Motif discovery

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Tutorial 5
Motif discovery
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Multiple sequence alignments and motif discovery

• Motif discovery
• MEME
• MAST
• TOMTOM
• GOMO
• PROSITE

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Can we find motifs using multiple sequence
alignment?
..YDEEGGDAEE.. ..YDEEGGDAEE.. ..YGEEGADYED.. ..
YDEEGADYEE.. ..YNDEGDDYEE.. ..YHDEGAADEE..

• Motif
• A widespread pattern with a biological
  significance

1 2 3 4 5 6 7 8 9 10
A 0 0 0 0 0 3/6 1/6 2/6 0 0
D 0 3/6 2/6 0 0 1/6 5/6 1/6 0 1/6
E 0 0 4/6 1 0 0 0 0 1 5/6
G 0 1/6 0 0 1 1/3 0 0 0 0
H 0 1/6 0 0 0 0 0 0 0 0
N 0 1/6 0 0 0 0 0 0 0 0
Y 1 0 0 0 0 0 3/6 3/6 0 0
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Can we find motifs using multiple sequence
alignment (MSA)?
YES!
NO
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Using MSA for motif discovery

• Can only work if things align nicely alone

• For most motifs this is not the case!

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ClustalW - Input
http//www.ebi.ac.uk/Tools/clustalw2/index.html
Input sequences
Scoring matrix
Gap scoring
Output format
Email address
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Muscle
http//www.ebi.ac.uk/Tools/muscle/index.html
Input sequences
Output format
Email address
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Motif search from de-novo motifs to motif
annotation
gapped motifs
Large DNA data
http//meme.sdsc.edu/
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MEME Multiple EM for Motif finding

• http//meme.sdsc.edu/
• Motif discovery from unaligned sequences
• Genomic or protein sequences
• Flexible model of motif presence (Motif can be
  absent in some sequences or appear several times
  in one sequence)

Expectation-maximization
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MEME - Input
Email address
How many times in each sequence?
Input file (fasta file)
Range of motif lengths
How many motifs?
How many sites?
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MEME - Output
Motif score
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MEME - Output
Motif score
Motif length
Number of times
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MEME - Output
Low uncertainty High information content
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MEME - Output
Multilevel Consensus
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Patterns can be presented as regular expressions

• AG-x-V-x(2)-YW
• - Either residue
• x - Any residue
• x(2) - Any residue in the next 2 positions
• - Any residue except these
• Examples AYVACM, GGVGAA

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MEME - Output
Position in sequence
Strength of match
Sequence names
Motif within sequence
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MEME - Output
Sequence names
Motif location in the input sequence
Overall strength of motif matches
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What can we do with motifs?

• MAST - Search for them in non annotated sequence
  databases (protein and DNA)
• TOMTOM - Find the protein who binds the DNA
  motifs.
• GOMO - Find putative target genes (DNA) of motifs
  and analyze their associated annotation terms.
• PROSITE - Search for them in annotated protein
  sequence databases.

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MAST
http//meme.sdsc.edu/meme4_4_0/cgi-bin/mast.cgi

• Searches for motifs (one or more) in sequence
  databases
• Like BLAST but motifs for input
• Similar to iterations of PSI-BLAST
• Profile defines strength of match
• Multiple motif matches per sequence
• Combined E value for all motifs
• MEME uses MAST to summarize results
• Each MEME result is accompanied by the MAST
  result for searching the discovered motifs on the
  given sequences.

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MAST - Input
Email address
Database
Input file (motifs)
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MAST - Output
Input motifs
Presence of the motifs in a given database
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TOMTOM
http//meme.sdsc.edu/meme/doc/tomtom.html

• Searches one or more query DNA motifs against one
  or more databases of target motifs, and reports
  for each query a list of target motifs, ranked
  by p-value.
• The output contains results for each query, in
  the order that the queries appear in the input
  file.

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TOMTOM - Input
Input motif
Background frequencies
Database
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DNA IUPAC code

• A --gt adenosine M --gt A C (amino)
  
• C --gt cytidine S --gt G C
  (strong)
• G --gt guanine W --gt A T
  (weak)
• T --gt thymidine
• B --gt G T C D --gt G A T
• R --gt G A (purine) H --gt A C T
• Y --gt T C (pyrimidine) V --gt G C A
• K --gt G T (keto) N --gt A G C T
  (any)

Example YCAY TCCATC
IUPAC International Union of Pure and Applied
Chemistry
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TOMTOM - Output
Input motif
Matching motifs
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TOMTOM Output
Wrong input, ok results
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JASPAR

• Profiles
• Transcription factor binding sites
• Multicellular eukaryotes
• Derived from published collections of experiments
• Open data accesss

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logo
score
organism
Name of gene/protein
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GOMO

• GOMO takes DNA binding motifs to find putative
  target genes and analyze their associated GO
  terms. A list of significant GO terms that can be
  linked to the given motifs will be produced.
• GOMO returns a list of GO-terms that are
  significantly associated with target genes of the
  motif.
• Gene Ontology provides a controlled vocabulary to
  describe gene and gene product attributes in any
  organism.

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GOMO - Input
Email address
Database
Input file (motifs)
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GOMO - Output
Input motifs
GO annotation
MF - Molecular function BP - Biological
process  CC - Cellular compartment
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Prosite
http//www.expasy.org/tools/scanprosite

• ProSite is a database of protein domains and
  motifs that can be searched by either regular
  expression patterns or sequence profiles.

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(No Transcript)
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Prosite - input
Input motif a regular expression
Database
Filters
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Input motif
Prosite - Output
Location in the protein sequence
protein