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Lecture 2: Introduction to Computational Biology Alexei Drummond Outline • • • • • CS369 2007 Sequences and sequence databases Similarity and Homology Sequence alignment Dot plots Database searches for similar sequences 2 Sequence • Definition: A sequence S is an ordered set of n characters (si) representing nucleotides or amino acids. S = {s1, s2,…,sn-1 , sn} – DNA is composed of four nucleotides or bases: si {A, C, G, T} – RNA is composed of four nucleotides: si {A, C, G, U}(T is transcribed as U) – Proteins are composed of twenty amino acids CS369 2007 3 Biomolecular sequences DNA 5’-ACGATCGACTGGTATATCGATGCT-3’ Xi {A,C,G,T} RNA Protein CS369 2007 5’-ACGAUCGACUGGUAUAUCGAUGCU-3’ Xi {A,C,G,U} MFINRWLFSTNHKDIGTLYLLFGAW Xi {A,R,N,D,C,E,Q,G,H,I,L,K, M,F,P,S,T,W,Y,V} 4 What is a gene? Intergenic DNA DNA Start codon Splice sites Stop codon 5’ 3’ 3’ 5’ Exon 1 Intron 1 Exon 2 Intron 2 Exon 3 Both the exons and introns are transcribed Primary RNA transcript 5’ 3’ The introns are removed Messenger RNA (mRNA) Translated to protein CS369 2007 5 Eukaryotes versus Prokaryotes Note: There is no cellular biology in the exam! • • • • • • • • Bacteria and Archaea Small No nucleus No introns Not much intergenic DNA Typically 1-10Mb genomes CS369 2007 • • • • Plants, animals and fungi Larger cells, often multicellular Well defined nucleus, and specialized organelles Introns Lots of intergenic DNA 100Mb -100 Gb genomes Graphics from MIT: http://web.mit.edu/hst.035/labs/labs.html 6 Sequence databases • Where do biologists store their data? – Databases • Public, private proprietary • General, specialist – Hard drive • Chromatograms/Electropherograms • Flat file sequence formats – Fasta, Genbank et cetera • Flat file alignment formats – Nexus, ClustalX, GCG et cetera CS369 2007 7 CS369 2007 8 NCBI Nucleotide database CS369 2007 9 Searching by accession number CS369 2007 10 Genbank record CS369 2007 11 Genbank headers LOCUS DEFINITION ACCESSION VERSION KEYWORDS SOURCE ORGANISM REFERENCE AUTHORS TITLE JOURNAL PUBMED CS369 2007 X00166 711 bp DNA linear PHG 10-FEB-1999 Bacteriophage lambda cI gene encoding the repressor protein for transcriptional control of tetracycline resistance on plasmid pTR 262. X00166 X00166.1 GI:15056 repressor; tetracycline resistance. Enterobacteria phage lambda Enterobacteria phage lambda Viruses; dsDNA viruses, no RNA stage; Caudovirales; Siphoviridae; Lambda-like viruses. 1 (bases 1 to 711) Nilsson,B., Uhlen,M., Josephson,S., Gatenbeck,S. and Philipson,L. An improved positive selection plasmid vector constructed by oligonucleotide mediated mutagenesis Nucleic Acids Res. 11 (22), 8019-8030 (1983) 6316281 12 Genbank feature table FEATURES source CDS CS369 2007 Location/Qualifiers 1..711 /organism="Enterobacteria phage lambda" /mol_type="genomic DNA" /db_xref="taxon:10710" 1..>711 /note="unnamed protein product; coding sequence cI gene" /codon_start=1 /transl_table=11 /protein_id="CAA24991.1" /db_xref="GI:15057" /db_xref="GOA:P03034" /db_xref="InterPro:IPR001387" /db_xref="InterPro:IPR006198" /db_xref="InterPro:IPR010982" /db_xref="InterPro:IPR011056" /db_xref="PDB:1F39" /db_xref="PDB:1GFX" /db_xref="PDB:1J5G" /db_xref="PDB:1LLI" /db_xref="PDB:1LMB" /db_xref="PDB:1LRP" … 13 Genbank sequence ORIGIN 1 61 121 181 241 301 361 421 481 541 601 661 atgagcacaa gcaatttatg atggggatgg tataacgccg atcgccagag gagtatgagt acctttacca gcattctggc tttcctgacg tgcatagcca caggtgtttt tccgttgtgg aaaagaaacc aaaaaaagaa ggcagtcagg cattgcttgc aaatctacga accctgtttt aaggtgatgc ttgaggttga gaatgttaat gacttggggg tacaaccact ggaaagttat attaacacaa aaatgaactt cgttggtgct aaaaattctc gatgtatgaa ttctcatgtt ggagagatgg aggtaattcc tctcgttgac tgatgagttt aaacccacag cgctagtcag gagcagcttg ggcttatccc ttatttaatg aaagttagcg gcggttagta caggcaggga gtaagcacaa atgaccgcac cctgagcagg accttcaaga tacccaatga tggcctgaag aggacgcacg aggaatctgt gcatcaatgc ttgaagaatt tgcagccgtc tgttctcacc ccaaaaaagc caacaggctc ctgttgagcc aactgatcag tcccatgcaa agacgtttgg tcgccttaaa cgcagacaag attaaatgct tagcccttca acttagaagt tgagcttaga cagtgattct caagccaagc aggtgatttc ggatagcggt tgagagttgt c // CS369 2007 14 Fasta format >gi|15056|emb|X00166.1| Bacteriophage lambda cI gene encoding the… ATGAGCACAAAAAAGAAACCATTAACACAAGAGCAGCTTGAGGACGCACGTCGCCTTAAAGCAATTTATG AAAAAAAGAAAAATGAACTTGGCTTATCCCAGGAATCTGTCGCAGACAAGATGGGGATGGGGCAGTCAGG CGTTGGTGCTTTATTTAATGGCATCAATGCATTAAATGCTTATAACGCCGCATTGCTTGCAAAAATTCTC AAAGTTAGCGTTGAAGAATTTAGCCCTTCAATCGCCAGAGAAATCTACGAGATGTATGAAGCGGTTAGTA TGCAGCCGTCACTTAGAAGTGAGTATGAGTACCCTGTTTTTTCTCATGTTCAGGCAGGGATGTTCTCACC TGAGCTTAGAACCTTTACCAAAGGTGATGCGGAGAGATGGGTAAGCACAACCAAAAAAGCCAGTGATTCT GCATTCTGGCTTGAGGTTGAAGGTAATTCCATGACCGCACCAACAGGCTCCAAGCCAAGCTTTCCTGACG GAATGTTAATTCTCGTTGACCCTGAGCAGGCTGTTGAGCCAGGTGATTTCTGCATAGCCAGACTTGGGGG TGATGAGTTTACCTTCAAGAAACTGATCAGGGATAGCGGTCAGGTGTTTTTACAACCACTAAACCCACAG TACCCAATGATCCCATGCAATGAGAGTTGTTCCGTTGTGGGGAAAGTTATCGCTAGTCAGTGGCCTGAAG AGACGTTTGGC CS369 2007 15 Hepatitis C sequence database • Specialist databases usually refer to sequences in the public databases, but have extra information and search criteria specific to the domain. CS369 2007 16 Hepatitis C sequence database CS369 2007 17 Problem 1: detecting sequence similarity between two sequences • Biologists often want to detect if two sequences are similar – How is sequence similarity defined? – What is it used for? – Are there different types of similarity? CS369 2007 18 How is sequence similarity defined? • The number of matching nucleotides (when aligned)? • The amount of shared information? • The “distance” between the two sequences under some metric? 38 out of 60 sites are identical in this alignment CS369 2007 19 How is sequence similarity defined? • • • • CS369 2007 A1 is 42 nucleotides long A2 is 60 nucleotides long So 38/42 = 90% of A1 is “explained” by A2 Whereas 38/60 = 63% of A2 is “explained” by A1 20 What is similarity used for? • Detecting homology (shared evolutionary history) • Reconstructing evolutionary history to better understand biology • Determining the structure and function of new sequences, by matching them with sequences of known structure/function • Grouping sequences together to increase statistical power of single-sequence analyses • Many many more uses… CS369 2007 21 Are their different types of similarity? • Chance similarity – For example: if you compare two long random sequences of DNA you will always find some small region containing the same sequence. • Similarity due to a common origin, followed by divergent/independent evolution (called homology) • Similarity due to convergence – Bird wings and bat wings – Lysozyme gut enzyme in cows and colobus monkeys CS369 2007 22 Sequence Homology x • Homologous protein or DNA sequences share common ancestry – A statement of homology is therefore an evolutionary hypothesis • Homology need not imply similar function • Homology is a binary property, a pair of sequences are either homologous or not homologous. t a, b homologous a b x y – No such thing as degree of homology • Homology is often inferred by sequence similarity a, b not homologous a CS369 2007 b 23 Origin of similar genes • Similar genes in the same genome arise by gene duplication • Similar genes in different genomes arise from common ancestry • A copy of a gene might be inserted next to the original • Two copies mutate independently • Each can take on separate functions • All or part can be transferred from one part of genome to another A Gene duplication A Speciation A B Species I CS369 2007 B A’ B’ Species II 24 Orthology and paralogy "Where the homology is a result of gene duplication so that both copies have descended side by side during the history of an organism, (for example, alpha and beta hemoglobin) the genes should be called paralogous (para=in parallel). Where the homology is the result of speciation so that the history of the gene reflects the history of the species (for example alpha hemoglobin in man and mouse) the genes should be called orthologous (ortho=exact). " Fitch WM. Distinguishing homologous from analogous proteins. Systematic Zoology 1970 Jun;19(2):99-113. CS369 2007 25 Orthology and paralogy CS369 2007 26 Orthology, paralogy and multigene families CS369 2007 Reproduced from NCBI education website 27 Solution 1: Pairwise sequence alignment • Definition: Procedure for optimizing a score function on a pair of sequence S1 and S2 by introducing gap characters into a subsequence of one or both of the sequences so as to construct aligned sequences A1 and A2. The objective is to find the similarity regions in the two sequences. – A1 and A2 will be the same length. – Ai will consist only of a subsequence of Si once gap characters are removed. CS369 2007 28 Pairwise sequence alignment Sequences S1 = a c g g t S2 = a g g c t t Alignment A1 = a c g g – t | || | A2 = a – g g c t t CS369 2007 29 Global versus Local Alignment • We distinguish – Global alignment algorithms which optimize overall alignment between two sequences – Local alignment algorithms which seek only highly similar subsequences • Alignment stops at the ends of regions of strong similarity • Favors finding conserved patterns in otherwise dissimilar sequences CS369 2007 30 Global vs. Local Alignment • Global LGPSSKQTGKGS-SRIWDN | | ||| | | LN-ITKSAGKGAIMRLGDA • Local --------GKG-------||| --------GKG-------- CS369 2007 31 Solution 2: The dot plot G C T A G G A G A C T A G G C CS369 2007 Window size = 1 Matches = 1 0/1 1/1 32 Filtering the dot plot G C T A G G A G A C T A G G C CS369 2007 Window size = 3 Matches = 2 0/3 1/3 2/3 3/3 33 Dot plots 1,1 2,2 The dot plot is a graphical method that can be tuned CS369 2007 34 Dot plots 3,3 CS369 2007 5,22 35 Dot matrix analysis with Geneious • Get phage l cI and phage P22 c2 repressor sequences from Genbank Nucleotide database – Accessions X00166 and V01153 respectively • Use Geneious 2.5.4 (http://www.geneious.com) • Use window size of 11 and stringency of 7 • See figure 3.X in Mount CS369 2007 36 Dot matrix analysis with Geneious CS369 2007 37 Dot matrix analysis with Geneious (2) • Get human LDL receptor protein sequence from Genbank (accession P01130) • Make copy, and look at self-similarity • Use window size of 1 and stringency of 1 • Use window size of 23 and stringency of 7 CS369 2007 38 Human LDL receptor self similarity 1,1 CS369 2007 23,7 39 Dot plots • Two 100 nucleotide fragments of the nef gene • Low complexity repetitive region is visible as dense region of parallel lines CS369 2007 40 Which alignment is best? CS369 2007 41 Problem 2: finding similar sequences in a database using query sequence • Biologists often want to find known sequences that are similar to a newly obtained sequence – How to rapidly compare the new sequence to the hundreds of billions of bases already sequenced? – Pairwise align new sequence to all the sequences in the database? – Which database to search? CS369 2007 42 Similarity searching • Many heuristic algorithms – BLAST – FASTA • Exact algorithms – Pairwise alignment on all database entries – Only possible for small databases CS369 2007 43 BLAST CS369 2007 44 CS369 2007 45