Download Gene!

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

Copy-number variation wikipedia, lookup

Oncogenomics wikipedia, lookup

Genomic library wikipedia, lookup

Epigenetics in learning and memory wikipedia, lookup

Public health genomics wikipedia, lookup

Non-coding RNA wikipedia, lookup

Transfer RNA wikipedia, lookup

Gene therapy wikipedia, lookup

Epigenetics of diabetes Type 2 wikipedia, lookup

Short interspersed nuclear elements (SINEs) wikipedia, lookup

Genetic engineering wikipedia, lookup

Epigenetics of neurodegenerative diseases wikipedia, lookup

Pathogenomics wikipedia, lookup

Transposable element wikipedia, lookup

Long non-coding RNA wikipedia, lookup

Biology and consumer behaviour wikipedia, lookup

Gene nomenclature wikipedia, lookup

Gene expression programming wikipedia, lookup

Genomic imprinting wikipedia, lookup

Genomics wikipedia, lookup

Frameshift mutation wikipedia, lookup

Ridge (biology) wikipedia, lookup

Messenger RNA wikipedia, lookup

NEDD9 wikipedia, lookup

Gene desert wikipedia, lookup

Epitranscriptome wikipedia, lookup

Human genome wikipedia, lookup

Vectors in gene therapy wikipedia, lookup

Nutriepigenomics wikipedia, lookup

Point mutation wikipedia, lookup

Non-coding DNA wikipedia, lookup

History of genetic engineering wikipedia, lookup

Expanded genetic code wikipedia, lookup

Genome (book) wikipedia, lookup

Minimal genome wikipedia, lookup

Primary transcript wikipedia, lookup

Genome editing wikipedia, lookup

Epigenetics of human development wikipedia, lookup

RNA-Seq wikipedia, lookup

Gene expression profiling wikipedia, lookup

Site-specific recombinase technology wikipedia, lookup

Microevolution wikipedia, lookup

Designer baby wikipedia, lookup

Therapeutic gene modulation wikipedia, lookup

Helitron (biology) wikipedia, lookup

Gene wikipedia, lookup

Genome evolution wikipedia, lookup

Artificial gene synthesis wikipedia, lookup

Genetic code wikipedia, lookup

Transcript
BIOINFORMATICS
Lecture 4
Gene Prediction
Dr. Aladdin Hamwieh
Khalid Al-shamaa
Abdulqader Jighly
Aleppo University
Faculty of technical engineering
Department of Biotechnology
2010-2011
GENE PREDICTION:
COMPUTATIONAL CHALLENGE
Gene:
A sequence of nucleotides
coding for protein
Gene
Prediction
Problem:
Determine the beginning and end
positions of genes in a genome
GENE PREDICTION: COMPUTATIONAL CHALLENGE
aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatg
cggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatcc
gatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctg
ggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatg
ctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcat
gcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggct
atgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagc
tgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggat
ccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaa
tgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaa
tgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctg
cggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatg
cggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgc
aagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagct
cggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaa
tgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggct
atgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggct
atgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgcta
atgcatgcggctatgctaagctcatgcgg
GENE PREDICTION: COMPUTATIONAL CHALLENGE
aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatg
cggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatcc
gatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctg
ggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatg
ctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcat
gcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggct
atgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagc
tgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggat
ccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaa
tgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaa
tgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctg
cggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatg
cggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgc
aagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagct
cggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaa
tgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggct
atgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggct
atgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgcta
atgcatgcggctatgctaagctcatgcgg
GENE PREDICTION: COMPUTATIONAL CHALLENGE
aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatg
cggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgggatcc
gatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctg
ggatccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatg
ctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcat
gcggctatgcaagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggct
atgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgcaagc
tgggatcctgcggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggat
ccgatgacaatgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaa
tgcatgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaa
tgcatgcggctatgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctg
cggctatgctaatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatgcatg
cggctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgcatgcggctatgc
aagctgggatccgatgactatgctaagctgcggctatgctaatgcatgcggctatgctaagct
cggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgggatccgatgacaa
tgcatgcggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgcatgcggct
atgctaagctgggaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcggct
atgctaatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgcta
atgcatgcggctatgctaagctcatgcgg
Gene!
CENTRAL DOGMA: DNA -> RNA -> PROTEIN
DNA
CCTGAGCCAACTATTGATGAA
transcription
RNA
CCUGAGCCAACUAUUGAUGAA
translation
Protein
PEPTIDE
CODONS
 In
1961 Sydney Brenner and Francis
Crick discovered frameshift mutations
 Systematically deleted nucleotides from
DNA
 Single
and
double
deletions
dramatically altered protein product
 Effects of triple deletions were minor
 Conclusion: every triplet of nucleotides,
each codon, codes for exactly one amino
acid in a protein
THE SLY FOX
 In
the following string
THE SLY FOX AND THE SHY DOG
 Delete 1, 2, and 3 nucleotifes after the
first ‘S’:
THE SYF OXA NDT HES HYD OG
THE SFO XAN DTH ESH YDO G
THE SOX AND THE SHY DOG
 Which of the above makes the most sense?
TRANSLATING NUCLEOTIDES
INTO AMINO ACIDS
Codon:
3 consecutive nucleotides
43 = 64 possible codons
Genetic code is degenerative and
redundant
 Includes start and stop codons
 An amino acid may be coded by
more than one codon
Genetic Code and Stop Codons
UAA, UAG and
UGA correspond
to 3 Stop codons
that (together
with Start codon
ATG) delineate
Open Reading
Frames
TERMINOLOGY

ORF


Exons


Portions of the ORF that are transcribed and when
combined form the coding sequence (CDS) for the gene
Introns


A series of DNA codons, including a 5’ initiation codon and
a termination codon, that encodes a putative or known
gene.
Portions of the ORF that are transcribed and are spliced
out of the mRNA before translation.
Untranslated regions (UTRs)



Non-coding regions that are transcribed and flank the
ORF (for DNA) and CDS (for mRNA)
5’ end (relative to mRNA) UTRs (leader, regulatory sites)
3’ end UTRs (terminator sites, trailer)
TRANSCRIPTION IN PROKARYOTES
Transcribed region
start codon
stop codon
Coding region
5’
3’
Untranslated regions
Promoter
Transcription start side
upstream
downstream
Transcription stop side
GENE STRUCTURE IN EUKARYOTS
Transcribed region
exons
introns
start codon
stop codon
5’
3’
donor
GT
AG acceptor
and
sites
Promoter
Transcription stop site
Untranslated regions
Transcription start site
BIOLOGICAL BACKGROUND
GENE STRUCTURE
5’ UTR
Ex1
In1
Ex2
Stop
codon
ATG
Ex2
In2
Ex3
In3
GT
CAP
Ex1
Ex2
Ex2
Ex3
Ex4
In4
Ex5
Ex5
Ex5
Poly A
AG
Ex4
3’ UTR
Ex5
DISCOVERY OF SPLIT GENES
 In
1977, Phillip Sharp and Richard Roberts
experimented with mRNA of hexon, a viral
protein.
 Map hexon mRNA in viral genome by
hybridization to adenovirus DNA and electron
microscopy
 mRNA-DNA hybrids formed three curious
loop structures instead of contiguous duplex
segments
EXONS AND INTRONS
 In
eukaryotes, the gene is a
combination of coding segments
(exons) that are interrupted by noncoding segments (introns)
 This
makes computational gene
prediction in eukaryotes even MORE
DIFFICULT
 Prokaryotes
don’t have introns Genes in prokaryotes are continuous
CENTRAL DOGMA AND SPLICING
exon1
intron1
exon2
intron2
exon3
transcription
splicing
exon = coding
intron = non-coding
translation
Batzoglou
SPLICING SIGNALS
Exons are interspersed with introns and
typically flanked by GT and AG
CONSENSUS SPLICE SITES
TWO APPROACHES TO GENE
PREDICTION
 Statistical:
coding segments (exons) have
typical sequences on either end and use
different subwords than non-coding segments
(introns).
 Similarity-based:
many human genes are
similar to genes in mice, chicken, or even
bacteria. Therefore, already known mouse,
chicken, and bacterial genes may help to find
human genes.
STATISTICAL APPROACH: METAPHOR IN
UNKNOWN LANGUAGE
Noting the differing frequencies of symbols (e.g. ‘%’, ‘.’, ‘-’)
and numerical symbols could you distinguish between a story
and the stock report in a foreign newspaper?
SIMILARITY-BASED APPROACH:
METAPHOR IN DIFFERENT LANGUAGES
If you could compare the day’s news in English, side-by-side
to the same news in a foreign language, some similarities
may become apparent
WHICH ISSUES HELP US TO DETECT GENES?
1.
2.
3.
4.
5.
6.
7.
ORFs constructions
ORFs Long
ORFs Codon usage
Regulatory motifs
Ribosomal Binding Sites
CG islands
Similarities with other genes
OPEN READING FRAMES (ORFS)

Detect potential coding regions by looking at ORFs
 A genome of length n is comprised of (n/3) codons
 Stop codons break genome into segments
between consecutive Stop codons
 The subsegments of these that start from the
Start codon (ATG) are ORFs
 ORFs in different frames may overlap
ATG
TGA
Genomic Sequence
Open reading frame
LONG VS.SHORT ORFS
 Long
open reading frames may be a gene
 At random, we should expect one stop
codon every (64/3) ~= 21 codons
 However, genes are usually much longer
than this
 A basic approach is to scan for ORFs
whose length exceeds certain threshold
 This is naive because some genes (e.g.
some neural and immune system genes)
are relatively short
9-kb fungal plasmid sequence in looking for ORFs
(potential genes).
 Six possible reading frames, three in each
direction.
 Two large ORFs, 1 and 2, are the most likely
candidates as potential genes.
 The yellow ORFs are too short to be genes

TESTING ORFS: CODON USAGE
 Create
a 64-element hash table and
count the frequencies of codons in an
ORF
 Amino acids typically have more than
one codon, but in nature certain codons
are more in use
 Uneven
use of the codons may
characterize a real gene
 This compensate for pitfalls of the ORF
length test
CODON USAGE IN HUMAN GENOME
CODON USAGE IN MOUSE GENOME
AA
codon
Ser
Ser
Ser
Ser
Ser
Ser
TCG
TCA
TCT
TCC
AGT
AGC
4.31
11.44
15.70
17.92
12.25
19.54
/1000
frac
0.05
0.14
0.19
0.22
0.15
0.24
Pro
Pro
Pro
Pro
CCG
CCA
CCT
CCC
6.33
17.10
18.31
18.42
0.11
0.28
0.30
0.31
AA
codon
/1000
frac
0.40
0.08
0.13
0.20
Leu
Leu
Leu
Leu
CTG
CTA
CTT
CTC
39.95
7.89
12.97
20.04
Ala
Ala
Ala
GCG
GCA
GCT
Ala
6.72 0.10
15.80 0.23
20.12 0.29
GCC
26.51
0.38
Gln
CAG
Gln
34.18 0.75
CAA
11.51
0.25
CODON USAGE AND LIKELIHOOD RATIO
 An
ORF is more “believable” than another if it
has more “likely” codons
 Do sliding window calculations to find ORFs
that have the “likely” codon usage
 Allows for higher precision in identifying true
ORFs; much better than merely testing for
length.
 However, average vertebrate exon length is
130 nucleotides, which is often too small to
produce reliable peaks in the likelihood ratio
 Further improvement: in-frame hexamer count
(frequencies of pairs of consecutive codons)
GENE PREDICTION AND MOTIFS
 Upstream
regions of genes often contain motifs
that can be used for gene prediction
ATG
-35
-10
0
TTCCAA TATACT
Pribnow Box
10
GGAGG
Ribosomal binding site
Transcription start site
STOP
RIBOSOMAL BINDING SITE
CG-ISLANDS
 Given
4 nucleotides: probability of
occurrence is ~ 1/4. Thus, probability of
occurrence of a dinucleotide is ~ 1/16.
 However, the frequencies of dinucleotides
in DNA sequences vary widely.
 In particular, CG is typically
underepresented (frequency of CG is
typically < 1/16)
34
WHY CG-ISLANDS?
 CG
is the least frequent dinucleotide
because C in CG is easily methylated and
has the tendency to mutate into T
afterwards
 However, the methylation is suppressed
around genes in a genome. So, CG
appears at relatively high frequency
within these CG islands
 So, finding the CG islands in a genome is
an important problem
35
THANK YOU