1. Tutorial 5
Motif discovery

2. Multiple sequence alignments and motif discovery
MEME
MAST
TOMTOM
GOMO
PROSITE

3. Can we find motifs using multiple sequence alignment?
A widespread pattern with a biological significance
..YDEEGGDAEE..
..YGEEGADYED..
..YDEEGADYEE..
..YNDEGDDYEE..
..YHDEGAADEE.. 1 2 3 4 5 6 7 8 9 10 A
3/6
1/6
2/6 D
5/6 E
4/6 G
1/3 H N Y

4. Can we find motifs using multiple sequence alignment (MSA)?
YES! NO

5. Using MSA for motif discovery
Can only work if things align nicely alone
For most motifs this is not the case!

6. ClustalW - Input Input sequences Scoring matrix Gap scoring
Input sequences
Scoring matrix
Gap scoring
Output format
address

7. Muscle Input sequences Output format Email address
Input sequences
Output format
address

8. Motif search: from de-novo motifs to motif annotation
gapped motifs
Large DNA data

9. MEME – Multiple EM* for Motif finding
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

10. How many times in each sequence? Input file (fasta file)
MEME - Input
address
How many times in each sequence?
Input file (fasta file)
Range of motif lengths
How many motifs?
How many sites?

11. MEME - Output
Motif score

12. MEME - Output
Motif score
Motif length
Number of times

13. High information content
MEME - Output
Low uncertainty =
High information content

14. MEME - Output
Multilevel Consensus

15. 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

16. MEME - Output Position in sequence Strength of match Sequence names
Motif within sequence

17. Motif location in the input sequence Overall strength of motif matches
MEME - Output
Sequence names
Motif location in the input sequence
Overall strength of motif matches

18. 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.

19. MAST Searches for motifs (one or more) in sequence databases:
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.

20. MAST - Input
address
Database
Input file (motifs)

21. Presence of the motifs in a given database
MAST - Output
Input motifs
Presence of the motifs in a given database

22. TOMTOM
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.

23. Background frequencies
TOMTOM - Input
Input motif
Background frequencies
Database

24. DNA IUPAC* code Example: YCAY = [TC]CA[TC]
A --> adenosine M --> A C (amino) C --> cytidine S --> G C (strong) G --> guanine W --> A T (weak) T --> thymidine B --> G T C D --> G A T R --> G A (purine) H --> A C T Y --> T C (pyrimidine) V --> G C A K --> G T (keto) N --> A G C T (any)
Example: YCAY = [TC]CA[TC]
*IUPAC = International Union of Pure and Applied Chemistry

25. TOMTOM - Output
Input motif
Matching motifs

26. TOMTOM – Output
Wrong input, ok results

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

28. logo
Name of gene/protein
organism
score

29. 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.

30. GOMO - Input
address
Database
Input file (motifs)

31. GOMO - Output MF - Molecular function BP - Biological process
Input motifs
GO annotation
MF - Molecular function
BP - Biological process 
CC - Cellular compartment

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

34. Input motif a regular expression
Prosite - input
Database
Filters

35. Location in the protein sequence
Input motif
Prosite - Output
Location in the protein sequence
protein