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Sequence motifs
What are sequence motifs?
• Sequences are translated into electron densities with different affinities of interacting with other molecules.
• Motifs represent a short common sequence– Regulatory motifs (TF binding sites) – Functional site in proteins (DNA binding motif)
DNA Regulatory Motifs• Transcription Factors bind to regulatory
motifs with high affinity– TF binding motifs are usually 6 – 20
nucleotides long– Usually located near target gene, mostly
upstream the transcription start site
Transcription Start Site
SBFmotif
MCM1motif
Gene X
MCM1 SBF
Identification of Known Motifs within Genomic Sequences
• Main Motivation: - Identifying the target of regulatory proteins
(e.g. Transcription Factors) in the cell
In many cancers specific TFs are known to be mutated. How do we identify the genes that are affected downstream?
P53 the guardian of the cell
How can we start looking for p53 (or any other transcription factor) targets
using bioinformatics?
Scenario 1 : Binding motif is known (easier case)
Scenario 2 : Binding motif is unknown (hard case)
Challenges
• How to recognize a regulatory motif?• Can we identify new occurrences of known
motifs in genome sequences?• Can we discover new motifs within
upstream sequences of genes?
Scenario 1 : Binding targets are known
1. Motif Representation
• Consensus: represent only ‘common’ nucleotides
• NANCATNNCCTTTTTATACAGNNNTTNNNTNN
• N stands for any nucleotide.
• Representing only consensus loses information. How can this be avoided?
GTTCTTCGTGTTTATTTTTAGGAAATTGATGATTGTTTCTCCTTTTAAAATAGTACTGCTGTTTTTTACTAACGACACATTGAAGAAATCACTTTGGATACGCTTACCGTTATCCAGAGCTACAGCGCTACTAATATGTAATACTTCAGCTCCCCTTAATATTGAGATCTTTTTTAACTAGTTAGGTCTACCTTCTCCCCTTCTTCATTTTAGCCTGTTTGGACTAACATAACTTATTTACATAGTGCCATTGAACGATATTTCCCGTTGTGTTAAGGCTGAGAAGAATTTTCCCGACCATCAAGACAGGTGATTTATCATGCAAAAACTTTTTTTCACAGGGCTAACTTGCGTTTATTGTGTTTCCACTCAGTTAAAAAACGAAACGTACTTTAATATTTATAGTACTTCATTCGAACATGCTATTTTTCATACAGCAACCTCACATCTGCACTCATCATTAGATTAGAGGAACATGGATACTTTTCTTTATCTAAGCAGCTAACTCAACTATCAACATGCTATTGAACTAGAGATCCACCTATAACTAACATGACTTTAACAGGGCTAATTTACAGTACTAACTAATTAACTTAGAACATTAACATGATCACCGTCACATTTATTAGAATTTCAAACGCAGTGGAATTTTTTTTTCTAGAAATGGTATCGCTCTATGACCAATAAAAACAGACTGTACTTTCAAATGGTATTATTTATAACAGTTGAACATTTCATAAATATGCGATCAATATAGACCGTTGATATATTTTACTTTTTTTTTTTTAGGAGCTCCAAGAATTTATTTCCTTATAATACAGACACGGTTACATCGCAATTAATTTTCTAATAGTTTTTCATTTTGACCATCTTTCTTTTCCCCAGTGCTAAACACGAACCTTCTTTCTCATTCGTAGATTACTGTTGCAATTACTAACAGCTGTAATAGCCGACAAATTTCTCTCTGCGCGTCCAATTTAGCTATACTGTTGTTGTTTTGTTTTGTCGTACAGTGTTTGGAGAAAAACTTCCATTTCTTACATAGATCATCGCCATTCCTTTCCATAATTTATTCAGCGCTTTGGTATCGATTTACTATTTCCATTTAGACGTTGTTCAAAATTTACTAACAATACTTCAGTTTATAATGGATCCTATACTAACAATTTGTAGTTCATAAATAA
Entropy - Definition
Claude E. Shannon 1948, “A mathematical theory of communication”.
Entropy - Definition
Entropy - Example
Relative EntropyThe Kullback-Leibler distance D
Information content
Information content
Information content
GTTCTTCGTGTTTATTTTTAGGAAATTGATGATTGTTTCTCCTTTTAAAATAGTACTGCTGTTTTTTACTAACGACACATTGAAGAAATCACTTTGGATACGCTTACCGTTATCCAGAGCTACAGCGCTACTAATATGTAATACTTCAGCTCCCCTTAATATTGAGATCTTTTTTAACTAGTTAGGTCTACCTTCTCCCCTTCTTCATTTTAGCCTGTTTGGACTAACATAACTTATTTACATAGTGCCATTGAACGATATTTCCCGTTGTGTTAAGGCTGAGAAGAATTTTCCCGACCATCAAGACAGGTGATTTATCATGCAAAAACTTTTTTTCACAGGGCTAACTTGCGTTTATTGTGTTTCCACTCAGTTAAAAAACGAAACGTACTTTAATATTTATAGTACTTCATTCGAACATGCTATTTTTCATACAGCAACCTCACATCTGCACTCATCATTAGATTAGAGGAACATGGATACTTTTCTTTATCTAAGCAGCTAACTCAACTATCAACATGCTATTGAACTAGAGATCCACCTATAACTAACATGACTTTAACAGGGCTAATTTACAGTACTAACTAATTAACTTAGAACATTAACATGATCACCGTCACATTTATTAGAATTTCAAACGCAGTGGAATTTTTTTTTCTAGAAATGGTATCGCTCTATGACCAATAAAAACAGACTGTACTTTCAAATGGTATTATTTATAACAGTTGAACATTTCATAAATATGCGATCAATATAGACCGTTGATATATTTTACTTTTTTTTTTTTAGGAGCTCCAAGAATTTATTTCCTTATAATACAGACACGGTTACATCGCAATTAATTTTCTAATAGTTTTTCATTTTGACCATCTTTCTTTTCCCCAGTGCTAAACACGAACCTTCTTTCTCATTCGTAGATTACTGTTGCAATTACTAACAGCTGTAATAGCCGACAAATTTCTCTCTGCGCGTCCAATTTAGCTATACTGTTGTTGTTTTGTTTTGTCGTACAGTGTTTGGAGAAAAACTTCCATTTCTTACATAGATCATCGCCATTCCTTTCCATAATTTATTCAGCGCTTTGGTATCGATTTACTATTTCCATTTAGACGTTGTTCAAAATTTACTAACAATACTTCAGTTTATAATGGATCCTATACTAACAATTTGTAGTTCATAAATAA
A 94 88 84 75 78 78 71 69 70 60 68 77 32 49 87 93 93 134 9 266 0 86 66 85 81 89 81 88 82
C 31 45 52 44 56 46 62 54 56 51 46 37 30 42 32 44 30 25 122 1 0 38 65 52 43 62 62 57 43
T 113 110 113 117 104 117 111 120 118 125 136 140 182 155 122 100 124 75 137 0 0 72 85 82 91 83 73 67 96
G 30 25 19 32 30 27 24 25 24 32 18 14 24 22 27 31 21 34 0 1 268 72 52 49 53 34 52 56 47
Count nucleotides at each position:
A 0,35 0,33 0,31 0,28 0,29 0,29 0,26 0,26 0,26 0,22 0,25 0,29 0,12 0,18 0,32 0,35 0,35 0,50 0,03 0,99 0,00 0,32 0,25 0,32 0,30 0,33 0,30 0,33 0,31
C 0,12 0,17 0,19 0,16 0,21 0,17 0,23 0,20 0,21 0,19 0,17 0,14 0,11 0,16 0,12 0,16 0,11 0,09 0,46 0,00 0,00 0,14 0,24 0,19 0,16 0,23 0,23 0,21 0,16
T 0,42 0,41 0,42 0,44 0,39 0,44 0,41 0,45 0,44 0,47 0,51 0,52 0,68 0,58 0,46 0,37 0,46 0,28 0,51 0,00 0,00 0,27 0,32 0,31 0,34 0,31 0,27 0,25 0,36
G 0,11 0,09 0,07 0,12 0,11 0,10 0,09 0,09 0,09 0,12 0,07 0,05 0,09 0,08 0,10 0,12 0,08 0,13 0,00 0,00 1,00 0,27 0,19 0,18 0,20 0,13 0,19 0,21 0,18
Convert to frequencies:
Frequency-logo:
Logo plots - HowTo
GTTCTTCGTGTTTATTTTTAGGAAATTGATGATTGTTTCTCCTTTTAAAATAGTACTGCTGTTTTTTACTAACGACACATTGAAGAAATCACTTTGGATACGCTTACCGTTATCCAGAGCTACAGCGCTACTAATATGTAATACTTCAGCTCCCCTTAATATTGAGATCTTTTTTAACTAGTTAGGTCTACCTTCTCCCCTTCTTCATTTTAGCCTGTTTGGACTAACATAACTTATTTACATAGTGCCATTGAACGATATTTCCCGTTGTGTTAAGGCTGAGAAGAATTTTCCCGACCATCAAGACAGGTGATTTATCATGCAAAAACTTTTTTTCACAGGGCTAACTTGCGTTTATTGTGTTTCCACTCAGTTAAAAAACGAAACGTACTTTAATATTTATAGTACTTCATTCGAACATGCTATTTTTCATACAGCAACCTCACATCTGCACTCATCATTAGATTAGAGGAACATGGATACTTTTCTTTATCTAAGCAGCTAACTCAACTATCAACATGCTATTGAACTAGAGATCCACCTATAACTAACATGACTTTAACAGGGCTAATTTACAGTACTAACTAATTAACTTAGAACATTAACATGATCACCGTCACATTTATTAGAATTTCAAACGCAGTGGAATTTTTTTTTCTAGAAATGGTATCGCTCTATGACCAATAAAAACAGACTGTACTTTCAAATGGTATTATTTATAACAGTTGAACATTTCATAAATATGCGATCAATATAGACCGTTGATATATTTTACTTTTTTTTTTTTAGGAGCTCCAAGAATTTATTTCCTTATAATACAGACACGGTTACATCGCAATTAATTTTCTAATAGTTTTTCATTTTGACCATCTTTCTTTTCCCCAGTGCTAAACACGAACCTTCTTTCTCATTCGTAGATTACTGTTGCAATTACTAACAGCTGTAATAGCCGACAAATTTCTCTCTGCGCGTCCAATTTAGCTATACTGTTGTTGTTTTGTTTTGTCGTACAGTGTTTGGAGAAAAACTTCCATTTCTTACATAGATCATCGCCATTCCTTTCCATAATTTATTCAGCGCTTTGGTATCGATTTACTATTTCCATTTAGACGTTGTTCAAAATTTACTAACAATACTTCAGTTTATAATGGATCCTATACTAACAATTTGTAGTTCATAAATAA
• Multiple alignment of acceptor sites from 268 yeast DNA sequences
– What is the biological signal around the site ?– What are the important positions– How can it be visualized ?
Biological information
Sequence-logo
• Logo plot with Information Content
Exon Intron Exon
Logo plots - Information Content
Sequence-logo
Calculate Information ContentI = apalog2pa + log2(4), Maximal value is 2 bits
• X axis – Relative position. Y axis – Cross Entropy.• Total height at a position is the Information Content measured in bits.• Height of letter is the proportional to the frequency of that letter.• Stack order indicates importance, consensus is read at the top.• A Logo plot is a visualization of a multiple alignment.
~0.5 each
Completely conserved
Pseudocounts
PSSM – Position Specific Scoring Matrix
• Besides Entropy and Information content there are other ways to express a motif
-4 -3 -2 -1 0
A0.18 0.2 - 1 -
C0.05 0.02 0.5 - -
T 0.2 0.180.5 - -
G0.02 0.05 - - 1
Example:
Predicting the cAMP Receptor Protein (CRP) binding site motif by using a logo plot
Extract experimentally defined CRP Binding Sites GGATAACAATTTCACAAGTGTGTGAGCGGATAACAAAAGGTGTGAGTTAGCTCACTCCCCTGTGATCTCTGTTACATAGACGTGCGAGGATGAGAACACAATGTGTGTGCTCGGTTTAGTTCACCTGTGACACAGTGCAAACGCGCCTGACGGAGTTCACAAATTGTGAGTGTCTATAATCACGATCGATTTGGAATATCCATCACATGCAAAGGACGTCACGATTTGGGAGCTGGCGACCTGGGTCATGTGTGATGTGTATCGAACCGTGTATTTATTTGAACCACATCGCAGGTGAGAGCCATCACAGGAGTGTGTAAGCTGTGCCACGTTTATTCCATGTCACGAGTGTTGTTATACACATCACTAGTGAAACGTGCTCCCACTCGCATGTGATTCGATTCACA
Create a Multiple Sequence Alignment GGATAACAATTTCACATGTGAGCGGATAACAATGTGAGTTAGCTCACTTGTGATCTCTGTTACACGAGGATGAGAACACACTCGGTTTAGTTCACCTGTGACACAGTGCAAACCTGACGGAGTTCACAAGTGTCTATAATCACGTGGAATATCCATCACATGCAAAGGACGTCACGGGCGACCTGGGTCATGTGTGATGTGTATCGAATTTGAACCACATCGCAGGTGAGAGCCATCACATGTAAGCTGTGCCACGTTTATTCCATGTCACGTGTTATACACATCACTCGTGCTCCCACTCGCATGTGATTCGATTCACA
Generate a Logo plot
XXXXXTGTGAXXXXAXTCACAXXXXXXXXXXXXACACTXXXXTXAGTGTXXXXXXX
• http://weblogo.berkeley.eduWebLogo - Input
Genes:WebLogo - Outputs
Proteins:
PROBLEMS…
• When searching for a motif in a genome using PSSM or other methods – the motif is usually found all over the place– The motif is considered real if found in the vicinity of a gene.
• Checking experimentally for the binding sites of a specific TF (location analysis) – the sites that bind the motif are in some cases similar to the PSSM and sometimes not!
Scenario 2 : Binding targets are unknown
Finding new Motifs
• We are given a group of genes, which presumably contain a common regulatory motif.
• We know nothing of the TF that binds to the putative motif.
• The problem: discover the motif.
Motif Discovery
Motif Discovery
Computational Methods• This problem has received a lot of attention from
CS people.• Methods include:
– Probabilistic methods – hidden Markov models (HMMs), expectation maximization (EM), Gibbs sampling, etc.
– Enumeration methods – problematic for inexact motifs of length k>10. …
• Current status: Problem is still open.
MEME
"We need a name for the new replicator, a noun that conveys the idea of a unit of cultural transmission, or a unit of imitation. 'Mimeme' comes from a suitable Greek root, but I want a monosyllable that sounds a bit like 'gene'. I hope my classicist friends will forgive me, if I abbreviate mimeme to meme...“Richard Dawkins
• An (unsupervised) machine learning approach to motif discovery.
• Input: – Set of unaligned sequences.– Possible width of motifs.
• Output:– A set of gapless motifs.– Classifier for each motif.– Alignment of the occurrences of the motif to the
input set.
Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994.
MEME: Expectation Maximization
• Goal: Find motif profile and positions that have maximum likelihood
• Iteratively estimates a probabilistic model for a random motif to be statistically overrepresented in the dataset. Converges at local optimum.
MEME result example
MEME Pros and cons
• The number of motifs or their occurrences are not required in the input.
• Only allows exact matches.
• High time complexity.• Very pessimistic, can
miss signals.
• DRIM is a tool for discovering short motifs in a ranked list of nucleic acid sequences.
• From a mathematical point of view, DRIM identifies subsequences that tend to appear at the top of the list more often than in the rest of the list. – The definition of TOP in this context is flexible and driven
by the data.
E. Eden, D. Lipson, S. Yogev & Z. Yakhini. Discovering Motifs in Ranked Lists of DNA Sequences, PLoS Computational Biology, 2007.
The HyperGeometric (HG) score• The HG score estimates the significance of the
intersection (of size b)
N genes
B nb
N all genes, ranked according to some criterionB of them contain the motifn of them are located at the top of the listb contain the motif and are located at the top of the list
The mHG score• DRIM checks all the possibilities for n, in order to optimize
the significance of the intersection.– It chooses the ni which has the minimal HG score – denoted
as the mHG score.
N genes
B nibi
The mHG score reflects the surprise of seeing the observed density of motif occurrences at the top of the list compared with the rest of the list.
(STILL NEEDS TO BE CORRECTED FOR MULTIPLE HYPOTHESIS)
Puf2 – an RNA binding proteinYeast 3’UTR sequences were ranked according to Puf2 binding affinity. >YDR222W, affinity = 5.962
ACAAAAGCGUGAACACUUCCACAUGAAAUUCGUUUUUUGUCCUUUUUUUUCUCUUCUUUUUCUCUCCUGUUUCU>YLR297W, affinity = 5.937AAUAAAAAUAGAUAUAAUAGAUGGCACCGCUCUUCACGCCCGAAAGUUGGACAUUUUAAAUUUUAAUUCUCAUGA>YOL109W, affinity = 5.763UCACACUUGAAUGUGCUGCACUUUACUAGAAGUUUCUUUUUCUUUUUUUAAAAAUAAAAAAAGAGGAGAAAAAUGC>YGR138C, affinity = 5.498GCUGGUGCAAGUUUCCGGUAAAAAUAAUGAUGUUCUAGUCAUUCAUAUAUACGAUACAAAAAUAACA>YGL035C, affinity = 5.091UACGCUGACAAGUUUUUGGCGGUGCAGAUAAAUCAAAAGACAAUAGACAAGAAUUAAUAAUAUUAACAAUUAA...
DRIM
(mHG p-value= 9.9 10∙ -49)
DRIM pros and cons
• Finds relations between ranking variable and motifs (enrichment).
• Returns best possible match without the need of a significance threshold.
• Impossible to build a dictionary for motifs of > ~10-mers.
Tools on the Web• MEME – Multiple EM for Motif Elicitation.
http://meme.sdsc.edu/meme– metaMEME- Uses HMM method– MAST-Motif Alignment and Search Tool– Etc…
• TRANSFAC - database of eukaryotic cis-acting regulatory DNA elements and trans-acting factors. http://transfac.gbf.de/TRANSFAC/
• eMotif - allows to scan, make and search for motifs at the protein level. http://motif.stanford.edu/emotif/
• DRIM – Finds short motifs enriched in ranked lists.http://bioinfo.cs.technion.ac.il/drim/