Introduction to NCBI & Ensembl tools including BLAST and database searching Incorporating Bioinformatics into the High School Biology Curriculum Fran Lewitter, Ph.D. Director, Bioinformatics & Research Computing Whitehead Institute for Biomedical Research Lewitter AT wi.mit.edu

Lewitter-Whitehead Institute – August 15, 2012 What I hope you’ll learn What do we learn from database searching and sequence alignments What tools are available at NCBI What tools are available through Ensembl 2

First some background info Lewitter-Whitehead Institute – August 15, 20123

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Doolittle RF, Hunkapiller MW, Hood LE, Devare SG, Robbins KC, Aaronson SA, Antoniades HN. Science 221: , Simian sarcoma virus onc gene, v-sis, is derived from the gene (or genes) encoding a platelet-derived growth factor. 5

Lewitter-Whitehead Institute – August 15, 2012 Why do alignments Use sequence similarity to infer homology and/or structural similarity between 2 or more genes/proteins Identify more conserved regions of a protein, potentially identifying regions of most functional importance Compare and contrast homologs (perhaps into groups) based on shared positions or regions Infer evolutionary distance from sequence dissimilarity 6

Lewitter-Whitehead Institute – August 15, 2012 Evolutionary Basis of Sequence Alignment Similarity - observable quantity, such as percent identity Homology - conclusion drawn from data that two genes share a common evolutionary history; no metric is associated with this –Paralog – genes related by duplication –Ortholog – genes related by speciation 7

Lewitter-Whitehead Institute – August 15, 2012 Local vs Global Alignment From Mount, Bioinformatics, 2004, pg 71 GLOBAL LOCAL 8

Nucleotide vs Protein If comparing protein coding genes, use protein sequences because of less noise If protein sequences are very similar, it might be more instructive to use DNA sequences Lewitter-Whitehead Institute – August 15, 20129

Example of simple scoring system for nucleic acids Match = +1 (ex. A-A, T-T, C-C, G-G) Mismatch = -1 (ex. A-T, A-C, etc) Gap opening = - 5 Gap extension = -2 T C A G A C G A G T G T C G G A - - G C T G = -4 10

11WIBR Sequence Analysis Course, © Whitehead Institute, February 2005 Possible Alignments A:T C A G A C G A G T G B:T C G G A G C T G I.T C A G A C G A G T G T C G G A - - G C T G II.T C A G A C G A G T G T C G G A - G C - T G III.T C A G A C G A G T G T C G G A - G - C T G score=-4 score=-5

Lewitter-Whitehead Institute – August 15, 2012 Scoring for Protein Alignments - Amino Acid Substitution Matrices PAM - point accepted mutation based on global alignment [evolutionary model] BLOSUM - block substitutions based on local alignments [similarity among conserved sequences] 12

Lewitter-Whitehead Institute – August 15, 2012 AA Scoring Matrices PAM - point accepted mutation based on global alignment [evolutionary model] BLOSUM - block substitutions based on local alignments [similarity among conserved sequences] Log-odds= pair in homologous proteins pair in unrelated proteins by chance Log-odds= obs freq of aa substitutions freq expected by chance 13

Lewitter-Whitehead Institute – August 15, 2012 Substitution Matrices BLOSUM 30 BLOSUM 62 BLOSUM 80 % identity PAM 250 (80) PAM 120 (66) PAM 90 (50) % change Increasing similarity 14

Lewitter-Whitehead Institute – August 15, 2012 Scoring for BLAST Alignments Score = 94.0 bits (230), Expect = 6e-19 Identities = 45/101 (44%), Positives = 54/101 (52%), Gaps = 7/101 (6%) Query: 204 YTGPFCDV----DTKASCYDGRGLSYRGLARTTLSGAPCQPWASEATYRNVTAEQ---AR 256 Y+ FC + + CY G G +YRG T SGA C PW S V Q A+ Sbjct: 198 YSSEFCSTPACSEGNSDCYFGNGSAYRGTHSLTESGASCLPWNSMILIGKVYTAQNPSAQ 257 Query: 257 NWGLGGHAFCRNPDNDIRPWCFVLNRDRLSWEYCDLAQCQT 297 GLG H +CRNPD D +PWC VL RL+WEYCD+ C T Sbjct: 258 ALGLGKHNYCRNPDGDAKPWCHVLKNRRLTWEYCDVPSCST 298 Position 1: Y - Y = 7 Position 2: T - S = 1 Position 3: G - S = 0 Position 4: P - E = Position 9: - - P = -11 Position 10: - - A = Sum 230 Based on BLOSUM62 15

Lewitter-Whitehead Institute – August 15, 2012 Statistical Significance Raw Scores - score of an alignment equal to the sum of substitution and gap scores. Bit scores - scaled version of an alignment ’ s raw score that accounts for the statistical properties of the scoring system used. E-value - expected number of distinct alignments that would achieve a given score by chance. Lower E-value => more significant. 16

Lewitter-Whitehead Institute – August 15, 2012 A formula E = Kmn e - S This is the Expected number of high-scoring segment pairs (HSPs) with score at least S for sequences of length m and n. This is the E value for the score S. The parameters K and can be thought of simply as natural scales for the search space size and the scoring system respectively. 17

What’s significant? High confidence - >40% identity for long alignments (Rost, 1999 found that sequence alignments unambiguously distinguish between protein pairs of similar and non-similar structure when the pairwise sequence identity >40%) “Twilight zone” – blurry % identity “Midnight zone” - <20% identity Lewitter-Whitehead Institute – August 15,

Tools of Interest NCBI (US) - –BLAST –Entrez EBI/EMBL/WTSI (Europe) –Ensembl ( Lewitter-Whitehead Institute – August 15,

Lewitter-Whitehead Institute – August 15, 2012 NCBI Home Page 20

Lewitter-Whitehead Institute – August 15, 2012 Ensembl Home Page 21

Hands-On Entrez – finding sequences BLAST – look for similar sequences Ensembl – finding sequences Lewitter-Whitehead Institute – August 15,

How is BLAST WIBR? Looking for homologs Identifying domains in proteins Peptide identification Genome annotation Lewitter-Whitehead Institute – August 15,

Lewitter-Whitehead Institute – August 15, 2012 Remember 1.Don’t be afraid to look at help pages for each website Click, click, click 2.Any analysis tool will give you results 3.Interpret results you find 24

Lewitter-Whitehead Institute – August 15, 2012 Sequence of Note 1 GCGTTGCTGG CGTTTTTCCA TAGGCTCCGC 31 CCCCCTGACG AGCATCACAA AAATCGACGC 61 GGTGGCGAAA CCCGACAGGA CTATAAAGAT ………… GTAAAGTCTG GAAACGCGGA AGTCAGCGCC “ Here you see the actual structure of a small fragment of dinosaur DNA, ” Wu said. “ Notice the sequence is made up of four compounds - adenine, guanine, thymine and cytosine. This amount of DNA probably contains instructions to make a single protein - say, a hormone or an enzyme. The full DNA molecule contains three billion of these bases. If we looked at a screen like this once a second, for eight hours a day, it ’ d still take more than two years to look at the entire DNA strand. It ’ s that big. ” (page 103) (Published in 1990) Biotechniques,

Lewitter-Whitehead Institute – August 15, 2012 DinoDNA "Dinosaur DNA" from Crichton's THE LOST WORLD p. 135 GAATTCCGGAAGCGAGCAAGAGATAAGTCCTGGC ATCAGATACAGTTGGAGATAAGGACGACGTGTGG CAGCTCCCGCAGAGGATTCACTGGAAGTGCATTA CCTATCCCATGGGAGCCATGGAGTTCGTGGCGCT GGGGGGGCCGGATGCGGGCTCCCCCACTCCGTTC CCTGATGAAGCCGGAGCCTTCCTGGGGCTGGGGG GGGGCGAGAGGACGGAGGCGGGGGGGCTGCTGGC CTCCTACCCCCCCTCAGGCCGCGTGTCCCTGGTG CCGTGGCAGACACGGGTACTTTGGGGACCCCCCA GTGGGTGCCGCCCGCCACCCAAATGGAGCCCCCC CACTACCTGGAGCTGCTGCAACCCCCCCGGGGCA GCCCCCCCCATCCCTCCTCCGGGCCCCTACTGCC ACTCAGCAGCGCCTGCGGCCTCTACTACAAAC…… Published in

Lewitter-Whitehead Institute – August 15, 2012 >Erythroid transcription factor (NF-E1 DNA-binding protein) Query: 121 MEFVALGGPDAGSPTPFPDEAGAFLGLGGGERTEAGGLLASYPPSGRVSLVPWADTGTLG 300 MEFVALGGPDAGSPTPFPDEAGAFLGLGGGERTEAGGLLASYPPSGRVSLVPWADTGTLG Sbjct: 1 MEFVALGGPDAGSPTPFPDEAGAFLGLGGGERTEAGGLLASYPPSGRVSLVPWADTGTLG 60 Query: 301 TPQWVPPATQMEPPHYLELLQPPRGSPPHPSSGPLLPLSSGPPPCEARECVMARKNCGAT 480 TPQWVPPATQMEPPHYLELLQPPRGSPPHPSSGPLLPLSSGPPPCEARECV NCGAT Sbjct: 61 TPQWVPPATQMEPPHYLELLQPPRGSPPHPSSGPLLPLSSGPPPCEARECV----NCGAT 116 Query: 481 ATPLWRRDGTGHYLCNWASACGLYHRLNGQNRPLIRPKKRLLVSKRAGTVCSHERENCQT 660 ATPLWRRDGTGHYLCN ACGLYHRLNGQNRPLIRPKKRLLVSKRAGTVCS NCQT Sbjct: 117 ATPLWRRDGTGHYLCN---ACGLYHRLNGQNRPLIRPKKRLLVSKRAGTVCS----NCQT 169 Query: 661 STTTLWRRSPMGDPVCNNIHACGLYYKLHQVNRPLTMRKDGIQTRNRKVSSKGKKRRPPG 840 STTTLWRRSPMGDPVCN ACGLYYKLHQVNRPLTMRKDGIQTRNRKVSSKGKKRRPPG Sbjct: 170 STTTLWRRSPMGDPVCN---ACGLYYKLHQVNRPLTMRKDGIQTRNRKVSSKGKKRRPPG 226 Query: 841 GGNPSATAGGGAPMGGGGDPSMPPPPPPPAAAPPQSDALYALGPVVLSGHFLPFGNSGGF 1020 GGNPSATAGGGAPMGGGGDPSMPPPPPPPAAAPPQSDALYALGPVVLSGHFLPFGNSGGF Sbjct: 227 GGNPSATAGGGAPMGGGGDPSMPPPPPPPAAAPPQSDALYALGPVVLSGHFLPFGNSGGF 286 Query: 1021 FGGGAGGYTAPPGLSPQI 1074 FGGGAGGYTAPPGLSPQI Sbjct: 287 FGGGAGGYTAPPGLSPQI

Lewitter-Whitehead Institute – August 15, 2012 >Erythroid transcription factor (NF-E1 DNA-binding protein) Query: 121 MEFVALGGPDAGSPTPFPDEAGAFLGLGGGERTEAGGLLASYPPSGRVSLVPWADTGTLG 300 MEFVALGGPDAGSPTPFPDEAGAFLGLGGGERTEAGGLLASYPPSGRVSLVPWADTGTLG Sbjct: 1 MEFVALGGPDAGSPTPFPDEAGAFLGLGGGERTEAGGLLASYPPSGRVSLVPWADTGTLG 60 Query: 301 TPQWVPPATQMEPPHYLELLQPPRGSPPHPSSGPLLPLSSGPPPCEARECVMARKNCGAT 480 TPQWVPPATQMEPPHYLELLQPPRGSPPHPSSGPLLPLSSGPPPCEARECV NCGAT Sbjct: 61 TPQWVPPATQMEPPHYLELLQPPRGSPPHPSSGPLLPLSSGPPPCEARECV----NCGAT 116 Query: 481 ATPLWRRDGTGHYLCNWASACGLYHRLNGQNRPLIRPKKRLLVSKRAGTVCSHERENCQT 660 ATPLWRRDGTGHYLCN ACGLYHRLNGQNRPLIRPKKRLLVSKRAGTVCS NCQT Sbjct: 117 ATPLWRRDGTGHYLCN---ACGLYHRLNGQNRPLIRPKKRLLVSKRAGTVCS----NCQT 169 Query: 661 STTTLWRRSPMGDPVCNNIHACGLYYKLHQVNRPLTMRKDGIQTRNRKVSSKGKKRRPPG 840 STTTLWRRSPMGDPVCN ACGLYYKLHQVNRPLTMRKDGIQTRNRKVSSKGKKRRPPG Sbjct: 170 STTTLWRRSPMGDPVCN---ACGLYYKLHQVNRPLTMRKDGIQTRNRKVSSKGKKRRPPG 226 Query: 841 GGNPSATAGGGAPMGGGGDPSMPPPPPPPAAAPPQSDALYALGPVVLSGHFLPFGNSGGF 1020 GGNPSATAGGGAPMGGGGDPSMPPPPPPPAAAPPQSDALYALGPVVLSGHFLPFGNSGGF Sbjct: 227 GGNPSATAGGGAPMGGGGDPSMPPPPPPPAAAPPQSDALYALGPVVLSGHFLPFGNSGGF 286 Query: 1021 FGGGAGGYTAPPGLSPQI 1074 FGGGAGGYTAPPGLSPQI Sbjct: 287 FGGGAGGYTAPPGLSPQI