485 research outputs found
2017 ISCB Accomplishment by a Senior Scientist Award: Pavel Pevzner
No full textThe International Society for Computational Biology (ISCB) recognizes an established scientist each year with the Accomplishment by a Senior Scientist Award for significant contributions he or she has made to the field. This award honors scientists who have contributed to the advancement of computational biology and bioinformatics through their research, service, and education work. Pavel Pevzner, PhD, Ronald R. Taylor Professor of Computer Science and Director of the NIH Center for Computational Mass Spectrometry at University of California, San Diego, has been selected as the winner of the 2017 Accomplishment by a Senior Scientist Award. The ISCB awards committee, chaired by Dr. Bonnie Berger of the Massachusetts Institute of Technology, selected Pevzner as the 2017 winner. Pevzner will receive his award and deliver a keynote address at the 2017 Intelligent Systems for Molecular Biology-European Conference on Computational Biology joint meeting (ISMB/ECCB 2017) held in Prague, Czech Republic from July 21-July 25, 2017. ISMB/ECCB is a biennial joint meeting that brings together leading scientists in computational biology and bioinformatics from around the globe.</ns4:p
CONFORMAL SYMMETRY BREAKING OPERATORS FOR DIFFERENTIAL FORMS ON SPHERES
No full textInternational audienceWe give a complete classification of conformally covariant differential operators between the spaces of i-forms on the sphere S n and j-forms on the totally geodesic hypersphere S n−1. Moreover, we find explicit formulae for these new matrix-valued operators in the flat coordinates in terms of basic operators in differential geometry and classical orthogonal polynomials. We also establish matrix-valued factorization identities among all possible combinations of conformally co-variant differential operators. The main machinery of the proof is the "F-method" based on the "algebraic Fourier transform of Verma modules" (Kobayashi-Pevzner [Selecta Math. 2016]) and its extension to matrix-valued case developed here. A short summary of the main results was announced in [C. R. Acad. Sci. Paris, 2016]
Genomic distance under gene substitutions
Get PDFDias Vieira Braga M, Machado R, Ribeiro LC, Stoye J. Genomic distance under gene substitutions. BMC Bioinformatics. 2011;12(Suppl 9: Proc. of RECOMB-CG 2011): S8.Background:
The distance between two genomes is often computed by comparing only the common markers between them. Some approaches are also able to deal with non-common markers, allowing the insertion or the deletion of such markers. In these models, a deletion and a subsequent insertion that occur at the same position of the genome count for two sorting steps.
Results:
Here we propose a new model that sorts non-common markers with substitutions, which are more powerful operations that comprehend insertions and deletions. A deletion and an insertion that occur at the same position of the genome can be modeled as a substitution, counting for a single sorting step.
Conclusions:
Comparing genomes with unequal content, but without duplicated markers, we give a linear time algorithm to compute the genomic distance considering substitutions and double-cut-and-join (DCJ) operations. This model provides a parsimonious genomic distance to handle genomes free of duplicated markers, that is in practice a lower bound to the real genomic distances. The method could also be used to refine orthology assignments, since in some cases a substitution could actually correspond to an unannotated orthology
Minimal Principal Series Representations of SL(3,R)
No full textWe discuss the properties of principal series representations of SL(3,R) induced from a minimal parabolic subgroup. We present the general theory of induced representations in the language of fiber bundles, and outline the construction of principal series from structure theory of semisimple Lie groups. For SL(3,R), we show the explicit realization a novel picture of principal series based on the nonstandard picture introduced by Kobayashi, Orsted, and Pevzner for symplectic groups. We conclude by studying the K-types of SL(3,R) through Frobenius reciprocity, and evaluate prospects in developing simple intertwiners between principal series representations.MathematicsBachelors of Science (BS
On the weight of indels in genomic distances
Get PDFDias Vieira Braga M, Machado R, Ribeiro LC, Stoye J. On the weight of indels in genomic distances. BMC Bioinformatics. 2011;12(Suppl 9: RECOMB-CG 2011): S13.Background:
Classical approaches to compute the genomic distance are usually limited to genomes with the same content, without duplicated markers. However, differences in the gene content are frequently observed and can reflect important evolutionary aspects. A few polynomial time algorithms that include genome rearrangements, insertions and deletions (or substitutions) were already proposed. These methods often allow a block of contiguous markers to be inserted, deleted or substituted at once but result in distance functions that do not respect the triangular inequality and hence do not constitute metrics.
Results:
In the present study we discuss the disruption of the triangular inequality in some of the available methods and give a framework to establish an efficient correction for two models recently proposed, one that includes insertions, deletions and double cut and join (DCJ) operations, and one that includes substitutions and DCJ operations.
Conclusions:
We show that the proposed framework establishes the triangular inequality in both distances, by summing a surcharge on indel operations and on substitutions that depends only on the number of markers affected by these operations. This correction can be applied a posteriori, without interfering with the already available formulas to compute these distances. We claim that this correction leads to distances that are biologically more plausible
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No full textThe DNA motif discovery problem abstracts the task of discovering short, conserved sites in genomic DNA. Pevzner and Sze recently described a precise combinatorial formulation of motif discovery that motivates the following algorithmic challenge: find twenty planted occurrences of a motif of length fifteen in roughly twelve kilobases of genomic sequence, where each occurrence of the motif differs from its consensus in four randomly chosen positions. Such “subtle ” motifs, though statistically highly significant, expose a weakness in existing motif finding algorithms, which typically fail to discover them. Pevzner and Sze introduced new algorithms to solve their (15,4)-motif challenge, but these methods do not scale efficiently to more difficult problems in the same family, such as the (14,4)-, (16,5)-, and (18,6)-motif problems. We introduce a novel motif discovery algorithm, Projection, designed to enhance the perfor-mance of existing motif finders using random projections of the input’s substrings. Experiments on synthetic data demonstrate that Projection remedies the weakness observed in existing algo-rithms, typically solving the difficult (14,4)-, (16,5)-, and (18,6)-motif problems. Our algorithm is robust to nonuniform background sequence distributions and scales to larger amounts of sequence than that specified in the original challenge. A probabilistic estimate suggests that related motif-finding problems that Projection fails to solve are in all likelihood inherently intractable. We also test the performance of our algorithm on realistic biological examples, including transcription factor binding sites in eukaryotes and ribosome binding sites in prokaryotes. 1
Geometric analysis on small unitary representations of GL(N,R)
No full textAbstractThe most degenerate unitary principal series representations πiλ,δ (λ∈R, δ∈Z/2Z) of G=GL(N,R) attain the minimum of the Gelfand–Kirillov dimension among all irreducible unitary representations of G. This article gives an explicit formula of the irreducible decomposition of the restriction πiλ,δ|H (branching law) with respect to all symmetric pairs (G,H). For N=2n with n⩾2, the restriction πiλ,δ|H remains irreducible for H=Sp(n,R) if λ≠0 and splits into two irreducible representations if λ=0. The branching law of the restriction πiλ,δ|H is purely discrete for H=GL(n,C), consists only of continuous spectrum for H=GL(p,R)×GL(q,R) (p+q=N), and contains both discrete and continuous spectra for H=O(p,q) (p>q⩾1). Our emphasis is laid on geometric analysis, which arises from the restriction of ‘small representations’ to various subgroups
Prediction of the bonding state of cysteine residues in proteins with machine-learning methods
No full textIn this paper we evaluate the performance of machine learning methods in the task of predicting the bonding state of cysteines starting from protein sequences. This task is the first step for the identification of disulfide bonds in proteins. We score the performance of three different approaches: 1) Hidden Support Vector Machines (HSVMs) which integrate the SVM predictions with a Hidden Markov Model; 2) SVM-HMMs which discriminatively train models that are isomorphic to a kth-order hidden Markov model; 3) Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs) that we recently introduced. We evaluate two different encoding schemes based on sequence profile and position specific scoring matrix (PSSM) as computed with the PSI-BLAST program and we show that when the evolutionary information is encoded with PSSM all the methods perform better than with sequence profile. Among the different methods it appears that GRHCRFs perform slightly better than the others achieving a per protein accuracy of 87% with a Matthews correlation coefficient (C) of 0.73. Finally, we investigate the difference between disulfide bonding state predictions in Eukaryotes and Prokaryotes. Our analysis shows that the per-protein accuracy in Prokaryotic proteins is higher than that in Eukaryotes (0.88 vs 0.83). However, given the paucity of bonded cysteines in Prokaryotes as compared to Eukaryotes the Matthews correlation coefficient is drastically reduced (0.48 vs 0.80)
Geometric analysis on small unitary representations of GL(N,R).
No full textThe most degenerate unitary principal series representations () of attain the minimum of the Gelfand--Kirillov dimension among all irreducible unitary representations of . This article gives an explicit formula of the irreducible decomposition of the restriction (\textit{branching law}) with respect to all symmetric pairs . For with , the restriction remains irreducible for if and splits into two irreducible representations if . The branching law of the restriction is purely discrete for H = GL(n,\C), consists only of continuous spectrum for , and contains both discrete and continuous spectra for . Our emphasis is laid on geometric analysis, which arises from the restriction of `small representations' to various subgroups
Rankin-Cohen Operators and fusion matrices
No full textCe travail est consacré a l'étude des déformations covariantes des orbites co-adjointes du groupe de Lie SL(2,R).Nous établissons un lien entre des méthodes de quantification basées sur les crochets de Rankin-Cohen et les matrices de fusion pour les modules de Verma. Par ailleurs nous formalisons et étudions la notion associée d'algèbre de Rankin-Cohen qui contrôle l'associativité de ces déformations.This work is devoted to the study of covariant star-product on coadjointorbits of the Lie group SL(2,R). We establish a correspondence between two quantization methods. The first is based on the Rankin-Cohen brackets and the second is based in the canonical element associate to the Shapovalov form and fusion matrices for Verma modules.Furthermore we formalize and study the associated notion of non-commutative algebra that controls the associativity of these deformations
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