1 computational molecular biology mpi for molecular genetics dna sequence analysis gene prediction...

Computational Molecular BiologyMPI for Molecular Genetics

1

DNA sequence analysisGene prediction

Gene prediction methods

Gene indices

Mapping cDNA on genomic DNA

Applications


2

DNA sequence analysisGene prediction

exon 2exon 1 exon npromotor

5‘UTR

3‘UTRProtein coding sequence

exon n-1


3

Gene predictionStrategies for detecting ORFs / exons

Distribution of Stop-codons

Codon usage

Hexamer frequencies

Prediction of the coding frame

Splice site recognition (Eucaryotes only)


4

Gene predictionCodon usage (single exon)

Frame 1

Frame 2

Frame 3

coding

non-coding


5

Gene predictionCodon usage (single exon)

Frame 1

Frame 2

Frame 3

coding

non-coding

correct start

coding sequence


6

Gene predictionCodon usage (multiple exons)

Frame 1

Frame 2

Frame 3

coding

non-coding

Splice sites

Exons:208. .2951029. .13491500. .16882686. .29343326. .34443573. .36804135. .43094708. .48464993. .50967301. .73897860. .80138124. .84058553. .87139089. .922513841. .14244


7

Gene predictionCodon usage (multiple exons)

Frame 1

Frame 2

Frame 3

coding

non-coding

Splice sites

Exons:208. .2951029. .13491500. .16882686. .29343326. .34443573. .36804135. .43094708. .48464993. .50967301. .73897860. .80138124. .84058553. .87139089. .922513841. .14244


8

Gene predictionAdditional criteria

Detection of start codons

Detection of potential promotor elements

Detection of repetitive sequences (mostly untranslated)

Homology to known genes of related

organisms


9

Gene predictionSoftware

GENSCAN (C.Burge & S.Karlin)

Grail (neural network; Ueberbacher et al.)

MZEF (M. Zhang,1997)

FGeneH, Hexon (V.Solovyev et al., 1994)

Genie, etc.All programs are using dynamic programming for detection of theoptimal solution


10

DNA sequences in public databases

Human

~ 4 million ESTs + 130 000 RNAs

Mouse

~ 2.7 million ESTs + 30 000 RNAs


11

Expressed sequence tags (EST)

AAAAAA...mRNATTTTTT...

cDNA is usually oligo dT primed, or by random primers

Reverse transcriptase stops ‚randomly‘

cDNA

Several cDNAs for the same mRNA may be generated


12


Average: 1500 bp

<700 bpVector

(known sequence)

Clone = mRNA fragmentDechiffered sequence (EST)

3‘-primer


13


Isolation of mRNAs from tissue(s)

Generation of cDNAs reflecting parts of the RNAs

Cloning of cDNAs into a vector (often random orientation)

End sequencing of the clones


14

Generation of ESTsbasecalling problems

close to 3‘ end of EST

close to 5‘ end of EST

missing bases


15

Coverage of an mRNA by ESTs

AAAAAA...putativemRNA exon 15‘UTR exon 2 3‘UTR

expressed sequence tags(ESTs)


16

Characteristics of ESTs

Highly redundant

Low sequence quality

(Cheap)

Reflect expressed genes

May be tissue/stage specific


17

Gene indices

UniGene (NCBI)

TIGR Gene Indices

STACK (SANBI)

GeneNest (DKFZ,MPI)

Clustering of EST and mRNA sequences of an organism toreduce redundance in sequence data.

Goal: Each cluster represents one gene or mRNA


18

Gene indicesGeneNest workflow

EMBL database Unigene database

Quality clipping Quality clipping

BLAST/QUASARsearch, clustering

Assembly,Consensus sequences

Visualization


19

Gene indicesQuality clipping

Removal of vector sequence

Masking of repetitive sequences (e.g. Alu)

Removal of terminal sequences of low quality

In order to cluster based on gene-specific sequence datathe following steps have to be performed:


20

Gene indices Clustering

Minimal % identity (e.g. > 95%)

Minimal length of match (e.g. >40 bp)

No internal matches (TIGR gene indices)

Same origin of tissue (only STACK)

Sequences are usually clustered if the matching part between two sequences fullfills several (empirical) criteria:


21

Gene indices Assembly

Contigs, reflecting parts of different transcripts

One consensus sequence per contig

A relative order of the sequences (alignment)

Sequences in a cluster are assembled to group those sequences which are globally similar, resulting in


22

Gene indicesConsensus sequences

Reduced error rate

Consensus often longer than any single sequence contributing

Efficient database search

Detection of exon/intron boundaries and alternative splice variants


23

Gene indices Alignment

consensus


24

Gene indices Alignment Software

Phrap (Phil Green)

CAP3 (X. Huang)

TIGR assembler

GAP4 (R. Staden)


25

GeneNest visualization(http://genenest.molgen.mpg.de)


26

GeneNest visualization(http://genenest.molgen.mpg.de)


27

TIGR Gene Indices(http://www.tigr.org/)

Alignment scheme


28

UniGene(http://www.ncbi.nih.nlm.gov/UniGene)


29

UniGene(http://www.ncbi.nih.nlm.gov/UniGene)


30

Mapping of consensus sequences on genomic DNA

genomic sequence

exons

consensus sequence( mRNA)

missing intron


31

Mapping cDNA on genomic DNA


32

Gene indicesApplications

Detection of exon/intron boundaries

Detection of alternative splicing

Detection of Single Nucleotide Polymorphisms

Genome annotation

Analysis of gene expression

Genome-genome comparison


33

Alternative Splicing

hnRNA

mRNA 2exon 15‘UTR exon 2

mRNA 1exon 15‘UTR exon 3

exon 15‘UTR exon 2 exon 3


34

Alignment of EST consensus sequences and genomic target

genomic sequence


35

Detection of the appropriate genomic target sequence

Local similarity of EST consensus and genomic DNA>96% identity

genomic sequence


36

Cutting out genomic target sequence

genomic sequence


37

Alternative Splicing(mapping on genomic DNA)

genomic sequence

exons

consensus sequence( mRNA)

splice variant


38

SpliceNest(http://SpliceNest.molgen.mpg.de)

putative exons

genomic sequence

aligned GeneNestconsensus

alternative exon


39

Alternative Splicing(additional exon)

skipped exon

Splice variants of adenylsuccinate lyase

gene prediction errors ?

unspliced ?


40


Splice variants of APECED gene

number of sequences genomic sequencealternative variants


41

Alternative splicing


42

Alternative Splicing (alternative donor site)


43



44

Alternative Splicing(alternative exons)


45

SpliceNest(hypothetical gene Hs16936)


46

Single Nucleotide Polymorphisms(SNP)

SNPs are single base differences within one species

Several million SNPs detected in Human

SNPs may be related to diseases


47

Single Nucleotide Polymorphisms(SNP)

SNP or basecalling error ?


48

Genome Annotation / Ensembl(http://www.ensembl.org)


49

Analysis of gene expressiontissue-specificity

Counting frequency of EST derived from a specific tissue within one sequence cluster

Searching for cluster/contigs which are tissue specific (e.g. tumor)

Searching for alternative splice variants which are potentially tissue specific


50

Analysis of gene expressionPDZ-domain containing protein PDZK1 (Hs.15456)

liver tumor

kidney


51

Analysis of gene expressionsmall muscular protein, SMPX (Hs.88492)

heart

muscle


52

Analysis of gene expressionhypothetical protein (Hs.32343)

thyroid tumor

heart

ovary


53

Analysis of gene expressionnon-redundant gene set

Selection of ‚optimal‘ clones

Generation of gene-specific PCR-products


54

Analysis of gene expression ‚optimal clones‘

clone availability

type of clone library

length of the clone

relative position to the consensus sequence

homology to other genes

existence of repetitive elements


55

Analysis of gene expressiongene-specific PCR-products

putative gene consensussequence exon A exon Cexon B

repetitive sequencesimilarity to another gene

potential gene-specific fragment

potential gene-specific fragment


56

Analysis of gene expressionoptimal gene-specific PCR-product

minimal similarity to other genes

minimal content of repetitive sequences

not spanning over several exons

+/- constant length of PCR-products of different genes

1 computational molecular biology mpi for molecular genetics dna sequence analysis gene prediction...

Documents

coding noncoding slide

rnas slide

optimal solution slide

exon npromotor

sequence tags est aaaaaa

sequence tags est average

stack sequences

sequences fullfills