Sequencing and Sequence Alignment

Click to edit Master title style,Click to edit Master text styles,Second level,Third level,Fourth level,Fifth level,*,Lecture 2.4,1,Sequencing & Sequence Alignment,2,Objectives,Understand how DNA sequence data is collected and prepared,Be aware of the importance of sequence searching and sequence alignment in biology and medicine,Be familiar with the different algorithms and scoring schemes used in sequence searching and sequence alignment,3,High Throughput DNA Sequencing,4,30,000,5,Shotgun Sequencing,Isolate,Chromosome,ShearDNA,into Fragments,Clone into,Seq. Vectors,Sequence,6,Principles of DNA Sequencing,Primer,PBR322,Amp,Tet,Ori,DNA fragment,Denature with,heat to produce,ssDNA,Klenow + ddNTP,+ dNTP + primers,7,The Secret to Sanger Sequencing,8,Principles of DNA Sequencing,5,5 Primer,3 Template,G C A T G C,dATP,dCTP,dGTP,dTTP,ddATP,dATP,dCTP,dGTP,dTTP,ddCTP,dATP,dCTP,dGTP,dTTP,ddTTP,dATP,dCTP,dGTP,dTTP,ddCTP,G,ddC,GCATG,ddC,GC,ddA,GCA,ddT,ddG,GCAT,ddG,9,Principles of DNA Sequencing,G,C,T,A,+,_,+,_,G,C,A,T,G,C,10,Capillary Electrophoresis,Separation by Electro-osmotic Flow,11,Multiplexed CE with Fluorescent detection,ABI 3700,96x700 bases,12,Shotgun Sequencing,Sequence,Chromatogram,Send to Computer,Assembled,Sequence,13,Shotgun Sequencing,Very efficient process for small-scale (10 kb) sequencing (preferred method),First applied to whole genome sequencing in 1995 (,H. influenzae,),Now standard for all prokaryotic genome sequencing projects,Successfully applied to,D. melanogaster,Moderately successful for,H. sapiens,14,The Finished Product,GATTACAGATTACAGATTACAGATTACAGATTACAG,ATTACAGATTACAGATTACAGATTACAGATTACAGA,TTACAGATTACAGATTACAGATTACAGATTACAGAT,TACAGATTAGAGATTACAGATTACAGATTACAGATT,ACAGATTACAGATTACAGATTACAGATTACAGATTA,CAGATTACAGATTACAGATTACAGATTACAGATTAC,AGATTACAGATTACAGATTACAGATTACAGATTACA,GATTACAGATTACAGATTACAGATTACAGATTACAG,ATTACAGATTACAGATTACAGATTACAGATTACAGA,TTACAGATTACAGATTACAGATTACAGATTACAGAT,15,Sequencing Successes,T7 bacteriophage,completed in 1983,39,937 bp, 59 coded proteins,Escherichia coli,completed in 1998,4,639,221 bp, 4293 ORFs,Sacchoromyces cerevisae,completed in 1996,12,069,252 bp, 5800 genes,16,Sequencing Successes,Caenorhabditis elegans,completed in 1998,95,078,296 bp, 19,099 genes,Drosophila melanogaster,completed in 2000,116,117,226 bp, 13,601 genes,Homo sapiens,1st draft completed in 2001,3,160,079,000 bp, 31,780 genes,17,So what do we do with all this sequence data?,18,Sequence Alignment,19,Alignments tell us about.,Function or activity of a new gene/protein,Structure or shape of a new protein,Location or preferred location of a protein,Stability of a gene or protein,Origin of a gene or protein,Origin or phylogeny of an organelle,Origin or phylogeny of an organism,20,Factoid:,Sequence comparisons,lie at the heart of all,bioinformatics,21,Similarity versus Homology,Similarity refers to the likeness or % identity between 2 sequences,Similarity means sharing a statistically significant number of bases or amino acids,Similarity does not imply homology,Homology refers to shared ancestry,Two sequences are homologous is they are derived from a common ancestral sequence,Homology usually implies similarity,22,Similarity versus Homology,Similarity can be quantified,It is correct to say that two sequences are X% identical,It is correct to say that two sequences have a similarity score of Z,It is generally,incorrect,to say that two sequences are X%,similar,23,Homology cannot be quantified,If two sequences have a high % identity it is OK to say they are homologous,It is,incorrect,to say two sequences have a homology score of Z,It is,incorrect,to say two sequences are X% homologous,Similarity versus Homology,24,Sequence Complexity,MCDEFGHIKLAN.,High Complexity,ACTGTCACTGAT.,Mid Complexity,NNNNTTTTTNNN.,Low Complexity,Translate those DNA sequences!,25,Assessing Sequence Similarity,THESTORYOFGENESIS,THISBOOKONGENETICS,THESTORYOFGENESI-S,THISBOOKONGENETICS,THE STORY OF GENESIS,THIS BOOK ON GENETICS,Two Character,Strings,Character,Comparison,Context,Comparison,* * * * * * * * * * *,26,Assessing Sequence Similarity,Rbn KETAAAKFERQHMD,LszKVFGRCELAAAMKRHGLDNYRGYSLGNWVCAAKFESNFNT,RbnSST SAASSSNYCNQMMKSRNLTKDRCKPMNTFVHESLA,LszQATNRNTDGSTDYGILQINSRWWCNDGRTP GSRN,RbnDVQAVCSQKNVACKNGQTNCYQSYSTMSITDCRETGSSKY,LszLCNIPCSALLSSDITASVNC AKKIVSDGDGMNAWVAWR,RbnPNACYKTTQANKHIIVACEGNPYVPHFDASV,LszNRCKGTDVQA WIRGCRL,is this alignment significant?,27,Is This Alignment Significant?,28,Some Simple Rules,If two sequence are 100 residues and 25% identical, they are likely related,If two sequences are 15-25% identical they,may,be related, but more tests are needed,If two sequences are 15% identical they are probably not related,If you need more than 1 gap for every 20 residues the alignment is suspicious,29,Doolittles Rules of Thumb,Twilight Zone,30,Sequence Alignment - Methods,Dot Plots,Dynamic Programming,Heuristic (Fast) Local Alignment,Multiple Sequence Alignment,Contig Assembly,31,PAM Matrices,Developed by M.O. Dayhoff (1978),PAM = Point Accepted Mutation,Matrix assembled by looking at patterns of substitutions in closely related proteins,1 PAM corresponds to 1 amino acid change per 100 residues,1 PAM = 1% divergence or 1 million years in evolutionary history,32,Developed by Lipman & Pearson (1985/88),Refined by Altschul et al. (1990/97),Ideal for large database comparisons,Uses heuristics & statistical simplification,Fast N-type algorithm (similar to Dot Plot),Cuts sequences into short words (k-tuples),Uses “Hash Tables” to speed comparison,Fast Local Alignment Methods,33,FASTA,Developed in 1985 and 1988 (W. Pearson),Looks for clusters of nearby or locally dense “,identical,” k-tuples,init1 score,= score for first set of k-tuples,initn score,= score for gapped k-tuples,opt score,= optimized alignment score,Z-score,= number of S.D. above random,expect,= expected # of random matches,34,FASTA,35,Multiple Sequence Alignment,Multiple alignment of Calcitonins,36,Multiple Alignment Algorithm,Take all “n” sequences and perform all possible pairwise (n/2(n-1) alignments,Identify highest scoring pair, perform an alignment & create a consensus sequence,Select next most similar sequence and align it to the initial consensus, regenerate a second consensus,Repeat step 3 until finished,37,Multiple Sequence Alignment,Developed and refined by many (Doolittle, Barton, Corpet) through the 1980s,Used extensively for extracting hidden phylogenetic relationships and identifying sequence families,Powerful tool for extracting new sequence motifs and signature sequences,38,Multiple Alignment,Most commercial vendors offer good multiple alignment programs including:,GCG (Accelerys),PepTool/GeneTool (BioTools Inc.),LaserGene (DNAStar),Popular web servers include T-COFFEE, MULTALIN and CLUSTALW,Popular freeware includes PHYLIP & PAUP,39,Mutli-Align Websites,Match-Box,MUSCA,T-Coffee,MULTALIN,CLUSTALW,40,Multi-alignment & Contig Assembly,ATCGATGCGTAGCAGACTACCGTTACGATGCCTT,TAGCTACGCATCGTCTGATGGCAATGCTACGGAA.,ATCGATGCG,TAGC,TAGC,AGACTACC,GTT,GTT,ACGATGCCTT,TAGCTACGC,ATCGT,41,Contig Assembly,Read, edit & trim DNA chromatograms,Remove overlaps & ambiguous calls,Read in all sequence files (10-10,000),Reverse complement all sequences (doubles # of sequences to align),Remove vector sequences (vector trim),Remove regions of low complexity,Perform multiple sequence alignment,42,Chromatogram Editing,43,Sequence Loading,44,Sequence Alignment,45,Contig Alignment - Process,ATCGATGCG,TAGC,A,GACTACC,GTT,ACGATGCCTT,ATCGATGCG,TAGC,TAGC,AGACTACC,GTT,GTT,ACGATGCCTT,CGATGCG,TAGCA,ATCGATGCG,TAGC,TAGC,AGACTACC,GTT,GTT,ACGATGCCTT,TGCTA,CGCATCG,CGATGCG,TAGCA,46,Sequence Assembly Programs,Phred - base calling program that does detailed statistical analysis (UNIX),Phrap - sequence assembly program (UNIX),TIGR Assembler - microbial genomes (UNIX),The Staden Package (UNIX),GeneTool/ChromaTool/Sequencher (PC/Mac),47,Conclusions,Sequence alignments and database searching are key to all of bioinformatics,There are four different methods for doing sequence comparisons 1) Dot Plots; 2) Dynamic Programming; 3) Fast Alignment; and 4) Multiple Alignment,Understanding the significance of alignments requires an understanding of statistics and distributions,