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Exploring Protein Sequences

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.

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Exploring Protein Sequences

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  1. Tutorial 5 Exploring Protein Sequences

  2. Exploring Protein Sequences • Multiple alignment • ClustalW • Motif discovery • MEME • Jaspar

  3. A C D B Multiple Sequence Alignment • More than two sequences • DNA • Protein • Evolutionary relation • Homology  Phylogenetic tree • Detect motif GTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC

  4. A C D B Multiple Sequence Alignment • Dynamic Programming • Optimal alignment • Exponential in #Sequences • Progressive • Efficient • Heuristic GTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC

  5. 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

  6. ClustalW • Progressive • At each step align two existing alignments or sequences • Gaps present in older alignments remain fixed GTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC GTCGTAGTCG-GC-TGTC-TAG-CGAGCGTGC-GAAG-AG-GCG-GCCGTCG-CG-TCGT

  7. ClustalW - Input Scoring matrix Gap scoring Input sequences

  8. ClustalW - Output

  9. ClustalW - Output Input sequences Pairwise alignment scores Building alignment Final score

  10. ClustalW - Output

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

  12. http://http://www.megasoftware.net/

  13. Can we find motifs using multiple sequence alignment? 1 3 5 7 9 ..YDEEGGDAEE.. ..YDEEGGDAEE.. ..YGEEGADYED.. ..YDEEGADYEE.. ..YNDEGDDYEE.. ..YHDEGAADEE.. * :** *: Motif A widespread pattern with a biological significance

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

  15. 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)

  16. MEME - Input Email address Multiple input sequences Range of motif lengths How many motifs? How many times in each sequence? How many sites?

  17. MEME - Output Like BLAST Motif length Number of times

  18. MEME - Output Probability * 10 ‘a’=10, ‘:’=0

  19. MEME - Output Low uncertainty = High information content

  20. MEME - Output Multilevel Consensus

  21. MEME - Output Position in sequence Strength of match Sequence names Reverse complement (genomic input only) Motif within sequence

  22. MEME - Output Motif instance ‘-’=Other strand sequence lengths Overall strength of motif matches

  23. 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.

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

  25. JASPAR • profiles • Modeled as matrices. • can be converted into PSSM for scanning genomic sequences.

  26. Search profile http://jaspar.cgb.ki.se/

  27. http://jaspar.cgb.ki.se/

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