Genomics 101

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• Genomics 101
• DNA sequencing
• Alignment
• Gene identification
• Gene expression
• Genome evolution

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Next Few Topics

• Gene Recognition
• Finding genes in DNA with computational methods
• Large-scale alignment multiple alignment
• Comparing whole genomes, or large families of
  genes
• Gene Expression and Regulation
• Measuring the expression of many genes at a time
• Finding elements in DNA that control the
  expression of genes

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Gene Recognition
Credits for slides Marina Alexandersson Lior
Pachter Serge Saxonov
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Reading

• GENSCAN
• EasyGene
• SLAM
• Twinscan
• Optional
• Chris Burges Thesis

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Gene expression
CCTGAGCCAACTATTGATGAA
CCUGAGCCAACUAUUGAUGAA
PEPTIDE
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Gene structure
intron1
intron2
exon2
exon3
exon1
transcription
splicing
translation
Codon A triplet of nucleotides that is converted
to one amino acid
exon protein-coding intron non-coding
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Where are the genes?
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In humans 22,000 genes 1.5 of human DNA
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Finding Genes

• Exploit the regular gene structure
• ATGExon1Intron1Exon2ExonNSTOP
• Recognize coding bias
• CAG-CGA-GAC-TAT-TTA-GAT-AAC-ACA-CAT-GAA-
• Recognize splice sites
• IntroncAGtExongGTgagIntron
• Model the duration of regions
• Introns tend to be much longer than exons, in
  mammals
• Exons are biased to have a given minimum length
• Use cross-species comparison
• Gene structure is conserved in mammals
• Exons are more similar (85) than introns

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Approaches to gene finding

• Homology
• BLAST, Procrustes.
• Ab initio
• Genscan, Genie, GeneID.
• Hybrids
• GenomeScan, GenieEST, Twinscan, SGP, ROSETTA,
  CEM, TBLASTX, SLAM.

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1. Exploit the regular gene structure
Splice sites
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Next Exon Frame 0
Next Exon Frame 1
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2. Recognize coding bias

• Each exon can be in one of three frames
• aggattacagattacagattacagtaag Frame 0
• aggattacagattacagattacagtaag Frame 1
• aggattacagattacagattacagtaag Frame 2
• Frame of next exon depends on how many
  nucleotides are left over from previous exon
• Codons tag, tga, and taa are STOP
• No STOP codon appears in-frame, until end of gene
• Absence of STOP is called open reading frame
  (ORF)
• Different codons appear with different
  frequenciescoding bias

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2. Recognize coding bias
Amino Acid SLC DNA codons Isoleucine I ATT, ATC,
ATA Leucine L CTT, CTC, CTA, CTG, TTA,
TTG Valine V GTT, GTC, GTA, GTG Phenylalanine F T
TT, TTC Methionine M ATG Cysteine C TGT,
TGC Alanine A GCT, GCC, GCA, GCG
Glycine G GGT, GGC, GGA, GGG Proline P CCT,
CCC, CCA, CCG Threonine T ACT, ACC, ACA,
ACG Serine S TCT, TCC, TCA, TCG, AGT,
AGC Tyrosine Y TAT, TAC Tryptophan W TGG Glutamin
e Q CAA, CAG Asparagine N AAT,
AAC Histidine H CAT, CAC Glutamic acid E GAA,
GAG Aspartic acid D GAT, GAC Lysine K AAA,
AAG Arginine R CGT, CGC, CGA, CGG, AGA, AGG Stop
codons Stop TAA, TAG, TGA Can map 61 non-stop
codons to frequencies take log-odds ratios
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atg
caggtg
ggtgag
cagatg
ggtgag
cagttg
ggtgag
caggcc
ggtgag
tga
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Biology of Splicing
(http//genes.mit.edu/chris/)
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3. Recognize splice sites
Donor 7.9 bits Acceptor 9.4 bits (Stephens
Schneider, 1996)
(http//www-lmmb.ncifcrf.gov/toms/sequencelogo.ht
ml)
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3. Recognize splice sites
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3. Recognize splice sites

• WMM weight matrix model PSSM (Staden 1984)
• WAM weight array model 1st order Markov (Zhang
  Marr 1993)
• MDD maximal dependence decomposition (Burge
  Karlin 1997)
• Decision-tree algorithm to take pairwise
  dependencies into account
• For each position I, calculate Si ?j?i?2(Ci,
  Xj)
• Choose i such that Si is maximal and partition
  into two subsets, until
• No significant dependencies left, or
• Not enough sequences in subset
• Train separate WMM models for each subset

G5
G5G-1
G5G-1 A2
G5G-1 A2U6
All donor splice sites
not G5
G5 not G-1
G5G-1 not A2
G5G-1A2 not U6
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4. Model the duration of regions
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Hidden Markov Models for Gene Finding
First Exon State
Intron State
Intergene State
GTCAGATGAGCAAAGTAGACACTCCAGTAACGCGGTGAGTACATTAA
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Hidden Markov Models for Gene Finding
First Exon State
Intron State
Intergene State
GTCAGATGAGCAAAGTAGACACTCCAGTAACGCGGTGAGTACATTAA
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Duration HMM for Gene Finding
Duration Modeling Introns regular HMM
statesgeometric duration Exons special duration
model VE0,0(i) maxd1D Probduration(E0,0)d
?aIntron0,E0,0? ?ji-d1ieE0,0(xj)
where i is an admissible exon-ending
state, D is restricted by the longest ORF
GENSCAN Chris Burge and Sam Karlin, 1997 Best
performing de novo gene finder HMM with duration
modeling for Exon states
duration
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HMM-based Gene Finders

• GENSCAN (Burge 1997)
• Big jump in accuracy of de novo gene finding
• Currently, one of the best
• HMM with duration modeling for Exon states
• FGENESH (Solovyev 1997)
• Currently one of the best
• HMMgene (Krogh 1997)
• GENIE (Kulp 1996)
• GENMARK (Borodovsky McIninch 1993)
• VEIL (Henderson, Salzberg, Fasman 1997)

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Better way to do it negative binomial

• EasyGene
• Prokaryotic
• gene-finder
• Larsen TS, Krogh A
• Negative binomial with n 3

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GENSCANs hidden weapon

• CG content is correlated with
• Gene content ()
• Mean exon length ()
• Mean intron length ()
• These quantities affect parameters of model
• Solution
• Train parameters of model in four different CG
  content ranges!

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Evaluation of Accuracy
Coding / No Coding
(Slide by NF Samatova)
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Results of GENSCAN

• On the initial test dataset (Burset Guigo)
• 80 exact exon detection
• 10 partial exons
• 10 wrong exons
• In general
• HMMs have been best in de novo prediction
• In practice they overpredict human genes by 2x