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  • Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using
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Evaluating alignments using motif detection • Let’s evaluate alignments by searching for motifs • If alignment X reveals more functional motifs than Y using technique Z then X is better than Y w.r.t. Z • Motifs could be functional sites in proteins or functional regions in non-coding DNA

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Page 1: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Evaluating alignments using motif detection

• Let’s evaluate alignments by searching for motifs

• If alignment X reveals more functional motifs than Y using technique Z then X is better than Y w.r.t. Z

• Motifs could be functional sites in proteins or functional regions in non-coding DNA

Page 2: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Protein Functional Site Prediction

• The identification of protein regions responsible for stability and function is an especially important post-genomic problem

• With the explosion of genomic data from recent sequencing efforts, protein functional site prediction from only sequence is an increasingly important bioinformatic endeavor.

Page 3: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

What is a “Functional Site”?

• Defining what constitutes a “functional site” is not trivial

• Residues that include and cluster around known functionality are clear candidates for functional sites

• We define a functional site as catalytic residues, binding sites, and regions that clustering around them.

Page 4: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Protein

Page 5: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Protein + Ligand

Page 6: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Functional Sites (FS)

Page 7: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Regions that Cluster Around FS

Page 8: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Phylogenetic motifs

• PMs are short sequence fragments that conserve the overall familial phylogeny

• Are they functional?

• How do we detect them?

Page 9: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Phylogenetic motifs

• PMs are short sequence fragments that conserve the overall familial phylogeny

• Are they functional?• How do we detect them? • First we design a simple heuristic to find

them• Then we see if the detected sites are

functional

Page 10: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Whole Tree

Page 11: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Whole Tree

Page 12: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Windowed Tree Whole Tree

Page 13: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 14: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 8

Windowed Tree Whole Tree

Page 15: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 4

Windowed Tree Whole Tree

Page 16: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 17: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 8

Windowed Tree Whole Tree

Page 18: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 19: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 20: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 0

Windowed Tree Whole Tree

Page 21: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 22: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 23: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 8

Windowed Tree Whole Tree

Page 24: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 0

Windowed Tree Whole Tree

Page 25: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 26: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 27: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Scan for Similar Trees

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Partition Metric Score: 6

Windowed Tree Whole Tree

Page 28: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Phylogenetic Motif Identification

• Compare all windowed trees with whole tree and keep track of the partition metric scores

• Normalize all partition metric scores by calculating z-scores

• Call these normalized scores Phylogenetic Similarity Z-scores (PSZ)

• Set a PSZ threshold for identifying windows that represent phylogenetic motifs

Page 29: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Set PSZ Threshold

Page 30: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Regions of PMs

Page 31: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Map PMs to the Structure

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Page 32: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Map PMs to the Structure

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Set PSZ Threshold

Page 33: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Map PMs to the Structure

Map

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Set PSZ Threshold

Page 34: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Map PMs to the Structure

Map

2DBL DVVMTQIPLSLPVNL GDQASIS CRSSQSLIHSNGNTYLH WYLQKPGQS PKLLMYKVSNRF 1NCA DIVMTQSPKFMSTSV GDRVTIT CKASQ----- DVSTAVV WYQQKPGQS PKLLIYWASTRH 2JEL DVLMTQTPLSLPVSL GDQASIS CRSSQSIVHGNGNTYLE WYLQKPGQS PKLLIYKISNRF 2IGF DVLMTQTPLSLPVSL GDQASIS CRSNQTILLSDGDTYLE WYLQKPGQS PKLLIYKVSNRF 3HFM DIVLTQSPATLSVTP GNSVSLS CRASQS ----- IGNNLH WYQQKSHES PRLLIKYASQSI 3HFL DIVLTQSPAIMSASP GEKVTMT CSASSS ------ VNYMY WYQQKSGTS PKRWIYDT SKLA 1NGP QAVV TQES-ALTTSP GETVTLT CRSSTG --AVTTSNYAN WVQEKPDHLFTG LIGGTNNRA 2DBL YGVPDRFSGS GSGTDFTLKISRVEA EDLGIYFCS QSSHVPPTFGGGTKLEIK -RADAAPT 1NCA IGVPDRFAGSGSGTDYTLTISSVQA EDLALYYCQQHYSPPWTFGGGTKLEIK -RADAAPT 2JEL SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPYTFGGGTKLEIK -RADAAPT 2IGF SGVPDRFSGS GSGTDFTLKISRVEA EDLGVYYCFQGSHVPPTFGGGTKLEIK -RADAAPT 3HFM SGIPSRFSGS GSGTDFTLSINSVET EDFGMYFCQQSNSWPYTFGGGTKLEIK -RADAAPT 3HFL SGVPVRFSGS GSGTSYSLTISSMET EDAATYYCQQWGRNP-TFGGGTKLEIK -RADAAPT 1NGP PGVPARFSGS LIGDKAA LTITGAQT EDEAIYFCALWYSNHWV FGGGTKL TVLGQPKSSPS 2DBL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR ---QIQLVQSGPELKKPGETVKI 1NCA MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECQI QLVQSGPELKKPGETVKI 2JEL MSSTLTLTKDEYERHNSYTCEATHKTS DSPIVKSFNR N--QVQLAQSGPELVRPGVSVKI 2IGF MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECEV QLVESGGDLVKPGGSLK L 3HFM MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECDV QLQESGPSLVKPSQTLS L 3HFL MSSTLTLTKDEYERHNSYTCEATHKTSTSPIVKSFNR NECXV QLQQSGAELMKPGASVKI 1NGP ASSYLTLTARAWERHSSYSCQVTHEGHT--VEKSLSR---QVQLQQPGAELVKPGASVK L

Set PSZ Threshold

Page 35: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

PMs in Various Structures

Page 36: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

PMs and Traditional Motifs

Page 37: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

TIM

Phylogenetic Similarity False Positive Expectation

Page 38: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

TIM

Phylogenetic Similarity False Positive Expectation

Page 39: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

TIM

Phylogenetic Similarity False Positive Expectation

Page 40: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

TIM

Phylogenetic Similarity False Positive Expectation

Page 41: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Cytochrome P450

Phylogenetic Similarity False Positive Expectation

Page 42: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Cytochrome P450

Phylogenetic Similarity False Positive Expectation

Page 43: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Enolase

Phylogenetic Similarity False Positive Expectation

Page 44: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Glycerol Kinase

Phylogenetic Similarity False Positive Expectation

Page 45: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Glycerol Kinase

Phylogenetic Similarity False Positive Expectation

Page 46: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Myoglobin

Phylogenetic Similarity False Positive Expectation

Page 47: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Myoglobin

Phylogenetic Similarity False Positive Expectation

Page 48: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Evaluating alignments

• For a given alignment compute the PMs

• Determine the number of functional PMs

• Those identifying more functional PMs will be classified as better alignments

Page 49: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Protein datasets

Page 50: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Running time

Page 51: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Functional PMsPAl=blueMUSCLE=redBoth=green

(a)=enolase, (b)ammonia channel,(c)=tri-isomerase, (d)=permease,(e)=cytochrome

Page 52: Evaluating alignments using motif detection Let’s evaluate alignments by searching for motifs If alignment X reveals more functional motifs than Y using

Sequence Motifs. Motifs Motifs represent a short common sequence –Regulatory motifs (TF binding sites) –Functional site in proteins (DNA binding motif)

RNA Tertiary Structure. Additional Motifs of Tertiary Structure Coaxial helix A minor motif Pseudoknots Tetraloops Loop-loop Ribose zipper Kink turn motif

Motif discovery Tutorial 5. Motif discovery –MEME –MAST –TOMTOM –GOMO –PROSITE Multiple sequence alignments and motif discovery

E1 Alignments - National Institutes of Health · II-E1-1 SEP 97 E1 Alignments E1 Alignments E1 Alignments E1 Alignments E1 Alignments E1 Alignments E1 Alignments E1 Alignments E1

FANMOD fast network motif detection – manual - …theinf1.informatik.uni-jena.de/motifs/fanmod-manual.pdf · FANMOD fast network motif detection – manual – Florian Rasche and

Speeding the Database Searches and Sequence Alignments with Multi-Motif PHI-BLAST

Multiple Alignment - UCF Computer Sciencexiaoman/SIOMICS/download/SIOMICS...Jump to: Multiple Alignment Motif Tree Motif Matching Input file: 99 motifs loaded Settings: Metric=PCC,

Motif Simplification: Improving Network Visualization Readability … · Motif census (counting the kinds of motifs) and analysis is used extensively to analyze the behavior of complex

Evaluating alignments using motif detection

The MHC Motif Viewer: A Visualizationtheory.bio.uu.nl/pdf/Rapin.cpi10-0.pdfThe MHC Motif Viewer: A Visualization UNIT 18.17 Tool for MHC Binding Motifs Nicolas Rapin,1 Ilka Hoof,2,3

Multiple Alignments Motifs/Profiles

Motifs in Protein Sequencesgiri/teach/Bioinf/S06/Lec9.pdf · Motifs in Protein Sequences Examples: Helix-Turn-Helix, Zinc-finger, Homeobox domain, Hairpin-beta motif, Calcium-binding

E7 Alignments - PaVE: Papilloma virus genome … · E7 Protein Alignment II-E7-5 SEP 94 Rb binding area

Intro Protein structure Motifs Motif databases End … Protein structure Motifs Motif databases End Last time • Probability based methods • How find a good root? • Reliability

Functional Motifspeople.uleth.ca/~steven.mosimann/bchm4000a/Bchm4000PSA_L7.pdf · Functional Motifs Functional Motif Definition(s): Originally – a structural motif that performs

JAIPURI RAZAI - Craft Mark Quilting.pdf · Designs/ Motifs The engraved design in brought about by tagai. Paan motif Thali motif . 13 Thali phool motif Sitara motif . 14 Lines Circles

Metabolic and Transcriptional Regulatory Network Models in ...• ~700 distinct sequence motifs • Run once per genome assembly Gene motif PWMs motif instances JASPAR FIMO HOCOMOCO

Analysis of DNA Motifs Based on a Novel Motif Comparison ...nir/Papers/Habib-MSC.pdf · Based on a Novel Motif Comparison Method A thesis submitted in partial fulfillment of the

MOTIFS MOTIFS MOTIFSMARTIFAMORIFSMOOTIFSMICIFC A sequence motif is a nucleotide or amino-acid sequence pattern that is widespread (repeated) and has

Multiple Alignments Motifs/Profiles What is multiple alignment? HOW does one do this? WHY does one do this? What do we mean by a motif or profile? BIO520

Linear motifs and phosphorylation sites. What is a linear motif? ( in molecular biology )

L2 Alignments - PaVE: Papilloma virus genome database · II-L2-1 OCT 95 L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2 Alignments

Gene Regulation and Microarrays …after which we come back to multiple alignments for finding regulatory motifs

Motif Variety of Sasirangan on Martapura Riverbank ...B. Motifs, Meanings, and Functions of Sasirangan Fabrics. Basically, the motif has a function as a decoration on various types

Table S4. Predicted novel regulatory motifs INTERGENIC ... · Table S4. Predicted novel regulatory motifs INTERGENIC Motif Motif associated genes AAAATCATATG F56A6.5 Y39F10A.1 gst-35

1 Outline More exhaustive search algorithms Today: Motif finding –regulatory motifs and profiles –the motif finding problem –brute force motif finding

Inhibition of translation by UAUUUAU and UAUUUUUAU motifs ... · C). To investigate if the 5th motif contributed to inhibition, the 5th motif was mutated in pM2 (Fig. 3A), resulting

Motif and PTM Antibodies - Ozyme · Motif and PTM-specific antibodies: :: recognize phosphorylated residues within characteristic sequence motifs (motif antibody), or other PTMs in

Modeling & Discovery of Sequence Motifs · C. Burge Lecture #9 Mar. 6, 2014 . Modeling & Discovery of Sequence Motifs Modeling & Discovery of Sequence Motifs • Motif Discovery with

L2 Alignments - National Institutes of Health · II-L2-1 SEP 94 L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2 Alignments L2

Motif-driven Retrieval of Greek Painted Pottery · 2019. 9. 18. · Motif-driven Retrieval of Greek Painted Pottery ... Our retrieval system is also capable of finding similar motifs

Tutorial 5 Multiple sequence alignments and motif discoverywebcourse.cs.technion.ac.il/.../ho/WCFiles/tutorial5_11.pdf · Multiple sequence alignments and motif discovery ... •Motif

Square Motif Harvest Runner - Crochet World Magazineof previous motif, ch 2, sl st in 2nd ch of first ch-2] 4 times, work rem of rnd as for first motif. REM MOTIFS Make 13 more motifs,

Multiple Alignment - UCF Computer Sciencexiaoman/SIOMICS/download/SIOMICS... · 2014. 1. 27. · Jump to: Multiple Alignment Motif Tree Motif Matching Input file: 91 motifs loaded

How to Crochet Squares, Circles and Crochet Motifs: Free ... · PDF fileContents | HOw tO crOcHet squares, circles and crOcHet mOtifs: free crOcHet mOtif patterns 2 HOw tO crOcHet