Sequence ComparisonSlide 2Similarity vs. HomologyParalogous vs. Orthologus RelationshipsMatch CriterionDot PlotsGCG DotPlotsPowerPoint PresentationSlide 9Stringency and SpecificityHigh StringencyLow StringencyWord Match ComparisonsWord DotPlot -WordSize=2Slide 15Slide 16Window/Stringency ComparisonsWindow DotPlot 4/2Slide 19Slide 20Slide 21Slide 22Slide 23Window DotPlot 4/1Window DotPlot 4/3Symbol Comparison Tables (Scoring Matrices)Nucleotide Tablescompardna.cmpSlide 29nwsgapdna.cmpSlide 31swgapdna.cmpSlide 33Amino Acid TablesPAM250 Dayhoff MatrixPAMPAM250 Matrixpam250.cmpSlide 39ProblemsBLOSUM TablesBLOSUM ReferenceBlocksBLOSUM AdvantagesBLOSUM62 TableSlide 46StructGapPep.cmpOther TablesCompareSlide 50Run Compare/DotPlot FirstConsiderationsIdentifying RepeatsIdentifying Inverted RepeatsWord Comparison-WordSize= Default SettingsWindow/Stringency ComparisonWindow Settings DefaultsWindow SizeSlide 60Slide 61Slide 62Slide 63DotPlotSlide 65Slide 66Slide 67Polio:Hepatitis A 30/9 (Protein)Polio:Hepatitis A 20/10 (Protein)Polio:Hepatitis A 20/15 (Protein)Polio:Hepatitis A -Word=2 -NoRandom (Protein)Polio:Hepatitis A (DNA)Pol 1 Polymerase vs. Complete Polio Sequence (DNA) 21/14Pol 1 Poly. vs. Polymerase Region (DNA)Pol 1 Poly. vs.Coxsackievirus B4 (DNA)Pol 1 Poly. vs.Rhinovirus 2 (DNA)Pol 1 Poly. vs Hepatitis A virus (DNA)Pol 1 Poly. vs EMC virus (DNA)Pol 1 Poly. vs. Pol 2 (Protein) 30/9Pol 1 Poly. vs. Pol 2 (Protein) 20/10Pol 1 Poly. vs. Pol 2 (Protein) 20/15Pol 1 Poly. vs. Rhino 2 (Protein) 20/15Pol 1 Poly. vs. EMC (Protein) 20/10Pol 1 Poly. vs. EMC (Protein) 20/15Pol 1 Poly. vs. HepA (Protein) -Word=2Pol 1 Poly. vs. HepA (Protein) -Word=2 (Stopped)Pol 1 Poly. vs. HepA (Protein) -Word=3Pol 1 Poly. vs. HepA (Protein) 20/15Rous Sarcoma Virus vs. ItselfE. coli tRNA-Cys vs. ItselfE. coli tRNA-Cys vs. Its Reverse-ComplementAngiotensin II Receptor mRNAApolipoprotein Human:RatBestFit and GapComputer Generated AlignmentsLocal vs. Global AlignmentsSlide 97BestFitQuality vs. Percent Identity or SimilarityPercent IdentityPercent SimilarityQualityQuality for Nucleotide AlignmentsQuality for Protein AlignmentsRatioBestFit AlgorithmPath Matrix TableFilling the Path MatrixPath Matrix FilledPath Matrix DeterminationPath Matrix Local HomologyPath Matrix Gobal AlignmentAlignment Significance-RandomLimitationsSlide 116-PairOutputSlide 119Slide 120Slide 121Slide 122Slide 123Slide 124Slide 125Slide 126Slide 127Slide 128Slide 129Slide 130Slide 131Slide 132Slide 133GapAdditional Considerations-EndWeightForced PairingSlide 138Slide 139Slide 140Slide 141Slide 142Slide 143Slide 144Slide 145Next upSequence ComparisonPair-wise SimilaritiesSequence ComparisonGraphical AlignmentsCompareDotPlotPairwise alignmentsBestFitGapSimilarity vs. HomologySimilarityTwo sequences which resemble each otherCan be measuredDegrees of similarity existHomologyTwo sequences which are similar due to common evolutionary originMust be inferredAll or noneParalogous vs. Orthologus RelationshipsImplies HomologyOrthologsSequences that have evolved from a common ancestor following speciation ParalogsSequences that have evolved within a single line of descent following gene duplicationMatch CriterionIs there a similarity between two sequences?Identical symbols (nucleotides or amino acids)Related symbols (amino acids)Do gaps/rearrangements allow for a higher degree of similarity?Dot PlotsAllow comparison of two sequences in all registersProduces a graph (Dotplot) of sequence similaritiesThe human brain interprets the resultsGCG DotPlotsCompareCompares the sequencesOutput is a text table containing the comparison informationDotPlotProduces a graph of Compare's resultsSimple 1:1 DotPlotR • •E • • • • M • I • • R • •P • S • • • I • • S • • • Y • L • A • • N • • A • • E • • • • C • N • • E • • • • U • Q • E • • • • S • • • S E Q U E N C E A N A L Y S I S P R I M E RStringency and SpecificityDegree to which programs parameters are set to detect more distant similaritiesDegree to which programs parameters are set to exclude unrelated “background” similarities or “noise”High StringencyLow background noiseOnly relatively close matches detectedLow StringencyHigh background noiseDistant relationships detectedWord Match ComparisonsIdentifies short, perfect matches (words)ktup (k-tuple)Fast1,000 times faster than window/stringency comparisonLess sensitive than window/stringencyWord DotPlot -WordSize=2Word DotPlot /WordSize=2R E M I R P S I S Y L A N A E C N E U Q E • S S E Q U E N C E A N A L Y S I S P R I M E R E SWord DotPlot -WordSize=2Word DotPlot /WordSize=2RER •E • M • I • R • P • S • I • S • Y • L • A • N • A • E • C • N • E • U • Q • E • S S E Q U E N C E A N A L Y S I S P R I M E RWord DotPlot -WordSize=2Window/Stringency ComparisonsIdentifies a given number of matches (stringency)Over a given range (window)SlowHigh sensitivityWindow DotPlot 4/2R E M I R P S I S Y L A N A E C N E U • Q E S S E Q U E N C E A N A L Y S I S