2007 RECOMB - LNBI 4453 CD-ROM
RB-Finder: An Improved Distance-Based Sliding Window Method to Detect Recombination Breakpoints
Wah-Heng Lee1,2 and Wing-Kin Sung1,2
1Genome 2National
Institute of Singapore
University of Singapore [e-mail] [e-mail] Abstract. Recombination detection is important before inferring phylogenetic relationships. This will eventually lead to a better understanding of pathogen evolution, more accurate genotyping and advancements in vaccine development. In this paper, we introduce RB-Finder, a fast and accurate distance-based window method to detect recombination in a multiple sequence alignment. Our method introduces a more informative distance measure and a novel weighting strategy to reduce the window size sensitivity problem and hence improve the accuracy of breakpoint detection. Furthermore, our method is faster than existing phylogenybased methods since we do not need to construct and compare complex phylogenetic trees. When compared with the current best method Pruned-PDM, we are about a few hundred times more efficient. Experimental evaluation of RB-Finder using synthetic and biological datasets showed that our method is more accurate than existing phylogeny-based methods. We also show how our method has potential use in other related applications such as genotyping. Keywords: Recombination detection, sliding window, phylogeny, genotyping. LNBI 4453, p. 518 ff. Full article in PDF | BibTeX
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Multivariate Segmentation in the Analysis of Transcription Tiling Array Data
Antonio Piccolboni Affymetrix, Inc., 6650 Vallejo St. Suite 100, Emeryville, CA 94608 [e-mail] Abstract. Tiling DNA microarrays extend current microarray technology by probing the nonrepeat portion of a genome at regular intervals in an unbiased fashion. A fundamental problem in the analysis of these data is the detection of genomic regions that are differentially transcribed across multiple conditions. We propose a linear time algorithm based on segmentation techniques and linear modeling that can work at a user-selected false discovery rate. It also attains a four-fold sensitivity gain over the only competing algorithm when applied to a whole genome transcription data set spanning the embryonic development of Drosophila melanogaster. LNBI 4453, p. 311 ff. Full article in PDF | BibTeX
[e-mail] Springer-Verlag Berlin Heidelberg 2007
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A Bayesian Model That Links Microarray mRNA Measurements to Mass Spectrometry Protein Measurements
Anitha Kannan1, Andrew Emili2, and Brendan J. Frey3 Research, Cambridge, UK [e-mail] http://www.research.microsoft.com/~ankannan
2Banting 1Microsoft
& Best Department of Medical Research, University of Toronto, Canada [e-mail] http://emililab.med.utoronto.ca
3Electrical
& Computer Engineering, University of Toronto, Canada [e-mail] http://www.psi.toronto.edu Abstract. An important problem in biology is to understand correspondences between mRNA microarray levels and mass spectrometry peptide counts. Recently, a compendium of mRNA expression levels and protein abundances were released for the entire genome of the laboratory mouse, Mus musculus. The availability of these two data sets facilitate using machine learning methods to automatically infer plausible correspondences between the gene products. Knowing these correspondences can be helpful either for predicting protein abundances from microarray data or as an independent source of information that can be used for learning richer models such as regulatory networks. We propose a probabilistic model that relates protein abundances to mRNA expression levels. Using cross-mapped data from the above-mentioned studies, we learn the model and then score the genes for their strength of relationship by performing probabilistic inference in the learned model. While we gave a simplified outline of our technique in a publication aimed at biologists (Cell 2006), in this paper, we give a complete
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description of the Bayesian model and the computational technique used to perform inference. In addition, we demonstrate that the Bayesian technique achieves mappings with higher statistical significance, compared to standard linear regression and a maximum likelihood version of the proposed model. LNBI 4453, p. 325 ff. Full article in PDF | BibTeX
[e-mail] Springer-Verlag Berlin Heidelberg 2007
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Rearrangements in Genomes with Centromeres Part I: Translocations*
Michal Ozery-Flato and Ron Shamir School of Computer Science, Tel-Aviv University, Tel Aviv 69978, Israel [e-mail] [e-mail] Abstract. A centromere is a special region in the chromosome that plays a vital role during cell division. Every new chromosome created by a genome rearrangement event must have a centromere in order to survive. This constraint has been ignored in the computational modeling and analysis of genome rearrangements to date. Unlike genes, the different centromeres are indistinguishable, they have no orientation, and only their location is known. A prevalent rearrangement event in the evolution of multi-chromosomal species is translocation, i.e., the exchange of tails between two chromosomes. A translocation may create a chromosome with no centromere in it. In this paper we study for the first time centromeres-aware genome rearrangements. We present a polynomial time algorithm for computing a shortest sequence of translocations transforming one genome into the other, where all of the intermediate chromosomes must contain centromeres. We view this as a first step towards analysis of more general genome rearrangement models that take centromeres into consideration.
*This
study was supported in part by the Israeli Science Foundation (grant 309/02).
LNBI 4453, p. 339 ff. Full article in PDF | BibTeX
[e-mail]
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Springer-Verlag Berlin Heidelberg 2007
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Identification of Deletion Polymorphisms from Haplotypes
Erik Corona1, Benjamin Raphael2, and Eleazar Eskin3
1Dept.
of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92092 [e-mail]
2Dept.
of Computer Science & Center for Computational Molecular Biology, Brown University, Providence, RI 02912 [e-mail]
3Dept.
of Computer Science, Dept. of Human Genetics, University of California, Los Angeles, CA 90095 [e-mail] Abstract. Numerous efforts are underway to catalog genetic variation in human populations. While the majority of studies of genetic variation have focused on single base pair differences between individuals, i.e. single nucleotide polymorphisms (SNPs), several recent studies have demonstrated that larger scale structural variation including copy number polymorphisms and inversion polymorphisms are also common. However, direct techniques for detection and validation of structural variants are generally much more expensive than detection and validation of SNPs. For some types of structural variation, in particular deletions, the polymorphism produces a distinct signature in the SNP data. In this paper, we describe a new probabilistic method for detecting deletion polymorphisms from SNP data. The key idea in our method is that we estimate the frequency of the haplotypes in a region of the genome both with and without the possibility of a deletion in the region and apply a generalized likelihood ratio test to assess the significance of a deletion. Application of our method to the HapMap Phase I data revealed 319 candidate deletions, 142 of these overlap with variants identified in earlier studies, while 177 are novel. Using Phase II HapMap data we predict 6730 deletions.
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LNBI 4453, p. 354 ff. Full article in PDF | BibTeX
[e-mail] Springer-Verlag Berlin Heidelberg 2007
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Minimizing and Learning Energy Functions for Side-Chain Prediction
Chen Yanover1, Ora Schueler-Furman2, and Yair Weiss1
1School
of Computer Science and Engineering, The Hebrew University of Jerusalem, 91904 Jerusalem, Israel [e-mail] [e-mail]
2Department
of Molecular Genetics and Biotechnology, Hadassah Medical School, The Hebrew University of Jerusalem, 91120 Jerusalem, Israel [e-mail] Abstract. Side-chain prediction is an important subproblem of the general protein folding problem. Despite much progress in side-chain prediction, performance is far from satisfactory. As an example, the ROSETTA program that uses simulated annealing to select the minimum energy conformations, correctly predicts the first two side-chain angles for approximately 72% of the buried residues in a standard data set. Is further improvement more likely to come from better search methods, or from better energy functions? Given that exact minimization of the energy is NP hard, it is difficult to get a systematic answer to this question. In this paper, we present a novel search method and a novel method for learning energy functions from training data that are both based on Tree Reweighted Belief Propagation (TRBP). We find that TRBP can find the global optimum of the ROSETTA energy function in a few minutes of computation for approximately 85% of the proteins in a standard benchmark set. TRBP can also effectively bound the partition function which enables using the Conditional Random Fields (CRF) framework for learning. Interestingly, finding the global minimum does not significantly improve side-chain prediction for an energy function based on ROSETTA's default energy terms (less than 0.1%), while
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learning new weights gives a significant boost from 72% to 78%. Using a recently modified ROSETTA energy function with a softer Lennard-Jones repulsive term, the global optimum does improve prediction accuracy from 77% to 78%. Here again, learning new weights improves side-chain modeling even further to 80%. Finally, the highest accuracy (82.6%) is obtained using an extended rotamer library and CRF learned weights. Our results suggest that combining machine learning with approximate inference can improve the state-of-the-art in side-chain prediction. LNBI 4453, p. 381 ff. Full article in PDF | BibTeX
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Protein Conformational Flexibility Analysis with Noisy Data
Anshul Nigham1 and David Hsu2
1SingaporeMIT
Alliance, Singapore 117576, Singapore [e-mail]
2National
University of Singapore, Singapore 117543, Singapore [e-mail] Abstract. Protein conformational changes play a critical role in biological functions such as ligand-protein and protein-protein interactions. Due to the noise in structural data, determining salient conformational changes reliably and efficiently is a challenging problem. This paper presents an efficient algorithm for analyzing protein conformational changes, using noisy data. It applies a statistical flexibility test to all contiguous fragments of a protein and combines the information from these tests to compute a consensus flexibility measure for each residue of the protein. We tested the algorithm, using data from the Protein Data Bank and the Macromolecular Movements Database. The results show that our algorithm can reliably detect different types of salient conformational changes, including well-known examples such as hinge and shear, as well as the flap motion of HIV-1 protease. The software implementing our algorithm is available at http://motion.comp.nus.edu.sg/projects/ proflexana/proflexana.html. LNBI 4453, p. 396 ff. Full article in PDF | BibTeX
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[e-mail] Springer-Verlag Berlin Heidelberg 2007
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Deterministic Pharmacophore Detection Via Multiple Flexible Alignment of Drug-Like Molecules
Yuval Inbar1, Dina Schneidman-Duhovny1, Oranit Dror1, Ruth Nussinov2,3, and Haim J. Wolfson1 of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel, Fax: [phone] [e-mail]
2Sackler 1School
Inst. of Molecular Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel Research Program, SAIC-Frederick, Inc. Center for Cancer Research, Nanobiology Program NCI-Frederick, Frederick, MD 21702, USA Abstract. We present a novel highly efficient method for the detection of a pharmacophore from a set of ligands/drugs that interact with a target receptor. A pharmacophore is a spatial arrangement of physico-chemical features in a ligand that is responsible for the interaction with a specific receptor. In the absence of a known 3D receptor structure, a pharmacophore can be identified from a multiple structural alignment of the ligand molecules. The key advantages of the presented algorithm are: (a) its ability to multiply align flexible ligands in a deterministic manner, (b) its ability to focus on subsets of the input ligands, which may share a large common substructure, resulting in the detection of both outlier molecules and alternative binding modes, and (c) its computational efficiency, allows which to detect pharmacophores shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a standard default parameter set, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large drug-like molecule databases.
3Basic
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Supplementary Material: http://bioinfo3d.cs.tau.ac.il/pharma/supp.html Keywords: Computer-Aided Drug Design (CADD), Rational Drug Discovery, 3D Molecular Similarity, 3D Molecular Superposition. LNBI 4453, p. 412 ff. Full article in PDF | BibTeX
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Design of Compact, Universal DNA Microarrays for Protein Binding Microarray Experiments
Anthony A. Philippakis1,3,4, Aaron M. Qureshi1,5, Michael F. Berger1,4, and Martha L. Bulyk1,2,3,4
1Division
of Genetics, Department of Medicine and
2Department
of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
3Harvard/MIT
Division of Health Sciences and Technology (HST), Cambridge, MA 02138 [e-mail]
4Harvard
University Graduate Biophysics Program, Cambridge, MA 02138 of Mathematics, University of Maryland, College Park, MD 20742
5Department
Abstract. Our group has recently developed a compact, universal protein binding microarray (PBM) that can be used to determine the binding preferences of transcription factors (TFs) [1]. This design represents all possible sequence variants of a given length k (i.e., all k-mers) on a single array, allowing a complete characterization of the binding specificities of a given TF. Here, we present the mathematical foundations of this design based on de Bruijn sequences generated by linear feedback shift registers. We show that these sequences represent the maximum number of variants for any given set of array dimensions (i.e., number of spots and spot lengths), while also exhibiting desirable pseudo-randomness properties. Moreover, de Bruijn sequences can be selected that represent gapped sequence patterns, further increasing the coverage of the array. This design yields a powerful experimental platform that allows the binding preferences of TFs to be determined with unprecedented resolution.
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Keywords: de Bruijn sequences, linear feedback shift registers, protein binding microarrays, motif, transcription factor. LNBI 4453, p. 430 ff. Full article in PDF | BibTeX
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Improved Ranking Functions for Protein and ModificationSite Identifications
Marshall Bern and David Goldberg Palo Alto Research Center, 3333 Coyote Hill Rd., Palo Alto, CA 94304, USA [e-mail] [e-mail] Abstract. There are a number of computational tools for assigning identifications to peptide tandem mass spectra, but many fewer tools for the crucial next step of integrating spectral identifications into higher-level identifications, such as proteins or modification sites. Here we describe a new program called ComByne for scoring and ranking higher-level identifications. We compare ComByne to existing algorithms on several complex biological samples, including a sample of mouse blood plasma spiked with known concentrations of human proteins. A Web interface to our software is at http://bio.parc.xerox.com. LNBI 4453, p. 444 ff. Full article in PDF | BibTeX
[e-mail] Springer-Verlag Berlin Heidelberg 2007
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Peptide Retention Time Prediction Yields Improved Tandem Mass Spectrum Identification for Diverse Chromatography Conditions
Aaron A. Klammer, Xianhua Yi, Michael J. MacCoss, and William Stafford Noble Genome Sciences Department, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195-5065 [e-mail] [e-mail] [e-mail] [e-mail] Abstract. Most tandem mass spectrum identification algorithms use information only from the final spectrum, ignoring precursor information such as peptide retention time (RT). Efforts to exploit peptide RT for peptide identification can be frustrated by its variability across liquid chromatography analyses. We show that peptide RT can be reliably predicted by training a support vector regressor on a single chromatography run. This dynamically trained model outperforms a published statically trained model of peptide RT across diverse chromatography conditions. In addition, the model can be used to filter peptide identifications that produce large discrepancies between observed and predicted RT. After filtering, estimated true positive peptide identifications increase by as much as 50% at a false discovery rate of 3%, with the largest increase for non-specific cleavage with elastase. Keywords: Mass spectrometry, proteomics, peptide identification, retention time, chromatography, machine learning, support vector regression. LNBI 4453, p. 459 ff. Full article in PDF | BibTeX
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A Fast and Accurate Algorithm for the Quantification of Peptides from Mass Spectrometry Data
Ole Schulz-Trieglaff1,2, Rene Hussong3, Clemens Grpl2, Andreas Hildebrandt3, and Knut Reinert2
1Max
Planck Research School, Berlin, Germany [e-mail]
2Department 3Center
of Computer Science and Mathematics, Free University Berlin
for Bioinformatics, Saarland University
Abstract. Liquid chromatography combined with mass spectrometry (LC-MS) has become the prevalent technology in high-throughput proteomics research. One of the aims of this discipline is to obtain accurate quantitative information about all proteins and peptides in a biological sample. Due to size and complexity of the data generated in these experiments, this problem remains a challenging task requiring sophisticated and efficient computational tools. We propose an algorithm that can quantify even low abundance peptides from LC-MS data. Our approach is flexible and can be applied to preprocessed and raw instrument data. It is based on a combination of the sweep line paradigm with a novel wavelet function tailored to detect isotopic patterns. We evaluate our technique on several data sets of varying complexity and show that we are able to rapidly quantify peptides with high accuracy in a sound algorithmic framework. LNBI 4453, p. 473 ff. Full article in PDF | BibTeX
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[e-mail] Springer-Verlag Berlin Heidelberg 2007
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Association Mapping of Complex Diseases with Ancestral Recombination Graphs: Models and Efficient Algorithms
Yufeng Wu Department of Computer Science, University of California, Davis, Davis, CA 95616, U.S.A. [e-mail] Abstract. Association, or LD (linkage disequilibrium), mapping is an intensely-studied approach to gene mapping (genome-wide or in candidate regions) that is widely hoped to be able to efficiently locate genes influencing both complex and Mendelian traits. The logic underlying association mapping implies that the best possible mapping results would be obtained if the genealogical history of the sampled individuals were explicitly known. Such a history would be in the form of an "ancestral recombination graph (ARG)". But despite the conceptual importance of genealogical histories to association mapping, few practical association mapping methods have explicitly used derived genealogical aspects of ARGs. Two notable exceptions are [35] and [23]. In this paper we develop an association mapping method that explicitly constructs and samples minARGs (ARGs that minimize the ...