Multiple sequence alignments and motif discovery Tutorial 5

Multiple sequence alignment –ClustalW –Muscle Motif discovery –MEME –Jaspar Multiple sequence alignments and motif discovery

More than two sequences –DNA –Protein Evolutionary relation –Homology  Phylogenetic tree –Detect motif Multiple Sequence Alignment GTCGTAGTCG-GC-TCGAC GTC-TAG-CGAGCGT-GAT GC-GAAG-AG-GCG-AG-C GCCGTCG-CG-TCGTA-AC A DB C GTCGTAGTCGGCTCGAC GTCTAGCGAGCGTGAT GCGAAGAGGCGAGC GCCGTCGCGTCGTAAC

Dynamic Programming –Optimal alignment –Exponential in #Sequences Progressive –Efficient –Heuristic Multiple Sequence Alignment GTCGTAGTCG-GC-TCGAC GTC-TAG-CGAGCGT-GAT GC-GAAG-AG-GCG-AG-C GCCGTCG-CG-TCGTA-AC A DB C GTCGTAGTCGGCTCGAC GTCTAGCGAGCGTGAT GCGAAGAGGCGAGC GCCGTCGCGTCGTAAC

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 Pairwise alignment – calculate distance matrix Guided tree Progressive alignment using the guide tree

ClustalW Progressive –At each step align two existing alignments or sequences –Gaps present in older alignments remain fixed -TGTTAAC -TGT-AAC -TGT--AC ATGT---C ATGT-GGC

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

ClustalW - Output Match strength in decreasing order: * :.

ClustalW - Output

Pairwise alignment scores Building alignment Final score Building tree

ClustalW - Output

ClustalW Output Sequence namesSequence positions Match strength in decreasing order: * :.

ClustalW - Output

Branch length

ClustalW - Output

Muscle

Muscle - output

What’s the difference between Muscle and ClustalW? ClustalWMuscle

Can we find motifs using multiple sequence alignment? A /61/300 D00.51/3001/65/61/60 E002/ /6 G01/60011/30000 H01/ N Y YDEEGGDAEE....YGEEGADYED....YDEEGADYEE....YNDEGDDYEE....YHDEGAADEE.. * :** *: Motif A widespread pattern with a biological significance

Can we find motifs using multiple sequence alignment? YES! NO

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

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

MEME - Output Motif score

MEME - Output Motif length Number of times Motif score

MEME - Output Low uncertainty = High information content

MEME - Output Multilevel Consensus

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

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

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.

MEME - Input address Input file (motifs) Database

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

JASPARJASPAR profiles –Modeled as matrices. –can be converted into PSSM for scanning genomic sequences A /61/300 D00.51/3001/65/61/60 E002/ /6 G01/60011/30000 H01/ N Y

Search profile

score organism logo Name of gene/protein