Table Of ContentTheoretical and
Computational Methods
in Genome Research
Theoretical and
COlTIputational Methods
in GenOlTIe Research
Edited by
Sandor Suhai
German Cancer Research Center
Heidelberg, Germany
Springer Science+Business Media, LLC
Llbrary of Congress Cataloglng-In-Publlcatlon Data
Theoretical and cONputational methods in genome research / edited by
Sandor Suhai.
p. cm.
Includes bibliographical references and index.
ISBN 978-1-4613-7708-5 ISBN 978-1-4615-5903-0 (eBook)
DOI 10.1007/978-1-4615-5903-0
1. GenoNes--Data processing--Congresses. 2. Gene libraries--Data
processing--Congresses. 3. Nucleotide sequence--Data processing
-Congresses. 1. Suhai, Sandor.
CH442.T45 1997
572.8'S'0285--dc21 97-1548
CIP
Proceedings of the International Symposium on Theoretical and Computational Genome Research,
held March 24-27, 1996, in Heidelberg, Germany
ISBN 978-1-4613-7708-5
© 1997 Springer Science+Business Media New York
Originally published by Plenum Press, New York in 1997
Softcover reprint of the hardcover Ist edition 1997
http://www.plenum.com
10987654321
AH rights reserved
No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by
any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written
permission from the Publisher
PREFACE
The application ofcomputational methods to solve scientific and practical problems
in genome research created a new interdisciplinary area that transcends boundaries tradi
tionally separating genetics, biology, mathematics, physics, and computer science. Com
puters have, ofcourse, been intensively used in the field oflife sciences for many years,
even before genome research started, to store and analyze DNA or protein sequences; to
explore and model the three-dimensional structure, the dynamics, and the function of
biopolymers; to compute genetic linkage or evolutionary processes; and more. The rapid
development ofnew molecular and genetic technologies, combined with ambitious goals
to explore the structure and function ofgenomesofhigherorganisms, has generated, how
ever, not only a huge and exponentially increasing body ofdata but also a new class of
scientific questions. The nature and complexity ofthese questions will also require, be
yondestablishing anew kindofalliance between experimental and theoretical disciplines,
the development ofnew generations both in computer software and hardware technolo
gies.
New theoretical procedures, combined with powerful computational facilities, will
substantially extend the horizon ofproblems that genome research can attack with suc
cess. Many ofus still feel that computational models rationalizing experimental findings
ingenome research fulfill theirpromisesmoreslowlythan desired. There is alsoanuncer
tainty concerning the real position ofa "theoretical genome research" in the network of
established disciplines integrating their efforts in this field. There seems to be an obvious
parallel betweenthe present situationofgenome research andthatofatomic physics at the
endofthe first quarterofourcentury. Advancedexperimental techniques made itpossible
at that time to fill huge data catalogues with the frequencies ofspectral lines, yet all at
temptsatanempirical systematizationorclassically founded explanationremainedunsuc
cessful until a completely new conceptual framework, quantum mechanics, emerged that
made senseofall the data.
The present situation ofthe life sciences is certainly more intricate due to the more
amorphous nature of the field. One has to ask whether it is a fair demand at all that
genome research should reach the internal coherence ofa unifying theoretical framework
like theoretical physics or (to a smallerextent) chemistry. The fear seems to be, however,
not unjustified that genomic data accumulation will get so far ahead of its assimilation
into an appropriate theoretical framework thatthe datathemselves might eventuallyprove
an encumbrance in developing such new concepts. The aim ofmost ofthe computational
methods presented in this volume is to improve upon this situation by trying to provide a
bridge betweenexperimental databases (information) onthe one handandtheoretical con
cepts(biological andgeneticknowledge) onthe other.
v
vi Preface
The content of this volume was presented as plenary lectures at the International
Symposium on Theoretical and Computational Genome Research held March 24-27,
1996,atthe DeutschesKrebsforschungszentrum (DKFZ) inHeidelberg. Itis agreatpleas
ure to thank here Professor Harald zur Hausen and the coworkers ofDKFZ for their help
and hospitalityextendedto the lecturers andparticipantsduring the meetingand the Com
mission ofthe European Communities for the funding ofthe symposium. The organizers
profitedmuchfrom the help ofthe scientific committeeofthe symposium: MartinBishop,
Philippe Dessen, Reinhold Haux, RalfHofestiidt, Willi Jiiger, Jens G. Reich, Otto Ritter,
Petre Tautu, and Martin Vingron. Furthermore, the editor is deeply indebted to Michaela
Knapp-Mohammadyand Anke Retzmannfor theirhelp inorganizingthe meetingandpre
paringthis volume.
SandorSuhai
Heidelberg, July 1996
CONTENTS
1. EvaluatingtheStatisticalSignificanceofMultipleDistinctLocalAlignments
StephenF.Altschul
2. HiddenMarkovModelsforHumanGenes: PeriodicPatternsinExonSequences 15
PierreBaldi,SmenBrunak,YvesChauvin,andAndersKrogh
3. IdentificationofMuscle-SpecificTranscriptionalRegulatoryRegions 33
JamesW. Fickett
4. ASystematicAnalysisofGeneFunctionsbytheMetabolicPathwayDatabase. .. 41
MinoruKanehisaandSusumuGoto
5. PolymerDynamicsofDNA,Chromatin,andChromosomes 57
JorgLangowski,LutzEhrlich,MarkusHammermann,ChristianMiinkel,and
Gero Wedemann
6. Is WholeHumanGenomeSequencingFeasible? 73
EugeneW. MyersandJamesL.Weber
7. SequencePatternsDiagnosticofStructureandFunction 91
TempleF. Smith,R. MarkAdams,SudeshnaDas,LihuaYu,
LoredanaLoConte,andJamesWhite
8. RecognizingFunctionalDomainsinBiologicalSequences 105
GaryD. Stormo
9. TheIntegratedGenomicDatabase(lGD)S: Enhancingthe ProductivityofGene
MappingProjects 117
StephenP.Bryant,AnastassiaSpiridou,andNigelK. Spurr
10. ErrorAnalysisofGeneticLinkageData 135
RobertCottingham,Jr.,MargaretGelderElm,andMarekKimmel
11. ManagingAcceleratingDataGrowthintheGenomeDatabase 145
KennethH. Fasman
12. AdvancesinStatisticalMethodsforLinkageAnalysis 153
JeffreyR. O'ConnellandDanielE. Weeks
vii
viii Contents
13. ExploringHeterogeneousMolecularBiologyDatabasesintheContextofthe
Object-ProtocolModel 161
VictorM. Markowitz,I.-MinA. Chen,andAnthonyS.Kosky
14. ComprehensiveGenomeInformationSystems 177
OttoRitter
15. VisualizingtheGenome 185
DavidB. Searls
16. DataManagementforLigand-BasedDrugDesign 205
KarlAberer,KlemensHemm,andManfredHendlich
17. PicturingtheWorkingProtein 231
HansFrauenfelderandPeterG. Wolynes
18. HIV-l ProteaseandItsInhibitors 237
Maciej Geller,JoannaTrylska,andJanAntosiewicz
19. DensityFunctionalandNeuralNetworkAnalysis: HydrationEffectsand
SpectrosopicandStructuralCorrelationsinSmallPeptidesandAmino
Acids... ... ... . .. . . 255
K. J. Jalkanen,S. Suhai,andH. Bohr
20. ComputerSimulationsofProtein-DNAInteractions. ...................... 279
MatsErikssonandLennartNilsson
21. TheRoleofNeutralMutationsintheEvolutionofRNAMolecules 287
PeterSchuster
22. HowThree-FingeredSnakeToxinsRecogniseTheirTargets:
Acetylcholinesterase-FasciculinComplex,aCaseStudy 303
KurtGiles,MiaL.Raves, Israel Silman,andJoelL. Sussman
23. ProteinSequenceandStructureComparisonUsingIterativeDoubleDynamic
Programming ................................................. 317
WilliamR.Taylor
Index 329
Theoretical and
Computational Methods
in Genome Research
1
EVALUATING THE STATISTICAL
SIGNIFICANCE OF MULTIPLE DISTINCT
LOCAL ALIGNMENTS
StephenF.Altschul
NationalCenterforBiotechnologyInformation
NationalLibraryofMedicine
NationalInstitutesofHealth
Bethesda,Maryland20894
ABSTRACT
A comparison oftwo sequences may uncover multiple regions of local similarity.
While the significance of each local alignment may be evaluated independently, some
times a combined assessment is appropriate. This paper discusses a variety ofstatistical
andalgorithmic issuesthatsuchanassessmentpresents.
1. INTRODUCTION
The most widely used techniques for comparing DNA and protein sequences are lo
cal alignmentalgorithms, which seeksimilarregions ofthe sequencesunder consideration
[1-3]. Given a particular measure ofsimilarity, an important question is how strong a lo
cal alignment must beto beconsideredstatisticallysignificant. Forabroadrange ofmeas
ures, including most of those in common use, this question has been addressed both
analyticallyandexperimentally[4-17].
Occasionally a sequence comparison program will uncover not one but multiple lo
cal alignments, representing several distinct regions ofsequence similarity [17-22]. This
is particularly common with the BLAST algorithms [3], which disallow gaps within the
alignments found, but is true also oflocal alignmentalgorithms that permitgaps [17-21].
Can one arrive at ajoint statistical assessment ofthese several alignments? We will dis
cuss below the various issues that arise in reaching such an assessment. We will confine
attention first to local alignments lacking gaps, for which analytic results are available,
and then discussthe generalizationofthese results to alignmentswithgaps.
TheoreticalandComputationalMethodsinGenomeResearch,editedbySuhai
PlenumPress,NewYork, 1997 1
2 s.F.Altschul
2. LOCALALIGNMENT STATISTICS
The simple measure of local similarity with which we start requires a substitution
matrix, or set ofscores, for aligning the various pairs ofamino acids. Assume we are
sij
given two protein sequencesto compare. Choosinganypairofequal-length segments, one
from each sequence, we may construct an ungapped subalignment. The score for such a
segmentpair may be taken as the aggregate score ofits aligned pairs ofamino acids. The
segment pair with greatest score is called the maximal segment pair (or MSP), and its
score the maximal segment pair score (or MSP score). The MSP score may be calculated
using asimplification, thatdisallows gaps, ofthe Smith-Waterman dynamic programming
algorithm [1].
To analyze the statistical behavior ofMSP scores, one may model proteins as ran
dom sequences ofindependently selected amino acids; the amino acids, labeled 1to 20,
occur with respective probabilitiesPi" For our theory to progress, we need to assume that
theexpectedscore:E7J=1PiP for aligning two random amino acids is negative. This is, in
j sij
fact, a desirable condition: were the expected score positive, long segment pairs would
have high score independent of any biological relationship. Given this constrain on the
scores, as well as theexistence ofat leastone positive score, itisalwayspossible to find a
uniquepositivesolutionAto theequation
20
LPiP e',f = 1.
j
(1)
i,;=\
The parameter Amay be thought ofas a natural scale for the scores. Asecondposi
tive parameter K important for the statistical theory, depends upon the Pi and SiP and is
givenby ageometricallyconvergentinfinite series [9,10,14]. Aprogram inC for calculat
ing AandK isavailablefrom theauthor.
When the lengths m and n of the two sequences being compared are sufficiently
large, asymptotic results concerning the distribution ofthe MSP score S come into play
[9,10,14]. TheprobabilitythatSisat leastx isthen well approximatedbythe formula
Prob(S;;::x) = 1- exp(-Kmne-1..x). (2)
Sis saidto follow an extreme value distribution [23]. Ifonly the highest scoring segment
pairwere everofinterest,we wouldneedprogressno further.
3. LOCALSIMILARITYAND MULTIPLE LOCALALIGNMENTS
Becausetwo sequencesmaysharemorethanasingleregion ofsimilarity, it isdesir
able to consider not only the maximal segment pair, but other segment pairs that also
achieve high scores. The immediate problem is that the second, third and fourth highest
scoring segment pairs are likely to be slight variations ofthe maximal segment pair. We
thereforeneedadefinitionofwhentwo subalignmentsaredistinct.
The first such definition, inthecontextofsubalignmentswithgaps, is due to Sellers
[18],who definedthe "neighborhood" ofasubalignmentto bethe setofall subalignments
it touches within a path graph. A subalignment is then considered "locally optimal" ifit
