on Expressed Sequence Tags (ESTs)

and biological intuition/rules.


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To be Continued

gene identification[1]

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