Algorithms in Bioinformatics: Second International Workshop, WABI 2002 Rome, Italy, September 17–21, 2002 Proceedings

543 Pages·2002·5.331 MB·English

by  L. R. Grate, C. Bhattacharyya, M. I. Jordan, I. S. Mian (auth.), Roderic Guigó, Dan Gusfield (eds.)

Preface
We are pleased to present the proceedings of the Second Workshop on Algo-
rithms in Bioinformatics (WABI 2002), which took place on September 17-21,
2002 in Rome, Italy. The WABI workshop was part of a three-conference meet-
ing, which, in addition to WABI, included the ESA and APPROX 2002. The
three conferences are jointly called ALGO 2002, and were hosted by the Fac-
ulty of Engineering, University of Rome “La Sapienza”. See http://www.dis.
uniroma1.it/˜algo02 for more details.
The Workshop on Algorithms in Bioinformatics covers research in all areas
of algorithmic work in bioinformatics and computational biology. The emphasis
is on discrete algorithms that address important problems in molecular biology,
genomics,andgenetics,thatarefoundedonsoundmodels,thatarecomputation-
ally efficient, and that have been implemented and tested in simulations and on
real datasets. The goal is to present recent research results, including significant
work in progress, and to identify and explore directions of future research.
Original research papers (including significant work in progress) or state-
of-the-art surveys were solicited on all aspects of algorithms in bioinformatics,
including, but not limited to: exact and approximate algorithms for genomics,
genetics, sequence analysis, gene and signal recognition, alignment, molecular
evolution, phylogenetics, structure determination or prediction, gene expression
and gene networks, proteomics, functional genomics, and drug design.
We received 83 submissions in response to our call for papers, and were able
to accept about half of the submissions. In addition, WABI hosted two invited,
distinguished lectures, given to the entire ALGO 2002 conference, by Dr. Ehud
ShapirooftheWeizmannInstituteandDr.GeneMyersofCeleraGenomics.An
abstract of Dr. Shapiro’s lecture, and a full paper detailing Dr. Myers lecture,
are included in these proceedings.
We would like to sincerely thank all the authors of submitted papers, and
the participants of the workshop. We also thank the program committee for
theirhardworkinreviewingandselectingthepapersfortheworkshop.Wewere
fortunate to have on the program committee the following distinguished group
of researchers:
Pankaj Agarwal (GlaxoSmithKline Pharmaceuticals, King of Prussia)
Alberto Apostolico (Universita` di Padova and Purdue University, Lafayette)
Craig Benham (University of California, Davis)
Jean-Michel Claverie (CNRS-AVENTIS, Marseille)
Nir Friedman (Hebrew University, Jerusalem)
Olivier Gascuel (Universit´e de Montpellier II and CNRS, Montpellier)
Misha Gelfand (IntegratedGenomics, Moscow)
Raffaele Giancarlo (Universit`a di Palermo)
VI Preface
David Gilbert (University of Glasgow)
Roderic Guigo (Institut Municipal d’Investigacions M`ediques,
Barcelona, co-chair)
Dan Gusfield (University of California, Davis, co-chair)
Jotun Hein (University of Oxford)
Inge Jonassen (Universitetet i Bergen)
Giuseppe Lancia (Universita` di Padova)
Bernard M.E. Moret (University of New Mexico, Albuquerque)
Gene Myers (Celera Genomics, Rockville)
Christos Ouzonis (European Bioinformatics Institute, Hinxton Hall)
Lior Pachter (University of California, Berkeley)
Knut Reinert (Celera Genomics, Rockville)
Marie-France Sagot (Universit´e Claude Bernard, Lyon)
David Sankoff (Universit´e de Montr´eal)
Steve Skiena (State University of New York, Stony Brook)
Gary Stormo (Washington University, St. Louis)
Jens Stoye (Universita¨t Bielefeld)
Martin Tompa (University of Washington, Seattle)
Alfonso Valencia (Centro Nacional de Biotecnolog´ıa, Madrid)
Martin Vingron (Max-Planck-Institut fu¨r Molekulare Genetik, Berlin)
Lusheng Wang (City University of Hong Kong)
Tandy Warnow (University of Texas, Austin)
We also would like to thank the WABI steering committee, Olivier Gascuel,
JotunHein,RaffaeleGiancarlo,ErikMeineche-Schmidt,andBernardMoret,for
inviting us to co-chair this program committee, and for their help in carrying
out that task.
We are particularly indebted to Terri Knight of the University of California,
Davis,RobertCastelooftheUniversitatPompeuFabra,Barcelona,andBernard
MoretoftheUniversityofNewMexico,Albuquerque,fortheextensivetechnical
and advisory help they gave us. We could not have managed the reviewing
process and the preparation of the proceedings without their help and advice.
Thanks again to everyone who helped to make WABI 2002 a success. We
hope to see everyone again at WABI 2003.
July, 2002 Roderic Guigo´ and Dan Gusfield
Table of Contents
Simultaneous Relevant Feature Identification
and Classification in High-Dimensional Spaces ......................... 1
L.R.Grate(LawrenceBerkeleyNationalLaboratory),C.Bhattacharyya,
M.I. Jordan, and I.S. Mian (University of California Berkeley)
Pooled Genomic Indexing (PGI): Mathematical Analysis
and Experiment Design............................................. 10
M. Csu˝ro¨s (Universit´e de Montr´eal), and A. Milosavljevic (Human
Genome Sequencing Center)
Practical Algorithms and Fixed-Parameter Tractability
for the Single Individual SNP Haplotyping Problem .................... 29
R.Rizzi(Universita`diTrento),V.Bafna,S.Istrail(CeleraGenomics),
and G. Lancia (Universita` di Padova)
Methods for Inferring Block-Wise Ancestral History
from Haploid Sequences............................................. 44
R. Schwartz, A.G. Clark (Celera Genomics), and S. Istrail (Celera
Genomics)
Finding Signal Peptides in Human Protein Sequences
Using Recurrent Neural Networks .................................... 60
M. Reczko (Synaptic Ltd.), P. Fiziev, E. Staub (metaGen Pharmaceu-
ticals GmbH), and A. Hatzigeorgiou (University of Pennsylvania)
Generating Peptide Candidates from Amino-Acid Sequence Databases
for Protein Identification via Mass Spectrometry ....................... 68
N. Edwards and R. Lippert (Celera Genomics)
Improved Approximation Algorithms for NMR Spectral Peak Assignment . 82
Z.-Z. Chen (Tokyo Denki University), T. Jiang (University of Califor-
nia, Riverside), G. Lin (University of Alberta), J. Wen (University of
California, Riverside), D. Xu, and Y. Xu (Oak Ridge National Labo-
ratory)
Efficient Methods for Inferring Tandem Duplication History ............. 97
L.Zhang(Nat.UniversityofSingapore),B.Ma(UniversityofWestern
Ontario), and L. Wang (City University of Hong Kong)
Genome Rearrangement Phylogeny Using Weighbor .................... 112
L.-S. Wang (University of Texas at Austin)
VIII Table of Contents
Segment Match Refinement and Applications.......................... 126
A.L. Halpern (Celera Genomics), D.H. Huson (Tu¨bingen University),
and K. Reinert (Celera Genomics)
Extracting Common Motifs under the Levenshtein Measure:
Theory and Experimentation ........................................ 140
E.F. Adebiyi and M. Kaufmann (Universita¨t Tu¨bingen)
Fast Algorithms for Finding Maximum-Density Segments
of a Sequence with Applications to Bioinformatics...................... 157
M.H.Goldwasser(LoyolaUniversityChicago),M.-Y.Kao(Northwest-
ern University), and H.-I. Lu (Academia Sinica)
FAUST: An Algorithm for Extracting Functionally Relevant Templates
from Protein Structures............................................. 172
M. Milik, S. Szalma, and K.A. Olszewski (Accelrys)
Efficient Unbound Docking of Rigid Molecules ......................... 185
D. Duhovny, R. Nussinov, and H.J. Wolfson (Tel Aviv University)
A Method of Consolidating and Combining EST and mRNA Alignments
to a Genome to Enumerate Supported Splice Variants .................. 201
R. Wheeler (Affymetrix)
AMethodtoImprovethePerformanceofTranslationStartSiteDetection
and Its Application for Gene Finding................................. 210
M. Pertea and S.L. Salzberg (The Institute for Genomic Research)
Comparative Methods for Gene Structure Prediction
in Homologous Sequences ........................................... 220
C.N.S. Pedersen and T. Scharling (University of Aarhus)
MultiProt – A Multiple Protein Structural Alignment Algorithm......... 235
M. Shatsky, R. Nussinov, and H.J. Wolfson (Tel Aviv University)
A Hybrid Scoring Function for Protein Multiple Alignment.............. 251
E. Rocke (University of Washington)
Functional Consequences in Metabolic Pathways
from Phylogenetic Profiles .......................................... 263
Y. Bilu and M. Linial (Hebrew University)
Finding Founder Sequences from a Set of Recombinants ................ 277
E. Ukkonen (University of Helsinki)
Estimating the Deviation from a Molecular Clock ...................... 287
L. Nakhleh, U. Roshan (University of Texas at Austin), L. Vawter
(Aventis Pharmaceuticals), and T. Warnow (University of Texas at
Austin)
Table of Contents IX
ExploringtheSetofAllMinimalSequencesofReversals–AnApplication
to Test the Replication-Directed Reversal Hypothesis................... 300
Y.Ajana,J.-F.Lefebvre(Universit´edeMontr´eal),E.R.M.Tillier(Uni-
versity Health Network), and N. El-Mabrouk (Universit´e de Montr´eal)
Approximating the Expected Number of Inversions
Given the Number of Breakpoints.................................... 316
N. Eriksen (Royal Institute of Technology)
Invited Lecture – Accelerating Smith-Waterman Searches ............... 331
G. Myers (Celera Genomics) and R. Durbin (Sanger Centre)
Sequence-Length Requirements for Phylogenetic Methods ............... 343
B.M.E.Moret(UniversityofNewMexico),U.Roshan,andT.Warnow
(University of Texas at Austin)
Fast and Accurate Phylogeny Reconstruction Algorithms
Based on the Minimum-Evolution Principle ........................... 357
R.Desper(NationalLibraryofMedicine,NIH)andO.Gascuel(LIRMM)
NeighborNet: An Agglomerative Method for the Construction
of Planar Phylogenetic Networks..................................... 375
D. Bryant (McGill University) and V. Moulton (Uppsala University)
On the Control of Hybridization Noise
in DNA Sequencing-by-Hybridization................................. 392
H.-W.Leong(NationalUniversityofSingapore),F.P.Preparata(Brown
University), W.-K. Sung, and H. Willy (National University of Singa-
pore)
Restricting SBH Ambiguity via Restriction Enzymes ................... 404
S. Skiena (SUNY Stony Brook) and S. Snir (Technion)
Invited Lecture – Molecule as Computation:
Towards an Abstraction of Biomolecular Systems ...................... 418
E. Shapiro (Weizmann Institute)
Fast Optimal Genome Tiling with Applications to Microarray Design
and Homology Search .............................................. 419
P. Berman (Pennsylvania State University), P. Bertone (Yale Univer-
sity), B. DasGupta (University of Illinois at Chicago), M. Gerstein
(Yale University), M.-Y. Kao (Northwestern University), and M. Sny-
der (Yale University)
Rapid Large-Scale Oligonucleotide Selection for Microarrays............. 434
S. Rahmann (Max-Planck-Institute for Molecular Genetics)
X Table of Contents
Border Length Minimization in DNA Array Design..................... 435
A.B. Kahng, I.I. Ma˘ndoiu, P.A. Pevzner, S. Reda (University of Cal-
ifornia at San Diego), and A.Z. Zelikovsky (Georgia State University)
The Enhanced Suffix Array and Its Applications to Genome Analysis..... 449
M.I. Abouelhoda, S. Kurtz, and E. Ohlebusch (University of Bielefeld)
The Algorithmic of Gene Teams ..................................... 464
A. Bergeron (Universit´e du Qu´ebec a Montreal), S. Corteel (CNRS -
Universit´edeVersailles),andM.Raffinot(CNRS-LaboratoireG´enome
et Informatique)
Combinatorial Use of Short Probes
for Differential Gene Expression Profiling ............................. 477
L.L. Warren and B.H. Liu (North Carolina State University)
Designing Specific Oligonucleotide Probes
for the Entire S. cerevisiae Transcriptome............................. 491
D. Lipson (Technion), P. Webb, and Z. Yakhini (Agilent Laboratories)
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data.. 506
Z. Bar-Joseph, E.D. Demaine, D.K. Gifford (MIT LCS), A.M. Hamel
(WilfridLaurierUniversity),T.S.Jaakkola(MITAILab),andN.Sre-
bro (MIT LCS)
Inversion Medians Outperform Breakpoint Medians
in Phylogeny Reconstruction from Gene-Order Data.................... 521
B.M.E. Moret (University of New Mexico), A.C. Siepel (University of
California at Santa Cruz), J. Tang, and T. Liu (University of New
Mexico)
Modified Mincut Supertrees ......................................... 537
R.D.M. Page (University of Glasgow)
Author Index ................................................. 553
Simultaneous Relevant Feature Identification
and Classification in High-Dimensional Spaces
L.R. Grate1, C. Bhattacharyya2,3, M.I. Jordan2,3, and I.S. Mian1
1 Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA 94720
2 Department of EECS, University of California Berkeley, Berkeley CA 94720
3 Department of Statistics, University of California Berkeley, Berkeley CA 94720
Abstract. Molecular profiling technologies monitor thousands of tran-
scripts, proteins, metabolites or other species concurrently in biologi-
calsamplesofinterest.Giventwo-class,high-dimensionalprofilingdata,
nominal Liknon [4] is a specific implementation of a methodology for
performing simultaneous relevant feature identification and classifica-
tion. It exploits the well-known property that minimizing an l norm
1
(via linear programming) yields a sparse hyperplane [15,26,2,8,17]. This
work (i) examines computational, software and practical issues required
to realize nominal Liknon, (ii) summarizes results from its application
to five real world data sets, (iii) outlines heuristic solutions to problems
posed by domain experts when interpreting the results and (iv) defines
some future directions of the research.
1 Introduction
Biologistsandcliniciansareadoptinghigh-throughputgenomics,proteomicsand
relatedtechnologiestoassistininterrogatingnormalandperturbedsystemssuch
asunaffectedandtumortissuespecimens.Suchinvestigationscangeneratedata
havingtheformD ={(xn,yn),n∈(1,...,N)}wherexn ∈RP and,fortwo-class
data, yn ∈{+1,−1}. Each element of a data point xn is the absolute or relative
abundanceofamolecularspeciesmonitored.Intranscriptprofiling,adatapoint
represents transcript (gene) levels measured in a sample using cDNA, oligonu-
cleotide or similar microarray technology. A data point from protein profiling
can represent Mass/Charge (M/Z) values for low molecular weight molecules
(proteins) measured in a sample using mass spectroscopy.
In cancer biology, profiling studies of different types of (tissue) specimens
are motivated largely by a desire to create clinical decision support systems
for accurate tumor classification and to identify robust and reliable targets,
“biomarkers”, for imaging, diagnosis, prognosis and therapeutic intervention
[14,3,13,27,18,23,9,25,28,19,21,24]. Meeting these biological challenges includes
addressing the general statistical problems of classification and prediction, and
relevant feature identification.
Support Vector Machines (SVMs) [30,8] have been employed successfully for
cancer classification based on transcript profiles [5,22,25,28]. Although mecha-
nisms for reducing the number of features to more manageable numbers include
R.Guig´oandD.Gusfield(Eds.):WABI2002,LNCS2452,pp.1–9,2002.
(cid:1)c Springer-VerlagBerlinHeidelberg2002
2 L.R. Grate et al.
discarding those below a user-defined threshold, relevant feature identification
is usually addressed via a filter-wrapper strategy [12,22,32]. The filter generates
candidate feature subsets whilst the wrapper runs an induction algorithm to
determine the discriminative ability of a subset. Although SVMs and the newly
formulated Minimax Probability Machine (MPM) [20] are good wrappers [4],
the choice of filtering statistic remains an open question.
Nominal Liknon is a specific implementation of a strategy for perform-
ing simultaneous relevant feature identification and classification [4]. It exploits
the well-known property that minimizing an l norm (via linear programming)
1
yields a sparse hyperplane [15,26,2,8,17]. The hyperplane constitutes the clas-
sifier whilst its sparsity, a weight vector with few non-zero elements, defines a
small number of relevant features. Nominal Liknon is computationally less de-
manding than the prevailing filter–(SVM/MPM) wrapper strategy which treats
the problems of feature selection and classification as two independent tasks
[4,16]. Biologically, nominal Liknon performs well when applied to real world
data generated not only by the ubiquitous transcript profiling technology, but
also by the emergent protein profiling technology.
2 Simultaneous Relevant Feature Identification
and Classification
Consider a data set D = {(xn,yn),n ∈ (1,...,N)}. Each of the N data points
(profiling experiments) is a P-dimensional vector of features (gene or protein
abundances) xn ∈ RP (usually N ∼ 101 −102;P ∼ 103 −104). A data point
n is assigned to one of two classes yn ∈ {+1,−1} such a normal or tumor tis-
sue sample. Given such two-class high-dimensional data, the analytical goal is
to estimate a sparse classifier, a model which distinguishes the two classes of
data points (classification) and specifies a small subset of discriminatory fea-
tures(relevantfeatureidentification).AssumethatthedataD canbeseparated
by a linear hyperplane in the P-dimensional input feature space. The learning
task can be formulated as an attempt to estimate a hyperplane, parameterized
in terms of a weight vector w and bias b, via a solution to the following N
inequalities [30]:
ynzn =yn(wTxn−b)≥0
∀n={1,...,N} . (1)
The hyperplane satisfying wTx−b=0 is termed a classifier. A new data point
x (abundances of P features in a new sample) is classified by computing z =
wTx−b. If z >0, the data point is assigned to one class otherwise it belongs to
the other class.
Enumerating relevant features at the same time as discovering a classifier
can be addressed by finding a sparse hyperplane, a weight vector w in which
most components are equal to zero. The rationale is that zero elements do not
contribute to determining the value of z:
Simultaneous Relevant Feature Identification and Classification 3
(cid:1)P
z = wpxp−b .
p=1
If wp = 0, feature p is “irrelevant” with regards to deciding the class. Since
only non-zero elements wp (cid:5)=0 influence the value of z, they can be regarded as
“relevant” features.
The task of defining a small number of relevant features can be equated
with that of finding a small set of non-zero elements. This can be formulated as
an optimization problem; namely that of minimizing the l norm (cid:6)w(cid:6) , where
0 0
(cid:6)w(cid:6)0 =number of{p:wp (cid:5)=0}, the number of non-zero elements of w. Thus we
obtain:
min (cid:6)w(cid:6)
0
w,b
subject toyn(wTxn−b)≥0
∀n={1,...,N} . (2)
Unfortunately, problem (2) is NP-hard [10]. A tractable, convex approxima-
tion to this problem can(cid:2)be obtained by replacing the l0 norm with the l1 norm
(cid:6)w(cid:6)1, where (cid:6)w(cid:6)1 = Pp=1|wp|, the sum of the absolute magnitudes of the
elements of a vector [10]:
(cid:2)
mwi,bn(cid:6)w(cid:6)1 = Pp=1|wp|
subject to yn(wTxn−b)≥0
∀n={1,...,N} . (3)
A solution to (3) yields the desired sparse weight vector w.
Optimization problem (3) can be solved via linear programming [11]. The
ensuingformulationrequirestheimpositionofconstraintsontheallowedranges
of variables. The introduction of new variables up,vp ∈ RP such that |wp| =
up+vp and wp =up−vp ensures non-negativity. The range of wp =up−vp is
unconstrained (positive or negative) whilst up and vp remain non-negative. up
andvp aredesignatedthe“positive”and“negative”partsrespectively.Similarly,
the bias b is split into positive and negative components b = b+−b−. Given a
solution to problem (3), either up or vp will be non-zero for feature p [11]:
(cid:2)
u,vm,bi+n,b− Pp=1(up+vp)
subject to yn((u−v)Txn−(b+−b−))≥1
up ≥0;vp ≥0;b+ ≥0;b− ≥0
∀n={1,...,N};∀p={1,...,P} . (4)
Adetaileddescriptionoftheoriginsofthe≥1constraintcanbefoundelsewhere
[30].
IfthedataD arenotlinearlyseparable,misclassifications(errorsintheclass
labelsyn)canbeaccountedforbytheintroductionofslackvariablesξn.Problem
(4) can be recast yielding the final optimization problem,
4 L.R. Grate et al.
(cid:2) (cid:2)
u,vm,bi+n,b− Pp=1(up+vp)+C Nn=1ξn
subject toyn((u−v)Txn−(b+−b−))≥1−ξn
up ≥0;vp ≥0;b+ ≥0;b− ≥0;ξn ≥0
∀n={1,...,N};∀p={1,...,P} . (5)
C is an adjustable parameter weighing the contribution of misclassified data
points. Larger values lead to fewer misclassifications being ignored: C = 0 cor-
responds to no outliers being ignored whereas C →∞ leads to the hard margin
limit.
3 Computational, Software and Practical Issues
Learning the sparse classifier defined by optimization problem (5) involves min-
imizing a linear function subject to linear constraints. Efficient algorithms for
solving such linear programming problems involving ∼10,000 variables (N) and
∼10,000 constraints (P) are well-known. Standalone open source codes include
lp solve1 and PCx2.
Nominal Liknon is an implementation of the sparse classifier (5). It incor-
poratesroutineswritteninMatlab3 andasystemutilizingperl4 andlp solve.
The code is available from the authors upon request. The input consists of a file
containing an N ×(P +1) data matrix in which each row represents a single
profiling experiment. The first P columns are the feature values, abundances of
molecular species, whilst column P +1 is the class label yn ∈ {+1,−1}. The
outputcomprisesthenon-zerovaluesoftheweightvectorw (relevantfeatures),
the bias b and the number of non-zero slack variables ξn.
The adjustable parameter C in problem (5) can be set using cross validation
techniques. The results described here were obtained by choosing C = 0.5 or
C =1.
4 Application of Nominal Liknon to Real World Data
Nominal Liknon was applied to five data sets in the size range (N = 19, P =
1,987)to(N =200,P =15,154).AdatasetD yieldedasparseclassifier,w and
b,andaspecificationofthelrelevantfeatures(P (cid:9)l).Sincetheprofilingstudies
produced only a small number of data points (N (cid:10)P), the generalization error
of a nominal Liknon classifier was determined by computing the leave-one-out
error for l-dimensional data points. A classifier trained using N −1 data points
wasusedtopredicttheclassofthewithhelddatapoint;theprocedurerepeated
N times. The results are shown in Table 1.
Nominal Liknon performs well in terms of simultaneous relevant feature
identification and classification. In all five transcript and protein profiling data
1 http://www.netlib.org/ampl/solvers/lpsolve/
2 http://www-fp.mcs.anl.gov/otc/Tools/PCx/
3 http://www.mathworks.com
4 http://www.perl.org/