Transcription Regulation Transcription Factor Motif Finding

1
Transcription RegulationTranscription Factor
Motif Finding

• Xiaole Shirley Liu
• STAT115, STAT215, BIO298, BIST520

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Outline

• Biology of transcription regulation and
  challenges of computational motif finding
• Scan for known TF motif sites
• TRASFAC and JASPAR, Sequence Logo
• De novo method
• Regular expression enumeration w-mer enumerate
• Position weight matrix update EM and Gibbs
• Motif finding in different organisms
• Motif clusters and conservation

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Imagine a Chef
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Each Cell Is Like a Chef
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Each Cell Is Like a Chef
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Understanding a Genome
Get the complete sequence (encoded cook book)
Observe gene expressions at different cell
states (meals prepared at different situations)
Decode gene regulation (decode the book,
understand the rules)
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Information in DNA
ATTTACCGATGGCTGCACTATGCCCTATCGATCGACCTCTC ATTTACCA
CATCGCATCACAGTTCAGGACTAGACACGGACG GCCTCGATTGACGGTG
GTACAGTTCAATGACAACCTGACTA TCTCGTTAGGACCCATGCGTACGA
CCCGTTTAAATCGAGAG CGCTAGCCCGTCATCGATCTTGTTCGAATCGC
GAATTGCCT
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Information in DNA

• Non-coding region 98
• Regulation When, Where,
• Amount, Other Conditions, etc
• ATTTACCGATGGCTGCACTATGCCCTATCGATCGACCTCTC
• ATTTACCACATCGCATCACTACGACGGACTAGACACGGACG
• GCCTCGATTGACGGTGGTACAGTTCAATGACAACCTGACTA
• TCTCGTTAGGACCCATGCGTACGACCCGTTTAAATCGAGAG
• CGCTAGGTCATCCCAGATCTTGTTCGAATCGCGAATTGCCT

Coding region 2
Milk-gtYogurt
Egg-gtOmelet
Fish-gtSushi
Flour-gtCake
Beef-gtBurger
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Measure Gene Expression

• Microarray or SAGE detects the expression of
  every gene at a certain cell state
• Clustering find genes that are co-expressed
  (potentially share regulation)

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Decode Gene Regulation

• GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC
• CACATCGCATATTTACCACCAGTTCAGACACGGACGGC
• GCCTCGATTTACCGTGGTACAGTTCAAACCTGACTAAA
• TCTCGTTAGGACCATATTTACCACCCACATCGAGAGCG
• CGCTAGCCATTTACCGATCTTGTTCGAGAATTGCCTAT

Look at genes always expressed together Upstrea
m Regions Co-expressed Genes
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Decode Gene Regulation

• GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC
• CACATCGCATATTTACCACCAGTTCAGACACGGACGGC
• GCCTCGATTTACCGTGGTACAGTTCAAACCTGACTAAA
• TCTCGTTAGGACCATATTTACCACCCACATCGAGAGCG
• CGCTAGCCATTTACCGATCTTGTTCGAGAATTGCCTAT

Look at genes always expressed together Upstrea
m Regions Co-expressed Genes
Scrambled Egg
Bacon
Cereal
Hash Brown
Orange Juice
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Decode Gene Regulation

• GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC
• CACATCGCATATTTACCACCAGTTCAGACACGGACGGC
• GCCTCGATTTACCGTGGTACAGTTCAAACCTGACTAAA
• TCTCGTTAGGACCATATTTACCACCCACATCGAGAGCG
• CGCTAGCCATTTACCGATCTTGTTCGAGAATTGCCTAT

Look at genes always expressed together Upstrea
m Regions Co-expressed Genes
Scrambled Egg
Bacon
Cereal
Hash Brown
Orange Juice
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Biology of Transcription Regulation

• ...acatttgcttctgacacaactgtgttcactagcaacctca...aaca
  gacaccATGGTGCACCTGACTCCTGAGGAGAAGTCT...

...agcaggcccaactccagtgcagctgcaacctgcccactcc...ggc
agcgcacATGTCTCTGACCAAGACTGAGAGTGCCGTC...
...cgctcgcgggccggcactcttctggtccccacagactcag...gat
acccaccgATGGTGCTGTCTCCTGCCGACAAGACCAA...
...gccccgccagcgccgctaccgccctgcccccgggcgagcg...gat
gcgcgagtATGGTGCTGTCTCCTGCCGACAAGACCAA...
Motif can only be computational discovered when
there are enough cases for machine learning
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Computational Motif Finding

• Input data
• Upstream sequences of gene expression profile
  cluster
• 20-800 sequences, each 300-5000 bps long
• Output enriched sequence patterns (motifs)
• Ultimate goals
• Which TFs are involved and their binding motifs
  and effects (enhance / repress gene expression)?
• Which genes are regulated by this TF, why is
  there disease when a TF goes wrong?
• Are there binding partner / competitor for a TF?

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Challenges Where/what the signal

• The motif should be abundant
• GAAATATGCACATTTACCTATGCCCTACGACCTCTCGC
• CACATCGCATATTTACCACCAAATAAGACACGGACGGC
• GCCTCGAAATAGCCATTTACCGTTCAAACCTGACTAAA
• TCTCGTATTTACCATATTAAATACCCACATCGAGAGCG
• CGCTAGCAAATATACGATTTACCTCGAGAATTGCCTAT

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Challenges Where/what the signal

• The motif should be abundant
• And Abundant with significance
• GAAATATGCACATTTACCTATGCCCTACGACCTCTCGC
• CACATCGCATATTTACCACCAAATAAGACACGGACGGC
• GCCTCGAAATAGCCATTTACCGTTCAAACCTGACTAAA
• TCTCGTATTTACCATATTAAATACCCACATCGAGAGCG
• CGCTAGCAAATATACGATTTACCTCGAGAATTGCCTAT

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Challenges Double stranded DNA

• Motif appears in both
• strands
• GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC
• CACATCGCATGGTAAATACCAGTTCAGACACGGACGGC
• TCTCAGGTAAATCAGTCATACTACCCACATCGAGAGCG

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Challenges Base substitutions

• Sequences do not have to match the motif
• perfectly, base substitutions are allowed
• GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC
• CACATCGCATATGTACCACCAGTTCAGACACGGACGGC
• GCCTCGATTTGCCGTGGTACAGTTCAAACCTGACTAAA
• TCTCGTTAGGACCATATTTATCACCCACATCGAGAGCG
• CGCTAGCCAATTACCGATCTTGTTCGAGAATTGCCTAT

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Challenges Variable motif copies

• Some sequences do not have the motif
• Some have multiple copies of the motif
• GATGATGCCTCGGACGGATATGCCCTACGACCTCTCGC
• CACATCGCAATGCAGCAATGCGTTCAGACACGGACGGC
• TCATGCTAATGCCAGTCATGCTACATGCATCGAGAGCG
• GCCTCTAGCTAGGCCGGTGAACATCAGACCTGACTAAA
• CGCAATATAGCATTAGCAGACAGACGAGAATTGCCTAT

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Challenges Variable motif copies

• Some sequences do not have the motif
• Some have multiple copies of the motif
• GATGATGCCTCGGACGGATATGCCCTACGACCTCTCGC
• CACATCGCAATGCAGCAATGCGTTCAGACACGGACGGC
• TCATGCTAATGCCAGTCATGCTACATGCATCGAGAGCG
• GCCTCTAGCTAGGCCGGTGAACATCAGACCTGACTAAA
• CGCAATATAGCATTAGCAGACAGACGAGAATTGCCTAT

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Challenges Two-block motifs

• Some motifs have two parts
• GACACATTTACCTATGC TGGCCCTACGACCTCTCGC
• CACAATTTACCACCA TGGCGTGATCTCAGACACGGACGGC
• GCCTCGATTTACCGTGGTATGGCTAGTTCTCAAACCTGACTAAA
• TCTCGTTAGATTTACCACCCA TGGCCGTATCGAGAGCG
• CGCTAGCCATTTACCGAT TGGCGTTCTCGAGAATTGCCTAT

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Scan for Known TF Motif Sites

• Experimental TF sites TRANSFAC, JASPAR
• Motif representation
• Regular expression Consensus CACAAAA
• binary decision Degenerate CRCAAAW
• IUPAC

A/T
A/G
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Scan for Known TF Motif Sites

• Experimental TF sites TRANSFAC, JASPAR
• Motif representation
• Regular expression Consensus CACAAAA
• binary decision Degenerate CRCAAAW
• Position weight matrix (PWM) need score cutoff

Motif Matrix
Pos 12345678 ATGGCATG AGGGTGCG
ATCGCATG TTGCCACG ATGGTATT ATTGCACG
AGGGCGTT ATGACATG ATGGCATG ACTGGATG
Sites
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IUPAC for DNA

• A adenosine
• C cytidine
• G guanine
• T thymidine
• U uridine
• R G A (purine)
• Y T C (pyrimidine)
• K G T (keto)

• M A C (amino)
• S G C (strong)
• W A T (weak)
• B C G T (not A)
• D A G T (not C)
• H A C T (not G)
• V A C G (not T)
• N A C G T (any)

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Protein Binding Microarrays

• In vitro protein-DNA interactions
• Better capture motifs

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JASPAR

• User defined cutoff to scan for a particular motif

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A Word on Sequence Logo

• SeqLogo consists of stacks of symbols, one stack
  for each position in the sequence
• The overall height of the stack indicates the
  sequence conservation at that position
• The height of symbols within the stack indicates
  the relative frequency of nucleic acid at that
  position

ATGGCATG AGGGTGCG ATCGCATG
TTGCCACG ATGGTATT ATTGCACG AGGGCGTT
ATGACATG ATGGCATG ACTGGATG
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Scan Known TF Motifs

• Drawbacks
• Limited number of motifs
• Limited number of sites to represent each motif
• Low sensitivity and specificity
• Poor description of motif
• Binding site borders not clear
• Binding site many mismatches
• Many motifs look very similar
• E.g. GC-rich motif, E-box (CACGTG)

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De novo Sequence Motif Finding

• Goal look for common sequence patterns enriched
  in the input data (compared to the genome
  background)
• Regular expression enumeration
• Pattern driven approach
• Enumerate patterns, check significance in
  dataset
• Oligonucleotide analysis, MobyDick
• Position weight matrix update
• Data driven approach, use data to refine motifs
• Consensus, EM Gibbs sampling
• Motif score and Markov background

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Regular Expression Enumeration

• Oligonucleotide Analysis check
  over-representation for every w-mer
• Expected w occurrence in data
• Consider genome sequence current data size
• Observed w occurrence in data
• Over-represented w is potential TF binding motif

Observed occurrence of w in the data
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MobyDick

• A sequence data and a dictionary of motif words

ATTTACCGATGGCTGCACTATGCCCTATCGATCGACCTCTC ATGCTTCA
CATCGCATCACCAGTTCAGGATAGACACGGACG GCCTCGATTGACGGTG
GTACAGTTCAATGACAACCTGACTA TCTCGTTAGGACCCATGCGTACGA
CCCGTTTAAATCGAGAG CGCTAGCCCGTCATCGATCTTGTTCGAATCGC
GAATTGCCT
D A, C, G, T Pw 0.22, 0.28,
0.28, 0.22
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MobyDick

• A sequence data and a dictionary of motif words
• Check over-representation of every word-pair

ATTTACCGATGGCTGCACTATGCCCTATCGATCGACCTCTC ATGCTTCA
CATCGCATCACCAGTTCAGGATAGACACGGACG GCCTCGATTGACGGTG
GTACAGTTCAATGACAACCTGACTA TCTCGTTAGGACCCATGCGTACGA
CCCGTTTAAATCGAGAG CGCTAGCCCGTCATCGATCTTGTTCGAATCGC
GAATTGCCT
D A, C, G, T Pw 0.28, 0.22,
0.22, 0.28
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MobyDick

• A sequence data and a dictionary of motif words
• Check over-representation of every word-pair

ATTTACCGATGGCTGCACTATGCCCTATCGATCGACCTCTC ATGCTTCA
CATCGCATCACCAGTTCAGGATAGACACGGACG GCCTCGATTGACGGTG
GTACAGTTCAATGACAACCTGACTA TCTCGTTAGGACCCATGCGTACGA
CCCGTTTAAATCGAGAG CGCTAGCCCGTCATCGATCTTGTTCGAATCGC
GAATTGCCT
D A, C, G, T Pw 0.28, 0.28,
0.22, 0.22
D A,C,G,T,AA,GA,TA,GG Pw ?
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MobyDick

• D A,C,G,T,AA,GA,TA,GG
• Seq AAGATAA
• Possible partitions
• A A G A T A A pA pA pG pA pT pA pA
• AA G A T A A pAA pG pA pT pA pA
• AA GA T A A pAA pGA pT pA pA
• AA GA TA A pAA pGA pTA pA
• A A GA T AA pAA pGA pT pAA
• Assign probabilities as to maximize total
  probability of generating the sequence

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MobyDick

• A sequence data and a dictionary of motif words
• Check over-representation of every word-pair
• Reassign word probability and consider every new
  word-pair to build even longer words

ATTTACCGATGGCTGCACTATGCCCTATCGATCGACCTCTC ATGCTTCA
CATCGCATCACCAGTTCAGGATAGACACGGACG GCCTCGATTGACGGTG
GTACAGTTCAATGACAACCTGACTA TCTCGTTAGGACCCATGCGTACGA
CCCGTTTAAATCGAGAG CGCTAGCCCGTCATCGATCTTGTTCGAATCGC
GAATTGCCT
D A, C, G, T Pw 0.28, 0.28,
0.22, 0.22
D A,C,G,T,AA,GA,TA,GG Pw ?
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Regular Expression Enumeration

• RE Enumeration Derivatives
• oligo-analysis, spaced dyads w1.ns.w2
• IUPAC alphabet
• Markov background (later)
• 2-bit encoding, fast index access
• Enumerate limited RE patterns known for a TF
  protein structure or interaction theme
• Exhaustive, guaranteed to find global optimum,
  and can find multiple motifs
• Not as flexible with base substitutions, long
  list of similar good motifs, and limited with
  motif width

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Consensus

• Starting from the 1st sequence, add one sequence
  at a time to look for the best motifs obtained
  with the additional sequence

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Consensus

• Starting from the 1st sequence, add one sequence
  at a time to look for the best motifs obtained
  with the additional sequence

Remaining good motifs

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Consensus

• Starting from the 1st sequence, add one sequence
  at a time to look for the best motifs obtained
  with the additional sequence
• G Stormo, algorithm runs very fast
• Sequence order plays a big role in performance
• First two sequences better contain the motif
• Sites stop accumulating at the first bad sequence
• Newer version allowing 0-n is much slower

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Expectation Maximization and Gibbs Sampling Model

• Objects
• Seq sequence data to search for motif
• ?0 non-motif (genome background) probability
• ? motif probability matrix parameter
• ? motif site locations
• Problem P(?, ? seq, ?0)
• Approach alternately estimate
• ? by P(? ?, seq, ?0)
• ? by P(? ?, seq, ?0)
• EM and Gibbs differ in the estimation methods

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Expectation Maximization

• E step ? ?, seq, ?0
• TTGACGACTGCACGT
• TTGAC p1
• TGACG p2
• GACGA p3
• ACGAC p4
• CGACT p5
• GACTG p6
• ACTGC p7
• CTGCA p8
• ...

• P1 likelihood ratio
• P(TTGAC ?)
• P(TTGAC ?0)

p0T ? p0T ? p0G ? p0A ? p0C 0.3 ? 0.3 ? 0.2 ?
0.3 ? 0.2
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Expectation Maximization

• E step ? ?, seq, ?0
• TTGACGACTGCACGT
• TTGAC p1
• TGACG p2
• GACGA p3
• ACGAC p4
• CGACT p5
• GACTG p6
• ACTGC p7
• CTGCA p8
• ...

• M step ? ?, seq, ?0
• p1 ? TTGAC
• p2 ? TGACG
• p3 ? GACGA
• p4 ? ACGAC
• ...
• Scale ACGT at each position, ? reflects weighted
  average of ?

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EM Derivatives

• First EM motif finder (C Lawrence)
• Deterministic algorithm, guarantee local optimum
• MEME (TL Bailey)
• Prior probability allows 0-n site / sequence
• Parallel running multiple
• EM with different seed
• User friendly results

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Gibbs Sampling

• Stochastic process, although still may need
  multiple initializations
• Sample ? from P(? ?, seq, ?0)
• Sample ? from P(? ?, seq, ?0)
• Collapsed form
• ? estimated with counts, not sampling from
  Dirichlet
• Sample site from one seq based on sites from
  other seqs
• Converged motif matrix ? and converged motif
  sites ? represent stationary distribution of a
  Markov Chain

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Gibbs Sampler

• Randomly initialize a probability matrix

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Gibbs Sampler

• Take out one sequence with its sites from current
  motif

?11
?21
?31
?41
?51
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Gibbs Sampler

• Score each possible segment of this sequence

Sequence 1
Segment (1-8)
?21
?31
?41
?51
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Gibbs Sampler

• Score each possible segment of this sequence

Sequence 1
Segment (2-9)
?21
?31
?41
?51
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Segment Score

• Use current motif matrix to score a segment

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Scoring Segments

• Motif 1 2 3 4 5 bg
• A 0.4 0.1 0.3 0.4 0.2 0.3
• T 0.2 0.5 0.1 0.2 0.2 0.3
• G 0.2 0.2 0.2 0.3 0.4 0.2
• C 0.2 0.2 0.4 0.1 0.2 0.2
• Ignore pseudo counts for now
• Sequence TTCCATATTAATCAGATTCCG score
• TAATC
• AATCA 0.4/0.3 x 0.1/0.3 x 0.1/0.3 x 0.1/0.2 x
  0.2/0.3 0.049383
• ATCAG 0.4/0.3 x 0.5/0.3 x 0.4/0.2 x 0.4/0.3 x
  0.4/0.2 11.85185
• TCAGA 0.2/0.3 x 0.2/0.3 x 0.3/0.3 x 0.3/0.2 x
  0.2/0.3 0.444444
• CAGAT

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Gibbs Sampler

• Sample site from one seq based on sites from
  other seqs

?21
?31
?41
?51
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How to Sample?
Pos 1 2 3 4 5 6 7 8 9
Score 3 1 12 5 8 9 1 2 6
SubT 3 4 16 21 29 38 39 41 47

• Rand(subtotal) X
• Find the first position with subtotal larger than
  X

Pos 1 2 3 4 5 6 7 8 9
Score 3 1 12 5 8 9 500 2 6
SubT 3 4 16 21 29 38 538 540 546
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Gibbs Sampler

• Repeat the process until motif converges

?21
?12
?31
?41
?51
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Gibbs Sampler Intuition

• Beginning
• Randomly initialized motif
• No preference towards any segment

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Gibbs Sampler Intuition

• Motif appears
• Motif should have enriched signal (more sites)
• By chance some correct sites come to alignment
• Sites bias motif to attract other similar sites

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Gibbs Sampler Intuition

• Motif converges
• All sites come to alignment
• Motif totally biased to sample sites every time

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Gibbs Sampler

• Column shift
• Metropolis algorithm
• Propose ? as ? shifted 1 column to left or right
• Calculate motif score u(?) and u(?)
• Accept ? with prob min(1, u(?) / u(?))

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Gibbs Sampling Derivatives

• Gibbs Motif Sampler (JS Liu)
• Add prior probability to allow 0-n site / seq
• Sample motif positions to consider
• AlignACE (F Roth)
• Look for motifs from both strands
• Mask out one motif to find more different motifs
• BioProspector (XS Liu)
• Use background model with Markov dependencies
• Sampling with threshold (0-n sites / seq), new
  scoring function
• Can find two-block motifs with variable gap

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Scoring Motifs

• Information Content (also known as relative
  entropy)
• Suppose you have x aligned segments for the motif
• pb(s1 from mtf) / pb(s1 from bg)
• pb(s2 from mtf) / pb(s2 from bg)
• pb(sx from mtf) / pb(sx from bg)

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Scoring Motifs

• Information Content (also known as relative
  entropy)
• Suppose you have x aligned segments for the motif
• pb(s1 from mtf) / pb(s1 from bg)
• pb(s2 from mtf) / pb(s2 from bg)
• pb(sx from mtf) / pb(sx from bg)

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Scoring Motifs

• pb(s1 from mtf) / pb(s1 from bg)
• pb(s2 from mtf) / pb(s2 from bg)
• pb(sx from mtf) / pb(sx from bg)
• (pA1/pA0)A1 (pT1/pT0)T1 (pT2/pT0)T2 (pG2/pG0)G2
  (pC2/pC0)C2
• Take log of this
• A1 log (pA1/pA0) T1 log (pT1/pT0)
• T2 log (pT2/pT0) G2 log (pG2/pG0)
• Divide by the number of segments (if all the
  motifs have same number of segments)
• pA1 log (pA1/pA0) pT1 log (pT1/pT0) pT2 log
  (pT2/pT0)

Pos 12345678 ATGGCATG AGGGTGCG
ATCGCATG TTGCCACG ATGGTATT ATTGCACG
AGGGCGTT ATGACATG ATGGCATG ACTGGATG
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Scoring Motifs

• Original function Information Content

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Scoring Motifs

• Original function Information Content

Good AGTCC AGTCC AGTCC AGTCC AGTCC AGTCC AGTCC
Bad ATAAA ATAAA ATAAA ATAAA ATAAA ATAAA ATAAA
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Scoring Motifs

• Original function Information Content
• Which is better?
• (data 8 seqs)

Motif 1 AGGCTAAC AGGCTAAC
Motif 2 AGGCTAAC AGGCTACC AGGCTAAC AGCCTAAC AGGCCA
AC AGGCTAAC TGGCTAAC AGGCTTAC AGGCTAAC AGGGTAAC
65
Scoring Motifs

• Motif scoring function
• Prefer conserved motifs with many sites, but are
  not often seen in the genome background

66
Markov Background Increases Motif Specificity

• Prefers motif segments enriched only in data,
  but not so likely to occur in the background
• Segment ATGTA score
• p(generate ATGTA from ?)
• p(generate ATGTA from ?0)

TCAGC .25 ? .25 ? .25 ? .25 ? .25 .3 ? .18
? .16 ? .22 ? .24 ATATA .25 ? .25 ? .25 ? .25
? .25 .3 ? .41 ? .38 ? .42 ? .30
67
Position Weight Matrix Update

• Advantage
• Can look for motifs of any widths
• Flexible with base substitutions
• Disadvantage
• EM and Gibbs sampling no guaranteed convergence
  time
• No guaranteed global optimum

68
Motif Finding in Bacteria

• Promoter sequences are short (200-300 bp)
• Motif are usually long (10-20 bases)
• Some have two blocks with a gap, some are
  palindromes
• Long motifs are usually very degenerate
• Single microarray experiment sometimes already
  provides enough information to search for TF
  motifs

69
Motif Finding in Lower Eukaryotes

• Upstream sequences longer (500-1000 bp), with
  some simple repeats
• Motif width varies (5 17 bases)
• Expression clusters provide decent input
  sequences quality for TF motif finding
• Motif combination and redundancy appears,
  although single motifs are usually significant
  enough for identification

70
Yeast Promoter Architecture

• Co-occurring regulators suggest physical
  interaction between the regulators

71
Motif Finding in Higher Eukaryotes

• Upstream sequences very long (3KB-20KB) with
  repeats, TF motif could appear downstream
• Motifs can be short or long (6-20 bases), and
  appear in combination and clusters
• Gene expression cluster not good enough input
• Need
• Comparative Genomics phastcons score
• Motif modules motif clusters
• ChIP-chip/seq

72
Yeast Regulatory Sequence Conservation
73
UCSC PhastCons Conservation

• Functional regulatory sequences are under
  stronger evolutionary constraint
• Align orthologous sequences together
• PhastCons conservation score (0 1) for each
  nucleotide in the genome can be downloaded from
  UCSC

74
Conserved Motif Clusters

• First find conserved regions in the genome
• Then look for repeated transcription factors (TF)
  binding sites
• They form transcription factor modules

75
Summary

• Biology and challenge of transcription regulation
• Scan for known TF motif sites TRANSFAC JASPAR
• De novo method
• Regular expression enumeration
• Oligonucleotide analysis
• MobyDick build long motifs from short ones
• Position weight matrix update
• CONSENSUS (sequence order)
• EM (iterate ?, ? ? weighted ? average)
• Gibbs Sampler (sample ?, ? Markov chain
  convergence)
• Motif score and Markov background
• Motif cluster and motif conservation