Genome Annotation Haixu Tang School of Informatics

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Genome AnnotationHaixu TangSchool of
Informatics

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Genome and genes

• Genome an organisms genetic material (Car
  encyclopedia)
• Gene a discrete units of hereditary information
  located on the chromosomes and consisting of DNA.
  (Chapters to make components of a car, or to use
  and drive a car).

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Gene Prediction Computational Challenge

• aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccg
  atgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggat
  ccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgct
  aatgaatggtcttgggatttaccttggaatgctaagctgggatccgatga
  caatgcatgcggctatgctaatgaatggtcttgggatttaccttggaata
  tgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcg
  gctatgctaatgcatgcggctatgcaagctgggatccgatgactatgcta
  agctgcggctatgctaatgcatgcggctatgctaagctgggatccgatga
  caatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctg
  cggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgg
  gatccgatgacaatgcatgcggctatgctaatgaatggtcttgggattta
  ccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcg
  gctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgca
  tgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgc
  taatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatg
  catgcggctatgctaagctgggatccgatgacaatgcatgcggctatgct
  aatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcgg
  ctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggt
  cttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgc
  ggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgca
  tgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatcc
  gatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctggga
  tccgatgactatgctaagctgcggctatgctaatgcatgcggctatgcta
  agctcatgcgg

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Gene Prediction Computational Challenge

• aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccg
  atgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggat
  ccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgct
  aatgaatggtcttgggatttaccttggaatgctaagctgggatccgatga
  caatgcatgcggctatgctaatgaatggtcttgggatttaccttggaata
  tgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcg
  gctatgctaatgcatgcggctatgcaagctgggatccgatgactatgcta
  agctgcggctatgctaatgcatgcggctatgctaagctgggatccgatga
  caatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctg
  cggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgg
  gatccgatgacaatgcatgcggctatgctaatgaatggtcttgggattta
  ccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcg
  gctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgca
  tgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgc
  taatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatg
  catgcggctatgctaagctgggatccgatgacaatgcatgcggctatgct
  aatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcgg
  ctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggt
  cttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgc
  ggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgca
  tgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatcc
  gatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctggga
  tccgatgactatgctaagctgcggctatgctaatgcatgcggctatgcta
  agctcatgcgg

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Gene Prediction Computational Challenge

• aatgcatgcggctatgctaatgcatgcggctatgctaagctgggatccg
  atgacaatgcatgcggctatgctaatgcatgcggctatgcaagctgggat
  ccgatgactatgctaagctgggatccgatgacaatgcatgcggctatgct
  aatgaatggtcttgggatttaccttggaatgctaagctgggatccgatga
  caatgcatgcggctatgctaatgaatggtcttgggatttaccttggaata
  tgctaatgcatgcggctatgctaagctgggatccgatgacaatgcatgcg
  gctatgctaatgcatgcggctatgcaagctgggatccgatgactatgcta
  agctgcggctatgctaatgcatgcggctatgctaagctgggatccgatga
  caatgcatgcggctatgctaatgcatgcggctatgcaagctgggatcctg
  cggctatgctaatgaatggtcttgggatttaccttggaatgctaagctgg
  gatccgatgacaatgcatgcggctatgctaatgaatggtcttgggattta
  ccttggaatatgctaatgcatgcggctatgctaagctgggaatgcatgcg
  gctatgctaagctgggatccgatgacaatgcatgcggctatgctaatgca
  tgcggctatgcaagctgggatccgatgactatgctaagctgcggctatgc
  taatgcatgcggctatgctaagctcatgcggctatgctaagctgggaatg
  catgcggctatgctaagctgggatccgatgacaatgcatgcggctatgct
  aatgcatgcggctatgcaagctgggatccgatgactatgctaagctgcgg
  ctatgctaatgcatgcggctatgctaagctcggctatgctaatgaatggt
  cttgggatttaccttggaatgctaagctgggatccgatgacaatgcatgc
  ggctatgctaatgaatggtcttgggatttaccttggaatatgctaatgca
  tgcggctatgctaagctgggaatgcatgcggctatgctaagctgggatcc
  gatgacaatgcatgcggctatgctaatgcatgcggctatgcaagctggga
  tccgatgactatgctaagctgcggctatgctaatgcatgcggctatgcta
  agctcatgcgg

Gene!
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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

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Central Dogma DNA -gt RNA -gt Protein
CCTGAGCCAACTATTGATGAA
CCUGAGCCAACUAUUGAUGAA
PEPTIDE
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Translating Nucleotides into Amino Acids

• Codon 3 consecutive nucleotides
• 4 3 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
  (codon degeneracy)

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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

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Genetic Code and Stop Codons
UAA, UAG and UGA correspond to 3 Stop codons that
(together with Start codon ATG) delineate Open
Reading Frames
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Six Frames in a DNA Sequence
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAA
GACTACCGTCTTACTAACAC CTGCAGACGAAACCTCTTGATGTAGTTGG
CCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC CTGCAGAC
GAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCG
TCTTACTAACAC
CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAA
GACTACCGTCTTACTAACAC
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTT
CTGATGGCAGAATGATTGTG
GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTT
CTGATGGCAGAATGATTGTG GACGTCTGCTTTGGAGAACTACATCAACC
GGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG GACGTCTG
CTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGC
AGAATGATTGTG

• stop codons TAA, TAG, TGA
• start codons - ATG

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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
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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 naïve because some genes (e.g. some
  neural and immune system genes) are relatively
  short

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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

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Codon Usage in Human Genome
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Codon Usage in Mouse Genome
AA codon /1000 frac Ser TCG 4.31
0.05 Ser TCA 11.44 0.14 Ser TCT 15.70
0.19 Ser TCC 17.92 0.22 Ser AGT 12.25
0.15 Ser AGC 19.54 0.24 Pro CCG 6.33
0.11 Pro CCA 17.10 0.28 Pro CCT 18.31
0.30 Pro CCC 18.42 0.31
AA codon /1000 frac Leu CTG 39.95
0.40 Leu CTA 7.89 0.08 Leu CTT 12.97
0.13 Leu CTC 20.04 0.20 Ala GCG 6.72
0.10 Ala GCA 15.80 0.23 Ala GCT 20.12
0.29 Ala GCC 26.51 0.38 Gln CAG 34.18
0.75 Gln CAA 11.51 0.25
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Transcription in prokaryotes
Transcribed region
start codon
stop codon
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Coding region
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Untranslated regions
Promoter
Transcription stop side
Transcription start side
upstream downstream
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Microbial gene finding

• Microbial genome tends to be gene rich (80-90
  of the sequence is coding sequence)
• Major problem finding genes without known
  homologue.

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Open Reading Frame
Open Reading Frame (ORF) is a sequence of codons
which starts with start codon, ends with a stop
codon and has no stop codons in-between.
Searching for ORFs consider all 6 possible
reading frames 3 forward and 3 reverse

• Is the ORF a coding sequence?
• Must be long enough (roughly 300 bp or more)
• Should have average amino-acid composition
  specific for a given organism.
• Should have codon usage specific for the given
  organism.

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Gene finding using codon frequency
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Example
Codon position A C T G
1 28 33 18 21
2 32 16 21 32
3 33 15 14 38
frequency in genome 31 18 19 31
Assume bases making codon are independent
P(xin coding) P(xrandom)

P(Ai at ith position) P(Ai in the sequence)
P
i
Score of AAAGAT
.28.32.33.21.26.14 .31.31.31.31.31.19
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Using codon frequency to find correct reading
frame
Consider sequence x1 x2 x3 x4 x5 x6 x7 x8 x9.
where xi is a nucleotide let p1 p x1 x2 x3 p
x3 x4 x5. p2 p x2 x3 x4 p x5 x6 x7.
p3 p x3 x4 x5 p x6 x7 x8. then
probability that ith reading frame is the coding
frame is pi p1 p2 p3

• Algorithm
• slide a window along the sequence and
• compute Pi
• Plot the results

Pi
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Eukaryotic gene finding

• On average, vertebrate gene is about 30KB long
• Coding region takes about 1KB
• Exon sizes vary from double digit numbers to
  kilobases
• An average 5 UTR is about 750 bp
• An average 3UTR is about 450 bp but both can be
  much longer.

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Exons and Introns

• In eukaryotes, the gene is a combination of
  coding segments (exons) that are interrupted by
  non-coding segments (introns)
• This makes computational gene prediction in
  eukaryotes even more difficult
• Prokaryotes dont have introns - Genes in
  prokaryotes are continuous

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Gene Structure
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Gene structure in eukaryotes
exons
Final exon
Transcribed region
start codon
stop codon
Initial exon
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5
GT AG
Untranslated regions
Promoter
Transcription stop side
Transcription start side
donor and acceptor sides
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Central Dogma and Splicing
intron1
intron2
exon2
exon3
exon1
transcription
splicing
translation
exon coding intron non-coding
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Splicing Signals

• Exons are interspersed with introns and typically
  flanked by GT and AG

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Splice site detection
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Consensus splice sites
Donor 7.9 bits Acceptor 9.4 bits
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Promoters

• Promoters are DNA segments upstream of
  transcripts that initiate transcription
• Promoter attracts RNA Polymerase to the
  transcription start site

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3
Promoter
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Two Approaches to Eukaryotic 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.

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Ribosomal Binding Site
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Donor and Acceptor Sites Motif Logos
Donor 7.9 bits Acceptor 9.4 bits (Stephens
Schneider, 1996)
(http//www-lmmb.ncifcrf.gov/toms/sequencelogo.ht
ml)
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Similarity-based gene finding

• Alignment of
• Genomic sequence and (assembled) EST sequences
• Genomic sequence and known (similar) protein
  sequences
• Two or more similar genomic sequences

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Expressed Sequence Tags
Cell or tissue
dbEST
Isolate mRNA and Reverse transcribe into cDNA
Clone cDNA into a vector to Make a cDNA library
Submit To dbEST
EST
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Vectors
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Pick a clone And sequence the 5 and 3 Ends of
cDNA insert
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Central Dogma and Splicing
intron1
intron2
exon2
exon3
exon1
transcription
splicing
translation
exon coding intron non-coding
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Splicing Sequence Alignment
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Using Similarities to Find the Exon Structure

• Human EST (mRNA) sequence is aligned to different
  locations in the human genome
• Find the best path to reveal the exon structure
  of human gene

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An annotated gene in human genome