exploring protein sequences
DESCRIPTION
Tutorial 5. Exploring Protein Sequences. Exploring Protein Sequences. Multiple alignment ClustalW Motif discovery MEME Jaspar. A. C. D. B. Multiple Sequence Alignment. More than two sequences DNA Protein Evolutionary relation Homology Phylogenetic tree Detect motif. - PowerPoint PPT PresentationTRANSCRIPT
Exploring Protein Sequences
Tutorial 5
Exploring Protein Sequences
• Multiple alignment– ClustalW
• Motif discovery– MEME– Jaspar
• More than two sequences– DNA– Protein
• Evolutionary relation– Homology Phylogenetic tree– Detect motif
Multiple Sequence Alignment
GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC
A
D B
CGTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC
• Dynamic Programming– Optimal alignment– Exponential in #Sequences
• Progressive– Efficient– Heuristic
Multiple Sequence Alignment
GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC
A
D B
CGTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC
ClustalW
“CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice”, J D Thompson et al
• Progressive– At each step align two existing alignments or sequences
– Gaps present in older alignments remain fixed
ClustalW
GTCGTAGTCG-GC-TGTC-TAG-CGAGCGTGC-GAAG-AG-GCG-GCCGTCG-CG-TCGT
GTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC
ClustalW - InputScoring matrix
Gap scoring
Input sequences
ClustalW - Output
ClustalW - Output
Input sequences
Pairwise alignment scores
Building alignment
Final score
ClustalW - Output
ClustalW Output
Sequence names Sequence positions
Match strength in decreasing order: * : .
http://http://www.megasoftware.net/
Can we find motifs using multiple sequence alignment?
1 2 3 4 5 6 7 8 9 10
A 0 0 0 0 0 0.5 1/6 1/3 0 0
D 0 0.5 1/3 0 0 1/6 5/6 1/6 0 1/6
E 0 0 2/3 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 0.5 0.5 0 0
1 3 5 7 9..YDEEGGDAEE....YDEEGGDAEE....YGEEGADYED....YDEEGADYEE....YNDEGDDYEE....YHDEGAADEE.. * :** *:
MotifA widespread pattern with a biological significance
Can we find motifs using multiple sequence alignment?
YES! NO
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)
MEME - InputEmail address
Multiple input sequences
How many times in each sequence?
How many motifs?
How many sites?
Range of motif lengths
MEME - OutputMotif length
Number of times
Like BLAST
MEME - Output
Probability * 10
‘a’=10, ‘:’=0
MEME - Output
Low uncertainty
=
High information content
MEME - Output
Multilevel Consensus
Sequence names
Reverse complement (genomic input only)
Position in
sequence
Strength of match
Motif within sequence
MEME - Output
Overall strength of motif matches
sequence lengths
Motif instance
MEME - Output
‘-’=Other strand
MAST• 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.
JASPAR• Profiles
– Transcription factor binding sites– Multicellular eukaryotes– Derived from published collections of
experiments
• Open data accesss
JASPAR• profiles
– Modeled as matrices.– can be converted into PSSM for scanning
genomic sequences.
1 2 3 4 5 6 7 8 9 10
A 0 0 0 0 0 0.5 1/6 1/3 0 0
D 0 0.5 1/3 0 0 1/6 5/6 1/6 0 1/6
E 0 0 2/3 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 0.5 0.5 0 0
Search profile
http://jaspar.cgb.ki.se/
http://jaspar.cgb.ki.se/