SequenceComparison:PairwiseAlignment

ShifraBen‐Dor

IritOrr

Theproblems:

IhaveaDNAsequence:Whatdoesitdo?

possiblecodingregion

possibleregulatoryregion

Ihaveaproteinsequence:

Whatdoesitdo??

SequenceComparison

•  Generally,sequencedeterminesstructureandstructuredeterminesfuncHon

•  Bystudyingsequencesimilarity,wehopetofindcorrelaHonsbetweenoursequenceandothersequenceswithknownstructureorfuncHon

•  ThisapproachisoKensuccessful,howevermanymoleculeshavelowsequencesimilarity,yetsHllsharesimilarstructureorfuncHon.

SequenceComparison

•  MoHfs/Domains‐similarityoversmallstretches

•  Sequencefamilies‐similarityoverlongersequences

•  Comparisoncanhelpuswith:

•  structure

•  funcHon

•  evoluHon

ComparisonQuesHons:

•  Arethesequencesrelated(homology)?

•  Canwequalifytheirsimilarity?

•  Dotheyhavesimilarsegments?

Terminology:

•  Homology

•  IdenHty

•  Similarity

Homology

•  Commonancestry

•  Sequence(andusuallystructure)conservaHon

•  HomologyisnotameasurablequanHty

•  Homologycanbeinferred,undersuitablecondiHons

IdenHty

•  ObjecHveandwelldefined

•  CanbequanHfiedbyseveralmethods:

•  Percent

•  ThenumberofidenHcalmatchesdividedbythelengthofthealignedregion

Similarity

•  Mostcommonmethodused

•  Notsowelldefined

•  Dependsontheparametersused(alphabet,scoringmatrix,etc.)

Whatarewecomparing?

•  DNAorRNA

•  Fournucleicacids(basicset)

•  Protein

•  Twentyaminoacids(basicset)

Alignment

•  Analignmentisanarrangementoftwosequencesoppositeoneanother

•  Itshowswheretheyaredifferentandwheretheyaresimilar

•  WewanttofindtheopHmalalignment‐themostsimilarityandtheleastdifferences

Alignment

•  Alignmentshavetwoaspects:

•  QuanHty:Towhatdegreearethesequencessimilar(percentage,otherscoringmethod)

•  Quality:Regionsofsimilarityinagivensequence

TheopHmalalignmentoftwo

sequencesisonethatfinds

thelongestsegmentofhigh

sequencesimilarity.

Howisanalignmentdone?

•  Whenwecomparesequences,wetaketwostringsofleXers(nucleoHdesoraminoacids)andalignthem.

•  WherethecharactersareidenHcal,wegivethemaposiHvescore,andwheretheydiffer,anegaHvevalue.

•  WecounttheidenHcalandnon‐idenHcalcharacters,andgivethealignmentascore(usuallycalledthequality)

Differencesinthesequencecanbe

causedbydeleHonsorinserHonsin

theDNA,orbypointmutaHons.These

changescanbeseenattheproteinlevel

aswell(changesinthetranslaHonof

theprotein)

Thisschemeworksfineaslongas

youassumethatallpossiblemutaHons

occuratthesamefrequency.

However,naturedoesn’tworkthisway.

IthasbeenfoundthatinDNA,transiHons

occurmoreoKenthantransversions.

Purines(A,G) are2‐ringbasesPyrimidines(C,T)are1‐ringbases

TransiHon:purinetopurineor pyrimidinetopyrimidine

Transversion:purinetopyrimidineorpyrimidinetopurine

TransiHonsconserveringnumberTransversionschangeringnumber

takenfromMolecularCellBiology,DarnellLodishBalHmore1990

Forproteins,thesituaHonisfarmorecomplex

•  AminoacidscanbegroupedbyanumberofclassificaHons:

•  Chemical:aromaHc,aliphaHc,sulphuric

•  FuncHonal:hydrophobic,hydrophilic,acidic,basic

•  Charge:posiHve,negaHve,neutral

•  Structural:internal,external

ScoringMatrices

•  Scoringmatricesareusedtoassignascoretoeachcomparisonofapairofcharacters

•  ThescoresinthematrixareintegervalueswhichassignaposiHvescoretoidenHcalorsimilarcharacterpairs,andanegaHvevaluetodissimilarpairs

•  Thematriceswereconstructedbyanalyzingknownfamiliesofproteins

Anexample:Blosum62Henikoff&Henikoff

A B C D E F G H I K L M N P Q R S T V W X Y Z A 4 -2 0 -2 -1 -2 0 -2 -1 -1 -1 -1 -2 -1 -1 -1 1 0 0 -3 -1 -2 -1 B -2 6 -3 6 2 -3 -1 -1 -3 -1 -4 -3 1 -1 0 -2 0 -1 -3 -4 -1 -3 2 C 0 -3 9 -3 -4 -2 -3 -3 -1 -3 -1 -1 -3 -3 -3 -3 -1 -1 -1 -2 -1 -2 -4 D -2 6 -3 6 2 -3 -1 -1 -3 -1 -4 -3 1 -1 0 -2 0 -1 -3 -4 -1 -3 2 E -1 2 -4 2 5 -3 -2 0 -3 1 -3 -2 0 -1 2 0 0 -1 -2 -3 -1 -2 5 F -2 -3 -2 -3 -3 6 -3 -1 0 -3 0 0 -3 -4 -3 -3 -2 -2 -1 1 -1 3 -3 G 0 -1 -3 -1 -2 -3 6 -2 -4 -2 -4 -3 0 -2 -2 -2 0 -2 -3 -2 -1 -3 -2 H -2 -1 -3 -1 0 -1 -2 8 -3 -1 -3 -2 1 -2 0 0 -1 -2 -3 -2 -1 2 0 I -1 -3 -1 -3 -3 0 -4 -3 4 -3 2 1 -3 -3 -3 -3 -2 -1 3 -3 -1 -1 -3 K -1 -1 -3 -1 1 -3 -2 -1 -3 5 -2 -1 0 -1 1 2 0 -1 -2 -3 -1 -2 1 L -1 -4 -1 -4 -3 0 -4 -3 2 -2 4 2 -3 -3 -2 -2 -2 -1 1 -2 -1 -1 -3 M -1 -3 -1 -3 -2 0 -3 -2 1 -1 2 5 -2 -2 0 -1 -1 -1 1 -1 -1 -1 -2 N -2 1 -3 1 0 -3 0 1 -3 0 -3 -2 6 -2 0 0 1 0 -3 -4 -1 -2 0 P -1 -1 -3 -1 -1 -4 -2 -2 -3 -1 -3 -2 -2 7 -1 -2 -1 -1 -2 -4 -1 -3 -1 Q -1 0 -3 0 2 -3 -2 0 -3 1 -2 0 0 -1 5 1 0 -1 -2 -2 -1 -1 2 R -1 -2 -3 -2 0 -3 -2 0 -3 2 -2 -1 0 -2 1 5 -1 -1 -3 -3 -1 -2 0 S 1 0 -1 0 0 -2 0 -1 -2 0 -2 -1 1 -1 0 -1 4 1 -2 -3 -1 -2 0 T 0 -1 -1 -1 -1 -2 -2 -2 -1 -1 -1 -1 0 -1 -1 -1 1 5 0 -2 -1 -2 -1 V 0 -3 -1 -3 -2 -1 -3 -3 3 -2 1 1 -3 -2 -2 -3 -2 0 4 -3 -1 -1 -2 W -3 -4 -2 -4 -3 1 -2 -2 -3 -3 -2 -1 -4 -4 -2 -3 -3 -2 -3 11 -1 2 -3 X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Y -2 -3 -2 -3 -2 3 -3 2 -1 -2 -1 -1 -2 -3 -1 -2 -2 -2 -1 2 -1 7 -2 Z -1 2 -4 2 5 -3 -2 0 -3 1 -3 -2 0 -1 2 0 0 -1 -2 -3 -1 -2 5

Alignmentalgorithms

•  Visualalignment

•  allowsintegraHonofrelevantdatanotavailabletocomputerizedalgorithms

•  Timeconsuming,notfeasibleforallbuttheshortestsequences

•  Fixedlengthalgorithms

•  donotconsiderinserHonsanddeleHons•  inserHonsanddeleHonsareneededevenforcloselyrelatedsequences

AlignmentAlgorithms

•  Thenaïveapproach:

•  generateallpossiblealignmentsfor2sequences(includinggaps)andchoosethealignmentwiththehighestscore

•  TooHmeconsuming

Dynamicprogrammingalgorithms

•  Eachcharacteralongbothsequencesisevaluated.AteachposiHontherearefourpossibilites•  idenHty

•  subsHtuHon

•  deleHoninsequence1

•  deleHoninsequence2

Dynamicprogramming

•  IdenHcalcharacters(matches)orsubsHtuHons(mismatches)arescoredaccordingtoamatrix.

•  DeleHonsineitherofthesequencesarecalledgaps.

•  GapsaregivenanegaHvescore,referredtoasthegappenalty

Thealignmentisgivenascore,calledthequality

Quality=matches‐(mismatches+gappenalty)

Theprogramwillfindthealignmentwiththehighestquality.

ThechoicebetweengapsandsubsHtuHonsismadetogivethehigherqualityofthetwo.

TheGapPenaltyConsiderthetwofollowingalignments:

V I T K L G T C V G S V I T K L G T C V G S

V I T . . . T C V G S V . T K . G T C V . S

Accordingtothealgorithmthese2caseswillgetthesamegappenalty:

Match=3Gap=‐2

8(3) + 3(-2) = 18 8(3) + 3(-2) = 18

Howevernatureisdifferent.Inmost

casesinserHons/deleHonsarelonger

thanasingleresidue,evenforvery

similarsequences.

Tocompensateforthis,andtodifferenHatebetweencasesliketheoneabove,thegappenaltyismadeupoftwofactors:

ThegapcreaHonpenalty‐subtractedfromthealignmentqualitywheneveragapisopened.

Thegapextensionpenalty‐subtractedfromthealignmentqualityaccordingtothelengthofthegap.

Thuswehave:

Quality=

matches‐(mismatches+gappenalty)

Gappenalty=

gapcreaHonpenalty+(gapextensionpenaltyXgaplength)

TheGapPenaltySonowwehave:

V I T K L G T C V G S V I T K L G T C V G S V I T . . . T C V G S V . T K . G T C V . S

Match=3Gapopen=‐4Gapextension=‐1

8(3)+[1(‐4)+3(‐1)]=178(3)+[3(‐4)+3(‐1)]=9

Gappenaltyparameters

InserHonofagapmustimprovethequalityofthealignment(raisethequalityscore).

IfthegapcreaHonandgapextensionpenalHesarehigh,lessgapswillbeinsertedintothealignment.

IfthegapcreaHonandgapextensionpenalHesarelow,moregapswillbeinsertedintothealignment.

SoifyouareinterestedinanalignmentbetweentwoverysimilarsequencesthegappenalHesshouldberaised,toreducethechancesofgejngsomethingrandom.

IfyouareinterestedindetecHnghomology(findingaweaksimilarity)betweentwodistantlyrelatedsequencesthegappenalHesshouldbelowered.

Ifyoudon'tknowwhattoexpect,startoffwiththedefaultparameters

Tosummarize:  Alignmentscoresaredependentonwhatwechoosefor:matches,mismatches,subsHtuHonsandgaps.

  Dynamicprogrammingcanbeusedforglobalorlocalalignment

Twotypesofalignment:

•  Globalalignment

•  Localalignment

Globalalignment

Aglobalpairwisealignmentisonewhereitisassumedthatthetwosequenceshavedivergedfromacommonancestorandthattheprogramshouldtrytostretchthetwosequences,introducinggapswherenecessary,inordertoshowthealignmentoverthewholelengthofthetwosequencesthatbestillustratestheirsimilariHes.

Globalalignment

•  Comparessequencesandgivesbestoverallalignment

•  Mayfailtofindthebestlocalregionofsimilarity(suchasasharedmoHf)amongdistantlyrelatedsequences

•  Will(generally)returnonlythebestmatchingsegmentforagivenpairofsequences

Globalalignment

•  TheclassicalalgorithmforglobalalignmentistheNeedleman‐Wunsch

LocalAlignment

•  SearchesforregionsoflocalsimilaritybetweentwosequencesandneednotincludetheenHrelengthofthesequences.

•  Findsregionsof(ungapped)sequencewithahighdegreeofsimilarity

•  BeXeratfindingmoHfs,especiallyforsequencesthataredifferentoverall

•  Canreturnmorethanonematchingsegmentforagivenpairofsequences

LocalAlignment

•  TheclassicalalgorithmforlocalalignmentistheSmith‐Waterman

SequenceComparisonPrograms

•  Global•  Gap(GCG)

•  Align(Fasta)•  Needle(EMBOSS)•  Stretcher(EMBOSS)–modifiedtoconservememory,goodforlongsequences

SequenceComparisonPrograms

•  Local•  Besmit(GCG)

•  Lalign(Fasta)–canreturnmorethanonesegment•  Matcher(EMBOSS)‐basedonlalign,canreturnmorethanonesegment

•  Water(EMBOSS)‐Smith‐Waterman,onlyonehit

LocalpairwisealignmentusingBL2SEQatNCBI

  ThistoolproducesthealignmentoftwogivensequencesusingBLASTalgorithmforlocalalignment.

  Reference:TaHanaA.Tatusova,ThomasL.Madden(1999),"Blast2sequences‐anewtoolforcomparingproteinandnucleoHdesequences",FEMSMicrobiolLeX.174:247‐250

LocalpairwisealignmentusingBL2SEQ

  ThistooluHlizestheBLASTengineforpairwisesequencecomparisonandisbasedonthesamealgorithmandstaHsHcsoflocalalignmentsthathavebeendescribedintheBLASTpaper.

  TheBLASTalgorithmgeneratesagappedalignmentbyusingdynamicprogrammingtoextendthecentralsegmentofalignedresidues.

  Becausetheparameterswerebasedondatabasesearching,somemayhavetobechangedtofindamatch

StaHsHcalEvaluaHonofAlignments

TheproblemwiththeseprogramsisnomaXerhowdissimilarthesequencesyoucompare,theprogramswillalwaysalignthem.

Evena5%idenHtywillbedisplayedasavalidresult.

SohowcanyoutellifthealignmentisstaHsHcallyvalid????

TherandomizeopHon

  TherandomizeopHonwilltakethesecondsequenceyouinputandshuffleit,toobtainarandomsequencewiththesamecharactercomposiHon.

  Thisrandomsequencewillbecomparedtothefirstsequence,usingeitheraglobalorlocalalgorithm(thesamethatyouusedoriginally),andaqualityscorewillbeobtained.

TherandomizeopHon

  ThisprocessisrepeatedanumberofHmes,specifiedbytheuser,inordertoobtainapopulaHonofsequencesthatcanbeusedforstaHsHcalanalysis.

  Thequalityofthesealignmentswillbeaveragedandcomparedtotheoriginalquality,andthenbeusedtogiveastaHsHcallymeaningfulanswertothealignment.

Theprogramgivestheoriginalalignment,

theoriginalquality,andanewqualityscore

madefromtheaverageoftherandomized

alignments+/‐thestandarddeviaHon.

Thesevaluesareusedtocalculatethedistanceofyouroriginalqualityfromthemean(Z‐score).

Z‐score=(OriginalQuality‐AverageQuality)StandardDeviaHon

AZ‐scoreofover4isconsideredsignificant.(MeaningthatthealignmentisstaHsHcallysignificanttoo)

AlternaHvestoGCG

•  IntheFASTApackage,therearetwoalternaHveprogramsforstaHsHcalanalysis:

•  PRDF‐calculatestheprobabilityofasimilarityscoremoreaccuratelybyusingafittoanextremevaluedistribuHon.

•  PRSS‐aversionofPRDFthatusesarigorousSmith‐WatermancalculaHontoscoresimilariHes

(Theseprogramsareavailableontheweb)

Dotplotsaretwodimensionalgraphs,showingacomparisonoftwosequences.

Thetwoaxesofthegraphrepresentthetwosequencesbeingcompared.

Everyregionofthesequenceiscomparedtoeveryregionoftheothersequence.

Dotplots

Dotplots

Dotplojngisthebestwaytoseeallofthestructuresincommonbetweentwosequences.

Dotplojngcanalsobeusedtoviewrepeatedstructuresorinvertedrepeatsinasinglesequence.Thisisaccomplishedbycomparingasequencetoitself.

Dotplojnghelpsrecognizelargeregionsofsimilarity.InmostcasesitisnotsensiHveenoughtoseesmallstructures.

ComparisonCriteria

Thematchcriterioncanbemetintwodifferentways:

Thewindow/stringencymethod.

Thewordmethod.

Thewindow/stringencymethod

Searchesforalltheplaceswhereagivennumberofmatches(stringency)occurwithinagivenrange(window).

ThismethodismoreHme‐consuming,butmoresensiHve.

Comparisonsaredoneaccordingtoascoringmatrix.

Mustbespecifiedonthecommandline(‐wordsize=X,whereXisthesizeyouchoose).

Searchesforshortperfectmatchesofasetlength(words).

Thismethodisabout1000Hmesfasterthanthewindow/stringencymethod,butismuchlesssensiHve.

Ifthesequencesdonotcontainshortperfectmatchesthenthismethodwillfindnothing.

Thewordmethod

HintsIfyouhavelongsequences,tryawordcomparisonfirst.Thisismuchfaster,andwillgiveyouanideaofwhatthedotplotforthemoresensiHvewindow/stringencymethodwilllooklike.

Whenusingthewordmethod,startoffwithawordsizeof6fornucleicacidsequencesofupto1,000bases,or8forsequencesofupto10,000.

Hints

ForpepHdesequences,startoffwithawordsizeof2‐3.

Whenusingthewindow/stringencymethodstartoffwithawindowof21andastringencyof14fornucleicacids.

ForpepHdesequencesstartoffwithawindowof30andastringencyof11.

Programsfordotplots

  GCG–  Compare(createthepoints)

–  Dotplot(plottheoutputofcompare)

  SeqWeb(GCG)–  Compare

  EMBOSS–  Dotmatcher‐window/stringency–  DoXup‐wordplot–  Dotpath‐non‐overlappingwordplot–  Polydot‐allagainstallwordplot

AlternaHve“dotplots”

DoXerisagraphicaldotplotprogramfordetailedcomparisonoftwosequences.

Tomakethescorematrixmoreintelligible,thepairwisescoresareaveragedoveraslidingwindowwhichrunsdiagonally.Theaveragedscorematrixformsathree‐dimensionallandscape,withthetwosequencesintwodimensionsandtheheightofthepeaksinthethird.

Thislandscapeisprojectedontotwodimensionsbyaidofgreyscales‐thedarkergreyofapeak,thehigheritis.

DoXerprovidesatooltoexplorethevisualappearanceofthislandscape,aswellasatooltoexaminethesequencealignmentitrepresents.