bluuebunny/crossref_metadata_2025 huggingface dataset:
Modulation of actin structure and function by phosphorylation of Tyr-53 and profilin binding
Authors: Baek Kyuwon , Liu Xiong , Ferron François , Shu Shi , Korn Edward D. , Dominguez Roberto
Published: August 2008

On starvation,
Dictyostelium
cells aggregate to form multicellular fruiting bodies containing spores that germinate when transferred to nutrient-rich medium. This developmental cycle correlates with the extent of actin phosphorylation at Tyr-53 (pY53-actin), which is low in vegetative cells but high in viable mature spores. Here we describe high-resolution crystal structures of pY53-actin and unphosphorylated actin in complexes with gelsolin segment 1 and profilin. In the structure of pY53-actin, the phosphate group on Tyr-53 makes hydrogen-bonding interactions with residues of the DNase I-binding loop (D-loop) of actin, resulting in a more stable conformation of the D-loop than in the unphosphorylated structures. A more rigidly folded D-loop may explain some of the previously described properties of pY53-actin, including its increased critical concentration for polymerization, reduced rates of nucleation and pointed end elongation, and weak affinity for DNase I. We show here that phosphorylation of Tyr-53 inhibits subtilisin cleavage of the D-loop and reduces the rate of nucleotide exchange on actin. The structure of profilin–
Dictyostelium
-actin is strikingly similar to previously determined structures of profilin–β-actin and profilin–α-actin. By comparing this representative set of profilin–actin structures with other structures of actin, we highlight the effects of profilin on the actin conformation. In the profilin–actin complexes, subdomains 1 and 3 of actin close around profilin, producing a 4.7° rotation of the two major domains of actin relative to each other. As a result, the nucleotide cleft becomes moderately more open in the profilin–actin complex, probably explaining the stimulation of nucleotide exchange on actin by profilin.
In vivo
assessment of pyruvate dehydrogenase flux in the heart using hyperpolarized carbon-13 magnetic resonance
Authors: Schroeder Marie A. , Cochlin Lowri E. , Heather Lisa C. , Clarke Kieran , Radda George K. , Tyler Damian J.
Published: August 2008

The advent of hyperpolarized
13
C magnetic resonance (MR) has provided new potential for the real-time visualization of
in vivo
metabolic processes. The aim of this work was to use hyperpolarized [1-
13
C]pyruvate as a metabolic tracer to assess noninvasively the flux through the mitochondrial enzyme complex pyruvate dehydrogenase (PDH) in the rat heart, by measuring the production of bicarbonate (H
13
CO
3
−
), a byproduct of the PDH-catalyzed conversion of [1-
13
C]pyruvate to acetyl-CoA. By noninvasively observing a 74% decrease in H
13
CO
3
−
production in fasted rats compared with fed controls, we have demonstrated that hyperpolarized
13
C MR is sensitive to physiological perturbations in PDH flux. Further, we evaluated the ability of the hyperpolarized
13
C MR technique to monitor disease progression by examining PDH flux before and 5 days after streptozotocin induction of type 1 diabetes. We detected decreased H
13
CO
3
−
production with the onset of diabetes that correlated with disease severity. These observations were supported by
in vitro
investigations of PDH activity as reported in the literature and provided evidence that flux through the PDH enzyme complex can be monitored noninvasively,
in vivo
, by using hyperpolarized
13
C MR.
Detection of 3D atomic similarities and their use in the discrimination of small molecule protein-binding sites
Authors: Najmanovich Rafael , Kurbatova Natalja , Thornton Janet
Published: August 2008

Motivation: Current computational methods for the prediction of function from structure are restricted to the detection of similarities and subsequent transfer of functional annotation. In a significant minority of cases, global sequence or structural (fold) similarities do not provide clues about protein function. In these cases, one alternative is to detect local binding site similarities. These may still reflect more distant evolutionary relationships as well as unique physico-chemical constraints necessary for binding similar ligands, thus helping pinpoint the function. In the present work, we ask the following question: is it possible to discriminate within a dataset of non-homologous proteins those that bind similar ligands based on their binding site similarities?

Methods: We implement a graph-matching-based method for the detection of 3D atomic similarities introducing some simplifications that allow us to extend its applicability to the analysis of large allatom binding site models. This method, called IsoCleft, does not require atoms to be connected either in sequence or space. We apply the method to a cognate-ligand bound dataset of non-homologous proteins. We define a family of binding site models with decreasing knowledge about the identity of the ligand-interacting atoms to uncouple the questions of predicting the location of the binding site and detecting binding site similarities. Furthermore, we calculate the individual contributions of binding site size, chemical composition and geometry to prediction performance.

Results: We find that it is possible to discriminate between different ligand-binding sites. In other words, there is a certain uniqueness in the set of atoms that are in contact to specific ligand scaffolds. This uniqueness is restricted to the atoms in close proximity of the ligand in which case, size and chemical composition alone are sufficient to discriminate binding sites. Discrimination ability decreases with decreasing knowledge about the identity of the ligand-interacting binding site atoms. The decrease is quite abrupt when considering size and chemical composition alone, but much slower when including geometry. We also observe that certain ligands are easier to discriminate. Interestingly, the subset of binding site atoms belonging to highly conserved residues is not sufficient to discriminate binding sites, implying that convergently evolved binding sites arrived at dissimilar solutions.

Availability: IsoCleft can be obtained from the authors.

Contact:  rafael.najmanovich@ebi.ac.uk
Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data
Authors: Kronek Louis-Philippe , Reddy Anupama
Published: August 2008

Motivation: Survival analysis involves predicting the time to event for patients in a dataset, based on a set of recorded attributes. In this study we focus on right-censored survival problems. Detecting high-degree interactions for the estimation of survival probability is a challenging problem in survival analysis from the statistical perspective.

Results: We propose a new methodology, Logical Analysis of Survival Data (LASD), to identify interactions between variables (survival patterns) without any prior hypotheses. Using these set of patterns, we predict survival distributions for each observation. To evaluate LASD we select two publicly available datasets: a lung adenocarcinoma dataset (gene-expression profiles) and the other a breast cancer dataset (clinical profiles). The performance of LASD when compared with survival decision trees improves the cross-validation accuracy by 18% for the gene-expression dataset, and by 2% for the clinical dataset.

Availability: Executable codes will be provided upon request.

Contact:  louis-philippe.kronek@g-scop.fr; areddy@rutcor.rutgers.edu
Logical modelling of the role of the Hh pathway in the patterning of the Drosophila wing disc
Authors: González Aitor , Chaouiya Claudine , Thieffry Denis
Published: August 2008

Motivations: The development of most tissues and organs relies on a limited number of signal transduction pathways enabling the coordination of cellular differentiation. A proper understanding of the roles of signal transduction pathways requires the definition of formal models capturing the main qualitative features of these patterning processes. This is a challenging task because the underlying processes, diffusion, regulatory modifications, reception and sequestration of signalling molecules, transcriptional regulation of target genes, etc. are only partly characterized. In this context, qualitative models can be more readily proposed on the basis of available (molecular) genetic data. But this requires novel computational tools and proper qualitative representations of phenomena such as diffusion or sequestration.

To assess the power and limits of a logical formalism in this context, we propose a multi-level model of the multi-cellular network involved in the definition of the anterior–posterior boundary during the development of the wing disc of Drosophila melanogaster. The morphogen Hedgehog (Hh) is the inter-cellular signal coordinating this process. It diffuses from the posterior compartment of the disc to activate its pathway in cells immediately anterior to the boundary. In these boundary cells, the Hh gradient induces target genes in distinct domains as a function of the Hh concentration. One target of Hh signalling is the gene coding for the receptor Patched (Ptc), which sequesters Hh and impedes further diffusion, thereby refining the boundary.

Results: We have delineated a logical model of the patterning process defining the cellular anterior–posterior boundary in the developing imaginal disc of Drosophila melanogaster. This model qualitatively accounts for the formation of a gradient of Hh, as well as for the transduction of this signal through a balance between the activatory (CiA) and inhibitory (CiR) products of the gene cubitus interruptus (ci). Wild-type and mutant simulations have been carried out to assess the coherence of the model with experimental data. Interestingly, our computational analysis provides novel insights into poorly understood processes such as the regulation of Ptc by CiR, the formation of a functional gradient of CiA across boundary cells, or yet functional En differences between anterior and posterior cells.

In conclusion, our model analysis demonstrates the flexibility of the logical formalism, enabling consistent qualitative representation of diffusion, sequestration and post-transcriptional regulatory processes within and between neighbouring cells.

Availability: An XML file containing the proposed model together with annotations can be downloaded from our website (http://gin.univ-mrs.fr/GINsim/), along with GINsim, a logical modelling and simulation software freely available to academic groups.

Contact:  thieffry@tagc.univ-mrs.fr
Gain and loss of phosphorylation sites in human cancer
Authors: Radivojac Predrag , Baenziger Peter H. , Kann Maricel G. , Mort Matthew E. , Hahn Matthew W. , Mooney Sean D.
Published: August 2008

Motivation: Coding-region mutations in human genes are responsible for a diverse spectrum of diseases and phenotypes. Among lesions that have been studied extensively, there are insights into several of the biochemical functions disrupted by disease-causing mutations. Currently, there are more than 60 000 coding-region mutations associated with inherited disease catalogued in the Human Gene Mutation Database (HGMD, August 2007) and more than 70 000 polymorphic amino acid substitutions recorded in dbSNP (dbSNP, build 127). Understanding the mechanism and contribution these variants make to a clinical phenotype is a formidable problem.

Results: In this study, we investigate the role of phosphorylation in somatic cancer mutations and inherited diseases. Somatic cancer mutation datasets were shown to have a significant enrichment for mutations that cause gain or loss of phosphorylation when compared to our control datasets (putatively neutral nsSNPs and random amino acid substitutions). Of the somatic cancer mutations, those in kinase genes represent the most enriched set of mutations that disrupt phosphorylation sites, suggesting phosphorylation target site mutation is an active cause of phosphorylation deregulation. Overall, this evidence suggests both gain and loss of a phosphorylation site in a target protein may be important features for predicting cancercausing mutations and may represent a molecular cause of disease for a number of inherited and somatic mutations.

Contact:  sdmooney@iupui.edu
Comprehensive in silico mutagenesis highlights functionally important residues in proteins
Authors: Bromberg Yana , Rost Burkhard
Published: August 2008

Motivation: Mutating residues into alanine (alanine scanning) is one of the fastest experimental means of probing hypotheses about protein function. Alanine scans can reveal functional hot spots, i.e. residues that alter function upon mutation. In vitro mutagenesis is cumbersome and costly: probing all residues in a protein is typically as impossible as substituting by all non-native amino acids. In contrast, such exhaustive mutagenesis is feasible in silico.

Results: Previously, we developed SNAP to predict functional changes due to non-synonymous single nucleotide polymorphisms. Here, we applied SNAP to all experimental mutations in the ASEdb database of alanine scans; we identified 70% of the hot spots (≥1 kCal/mol change in binding energy); more severe changes were predicted more accurately. Encouraged, we carried out a complete all-against-all in silico mutagenesis for human glucokinase. Many of the residues predicted as functionally important have indeed been confirmed in the literature, others await experimental verification, and our method is ready to aid in the design of in vitro mutagenesis.

Availability: ASEdb and glucokinase scores are available at http://www.rostlab.org/services/SNAP. For submissions of large/whole proteins for processing please contact the author.

Contact:  yb2009@columbia.edu
PhyloDetect: a likelihood-based strategy for detecting microorganisms with diagnostic microarrays
Authors: Rehrauer Hubert , Schönmann Susan , Eberl Leo , Schlapbach Ralph
Published: August 2008

Motivation: Detection and identification of microbes using diagnostic arrays is still subject of ongoing research. Existing significance-based algorithms consider an organism detected even if a significant number of the microarray probes that match the organism are called absent in a hybridization. Further, they do generate redundant results if the target organisms show high sequence similarity and the microarray probes cannot discriminate all of them.

Results: We propose a new analysis strategy that considers organism similarities and calls organisms only present if the probes that match the organism but are absent in a hybridization can be explained by random events. In our strategy, we first identify the groups of target organisms that are actually distinguishable by the array. Subsequently, these organism groups are placed in a hierarchical tree such that groups matching only less specific probes are closer to the tree root, and groups that are discriminated only by few probes are close to each other. Finally, we compute for each group a likelihood score that is based on a hypothesis test with the null hypothesis that the group was actually present in the hybridized sample. We have validated our strategy using datasets from two different array types and implemented it as an easy-to-use web application.

Availability:  http://www.fgcz.ethz.ch/PhyloDetect

Contact:  Hubert.Rehrauer@fgcz.uzh.ch

Supplementary information: Example data is available at

http://www.fgcz.ethz.ch/PhyloDetect
Towards de novo identification of metabolites by analyzing tandem mass spectra
Authors: Böcker Sebastian , Rasche Florian
Published: August 2008

Motivation: Mass spectrometry is among the most widely used technologies in proteomics and metabolomics. Being a highthroughput method, it produces large amounts of data that necessitates an automated analysis of the spectra. Clearly, database search methods for protein analysis can easily be adopted to analyze metabolite mass spectra. But for metabolites, de novo interpretation of spectra is even more important than for protein data, because metabolite spectra databases cover only a small fraction of naturally occurring metabolites: even the model plant Arabidopsis thaliana has a large number of enzymes whose substrates and products remain unknown. The field of bio-prospection searches biologically diverse areas for metabolites which might serve as pharmaceuticals. De novo identification of metabolite mass spectra requires new concepts and methods since, unlike proteins, metabolites possess a non-linear molecular structure.

Results: In this work, we introduce a method for fully automated de novo identification of metabolites from tandem mass spectra. Mass spectrometry data is usually assumed to be insufficient for identification of molecular structures, so we want to estimate the molecular formula of the unknown metabolite, a crucial step for its identification. The method first calculates all molecular formulas that explain the parent peak mass. Then, a graph is build where vertices correspond to molecular formulas of all peaks in the fragmentation mass spectra, whereas edges correspond to hypothetical fragmentation steps. Our algorithm afterwards calculates the maximum scoring subtree of this graph: each peak in the spectra must be scored at most once, so the subtree shall contain only one explanation per peak. Unfortunately, finding this subtree is NP-hard. We suggest three exact algorithms (including one fixedparameter tractable algorithm) as well as two heuristics to solve the problem. Tests on real mass spectra show that the FPT algorithm and the heuristics solve the problem suitably fast and provide excellent results: for all 32 test compounds the correct solution was among the top five suggestions, for 26 compounds the first suggestion of the exact algorithm was correct.

Availability:  http://www.bio.inf.uni-jena.de/tandemms

Contact:  florian.rasche@minet.uni-jena.de
Protein structure alignment considering phenotypic plasticity
Authors: Csaba Gergely , Birzele Fabian , Zimmer Ralf
Published: August 2008

Motivation: Protein structure comparison exhibits differences and similarities of proteins and protein families and may help to elucidate protein sequence and structure evolution. Despite many methods to score protein structure similarity with and without flexibility and to align proteins accurately based on their structures, a meaningful evolutionary distance measure and alignment method which models the cost of mutations, insertions and deletions occurring in protein sequences on the structure level is still missing.

Results: Here, we introduce a new measure for protein structure similarity and propose a novel method called phenotypic plasticity method (PPM) which explicitly tries to model the evolutionary distance of two proteins on the structure level by measuring the cost of ‘morphing’ one structure into the other one. PPM aligns protein structures taking variations naturally observed in groups of structures (‘phenotypic plasticity’) into account while preserving the overall topological arrangement of the structures. The performance of PPM in detecting similarities between protein structures is evaluated against well-known structure classification methods on two benchmark sets. The larger set consists of more than 3.6 million structure pairs from the SCOP database which are also consistently classified in CATH. In the current parameterization, PPM already performs comparable or better than other methods such as TM-Align and Vorolign on those two sets according to various evaluation criteria showing that the method is able to reliably classify known protein structures, to detect their similarities and to compute accurate alignments despite phenotypic plasticity.

Availability: Executables are available upon request. Datasets and supplementary data (datasets and superpositions) can be accessed on http://www.bio.ifi.lmu.de/PPM

Contact:  ralf.zimmer@bio.ifi.lmu.de
SIRENE: supervised inference of regulatory networks
Authors: Mordelet Fantine , Vert Jean-Philippe
Published: August 2008

Motivation: Living cells are the product of gene expression programs that involve the regulated transcription of thousands of genes. The elucidation of transcriptional regulatory networks is thus needed to understand the cell's working mechanism, and can for example, be useful for the discovery of novel therapeutic targets. Although several methods have been proposed to infer gene regulatory networks from gene expression data, a recent comparison on a large-scale benchmark experiment revealed that most current methods only predict a limited number of known regulations at a reasonable precision level.

Results: We propose SIRENE (Supervised Inference of Regulatory Networks), a new method for the inference of gene regulatory networks from a compendium of expression data. The method decomposes the problem of gene regulatory network inference into a large number of local binary classification problems, that focus on separating target genes from non-targets for each transcription factor. SIRENE is thus conceptually simple and computationally efficient. We test it on a benchmark experiment aimed at predicting regulations in Escherichia coli, and show that it retrieves of the order of 6 times more known regulations than other state-of-the-art inference methods.

Availability: All data and programs are freely available at http://cbio.ensmp.fr/sirene.

Contact:  Fantine.Mordelet@ensmp.fr
Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways
Authors: Willemsen A.M. , Jansen G.A. , Komen J.C. , van Hooff S. , Waterham H.R. , Brites P.M.T. , Wanders R.J.A. , van Kampen A.H.C.
Published: August 2008

Motivation: One important area of clinical genomics research involves the elucidation of molecular mechanisms underlying (complex) disorders which eventually may lead to new diagnostic or drug targets. To further advance this area of clinical genomics one of the main challenges is the acquisition and integration of data, information and expert knowledge for specific biomedical domains and diseases. Currently the required information is not very well organized but scattered over biological and biomedical databases, basic text books, scientific literature and experts’ minds and may be highly specific, heterogeneous, complex and voluminous.

Results: We present a new framework to construct knowledge bases with concept maps for presentation of information and the web ontology language OWL for the representation of information. We demonstrate this framework through the construction of a peroxisomal knowledge base, which focuses on four key peroxisomal pathways and several related genetic disorders. All 155 concept maps in our knowledge base are linked to at least one other concept map, which allows the visualization of one big network of related pieces of information.

Availability: The peroxisome knowledge base is available from www.bioinformaticslaboratory.nl (Support → Web applications).

Contact:  a.h.vankampen@amc.uva.nl
Temporal logic patterns for querying dynamic models of cellular interaction networks
Authors: Monteiro Pedro T. , Ropers Delphine , Mateescu Radu , Freitas Ana T. , de Jong Hidde
Published: August 2008

Motivation: Models of the dynamics of cellular interaction networks have become increasingly larger in recent years. Formal verification based on model checking provides a powerful technology to keep up with this increase in scale and complexity. The application of modelchecking approaches is hampered, however, by the difficulty for nonexpert users to formulate appropriate questions in temporal logic.

Results: In order to deal with this problem, we propose the use of patterns, that is, high-level query templates that capture recurring biological questions and can be automatically translated into temporal logic. The applicability of the developed set of patterns has been investigated by the analysis of an extended model of the network of global regulators controlling the carbon starvation response in Escherichia coli.

Availability: GNA and the model of the carbon starvation response network are available at http://www-helix.inrialpes.fr/gna

Contact:  Hidde.de-Jong@inrialpes.fr
Annotation of metagenome short reads using proxygenes
Authors: Dalevi Daniel , Ivanova Natalia N. , Mavromatis Konstantinos , Hooper Sean D. , Szeto Ernest , Hugenholtz Philip , Kyrpides Nikos C. , Markowitz Victor M.
Published: August 2008

Motivation: A typical metagenome dataset generated using a 454 pyrosequencing platform consists of short reads sampled from the collective genome of a microbial community. The amount of sequence in such datasets is usually insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short reads. As a result, analysis of such datasets usually involves comparisons in terms of relative abundances of various protein families. The latter requires assignment of individual reads to protein families, which is hindered by the fact that short reads contain only a fragment, usually small, of a protein.

Results: We have considered the assignment of pyrosequencing reads to protein families directly using RPS-BLAST against COG and Pfam databases and indirectly via proxygenes that are identified using BLASTx searches against protein sequence databases. Using simulated metagenome datasets as benchmarks, we show that the proxygene method is more accurate than the direct assignment. We introduce a clustering method which significantly reduces the size of a metagenome dataset while maintaining a faithful representation of its functional and taxonomic content.

Contact:  vmmarkowitz@lbl.gov
Improved network-based identification of protein orthologs
Authors: Yosef Nir , Sharan Roded , Noble William Stafford
Published: August 2008

Motivation: Identifying protein orthologs is an important task that is receiving growing attention in the bioinformatics literature. Orthology detection provides a fundamental tool towards understanding protein evolution, predicting protein functions and interactions, aligning protein–protein interaction (PPI) networks of different species and detecting conserved modules within these networks.

Results: Here, we present a novel diffusion-based framework that builds on the Rankprop algorithm for protein orthology detection and enhances it in several important ways. Specifically, we enhance the Rankprop algorithm to account for the presence of multiple paralogs, utilize PPI, and consider multiple (>2) species in parallel. We comprehensively benchmarked our framework using a variety of training datasets and experimental settings. The results, based on the yeast, fly and human proteomes, show that the novel enhancements of Rankprop provide substantial improvements over its original formulation as well as over a number of state of the art methods for network-based orthology detection.

Availability: datasets and source code are available upon request.

Contact:  niryosef@post.tau.ac.il
Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities
Authors: Gao Pei , Honkela Antti , Rattray Magnus , Lawrence Neil D.
Published: August 2008

Motivation: Inference of latent chemical species in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalization of these latent chemical species through Gaussian process priors.

Results: We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarsegrained discretization of continuous time functions, which would lead to a large number of additional parameters to be estimated. We develop exact (for linear regulation) and approximate (for non-linear regulation) inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.

Availability: The software and data for recreating all the experiments in this paper is available in MATLAB from http://www.cs.man.ac.uk/~neill/gpsim.

Contact:  neill@cs.man.ac.uk
Clinically driven semi-supervised class discovery in gene expression data
Authors: Steinfeld Israel , Navon Roy , Ardigò Diego , Zavaroni Ivana , Yakhini Zohar
Published: August 2008

Motivation: Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries.

Results: We develop an approach to semi-supervised class discovery. One component of our approach uses clinical sample information to constrain the search space and guide the class discovery process to yield biologically relevant partitions. A second component consists of using known biological annotation of genes to drive the search, seeking partitions that manifest strong differential expression in specific sets of genes. We develop efficient algorithmics for these tasks, implementing both approaches and combinations thereof. We show that our method is robust enough to detect known clinical parameters in accordance with expected clinical values. We also use our method to elucidate cardiovascular disease (CVD) putative risk factors.

Availability: MonoClaD (Monotone Class Discovery). See http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/

Supplementary information: Supplementary data is available at http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/software.html

Contact:  zohar_yakhini@agilent.com
ChIPmix: mixture model of regressions for two-color ChIP–chip analysis
Authors: Martin-Magniette Marie-Laure , Mary-Huard Tristan , Bérard Caroline , Robin Stéphane
Published: August 2008

Motivation: Chromatin immunoprecipitation (ChIP) combined with DNA microarray is a high-throughput technology to investigate DNA–protein binding or chromatin/histone modifications. ChIP–chip data require adapted statistical method in order to identify enriched regions. All methods already proposed are based on the analysis of the log ratio (Ip/Input). Nevertheless, the assumption that the log ratio is a pertinent quantity to assess the probe status is not always verified and it leads to a poor data interpretation.

Results: Instead of working on the log ratio, we directly work with the Ip and Input signals of each probe by modeling the distribution of the Ip signal conditional to the Input signal. We propose a method named ChIPmix based on a linear regression mixture model to identify actual binding targets of the protein under study. Moreover, we are able to control the proportion of false positives. The efficiency of ChIPmix is illustrated on several datasets obtained from different organisms and hybridized either on tiling or promoter arrays. This validation shows that ChIPmix is convenient for any two-color array whatever its density and provides promising results.

Availability: The ChIPmix method is implemented in R and is available at http://www.agroparistech.fr/mia/outil_A.html

Contact:  marie_laure.martin@agroparistech.fr
Segment-based multiple sequence alignment
Authors: Rausch Tobias , Emde Anne-Katrin , Weese David , Döring Andreas , Notredame Cedric , Reinert Knut
Published: August 2008

Motivation: Many multiple sequence alignment tools have been developed in the past, progressing either in speed or alignment accuracy. Given the importance and wide-spread use of alignment tools, progress in both categories is a contribution to the community and has driven research in the field so far.

Results: We introduce a graph-based extension to the consistency-based, progressive alignment strategy. We apply the consistency notion to segments instead of single characters. The main problem we solve in this context is to define segments of the sequences in such a way that a graph-based alignment is possible. We implemented the algorithm using the SeqAn library and report results on amino acid and DNA sequences. The benefit of our approach is threefold: (1) sequences with conserved blocks can be rapidly aligned, (2) the implementation is conceptually easy, generic and fast and (3) the consistency idea can be extended to align multiple genomic sequences.

Availability: The segment-based multiple sequence alignment tool can be downloaded from http://www.seqan.de/projects/msa.html. A novel version of T-Coffee interfaced with the tool is available from http://www.tcoffee.org. The usage of the tool is described in both documentations.

Contact:  rausch@inf.fu-berlin.de
Efficient representation and P-value computation for high-order Markov motifs
Authors: da Fonseca Paulo G. S. , Guimarães Katia S. , Sagot Marie-France
Published: August 2008

Motivation: Position weight matrices (PWMs) have become a standard for representing biological sequence motifs. Their relative simplicity has favoured the development of efficient algorithms for diverse tasks such as motif identification, sequence scanning and statistical significance evaluation. Markov chainbased models generalize the PWM model by allowing for interposition dependencies to be considered, at the cost of substantial computational overhead, which may limit their application.

Results: In this article, we consider two aspects regarding the use of higher order Markov models for biological sequence motifs, namely, the representation and the computation of P-values for motifs described by a set of occurrences. We propose an efficient representation based on the use of tries, from which empirical position-specific conditional base probabilities can be computed, and extend state-of-the-art PWM-based algorithms to allow for the computation of exact P-values for high-order Markov motif models.

Availability: The software is available in the form of a Java objectoriented library from http://www.cin.ufpe.br/~paguso/kmarkov.

Contact:  paguso@cin.ufpe.br
From minimal signed circuits to the dynamics of Boolean regulatory networks
Authors: Remy Elisabeth , Ruet Paul
Published: August 2008

It is acknowledged that the presence of positive or negative circuits in regulatory networks such as genetic networks is linked to the emergence of significant dynamical properties such as multistability (involved in differentiation) and periodic oscillations (involved in homeostasis). Rules proposed by the biologist R. Thomas assert that these circuits are necessary for such dynamical properties. These rules have been studied by several authors. Their obvious interest is that they relate the rather simple information contained in the structure of the network (signed circuits) to its much more complex dynamical behaviour. We prove in this article a nontrivial converse of these rules, namely that certain positive or negative circuits in a regulatory graph are actually sufficient for the observation of a restricted form of the corresponding dynamical property, differentiation or homeostasis. More precisely, the crucial property that we require is that the circuit be globally minimal. We then apply these results to the vertebrate immune system, and show that the two minimal functional positive circuits of the model indeed behave as modules which combine to explain the presence of the three stable states corresponding to the Th0, Th1 and Th2 cells.

Contact:  ruet@iml.univ-mrs.fr
RNA structure alignment by a unit-vector approach
Authors: Capriotti Emidio , Marti-Renom Marc A.
Published: August 2008

Motivation: The recent discovery of tiny RNA molecules such as µRNAs and small interfering RNA are transforming the view of RNA as a simple information transfer molecule. Similar to proteins, the native three-dimensional structure of RNA determines its biological activity. Therefore, classifying the current structural space is paramount for functionally annotating RNA molecules. The increasing numbers of RNA structures deposited in the PDB requires more accurate, automatic and benchmarked methods for RNA structure comparison. In this article, we introduce a new algorithm for RNA structure alignment based on a unit-vector approach. The algorithm has been implemented in the SARA program, which results in RNA structure pairwise alignments and their statistical significance.

Results: The SARA program has been implemented to be of general applicability even when no secondary structure can be calculated from the RNA structures. A benchmark against the ARTS program using a set of 1275 non-redundant pairwise structure alignments results in ¡«6% extra alignments with at least 50% structurally superposed nucleotides and base pairs. A first attempt to perform RNA automatic functional annotation based on structure alignments indicates that SARA can correctly assign the deepest SCOR classification to >60% of the query structures.

Availability: The SARA program is freely available through a World Wide Web server http://sgu.bioinfo.cipf.es/services/SARA/

Contact:  mmarti@cipf.es
An integrative approach for predicting interactions of protein regions
Authors: Schelhorn Sven-Eric , Lengauer Thomas , Albrecht Mario
Published: August 2008

Motivation: Protein–protein interactions are commonly mediated by the physical contact of distinct protein regions. Computational identification of interacting protein regions aids in the detailed understanding of protein networks and supports the prediction of novel protein interactions and the reconstruction of protein complexes.

Results: We introduce an integrative approach for predicting protein region interactions using a probabilistic model fitted to an observed protein network. In particular, we consider globular domains, short linear motifs and coiled-coil regions as potential protein-binding regions. Possible cooperations between multiple regions within the same protein are taken into account. A finegrained confidence system allows for varying the impact of specific protein interactions and region annotations on the modeling process. We apply our prediction approach to a large training set using a maximum likelihood method, compare different scoring functions for region interactions and validate the predicted interactions against a collection of experimentally observed interactions. In addition, we analyze prediction performance with respect to the inclusion of different region types, the incorporation of confidence values for training data and the utilization of predicted protein interactions.

Contact:  mario.albrecht@mpi-inf.mpg.de

Supplementary information:  Supplementary data are available at Bioinformatics online.
Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining
Authors: Yu Shi , Van Vooren Steven , Tranchevent Leon-Charles , De Moor Bart , Moreau Yves
Published: August 2008

Motivation: Computational gene prioritization methods are useful to help identify susceptibility genes potentially being involved in genetic disease. Recently, text mining techniques have been applied to extract prior knowledge from text-based genomic information sources and this knowledge can be used to improve the prioritization process. However, the effect of various vocabularies, representations and ranking algorithms on text mining for gene prioritization is still an issue that requires systematic and comparative studies. Therefore, a benchmark study about the vocabularies, representations and ranking algorithms in gene prioritization by text mining is discussed in this article.

Results: We investigated 5 different domain vocabularies, 2 text representation schemes and 27 linear ranking algorithms for disease gene prioritization by text mining. We indexed 288 177 MEDLINE titles and abstracts with the TXTGate text profiling system and adapted the benchmark dataset of the Endeavour gene prioritization system that consists of 618 disease-causing genes. Textual gene profiles were created and their performance for prioritization were evaluated and discussed in a comparative manner. The results show that inverse document frequency-basedrepresentation of gene term vectors performs better than the term-frequency inverse document-frequency representation. The eVOC and MESH domain vocabularies perform better than Gene Ontology, Online Mendelian Inheritance in Man's and London Dysmorphology Database. The ranking algorithms based on 1-SVM, Standard Correlation and Ward linkage method provide the best performance.

Availability: The MATLAB code of the algorithm and benchmark datasets are available by request.

Contact:  shi.yu@esat.kuleuven.be

Supplementary information:  Supplementary data are available at Bioinformatics online.
Analysis of segmental duplications via duplication distance
Authors: Kahn Crystal L. , Raphael Benjamin J.
Published: August 2008

Motivation: Segmental duplications are common in mammalian genomes, but their evolutionary origins remain mysterious. A major difficulty in analyzing segmental duplications is that many duplications are complex mosaics of fragments of numerous other segmental duplications.

Results: We introduce a novel measure called duplication distance that describes the minimum number of duplications necessary to create a target string by repeated insertions of fragments of a source string.We derive an efficient algorithm to compute duplication distance, and we use the algorithm to analyze segmental duplications in the human genome. Our analysis reveals possible ancestral relationships between segmental duplications including numerous examples of duplications that contain multiple, nested insertions of fragments from one or more other duplications. Using duplication distance, we also identify a small number of segmental duplications that appear to have seeded many other duplications in the genome, lending support to a two-step model of segmental duplication in the genome.

Availability: Software for computing duplication distance is available upon request.

Contact:  clkahn@cs.brown.edu; braphael@cs.brown.edu.
Mining significant tree patterns in carbohydrate sugar chains
Authors: Hashimoto Kosuke , Takigawa Ichigaku , Shiga Motoki , Kanehisa Minoru , Mamitsuka Hiroshi
Published: August 2008

Motivation: Carbohydrate sugar chains or glycans, the third major class of macromolecules, hold branch shaped tree structures. Glycan motifs are known to be two types: (1) conserved patterns called ‘cores’ containing the root and (2) ubiquitous motifs which appear in external parts including leaves and are distributed over different glycan classes. Finding these glycan tree motifs is an important issue, but there have been no computational methods to capture these motifs efficiently.

Results: We have developed an efficient method for mining motifs or significant subtrees from glycans. The key contribution of this method is: (1) to have proposed a new concept, ‘á-closed frequent subtrees’, and an efficient method for mining all these subtrees from given trees and (2) to have proposed to apply statistical hypothesis testing to rerank the frequent subtrees in significance. We experimentally verified the effectiveness of the proposed method using real glycans: (1)We examined the top 10 subtrees obtained by our method at some parameter setting and confirmed that all subtrees are significant motifs in glycobiology. (2) We applied the results of our method to a classification problem and found that our method outperformed other competing methods, SVM with three different tree kernels, being all statistically significant.

Contact:  mami@kuicr.kyoto-u.ac.jp
Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry
Authors: Käll Lukas , Storey John D. , Noble William Stafford
Published: August 2008

Motivation: A mass spectrum produced via tandem mass spectrometry can be tentatively matched to a peptide sequence via database search. Here, we address the problem of assigning a posterior error probability (PEP) to a given peptide-spectrum match (PSM). This problem is considerably more difficult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution.

Results: We demonstrate how to apply non-parametric logistic regression to this problem. The method makes no explicit assumptions about the form of the underlying score distribution; instead, the method relies upon decoy PSMs, produced by searching the spectra against a decoy sequence database, to provide a model of the null score distribution. We show that our non-parametric logistic regression method produces accurate PEP estimates for six different commonly used PSM score functions. In particular, the estimates produced by our method are comparable in accuracy to those of PeptideProphet, which uses a parametric or semiparametric model designed specifically to work with SEQUEST. The advantage of the non-parametric approach is applicability and robustness to new score functions and new types of data.

Availability: C++ code implementing the method as well as supplementary information is available at http://noble.gs.washington.edu/proj/qvality

Contact:  noble@gs.washington.edu
Functional coherence in domain interaction networks
Authors: Pandey Jayesh , Koyutürk Mehmet , Subramaniam Shankar , Grama Ananth
Published: August 2008

Motivation: Extracting functional information from protein–protein interactions (PPI) poses significant challenges arising from the noisy, incomplete, generic and static nature of data obtained from high-throughput screening. Typical proteins are composed of multiple domains, often regarded as their primary functional and structural units. Motivated by these considerations, domain–domain interactions (DDI) for network-based analyses have received significant recent attention. This article performs a formal comparative investigation of the relationship between functional coherence and topological proximity in PPI and DDI networks. Our investigation provides the necessary basis for continued and focused investigation of DDIs as abstractions for functional characterization and modularization of networks.

Results: We investigate the problem of assessing the functional coherence of two biomolecules (or segments thereof) in a formal framework. We establish essential attributes of admissible measures of functional coherence, and demonstrate that existing, well-accepted measures are ill-suited to comparative analyses involving different entities (i.e. domains versus proteins). We propose a statistically motivated functional similarity measure that takes into account functional specificity as well as the distribution of functional attributes across entity groups to assess functional similarity in a statistically meaningful and biologically interpretable manner. Results on diverse data, including high-throughput and computationally predicted PPIs, as well as structural and computationally inferred DDIs for different organisms show that: (i) the relationship between functional similarity and network proximity is captured in a much more (biologically) intuitive manner by our measure, compared to existing measures and (ii) network proximity and functional similarity are significantly more correlated in DDI networks than in PPI networks, and that structurally determined DDIs provide better functional relevance as compared to computationally inferred DDIs.

Contact:  jpandey@cs.purdue.edu