Sahinalp, S. C.

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Genome Biology 2007, 8:301Meeting reportThe intelligence in developing systems for molecular biologyS Cenk SahinalpAddress: School of Computing Science, Simon Fraser University, University Drive, Burnaby, BC, Canada V5A 1S6. Email: cenk@cs.sfu.caPublished: 31 January 2007Genome Biology 2007, 8:301 (doi:10.1186/gb-2007-8-1-301)The electronic version of this article is the complete one and can befound online at http://genomebiology.com/2007/8/1/301© 2007 BioMed Central Ltd A report on the 14th Annual International Conference onIntelligent Systems for Molecular Biology (ISMB), Fortaleza,Brazil, 6-10 August 2006.The 900 or so participants at the Annual InternationalConference on Intelligent Systems for Molecular Biology lastAugust were treated to talks on topics ranging fromsequence analysis, structural bioinformatics, andcomparative genomics through to proteomics and systemsbiology. It was evident that interest in RNA, especially non-coding RNA (ncRNA), is growing, with quite a few talks onlocating and predicting the structure of small (and not sosmall) ncRNAs. As well as such relatively new topics, theclassic problem of discovering sequence motifs andassessing their significance seems to be re-emerging,especially in the context of new applications. As thebiological problems scientists aim to address become morecomplex, the mathematical principles and computationaltools being developed to solve them must become moresophisticated. The conference showed that not only arecomputer science and mathematics being applied to solvingkey problems in molecular biology, but these problems areinspiring the development of new computer science, and, toa certain degree, new mathematics.Sequences and statisticsSequence analysis was still the theme running throughmost talks. Its application outside DNA and proteins wasillustrated by Kiyoko Aoki-Kinoshita (Kyoto University,Japan), who described motif discovery in carbohydratesugar chains (glycans), the third major class ofmacromolecules. Starting from a single monosaccharide,many glycans have a tree-like structure consisting ofbranching chains with various combinations ofmonosaccharides. Aoki-Kinoshita described a profileMarkov model using a probabilistic sibling-dependent tree(PST) that aims to recognize glycan motifs, which arebasically paths on their tree representation. The model hasbeen tested successfully on both synthetic glycans andglycan data from the KEGG GLYCAN database, accessedfrom [http://www.genome.jp/kegg/glycan].Eugene Fratkin (Stanford University, Palo Alto, USA)described a combinatorial technique for finding motifs.Combinatorial techniques, unlike commonly used machinelearning techniques, are based on a branch of mathematicscalled combinatorics (graph theory is part ofcombinatorics). The method, appropriately namedMotifCut, can be accessed at[http://motifcut.stanford.edu] and is a graph-theoreticalapproach to the problem which, through an optimizationmethod called convex optimization, can be solved inpolynomial time. The main idea of MotifCut is to build agraph in which the vertices represent all sequences of agiven length (k-mers) in the input sequences and the edgesrepresent the degree of sequence similarity. In this graph, amotif is defined as the maximum density subgraph; that is,a set of k-mers that have the most highly weighted edgesbetween each pair. The dense subgraph is computed byiterative application of the classic min-cut algorithm(hence the name MotifCut) of Gallo and colleagues (1989).Uri Keich (Cornell University, Ithaca, USA) introduced anew optimization function to improve the ability of theGibbs sampling algorithm to discover motifs, especiallyweak motifs. Keich showed that relying on entropy scoresand their E-values when finding weak motifs by Gibbssampling can lead to undesirable results. As an alternative,he suggested using the incomplete likelihood ratio as ascoring function, which performs much better on thefamed ‘implanted motif’ problem. The implanted motiffinding problem is an artificial problem in which a motif ofa given length (say 17 nucleotides) is randomly implantedin a number of genome sequences (say five); eachimplantation differs from others in at most a fixed numberof locations (for example, three). Knowing the length of themotif, and the differences between the occurrences of themotif, a motif finder is supposed to find the motif exactly.The problem of counting the occurrences of a positionweight matrix in a DNA sequence has applications in cis-regulatory analysis. Saurabh Sinha (University of Illinois,Urbana-Champaign, USA) described a probabilistic scoringmethod to solve this problem in a statistically soundframework. He also described a local search technique tosolve the discriminative motif-finding problem; that is, howto find position weight matrices that have high counts in oneset of sequences and low counts in another set.Also addressing fundamental statistical questions in bio-informatics, Karsten Borgwardt (University of Munich,Germany) introduced a test for determining whether twosets of biological observations have been generated by thesame probability distribution. This involves a ‘kernel’-basedstatistical test, which compares the maximum discrepancybetween the means of a set of functions. A discrepancybetween the means of any member of a kernel-function classin the two observations implies a difference in the distribu-tions that must have generated them. The test has beenapplied to various tasks, such as microarray data compari-son, cancer diagnosis and classification of protein function.One very important and timely problem in sequence analysiswas discussed by Tien-Ho Lin (Carnegie Mellon University,Pittsburgh, USA) - the identification of victims in a massdisaster using DNA fingerprints. In such a situation,hundreds of samples are taken from remains that must bematched to the pedigrees of the victims’ surviving relatives,and the DNA is also degraded by heat and exposure. Lindescribed a very interesting probabilistic framework forclustering samples while eliminating implausible sample-pedigree pairings. This framework handles both degradedsamples (missing values) and experimental errors inproducing and/or reading a genotype.Lutz Krause (Bielefeld University, Bielefeld, Germany)described the application of the powerful pyrosequencing-based technology (developed by the company 454 LifeSciences and now marketed by Roche Diagnostics) toexplore the genomes of organisms that are difficult toculture by conventional means, and which can be studiedonly through DNA extracted directly from environmentalsources. Krause described the development of a new gene-finding algorithm that aims to address the problems inidentifying genes from this DNA, namely the short lengths ofthe contigs and the existence of in-frame stop codons andframeshifts, which arise due to poor sequence quality inDNA extracted from environmental sources.Exploring gene expressionA popular theme in the contributions on transcriptomics wasnovel motif-discovery and modeling algorithms for transcrip-tion factor binding sites. Barret Foat (Columbia University,New York, USA) described a new algorithm, MatrixREDUCE,to model transcription factor binding sites. MatrixREDUCEcan be found at [http://bussemaker.bio.columbia.edu/software/MatrixREDUCE]. The algorithm uses genome-wideoccupancy data for a transcription factor and the associatednucleotide sequences to discover the sequence-specificbinding affinity of the factor.Yong Lu (Carnegie Mellon University, Pittsburgh, USA)described the identification of cycling (self-regulatory) genesfrom gene-expression data. The idea is to combinemicroarray data from multiple species with sequenceinformation in a graph-theoretical framework in which eachgene is represented by a node and each edge representssequence similarity. Starting from the measured expressionvalues for each species, a ‘belief propagation’ machinelearning approach is used to determine a posterior score,indicating expression, for genes, which is then used todetermine a new set of cycling genes from each species.Gene-expression profiling is commonly used as a tool foridentifying genes that are important for the developmentand maintenance of different cell types. Yuan Qi(Massachusetts Institute of Technology, Cambridge, USA)described work aimed at detecting relevant genes from alarge set of expression profiles via a novel Bayesian, ‘semi-supervised’ clustering method called BGEN. This newmethod trains a kernel classifier based on labeled andunlabeled gene-expression examples. The semi-supervisedtrained classifier can then be used to efficiently classify theremaining genes in the dataset.RNA bioinformatics and structural informaticsThe importance of ncRNAs was recognized in 2006 by theaward of the Nobel prize for Physiology or Medicine for workon RNA interference (RNAi), and interest in ncRNAs was clearin the number and quality of talks on this topic at the meeting.One theme was the detection of potential ncRNAs in genomesequences. Shaujie Zhang (University of California, San Diego,USA) introduced a framework for constructing and comparingsequence-based ncRNA filters. The use of this framework givesrise to a new formulation of the covariance model, which, inturn, speeds up the alignment of the potential RNA sequencewith the model and thus gives a much faster ncRNA filter thanthe available alternatives. Unlike short interfering RNAs(siRNAs) and micro RNAs (miRNAs), there are no currenteffective computational and experimental screening methodsfor the class of ncRNAs known as small modulatory RNAs(smRNAs). These are a novel class of small (approximately 20base pair) RNAs that are double-stranded, exist in the cellnucleus, and do not code for proteins. Despite their very smallsize, smRNAs perform a major role in the differentiation ofneural stem cells to neurons. There are currently no screeningmethods for them. Neil Jones (University of California, SanDiego, USA) addressed this question and described a graph-theoretical discovery method  for long and highly similar motifs301.2 Genome Biology 2007, Volume 8, Issue 1, Article 301 Sahinalp http://genomebiology.com/2007/8/1/301Genome Biology 2007, 8:301through a comparative genomics approach that does notrequire an alignment of orthologous upstream regions (whichdo not align well); which can be accessed  at[http://www.cse.ucsd.edu/groups/bioinformatics].At present, RNA structure prediction is based on thermo-dynamic models. Chuong Do (Stanford University, PaloAlto, USA) described a computational alternative to thesemodels that derives RNA-folding parameters throughstatistical learning tools. The computational tooldeveloped, called Contrafold and accessible at[http://contra.stanford.edu/contrafold/], is based onconditional log-linear models, a class of probabilisticmodels that generalize stochastic context-free grammars.By providing a means of distinguishing RNA stems ofdifferent lengths, Contrafold can predict the secondarystructure of treacherous RNA sequences, such as 5S rRNA,much more accurately than the thermodynamic models.Structural-similarity searching among small molecules is astandard tool in molecular classification and in silico drugdiscovery, and public databases of such information are nowbeing developed. I described our team’s work on a novelk-nearest-neighbor search method for structural similarityand classification of small molecules, represented by arrays ofchemical descriptors. This is aimed at finding the bestmethods to separate molecules that exhibit a given activityfrom those that do not. We have shown how to compute aweighted Minkowski distance, which aims to show howsimilar the molecules are in terms of the bioactivity inquestion, on the descriptor arrays for the best separationthrough a linear programming formulation. I also described adata structure that exploits all available memory to search forall similar small molecules to a query molecule through adistance-based approach.Visualizing systems biologyA common theme in contributions on systems biology wasthe integration of various data sources for visualizing,inferring the topologies of, or understanding the dynamicsof networks and subnetworks. Using genotype information,gene expression, protein-protein interaction, protein phos-phorylation and transcription-factor-binding information,Zhidong Tu (University of Southern California, LosAngeles, USA) described ways of showing which genescontrol the expression levels of a specific gene. Hedescribed a stochastic algorithm that infers the causalgenes and identifies significant pathways on the expressionnetwork where each node is either a protein or atranscription factor.Yanay Ofran (Columbia University, New York, USA) intro-duced a new platform for integrating molecular data andinsights about the qualities of individual proteins in anetwork visualizer, which goes beyond the traditionaltopology-oriented presentation. The platform generatesnetworks on the macro systems level and analyzes themolecular characteristics of each protein on the micro level atthe same time. It also annotates the function and subcellularlocalization of each protein and displays the process on animage of a cell. Adrien Faure (Institut de Biologie duDeveloppement de Narseille-Luminy, France) aims tounderstand the dynamics of a regulatory network by treating itas a Boolean logic circuit that can work synchronously orasynchronously. The idea makes a lot of sense, as most of theavailable data on regulation are qualitative. Faure showed howthis general approach can be applied to test some of thedynamical properties of the mammalian cell.Cells need to adapt the activity levels of metabolic functionsto changes in the environment. Jose Nacher (KyotoUniversity, Kyoto, Japan) explored the connections betweenthe gene-expression response to external changes and theinduction or repression of specific metabolic functions. Histeam has analyzed the transcriptional response ofSaccharomyces cerevisiae to different stress conditions orstress signals. These signal-induced expression data are thenintegrated with structural data about the yeast network andthe topological properties of the induced or repressedsubnetworks are analyzed. These subnetworks turn out to bequite different from random networks; for example, theirdegree of distribution, the number of vertices with a specificnumber of neighbors, seems to have a heavy tail, indicatingfew nodes with many neighbors.Mustafa Kirac (Case Western Reserve University, Cleveland,USA) addressed the question of automatic assignment ofGene Ontology (GO) annotations to partially annotatedproteins through a data mining approach. The most accurateprotein annotations are currently provided by curators, butthe possibility of automatically assigning annotationsthrough mining of protein-protein interaction networks isappealing. Kirac showed how to compute the probabilisticrelationships between GO annotations of proteins and assignhighly correlated GO terms of annotated proteins to non-annotated proteins in the target set to achieve a predictionaccuracy of up to 81%.The meeting showed how much bioinformatics has maturedin the past few years. The computational tools for what cannow be considered as ‘classic’ bioinformatics problems, suchas motif discovery and RNA structure prediction, now havemuch more solid foundations. The need for depth indeveloping both mathematical models and algorithm tools isvery evident for these problems, and their application is alsobeing broadened. As many of the talks, especially in systemsbiology, showed, new problems are emerging very rapidly,requiring development of new computational tools that needto integrate various types of data. These are all signs thatbioinformatics is maturing into an independent scientificfield with considerable depth and breadth.http://genomebiology.com/2007/8/1/301 Genome Biology 2007, Volume 8, Issue 1, Article 301 Sahinalp 301.3Genome Biology 2007, 8:301AcknowledgementsI thank the members of SFU Lab for Computational Biology, in particularEmre Karakoc, Rahaleh Salari, Cagri Aksay and Fereydoun Hormozdiari,for their help.301.4 Genome Biology 2007, Volume 8, Issue 1, Article 301 Sahinalp http://genomebiology.com/2007/8/1/301Genome Biology 2007, 8:301

The Intelligence in Developing Systems for Molecular Biology

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