1. Prosite UCSC Genome Browser MSAs and Phylogeny Exercise 2

2. Turning information into knowledge  The outcome of a sequencing project is masses of raw data  The challenge is to turn this raw data into biological knowledge  A valuable tool for this challenge is an automated diagnostic pipe through which newly determined sequences can be streamlined

3. From sequence to function  Nature tends to innovate rather than invent  Proteins are composed of functional elements: domains and motifs Domains are structural units that carry out a certain function Domains are structural units that carry out a certain function The same domains are The same domains are shared between different proteins Motifs are shorter Motifs are shorter sequences with certain biological activity

4. http://www.ebi.ac.uk/interpro/

5. InterPro  An integrated documentation resource for protein families, domains and sites  Groups signatures describing the same protein family or domain  Combines a number of databases that use different methodologies to derive protein signature: UniProt: UniProtKB Swiss-Prot, TrEMBL, UniRef,UniParc UniProt: UniProtKB Swiss-Prot, TrEMBL, UniRef,UniParc prosite: documented DB on domains, families and functional sites. prosite: documented DB on domains, families and functional sites. Pfam: a DB of protein families represented by MSAs Pfam: a DB of protein families represented by MSAs

6. InterPro search

9. http://www.expasy.ch/prosite/

10. prosite  A method for determining the function of uncharacterized translated protein sequences  Consists of a DB of annotated biologically important sites/patterns/motifs/signature/fingerprints

11. prosite  Entries are represented with patterns or profiles pattern 54321.0010.66A.1000T.00.6600.33C.00.3300G profile [AC]-A-[GC]-T-[TC]-[GC] Profiles are used in prosite when the motif is relatively divergent, and it is difficult to represent as a pattern

12. Scanning prosite Query: sequence Query: pattern Result: all patterns found in sequence Result: all sequences which adhere to this pattern

13. Patterns with a high probability of occurrence  Entries describing commonly found post- translational modifications or compositionally biased regions.  Found in the majority of known protein sequences  High probability of occurrence

14. prosite sequence query

16. prosite pattern query

19. UCSC Genome Browser

20. Reset all settings of previous user UCSC Genome Browser - Gateway

23. UCSC Genome Browser query results

24. UCSC Genome Browser Annotation tracks Vertebrate conservation mRNA (GenBank) RefSeq UCSC Genes Base position Single species compared SNPs Repeats Gene Direction Exon Intron UTR

25. USCS Gene

26. UCSC Genome Browser - movement Zoom x3 + Center

27. UCSC Genome Browser – Base view

28. Annotation track options dense squish full pack

29. Annotation track options Another option to toggle between ‘pack’ and ‘dense’ view is to click on the track title Sickle-cell anemia distr. Malaria distr.

30. BLAT  BLAT = Blast-Like Alignment Tool  BLAT is designed to find similarity of >95% on DNA, >80% for protein  Rapid search by indexing entire genome. Good for: 1. Finding genomic coordinates of cDNA 2. Determining exons/introns 3. Finding human (or chimp, dog, cow…) homologs of another vertebrate sequence

31. BLAT on UCSC Genome Browser

33. BLAT Results

34. Match Non-Match (mismatch/indel) Indel boundaries

35. BLAT Results

36. BLAT Results on the browser

37. Getting DNA sequence of region

39. Clustal X – A Multiple Alignment Tool

40. Input: multiple sequence Fasta file >gi|21536452|ref|NP_002762.2| mesotrypsin preproprotein [Homo sapiens] MNPFLILAFVGAAVAVPFDDDDKIVGGYTCEENSLPYQVSLNSGSHFCGGSLISEQWVVSAAHCYKTRIQVRLGEHNIKVLEGNEQFINAAKIIRHPKYNRDTLDNDIMLIKLSSPAVINARVSTISLPTAPPAAGTECLISGWGNTLSFGADYPDELKCLDAPVLTQAECKASYPGKITNSMFCVGFLEGGKDSCQRDSGGPVVCNGQLQGVVSWGHGCAWKNRPGVYTKVYNYVDWIKDTIAANS >gi|114051746|ref|NP_001040585.1| protease, serine, 2 [Macaca mulatta] MNPLLILAFVGVAVAAPFDDDDKIVGGYTCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAAHCYKTRIQVRLGEHNIEVLEGTEQFINAAKIIRHPDYDRKTLNNDILLIKLSSPAVINARVSTISLPTAPPAAGAEALISGWGNTLSSGADYPDELQCLEAPVLSQAECEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVSNGQLQGIVSWGYGCAQKNRPGVYTKVYNYVDWIRDTIAANS >gi|6755891|ref|NP_035775.1| mesotrypsin [Mus musculus] MNALLILALVGAAVAFPVDDDDKIVGGYTCQENSVPYQVSLNSGYHFCGGSLINDQWVVSAAHCYKTRIQVRLGEHNINVLEGNEQFVNAAKIIKHPNFNRKTLNNDIMLLKLSSPVTLNARVATVALPSSCAPAGTQCLISGWGNTLSFGVSEPDLLQCLDAPLLPQADCEASYPGKITGNMVCAGFLEGGKDSCQGDSGGPVVCNRELQGIVSWGYGCALPDNPGVYTKVCNYVDWIQDTIAAN >gi|6981422|ref|NP_036861.1| protease, serine, 2 [Rattus norvegicus] MRALLFLALVGAAVAFPVDDDDKIVGGYTCQENSVPYQVSLNSGYHFCGGSLINDQWVVSAAHCYKSRIQVRLGEHNINVLEGNEQFVNAAKIIKHPNFDRKTLNNDIMLIKLSSPVKLNARVATVALPSSCAPAGTQCLISGWGNTLSSGVNEPDLLQCLDAPLLPQADCEASYPGKITDNMVCVGFLEGGKDSCQGDSGGPVVCNGELQGIVSWGYGCALPDNPGVYTKVCNYVDWIQDTIAAN >gi|27819626|ref|NP_777115.1| pancreatic anionic trypsinogen [Bos taurus] MHPLLILAFVGAAVAFPSDDDDKIVGGYTCAENSVPYQVSLNAGYHFCGGSLINDQWVVSAAHCYQYHIQVRLGEYNIDVLEGGEQFIDASKIIRHPKYSSWTLDNDILLIKLSTPAVINARVSTLALPSACASGSTECL...

41. One of the options to get multiple sequence Fasta file

43. Input: multiple sequence Fasta file >gi|21536452|ref|NP_002762.2| mesotrypsin preproprotein [Homo sapiens] MNPFLILAFVGAAVAVPFDDDDKIVGGYTCEENSLPYQVSLNSGSHFCGGSLISEQWVVSAAHCYKTRIQVRLGEHNIKVLEGNEQFINAAKIIRHPKYNRDTLDNDIMLIKLSSPAVINARVSTISLPTAPPAAGTECLISGWGNTLSFGADYPDELKCLDAPVLTQAECKASYPGKITNSMFCVGFLEGGKDSCQRDSGGPVVCNGQLQGVVSWGHGCAWKNRPGVYTKVYNYVDWIKDTIAANS >gi|114051746|ref|NP_001040585.1| protease, serine, 2 [Macaca mulatta] MNPLLILAFVGVAVAAPFDDDDKIVGGYTCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAAHCYKTRIQVRLGEHNIEVLEGTEQFINAAKIIRHPDYDRKTLNNDILLIKLSSPAVINARVSTISLPTAPPAAGAEALISGWGNTLSSGADYPDELQCLEAPVLSQAECEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVSNGQLQGIVSWGYGCAQKNRPGVYTKVYNYVDWIRDTIAANS >gi|6755891|ref|NP_035775.1| mesotrypsin [Mus musculus] MNALLILALVGAAVAFPVDDDDKIVGGYTCQENSVPYQVSLNSGYHFCGGSLINDQWVVSAAHCYKTRIQVRLGEHNINVLEGNEQFVNAAKIIKHPNFNRKTLNNDIMLLKLSSPVTLNARVATVALPSSCAPAGTQCLISGWGNTLSFGVSEPDLLQCLDAPLLPQADCEASYPGKITGNMVCAGFLEGGKDSCQGDSGGPVVCNRELQGIVSWGYGCALPDNPGVYTKVCNYVDWIQDTIAAN >gi|6981422|ref|NP_036861.1| protease, serine, 2 [Rattus norvegicus] MRALLFLALVGAAVAFPVDDDDKIVGGYTCQENSVPYQVSLNSGYHFCGGSLINDQWVVSAAHCYKSRIQVRLGEHNINVLEGNEQFVNAAKIIKHPNFDRKTLNNDIMLIKLSSPVKLNARVATVALPSSCAPAGTQCLISGWGNTLSSGVNEPDLLQCLDAPLLPQADCEASYPGKITDNMVCVGFLEGGKDSCQGDSGGPVVCNGELQGIVSWGYGCALPDNPGVYTKVCNYVDWIQDTIAAN >gi|27819626|ref|NP_777115.1| pancreatic anionic trypsinogen [Bos taurus] MHPLLILAFVGAAVAFPSDDDDKIVGGYTCAENSVPYQVSLNAGYHFCGGSLINDQWVVSAAHCYQYHIQVRLGEYNIDVLEGGEQFIDASKIIRHPKYSSWTLDNDILLIKLSTPAVINARVSTLALPSACASGSTECL...

44. Input: multiple sequence Fasta file >gi|21536452|ref|NP_002762.2| mesotrypsin preproprotein [Homo sapiens] MNPFLILAFVGAAVAVPFDDDDKIVGGYTCEENSLPYQVSLNSGSHFCGGSLISEQWVVSAAHCYKTRIQVRLGEHNIKVLEGNEQFINAAKIIRHPKYNRDTLDNDIMLIKLSSPAVINARVSTISLPTAPPAAGTECLISGWGNTLSFGADYPDELKCLDAPVLTQAECKASYPGKITNSMFCVGFLEGGKDSCQRDSGGPVVCNGQLQGVVSWGHGCAWKNRPGVYTKVYNYVDWIKDTIAANS >gi|114051746|ref|NP_001040585.1| protease, serine, 2 [Macaca mulatta] MNPLLILAFVGVAVAAPFDDDDKIVGGYTCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAAHCYKTRIQVRLGEHNIEVLEGTEQFINAAKIIRHPDYDRKTLNNDILLIKLSSPAVINARVSTISLPTAPPAAGAEALISGWGNTLSSGADYPDELQCLEAPVLSQAECEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVSNGQLQGIVSWGYGCAQKNRPGVYTKVYNYVDWIRDTIAANS >gi|6755891|ref|NP_035775.1| mesotrypsin [Mus musculus] MNALLILALVGAAVAFPVDDDDKIVGGYTCQENSVPYQVSLNSGYHFCGGSLINDQWVVSAAHCYKTRIQVRLGEHNINVLEGNEQFVNAAKIIKHPNFNRKTLNNDIMLLKLSSPVTLNARVATVALPSSCAPAGTQCLISGWGNTLSFGVSEPDLLQCLDAPLLPQADCEASYPGKITGNMVCAGFLEGGKDSCQGDSGGPVVCNRELQGIVSWGYGCALPDNPGVYTKVCNYVDWIQDTIAAN >gi|6981422|ref|NP_036861.1| protease, serine, 2 [Rattus norvegicus] MRALLFLALVGAAVAFPVDDDDKIVGGYTCQENSVPYQVSLNSGYHFCGGSLINDQWVVSAAHCYKSRIQVRLGEHNINVLEGNEQFVNAAKIIKHPNFDRKTLNNDIMLIKLSSPVKLNARVATVALPSSCAPAGTQCLISGWGNTLSSGVNEPDLLQCLDAPLLPQADCEASYPGKITDNMVCVGFLEGGKDSCQGDSGGPVVCNGELQGIVSWGYGCALPDNPGVYTKVCNYVDWIQDTIAAN >gi|27819626|ref|NP_777115.1| pancreatic anionic trypsinogen [Bos taurus] MHPLLILAFVGAAVAFPSDDDDKIVGGYTCAENSVPYQVSLNAGYHFCGGSLINDQWVVSAAHCYQYHIQVRLGEYNIDVLEGGEQFIDASKIIRHPKYSSWTLDNDILLIKLSTPAVINARVSTLALPSACASGSTECL...

45. Step1: Load the sequences

46. Sequences and conservation view

47. Step2: Perform Alignment

48. Sequences and conservation view

50. Step 3: Create tree

51. Step 4: NJPlot

53. The Newick tree format is used to represent trees as strings CA D In Newick format: ((A,C),(B,D)); B Each pair of parenthesis () enclose a clade in the tree, and the comma separates the members of the corresponding clade. “;” – is always the last character

54. How robust is our tree?

55.  We need some statistical way to estimate the confidence in the tree topology  But we don’t know anything about the tree topology distribution or parameters  The only data source we have is our data (MSA)  So, we must rely on our own resources: “pull up by your own bootstraps” How robust is our tree?

56. Bootstrap (and jackknife)

57. Jackknife 1. We create n (typically 100-1000) new MSAs (pseudo-data sets) by randomly sampling half of the characters. (random samples without replacement) We do not change the number of sequences, just the number of positions! POS: 52316 1 : TATTT 2 : CATTT 3 : CACTT N : AACTT POS: 18745 1 : TTTAT 2 : TAACC 3 : TAACC N : TGGGA POS: 18394 1 : TTGTA 2 : TAGAC 3 : TAAAC N : TGAGG

58. Jackknife 2. We reconstruct a tree from each data set, using the same method used for reconstructing the original tree POS: 52316 1 : TATTT 2 : CATTT 3 : CACTT N : AACTT POS: 18745 1 : TTTAT 2 : TAACC 3 : TAACC N : TGGGA POS: 18394 1 : TTGTA 2 : TAGAC 3 : TAAAC N : TGAGG Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4

59. 3. For each node in our original tree, we count the number of times it appeared in the Jackknife analysis Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4 Back to Jackknife Sp1 Sp2 Sp3 Sp4 67% 100% In 67% of the data sets, the node SP1+SP2 was found

60. Bootstrap The same as jackknife, but instead of sampling K/2 positions, we sample K positions with replacement

61. Bootstrap 1. Resample K positions n times 12345 K 1 : ATCTG…A 2 : ATCTG…C 3 : ACTTA…C N : ACCTA…T 11244 K 1 : AATTT…T 2 : AATTT…G 3 : AACTT…T N : AACTT…T 47789…K 1 : TTTAT…T 2 : TAACC…G 3 : TAACC…T N : TGGGA…T 15578…K 1 : AGGTA…T 2 : AGGAC…G 3 : AAAAC…A N : AAAGG…C

62. Bootstrap 2. Reconstruct a tree from each data set using the same method used for reconstructing the original tree Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4 11244 K 1 : AATTT…T 2 : AATTT…G 3 : AACTT…T N : AACTT…T 47789…K 1 : TTTAT…T 2 : TAACC…G 3 : TAACC…T N : TGGGA…T 15578…K 1 : AGGTA…T 2 : AGGAC…G 3 : AAAAC…A N : AAAGG…C

63. Bootstrap 3. For each node in our original tree, we count the number of times it appeared in the bootstrap analysis Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4 Sp1 Sp2 Sp3 Sp4 67% 100%

64. Step 3.5 - Bootstrap

65. Bootstrap values on NJPlot Note: ClustalX saves trees as.ph file trees with bootstrap are saved as.phb You might have to reopen the tree…