DNA Motif Finding 2010

DNA Motif Finding

Stewart MacArthur

Bioinformatics Core

March 11th, 2010

Stewart MacArthur (Bioinformatics Core) DNA Motif Finding March 11th, 2010 1 / 33

Introduction

What is a DNA Motif?

DNA motifs are short, recurring patterns that are presumed to have abiological function.

• sequence-specific binding sites• nucleases

• ribosome binding• mRNA processing

• splicing• editing• polyadenylation

• transcription termination


Introduction

What is a DNA Motif?

DNA motifs are short, recurring patterns that are presumed to have abiological function.

• sequence-specific binding sites• transcription factors• nucleases

• ribosome binding• mRNA processing

• splicing• editing• polyadenylation

• transcription termination


Representing a motif

How to represent a DNA motif?How can we represent the binding specificity of a protein, such that wecan reliably predict its binding to any given sequence?Restriction enzymes sites can be written as simple DNA sequence,e.g. GAATTC for EcoRI

5’-G A A T T C-3’3’-C T T A A G-5’

These sequences can incorporate ambiguity, e.g. GTYRAC for HincII,using the IUPAC code.

GTYRACY = C or TR = A or C

All matching sites will be cut by the restriction enzyme


Representing a motif

Transcription Factors are different...

• Regulatory motifs are often degenerate,variable but similar.• Transcription factors are often pleiotropic, regulating several

genes, but they may need to be expressed at different levels.• A side effect of this degeneracy is spurious binding, where the

protein has affinity at positions in the genome other than theirfunctional sites.

• Degeneracy in restriction enzyme binding would be lethal• Non-specific binding competes for protein and requires more

protein to be produced than would be required otherwise


Representing a motif Consensus

The Consensus Sequence• A consensus binding site is often used to represent transcription

factor binding• Refers to a sequence that matches all examples of the binding

site closely but not exactly• There is a trade-off between the ambiguity in the consensus and

its sensitivity

TACGATTATAATTATAATGATACTTATGATTATGTT



The Consensus Sequence : Example

TACGATTATAATTATAATTATACTTATGATTATGTTTATAAT

Allowing 0 mismatches finds 2/6 Sites1 site every 4kb




TACGATTATAAT*TATAAT*TATACTTATGATTATGTTTATAAT

Allowing 0 mismatches finds 2/6 Sites1 site every 4kb




TACGATTATAAT*TATAAT*TATACTTATGAT*TATGTTTATAAT

Allowing at most 1 mismatch finds 3/6 Sites1 site every 200bp




TACGAT*TATAAT*TATAAT*TATACT*TATGAT*TATGTT*TATAAT

Allowing up to 2 mismatches finds 6/6 Sites1 site every 30bp


Representing a motif IUPAC

IUPAC codesA AdenineC CytosineG GuanineT ThymineR A or GY C or TS G or CW A or TK G or TM A or CB C or G or TD A or G or TH A or C or TV A or C or GN any base

. or - gapStewart MacArthur (Bioinformatics Core) DNA Motif Finding March 11th, 2010 7 / 33



TACGATTATAATTATAATTATACTTATGATTATGTTTATRNT

Allowing 0 mismatches finds 2/6 Sites




TACGATTATAAT*TATAAT*TATACTTATGAT*TATGTT*TATRNT

Exact match finds 4/6 Sites - 1 site every 500bp




TACGAT*TATAAT*TATAAT*TATACT*TATGAT*TATGTT*TATRNT

Up to one mismatch finds 6/6 Sites - 1 site every 30bp


Representing a motif Matrix

The Matrix• A position weight matrix (PWM)

• also called position-specific weight matrix (PSWM)• also called position-frequency matrix (PFM)• also called position-specific scoring matrix (PSSM)• or just matrix

• Alternative to the consensus.• There is a matrix element for all possible bases at every position.

1 2 3 4 5 6 7 8 9 10 11A 4 13 5 3 0 0 0 0 17 0 6C 4 1 2 0 0 0 0 0 0 1 0G 3 3 0 0 18 0 0 0 1 4 3T 7 1 11 15 0 18 18 18 0 13 9



Matrix FormatsCounts

A 4 13 5 3 0 0 0 0 17 0 6C 4 1 2 0 0 0 0 0 0 1 0G 3 3 0 0 18 0 0 0 1 4 3T 7 1 11 15 0 18 18 18 0 13 9

FrequencyA 0.2 0.7 0.3 0.2 0.0 0.0 0.0 0.0 0.9 0.0 0.3C 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0G 0.2 0.2 0.0 0.0 1.0 0.0 0.0 0.0 0.1 0.2 0.2T 0.4 0.1 0.6 0.8 0.0 1.0 1.0 1.0 0.0 0.7 0.5

Weight (log odds)A -0.1 1.0 0.1 -0.4 -2.9 -2.9 -2.9 -2.9 1.3 -2.9 0.3C -0.1 -1.3 -0.7 -2.9 -2.9 -2.9 -2.9 -2.9 -2.9 -1.3 -2.9G -0.4 -0.4 -2.9 -2.9 1.3 -2.9 -2.9 -2.9 -1.3 -0.1 -0.4T 0.4 -1.3 0.9 1.2 -2.9 1.3 1.3 1.3 -2.9 1.0 0.7



Sequence Logos

• A visual representation of themotif

• Each column of the matrix isrepresented as a stack ofletters whose size isproportional to thecorresponding residuefrequency

• The total height of eachcolumn is proportional to itsinformation content.

A 4 13 5 3 0 0 0 0 17 0 6C 4 1 2 0 0 0 0 0 0 1 0G 3 3 0 0 18 0 0 0 1 4 3T 7 1 11 15 0 18 18 18 0 13 9


Information theory

Information Theory

• Information theory is a branch of applied mathematics involvedwith the quantification of information

• It has been applied to DNA motifs in order to determine theamount of uncertainly at each position in a site

• Uncertainly is measured in bits of information, which is on a log2scale.

• Information is a decrease in uncertainty


Information theory

Information theory

• 1 base occurs every time - 2 bits• 2 bases occur 50% of time - 1bit• 4 bases occur equally - 0 bits

A 4 13 5 3 0 0 0 0 17 0 6C 4 1 2 0 0 0 0 0 0 1 0G 3 3 0 0 18 0 0 0 1 4 3T 7 1 11 15 0 18 18 18 0 13 9

Example

Ii = 2 +∑

fb,i log2 fb,i

1 = 2 + 0.5× log2(0.5) + 0.5× log2(0.5)


Information theory

Why do we want to find them?

Expression Microarrays• Find co-regulated genes• Suggest Pathways

ChIP seq/chip• Determine binding

preferences• Find co-factors


Information theory

Two Methods

Pattern MatchingFinding known motifs

• Does protein X bind upstreamof my genes?

• Does it bind more thanexpected by chance?

Pattern DiscoveryFinding unknown motifs

• What motifs are upstream ofmy genes?

• What are these motifs

e.g. Patser, Pscan, Mast.. e.g. MEME, Weeder, MDScan ...


Databases of Motifs

Where can we find known motifs?

Online databases• Multicellular Eukaryotes

• Jaspar• Transfac• Pazar

• Yeast• Yeastract• SCPD

• Prokaryotes• RegulonDB• Prodoric

• Other• UniProbe


Databases of Motifs

Where can we find known motifs?Online databases• Multicellular Eukaryotes

• Jaspar• Transfac• Pazar

• Yeast• Yeastract• SCPD

• Prokaryotes• RegulonDB• Prodoric

• Other• UniProbe


Finding known motifs

How do we find them?

TATATTGTTTATTTTCATGACTTCATGTCGCATGTATTGTTAATTAACACATGTCTCATGTACTGGACCATGTCTAAGGGGTGTAAGGGTACTAACGAATCGTAGCATGTCCAGAGGTGCGGAGTACGTAAGGAGGGTGCCCATACATGTCCGTTTCATATGAGCCTGCATTAATGTACCAACCTTCAACCATGTCTCAACATGTCGCGGGTGTGCCTCCACGTACGAGCCGGAAGTCGACTCGCATGTCTGTCAGTATTATCCAAAGCATGTCGACCTCTTCATGTCAGCGAACGCAAGATCTTCATATGAGCCTGCATTAATGTACC



Pattern MatchingCounts

A 4 13 5 3 0 0 0 0 17 0 6C 4 1 2 0 0 0 0 0 0 1 0G 3 3 0 0 18 0 0 0 1 4 3T 7 1 11 15 0 18 18 18 0 13 9

FrequencyA 0.2 0.7 0.3 0.2 0.0 0.0 0.0 0.0 0.9 0.0 0.3C 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0G 0.2 0.2 0.0 0.0 1.0 0.0 0.0 0.0 0.1 0.2 0.2T 0.4 0.1 0.6 0.8 0.0 1.0 1.0 1.0 0.0 0.7 0.5

Weight (log odds)A -0.1 1.0 0.1 -0.4 -2.9 -2.9 -2.9 -2.9 1.3 -2.9 0.3C -0.1 -1.3 -0.7 -2.9 -2.9 -2.9 -2.9 -2.9 -2.9 -1.3 -2.9G -0.4 -0.4 -2.9 -2.9 1.3 -2.9 -2.9 -2.9 -1.3 -0.1 -0.4T 0.4 -1.3 0.9 1.2 -2.9 1.3 1.3 1.3 -2.9 1.0 0.7



Pattern Matching

A -0.1 1.0 0.1 -0.4 -2.9 -2.9 -2.9 -2.9 1.3 -2.9 0.3C -0.1 -1.3 -0.7 -2.9 -2.9 -2.9 -2.9 -2.9 -2.9 -1.3 -2.9G -0.4 -0.4 -2.9 -2.9 1.3 -2.9 -2.9 -2.9 -1.3 -0.1 -0.4T 0.4 -1.3 0.9 1.2 -2.9 1.3 1.3 1.3 -2.9 1.0 0.7

TATATTGTTTATTTTCATGACTTCATGTCGCATGTATTGTTAATTAA



Pattern Matching

A -0.1 1.0 0.1 -0.4 -2.9 -2.9 -2.9 -2.9 1.3 -2.9 0.3C -0.1 -1.3 -0.7 -2.9 -2.9 -2.9 -2.9 -2.9 -2.9 -1.3 -2.9G -0.4 -0.4 -2.9 -2.9 1.3 -2.9 -2.9 -2.9 -1.3 -0.1 -0.4T 0.4 -1.3 0.9 1.2 -2.9 1.3 1.3 1.3 -2.9 1.0 0.7

T A T A T T G T T T A

TATATTGTTTA TTTTCATGACTTCATGTCGCATGTATTGTTAATTAA



Pattern Matching

A -0.1 1.0 0.1 -0.4 -2.9 -2.9 -2.9 -2.9 1.3 -2.9 0.3C -0.1 -1.3 -0.7 -2.9 -2.9 -2.9 -2.9 -2.9 -2.9 -1.3 -2.9G -0.4 -0.4 -2.9 -2.9 1.3 -2.9 -2.9 -2.9 -1.3 -0.1 -0.4T 0.4 -1.3 0.9 1.2 -2.9 1.3 1.3 1.3 -2.9 1.0 0.7

A T A T T G T T T A T

T ATATTGTTTAT TTTCATGACTTCATGTCGCATGTATTGTTAATTAA



Pattern Matching

A -0.1 1.0 0.1 -0.4 -2.9 -2.9 -2.9 -2.9 1.3 -2.9 0.3C -0.1 -1.3 -0.7 -2.9 -2.9 -2.9 -2.9 -2.9 -2.9 -1.3 -2.9G -0.4 -0.4 -2.9 -2.9 1.3 -2.9 -2.9 -2.9 -1.3 -0.1 -0.4T 0.4 -1.3 0.9 1.2 -2.9 1.3 1.3 1.3 -2.9 1.0 0.7

T A T T G T T T A T T

TA TATTGTTTATT TTCATGACTTCATGTCGCATGTATTGTTAATTAA



Pattern Matching

TA TATTGTTTATT TTCATGACTTCATGTCGCATG TATTGTTAATT AAStewart MacArthur (Bioinformatics Core) DNA Motif Finding March 11th, 2010 20 / 33

Pattern Discovery

Introduction to de-novo motif finding

de-novo or ab-initio motif finding refers to finding motifs “from thebeginning”, i.e. without previous knowledge

Various Methods• Word-based algorithms e.g. Oligo-Analysis, Weeder• Expectation-Maximization methods e.g. MEME• Gibbs sampling methods e.g. Gibbs sampler, MotifSampler


Pattern Discovery

Guidelines

• If possible, remove repeat patterns from the target sequences• Use multiple motif prediction algorithms.• Run probabilistic algorithms multiple times• Return multiple motifs• Try a range of motif widths and expected number of sites

“... we do not recommend to trust pattern discoveryresults with vertebrate genomes. ”

Jacques van Helden


Recommended Tools

Recommended Tools

Pattern Matching• RSAT

• Pscan• Galaxy• MotifMogul

Pattern Discovery

• RSAT• MEME• Weeder• WebMOTIFS


Recommended Tools

Recommended Tools

Pattern Matching• RSAT• Pscan

• Galaxy• MotifMogul

Pattern Discovery



Recommended Tools

Recommended Tools

Pattern Matching• RSAT• Pscan• Galaxy

• MotifMogul

Pattern Discovery



Recommended Tools

Recommended Tools

Pattern Matching• RSAT• Pscan• Galaxy• MotifMogul

Pattern Discovery



Recommended Tools

Recommended Tools


Pattern Discovery• RSAT

• MEME• Weeder• WebMOTIFS


Recommended Tools

Recommended Tools


Pattern Discovery• RSAT• MEME

• Weeder• WebMOTIFS


Recommended Tools

Recommended Tools


Pattern Discovery• RSAT• MEME• Weeder

• WebMOTIFS


Recommended Tools

Recommended Tools


Pattern Discovery• RSAT• MEME• Weeder• WebMOTIFS


Recommended Tools RSA Tools

Regulatory Sequence Analysis Toolshttp://rsat.ulb.ac.be/rsat/

Modular computer programs specifically designed for the detection ofregulatory signals in non-coding sequences.



Regulatory Sequence Analysis Tools

Nature Protocols Series: Volume 3 No 10 2008

• Using RSAT to scan genome sequences for transcription factor bindingsites and cis-regulatory modules

• Using RSAT oligo-analysis and dyad-analysis tools to discoverregulatory signals in nucleic sequences

• Analyzing multiple data sets by interconnecting RSAT programs viaSOAP Web services - an example with ChIP-chip data

• Network Analysis Tools: from biological networks to clusters andpathways



Example Workflow

ProblemI have some differentially expressed genes from a microarrayexperiment. I would like to know if P53 binds in their promoter regions,and if so where.

Workflow• BioMart: Convert Gene IDs, if necessary• RSAT: retrieve sequence• JASPAR: Get PWM (MA0106.1)• RSAT: matrix-scan• RSAT: feature map


Recommended Tools Pscan

Pscan“Finding over-represented transcriptionfactor binding site motifs in sequences fromco-regulated or co-expressed genes”


Recommended Tools Pscan

Example Workflow

ProblemI have some differentially expressed genes from a microarrayexperiment. I would like to know which transcription factors bind totheir promoters.

Workflow• BioMart: Convert Gene IDs, if necessary• Pscan: retrieve sequence


Recommended Tools Galaxy

Galaxyhttp://main.g2.bx.psu.edu

“Galaxy allows you to do analyses you cannot do anywhereelse without the need to install or download anything. You cananalyze multiple alignments, compare genomic annotations, profilemetagenomic samples and much much more...”

• Collection of online tools• Modular• Can create workflows• Saved Histories

• Reproducible analysis• Shared histories• In house version• Easily extendable

http://kinchie/galaxy





• Collection of online tools

• Modular• Can create workflows• Saved Histories







• Collection of online tools• Modular

• Can create workflows• Saved Histories







• Collection of online tools• Modular• Can create workflows

• Saved Histories








• Reproducible analysis

• Shared histories• In house version• Easily extendable







• Reproducible analysis• Shared histories

• In house version• Easily extendable







• Reproducible analysis• Shared histories• In house version

• Easily extendable



Recommended Tools MEME Suite

MEME SuiteSuite of web based tools for motif discovery

• MEME - de-novo motif finding

• MAST - find matches to knownmotifs (MEME output)

• TOMTOM - Compare motifs toTRANSFAC and Jaspar




• MEME - de-novo motif finding• MAST - find matches to known

motifs (MEME output)

• TOMTOM - Compare motifs toTRANSFAC and Jaspar




• MEME - de-novo motif finding• MAST - find matches to known

motifs (MEME output)• TOMTOM - Compare motifs to

TRANSFAC and Jaspar


Further Reading

Further Reading

• Stormo GD. DNA binding sites: representation and discovery.Bioinformatics. 2000 Jan;16(1):16-23. Review. PubMed PMID:10812473.

• D’haeseleer P. How does DNA sequence motif discovery work?Nat Biotechnol. 2006 Aug;24(8):959-61. Review. PubMed PMID:16900144.

• Das MK, Dai HK. A survey of DNA motif finding algorithms. BMCBioinformatics. 2007 Nov 1;8 Suppl 7:S21. Review. PubMedPMID: 18047721; PubMed Central PMCID: PMC2099490.

• Tompa M, Li N et.al. Assessing computational tools for thediscovery of transcription factor binding sites. Nat Biotechnol.2005 Jan;23(1):137-44. PubMed PMID: 15637633.


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Practical Session


DNA Motif Finding 2010

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