bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent experiments on Alfvén eigenmodes in MAST
Authors: Gryaznevich M.P. , Sharapov S.E. , Lilley M. , Pinches S.D. , Field A.R. , Howell D. , Keeling D. , Martin R. , Meyer H. , Smith H. , Vann R. , Denner P. , Verwichte E.
Published: June 2008

The developments of advanced tokamak scenarios as well as the employment of a new neutral beam injection (NBI) source with higher power and beam energy up to ≈65 keV have significantly broadened the frequency range and the variety of Alfvén eigenmodes (AEs) excited by the super-Alfvénic NBI on the spherical tokamak MAST. During recent experiments on MAST, several distinct classes of beam-driven AEs have been identified, with different modes being most unstable in different MAST scenarios. In MAST discharges with elevated monotonicq(r)-profiles and NBI power ⩾3 MW, chirping modes starting in the frequency range ⩽150 kHz decreased in frequency down to ≈20 kHz asq(0) decreased and then smoothly transformed to long-living modes with a weakly-varying frequency and an= 1 kink-mode structure. The bolometer data suggest that the long-living modes can be responsible for fast ion losses on MAST, while the charge-exchange data show that a coupling between these modes and other low-frequency modes can cause a collapse of toroidal plasma rotation with a subsequent disruption. In MAST discharges with reversed magnetic shear, Alfvén cascade eigenmodes in the frequency range 40–180 kHz were observed at a moderate NBI power ⩽2 MW allowing an additional assessment ofq(r)-profile evolution in time. A robust reproducible scenario was found on MAST, in which the instability of high-frequency modes in the range 0.4–3.8 MHz and typically with negative toroidal mode numbers was dominating the spectrum of beam-driven AEs. Since the highest frequency of such modes is close to the on-axis ion cyclotron frequency and the polarization study of these modes show a significant parallel perturbed magnetic field, these modes are identified as compressional Alfvén eigenmodes. For investigating the AE spectrum in plasmas with high β, an active AE antenna has been installed on MAST. First measurements of stable AE modes in MAST have been performed successfully and are described here.
Selecting anti-HIV therapies based on a variety of genomic and clinical factors
Authors: Rosen-Zvi Michal , Altmann Andre , Prosperi Mattia , Aharoni Ehud , Neuvirth Hani , Sönnerborg Anders , Schülter Eugen , Struck Daniel , Peres Yardena , Incardona Francesca , Kaiser Rolf , Zazzi Maurizio , Lengauer Thomas
Published: June 2008

Motivation: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy.

Results: Three different machine learning techniques were used: generative–discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome.

Availability: The combined prediction engine will be available from July 2008, see http://engine.euresist.org

Contact:  rosen@il.ibm.com
Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model
Authors: Wang Yuan , Zhou Xiaobo , Wang Honghui , Li King , Yao Lixiu , Wong Stephen T.C.
Published: June 2008

Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity.

Contact:  XZhou@tmhs.org
MACHOS: Markov clusters of homologous subsequences
Authors: Wong Simon , Ragan Mark A.
Published: June 2008

Motivation: The classification of proteins into homologous groups (families) allows their structure and function to be analysed and compared in an evolutionary context. The modular nature of eukaryotic proteins presents a considerable challenge to the delineation of families, as different local regions within a single protein may share common ancestry with distinct, even mutually exclusive, sets of homologs, thereby creating an intricate web of homologous relationships if full-length sequences are taken as the unit of evolution. We attempt to disentangle this web by developing a fully automated pipeline to delineate protein subsequences that represent sensible units for homology inference, and clustering them into putatively homologous families using the Markov clustering algorithm.

Results: Using six eukaryotic proteomes as input, we clustered 162 349 protein sequences into 19 697–77 415 subsequence families depending on granularity of clustering. We validated these Markov clusters of homologous subsequences (MACHOS) against the manually curated Pfam domain families, using a quality measure to assess overlap. Our subsequence families correspond well to known domain families and achieve higher quality scores than do groups generated by fully automated domain family classification methods. We illustrate our approach by analysis of a group of proteins that contains the glutamyl/glutaminyl-tRNA synthetase domain, and conclude that our method can produce high-coverage decomposition of protein sequence space into precise homologous families in a way that takes the modularity of eukaryotic proteins into account. This approach allows for a fine-scale examination of evolutionary histories of proteins encoded in eukaryotic genomes.

Contact:  m.ragan@imb.uq.edu.au

Supplementary information:  Supplementary data are available at Bioinformatics online. MACHOS for the six proteomes are available as FASTA-formatted files: http://research1t.imb.uq.edu.au/ragan/machos
Guided genome halving: hardness, heuristics and the history of the Hemiascomycetes
Authors: Zheng Chunfang , Zhu Qian , Adam Zaky , Sankoff David
Published: June 2008

Motivation: Some present day species have incurred a whole genome doubling event in their evolutionary history, and this is reflected today in patterns of duplicated segments scattered throughout their chromosomes. These duplications may be used as data to ‘halve’ the genome, i.e. to reconstruct the ancestral genome at the moment of doubling, but the solution is often highly nonunique. To resolve this problem, we take account of outgroups, external reference genomes, to guide and narrow down the search.

Results: We improve on a previous, computationally costly, ‘brute force’ method by adapting the genome halving algorithm of El-Mabrouk and Sankoff so that it rapidly and accurately constructs an ancestor close the outgroups, prior to a local optimization heuristic. We apply this to reconstruct the predoubling ancestor of Saccharomyces cerevisiae and Candida glabrata, guided by the genomes of three other yeasts that diverged before the genome doubling event. We analyze the results in terms (1) of the minimum evolution criterion, (2) how close the genome halving result is to the final (local) minimum and (3) how close the final result is to an ancestor manually constructed by an expert with access to additional information. We also visualize the set of reconstructed ancestors using classic multidimensional scaling to see what aspects of the two doubled and three unduplicated genomes influence the differences among the reconstructions.

Availability: The experimental software is available on request.

Contact:  sankoff@uottawa.ca
Estimating true evolutionary distances under the DCJ model
Authors: Lin Yu , Moret Bernard M.E.
Published: June 2008

Motivation: Modern techniques can yield the ordering and strandedness of genes on each chromosome of a genome; such data already exists for hundreds of organisms. The evolutionary mechanisms through which the set of the genes of an organism is altered and reordered are of great interest to systematists, evolutionary biologists, comparative genomicists and biomedical researchers. Perhaps the most basic concept in this area is that of evolutionary distance between two genomes: under a given model of genomic evolution, how many events most likely took place to account for the difference between the two genomes?

Results: We present a method to estimate the true evolutionary distance between two genomes under the ‘double-cut-and-join’ (DCJ) model of genome rearrangement, a model under which a single multichromosomal operation accounts for all genomic rearrangement events: inversion, transposition, translocation, block interchange and chromosomal fusion and fission. Our method relies on a simple structural characterization of a genome pair and is both analytically and computationally tractable. We provide analytical results to describe the asymptotic behavior of genomes under the DCJ model, as well as experimental results on a wide variety of genome structures to exemplify the very high accuracy (and low variance) of our estimator. Our results provide a tool for accurate phylogenetic reconstruction from multichromosomal gene rearrangement data as well as a theoretical basis for refinements of the DCJ model to account for biological constraints.

Availability: All of our software is available in source form under GPL at http://lcbb.epfl.ch

Contact:  bernard.moret@epfl.ch
The multiple gene duplication problem revisited
Authors: Bansal Mukul S. , Eulenstein Oliver
Published: June 2008

Motivation: Deciphering the location of gene duplications and multiple gene duplication episodes on the Tree of Life is fundamental to understanding the way gene families and genomes evolve. The multiple gene duplication problem provides a framework for placing gene duplication events onto nodes of a given species tree, and detecting episodes of multiple gene duplication. One version of the multiple gene duplication problem was defined by Guigó et al. in 1996. Several heuristic solutions have since been proposed for this problem, but no exact algorithms were known.

Results: In this article we solve this longstanding open problem by providing the first exact and efficient solution. We also demonstrate the improvement offered by our algorithm over the best heuristic approaches, by applying it to several simulated as well as empirical datasets.

Contact:  oeulenst@cs.iastate.edu
Nucleosome positioning from tiling microarray data
Authors: Yassour Moran , Kaplan Tommy , Jaimovich Ariel , Friedman Nir
Published: June 2008

Motivation: The packaging of DNA around nucleosomes in eukaryotic cells plays a crucial role in regulation of gene expression, and other DNA-related processes. To better understand the regulatory role of nucleosomes, it is important to pinpoint their position in a high (5–10 bp) resolution. Toward this end, several recent works used dense tiling arrays to map nucleosomes in a high-throughput manner. These data were then parsed and hand-curated, and the positions of nucleosomes were assessed.

Results: In this manuscript, we present a fully automated algorithm to analyze such data and predict the exact location of nucleosomes. We introduce a method, based on a probabilistic graphical model, to increase the resolution of our predictions even beyond that of the microarray used. We show how to build such a model and how to compile it into a simple Hidden Markov Model, allowing for a fast and accurate inference of nucleosome positions.

We applied our model to nucleosomal data from mid-log yeast cells reported by Yuan et al. and compared our predictions to those of the original paper; to a more recent method that uses five times denser tiling arrays as explained by Lee et al.; and to a curated set of literature-based nucleosome positions. Our results suggest that by applying our algorithm to the same data used by Yuan et al. our fully automated model traced 13% more nucleosomes, and increased the overall accuracy by about 20%. We believe that such an improvement opens the way for a better understanding of the regulatory mechanisms controlling gene expression, and how they are encoded in the DNA.

Contact:  nir@cs.huji.ac.il
Alignment and classification of time series gene expression in clinical studies
Authors: Lin Tien-ho , Kaminski Naftali , Bar-Joseph Ziv
Published: June 2008

Motivation: Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class).

Results: We present a method that utilizes hidden Markov models (HMMs) for the classification task. We use HMMs with less states than time points leading to an alignment of the different patient response rates. To focus on the differences between the two classes we develop a discriminative HMM classifier. Unlike the traditional generative HMM, discriminative HMM can use examples from both classes when learning the model for a specific class. We have tested our method on both simulated and real time series expression data. As we show, our method improves upon prior methods and can suggest markers for specific disease and response stages that are not found when using traditional classifiers.

Availability: Matlab implementation is available from http://www.cs.cmu.edu/~thlin/tram/

Contact:  zivbj@cs.cmu.edu
Towards the use of argumentation in bioinformatics: a gene expression case study
Authors: McLeod Kenneth , Burger Albert
Published: June 2008

Motivation: Due to different experimental setups and various interpretations of results, the data contained in online bioinformatics resources can be inconsistent, therefore, making it more difficult for users of these resources to assess the suitability and correctness of the answers to their queries. This work investigates the role of argumentation systems to help users evaluate such answers. More specifically, it looks closely at a gene expression case study, creating an appropriate representation of the underlying data and series of rules that are used by a third-party argumentation engine to reason over the query results provided by the mouse gene expression database EMAGE.

Results: A prototype using the ASPIC argumentation engine has been implemented and a preliminary evaluation carried out. This evaluation suggested that argumentation can be used to deal with inconsistent data in biological resources.

Availability: The ASPIC argumentation engine is available from http://www.argumentation.org. EMAGE gene expression data can be obtained from http://genex.hgu.mrc.ac.uk. The argumentation rules for the gene expression example are available from the lead author upon request.

Contact:  kcm1@hw.ac.uk
Assessing the functional structure of genomic data
Authors: Huttenhower C. , Troyanskaya O.G.
Published: June 2008

Motivation: The availability of genome-scale data has enabled an abundance of novel analysis techniques for investigating a variety of systems-level biological relationships. As thousands of such datasets become available, they provide an opportunity to study high-level associations between cellular pathways and processes. This also allows the exploration of shared functional enrichments between diverse biological datasets, and it serves to direct experimenters to areas of low data coverage or with high probability of new discoveries.

Results: We analyze the functional structure of Saccharomyces cerevisiae datasets from over 950 publications in the context of over 140 biological processes. This includes a coverage analysis of biological processes given current high-throughput data, a data-driven map of associations between processes, and a measure of similar functional activity between genome-scale datasets. This uncovers subtle gene expression similarities in three otherwise disparate microarray datasets due to a shared strain background. We also provide several means of predicting areas of yeast biology likely to benefit from additional high-throughput experimental screens.

Availability: Predictions are provided in supplementary tables; software and additional data are available from the authors by request.

Contact:  ogt@princeton.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
Prediction of drug–target interaction networks from the integration of chemical and genomic spaces
Authors: Yamanishi Yoshihiro , Araki Michihiro , Gutteridge Alex , Honda Wataru , Kanehisa Minoru
Published: June 2008

Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently.

Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery.

Availability: Softwares are available upon request.

Contact:  Yoshihiro.Yamanishi@ensmp.fr

Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.
Protein complex identification by supervised graph local clustering
Authors: Qi Yanjun , Balem Fernanda , Faloutsos Christos , Klein-Seetharaman Judith , Bar-Joseph Ziv
Published: June 2008

Motivation: Protein complexes integrate multiple gene products to coordinate many biological functions. Given a graph representing pairwise protein interaction data one can search for subgraphs representing protein complexes. Previous methods for performing such search relied on the assumption that complexes form a clique in that graph. While this assumption is true for some complexes, it does not hold for many others. New algorithms are required in order to recover complexes with other types of topological structure.

Results: We present an algorithm for inferring protein complexes from weighted interaction graphs. By using graph topological patterns and biological properties as features, we model each complex subgraph by a probabilistic Bayesian network (BN). We use a training set of known complexes to learn the parameters of this BN model. The log-likelihood ratio derived from the BN is then used to score subgraphs in the protein interaction graph and identify new complexes. We applied our method to protein interaction data in yeast. As we show our algorithm achieved a considerable improvement over clique based algorithms in terms of its ability to recover known complexes. We discuss some of the new complexes predicted by our algorithm and determine that they likely represent true complexes.

Availability: Matlab implementation is available on the supporting website: www.cs.cmu.edu/~qyj/SuperComplex

Contact:  zivbj@cs.cmu.edu
The effectiveness of position- and composition-specific gap costs for protein similarity searches
Authors: Stojmirović Aleksandar , Gertz E. Michael , Altschul Stephen F. , Yu Yi-Kuo
Published: June 2008

Motivation: The flexibility in gap cost enjoyed by hidden Markov models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments.

Results: We found that position-specific gap penalties have an advantage over uniform gap costs. We did not explore optimizing distinct uniform gap costs for each query. For Pfam, PSSMs iteratively constructed from seeds based on HMM consensus sequences perform equivalently to HMMs that were adjusted to have constant gap transition probabilities, albeit with much greater variance. We observed no effect of composition-specific gap costs on retrieval performance. These results suggest possible improvements to the PSI-BLAST protein database search program.

Availability: The scripts for performing evaluations are available upon request from the authors.

Contact:  yyu@ncbi.nlm.nih.gov
Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space
Authors: Loewenstein Yaniv , Portugaly Elon , Fromer Menachem , Linial Michal
Published: June 2008

Motivation: UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets.

Application: We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any practical memory size constraint, this framework guarantees the correct clustering solution without explicitly requiring all dissimilarities in memory. The algorithms are general and are applicable to any dataset. We present a data-dependent characterization of hardness and clustering efficiency. The presented concepts are applicable to any agglomerative clustering formulation.

Results: We apply our algorithm to the entire collection of protein sequences, to automatically build a comprehensive evolutionary-driven hierarchy of proteins from sequence alone. The newly created tree captures protein families better than state-of-the-art large-scale methods such as CluSTr, ProtoNet4 or single-linkage clustering. We demonstrate that leveraging the entire mass embodied in all sequence similarities allows to significantly improve on current protein family clusterings which are unable to directly tackle the sheer mass of this data. Furthermore, we argue that non-metric constraints are an inherent complexity of the sequence space and should not be overlooked. The robustness of UPGMA allows significant improvement, especially for multidomain proteins, and for large or divergent families.

Availability: A comprehensive tree built from all UniProt sequence similarities, together with navigation and classification tools will be made available as part of the ProtoNet service. A C++ implementation of the algorithm is available on request.

Contact:  lonshy@cs.huji.ac.il
A max-margin model for efficient simultaneous alignment and folding of RNA sequences
Authors: Do Chuong B. , Foo Chuan-Sheng , Batzoglou Serafim
Published: June 2008

Motivation: The need for accurate and efficient tools for computational RNA structure analysis has become increasingly apparent over the last several years: RNA folding algorithms underlie numerous applications in bioinformatics, ranging from microarray probe selection to de novo non-coding RNA gene prediction.

In this work, we present RAF (RNA Alignment and Folding), an efficient algorithm for simultaneous alignment and consensus folding of unaligned RNA sequences. Algorithmically, RAF exploits sparsity in the set of likely pairing and alignment candidates for each nucleotide (as identified by the CONTRAfold or CONTRAlign programs) to achieve an effectively quadratic running time for simultaneous pairwise alignment and folding. RAF's fast sparse dynamic programming, in turn, serves as the inference engine within a discriminative machine learning algorithm for parameter estimation.

Results: In cross-validated benchmark tests, RAF achieves accuracies equaling or surpassing the current best approaches for RNA multiple sequence secondary structure prediction. However, RAF requires nearly an order of magnitude less time than other simultaneous folding and alignment methods, thus making it especially appropriate for high-throughput studies.

Availability: Source code for RAF is available at:http://contra.stanford.edu/contrafold/

Contact:  chuongdo@cs.stanford.edu
Detection of IUPAC and IUPAC-like chemical names
Authors: Klinger Roman , Kolářik Corinna , Fluck Juliane , Hofmann-Apitius Martin , Friedrich Christoph M.
Published: June 2008

Motivation: Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. Several representations and nomenclatures for chemicals like SMILES, InChI, IUPAC or trivial names exist. Only SMILES and InChI names allow a direct structure search, but in biomedical texts trivial names and Iupac like names are used more frequent. While trivial names can be found with a dictionary-based approach and in such a way mapped to their corresponding structures, it is not possible to enumerate all IUPAC names. In this work, we present a new machine learning approach based on conditional random fields (CRF) to find mentions of IUPAC and IUPAC-like names in scientific text as well as its evaluation and the conversion rate with available name-to-structure tools.

Results: We present an IUPAC name recognizer with an F1 measure of 85.6% on a MEDLINE corpus. The evaluation of different CRF orders and offset conjunction orders demonstrates the importance of these parameters. An evaluation of hand-selected patent sections containing large enumerations and terms with mixed nomenclature shows a good performance on these cases (F1 measure 81.5%). Remaining recognition problems are to detect correct borders of the typically long terms, especially when occurring in parentheses or enumerations. We demonstrate the scalability of our implementation by providing results from a full MEDLINE run.

Availability: We plan to publish the corpora, annotation guideline as well as the conditional random field model as a UIMA component.

Contact:  roman.klinger@scai.fraunhofer.de
Integrating high dimensional bi-directional parsing models for gene mention tagging
Authors: Hsu Chun-Nan , Chang Yu-Ming , Kuo Cheng-Ju , Lin Yu-Shi , Huang Han-Shen , Chung I-Fang
Published: June 2008

Motivation: Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this article, we describe in detail our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2. Our tagger is interesting because it accomplished the highest F-scores among CRF-based methods and second over all. Moreover, we obtained our results by mostly applying open source packages, making it easy to duplicate our results.

Results: We first describe in detail how we developed our CRF-based tagger. We designed a very high dimensional feature set that includes most of information that may be relevant. We trained bi-directional CRF models with the same set of features, one applies forward parsing and the other backward, and integrated two models based on the output scores and dictionary filtering. One of the most prominent factors that contributes to the good performance of our tagger is the integration of an additional backward parsing model. However, from the definition of CRF, it appears that a CRF model is symmetric and bi-directional parsing models will produce the same results. We show that due to different feature settings, a CRF model can be asymmetric and the feature setting for our tagger in BioCreative 2 not only produces different results but also gives backward parsing models slight but constant advantage over forward parsing model. To fully explore the potential of integrating bi-directional parsing models, we applied different asymmetric feature settings to generate many bi-directional parsing models and integrate them based on the output scores. Experimental results show that this integrated model can achieve even higher F-score solely based on the training corpus for gene mention tagging.

Availability: Data sets, programs and an on-line service of our gene mention tagger can be accessed at http://aiia.iis.sinica.edu.tw/biocreative2.htm

Contact:  chunnan@iis.sinica.edu.tw
A maximum common substructure-based algorithm for searching and predicting drug-like compounds
Authors: Cao Yiqun , Jiang Tao , Girke Thomas
Published: June 2008

Motivation: The prediction of biologically active compounds is of great importance for high-throughput screening (HTS) approaches in drug discovery and chemical genomics. Many computational methods in this area focus on measuring the structural similarities between chemical structures. However, traditional similarity measures are often too rigid or consider only global similarities between structures. The maximum common substructure (MCS) approach provides a more promising and flexible alternative for predicting bioactive compounds.

Results: In this article, a new backtracking algorithm for MCS is proposed and compared to global similarity measurements. Our algorithm provides high flexibility in the matching process, and it is very efficient in identifying local structural similarities. To predict and cluster biologically active compounds more efficiently, the concept of basis compounds is proposed that enables researchers to easily combine the MCS-based and traditional similarity measures with modern machine learning techniques. Support vector machines (SVMs) are used to test how the MCS-based similarity measure and the basis compound vectorization method perform on two empirically tested datasets. The test results show that MCS complements the well-known atom pair descriptor-based similarity measure. By combining these two measures, our SVM-based model predicts the biological activities of chemical compounds with higher specificity and sensitivity.

Contact:  ycao@cs.ucr.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
BLASTing small molecules—statistics and extreme statistics of chemical similarity scores
Authors: Baldi Pierre , Benz Ryan W.
Published: June 2008

Motivation: Small organic molecules, from nucleotides and amino acids to metabolites and drugs, play a fundamental role in chemistry, biology and medicine. As databases of small molecules continue to grow and become more open, it is important to develop the tools to search them efficiently. In order to develop a BLAST-like tool for small molecules, one must first understand the statistical behavior of molecular similarity scores.

Results: We develop a new detailed theory of molecular similarity scores that can be applied to a variety of molecular representations and similarity measures. For concreteness, we focus on the most widely used measure—the Tanimoto measure applied to chem-ical fingerprints. In both the case of empirical fingerprints and fingerprints generated by several stochastic models, we derive accurate approximations for both the distribution and extreme value distribution of similarity scores. These approximation are derived using a ratio of correlated Gaussians approach. The theory enables the calculation of significance scores, such as Z-scores and P-values, and the estimation of the top hits list size. Empirical results obtained using both the random models and real data from the ChemDB database are given to corroborate the theory and show how it can be applied to mine chemical space.

Availability: Data and related resources are available through http://cdb.ics.uci.edu

Contact:  pfbaldi@ics.uci.edu
Performing Desire: Stendhal's Theatrical Heroines
Authors: Scott Maria
Published: June 2008

Stendhal's independent heroines, often associated with a taste for the theatrical, do not tend to win the preference of his male heroes. As a result, they are often considered by critics to be less successful than their more traditionally ‘feminine’ counterparts in achieving their goals. However, this paper argues that characters such as Mathilde de La Mole, la Sanseverina, Lamiel, Mina Wanghen and Mina de Vanghel achieve a level of control over their own destinies unknown to Stendhal's more retiring heroines, such Mme de Rênal, Clélia Conti, Mme de Chasteller and Armance. Consequently, the author's less ‘natural’ female characters turn out in fact to be more authentic, in existential terms at least, than their more traditional sisters. The authenticity of these heroines, interpreted here as their success in defining themselves as subjects rather than objects of desire, is shown by this article to be related, somewhat paradoxically, to their interest in theatrical fakery. In making this argument, the paper engages with the ideas of, among others, Simone de Beauvoir and Jacques Lacan.
Towards an Ethics of Witness, or the Story and History of ‘une minuscule détresse’ in Annie Ernaux's
L'Événement
and Nancy Huston's
Instruments des ténèbres
Authors: Sardin Pascale
Published: June 2008

This paper investigates what is at stake, ethically and aesthetically, in writing about the disturbing issues of abortion and neonaticide. It focuses on
L'Événement
by Annie Ernaux and on Nancy Huston's
Instruments des ténèbres
. Though very different in form and content, these two novels, written from a distinctively female viewpoint, are metatextual works which reflect on the act of writing such taboos, and of representing irrational, defective mothers in the context of poststructuralism and postmodernism. In her exploration of the genre of the memoir and that of historiography as defined by Paul Ricoeur, Ernaux seeks a form of writing which discards pathos and the moral involvement of the reader, while Huston attempts something similar through humour and the grotesque. Yet at the same time, these novelists renegotiate the notion of retelling experience as defined by Walter Benjamin in ‘The storyteller’ — the rewriting of the chronology of excruciating events, drawing a parallel between private and public history. In this perspective, their depiction of dark mothers, which goes against the comforting myth of the good mother, reads as a political and humanistic project. Ernaux's and Huston's respective aesthetics define a new kind of ethics involving the reader and centring on the notion of witness, exhorting their readers to undertake a performative reading.
Mimesis and Alterity: Michel Leiris's Ethnography and Poetics of Spirit Possession
Authors: Albers Irene
Published: June 2008

The title of Leiris's diary
L'Afrique fantôme
has mostly been understood as a critique of European Africanist discourse. However, Leiris does a lot more than just denounce (his own) exoticism. This article argues that the title of his diary has to be read with reference to the French Surrealist and ethnographer's experience during his stay in Gondar while studying the
zar
, a popular cult of spirit possession. These ecstatic cults have been described as performing a mimetic relation to alterity (Taussig) or as an ‘interpretation of the Other by mimesis’ (Kramer). Re-enacting the Other leads to a liberation from its power by means of imitation. It is shown that this corresponds to Leiris's own retrospective studies of the
zar
cult as a cathartic externalization of alterity and as a theatre of alterity. In his failed search for a truly sacral, authentically primitive or salutary ritual, Leiris above all discovered one particular ritual: writing, ‘poésie’. This refers to what now appears as a European form of spirit possession developing its own rituals — rituals of conjuring ‘phantoms’ as well as ones of defence and exorcism. In Leiris, the conjuring (as well as the therapy) becomes a literary domain, whereas the banishment of the phantom is the domain of theory or research. Talking as one who is possessed and talking about possession, inseparably linked in the diary, are associated with discourses of autobiographical self-description and ethnographic description of otherness, of ‘expérience poétique’ and ‘étude ethnologique’.
Integrating commercial and research surveys to estimate the harvestable biomass, and establish a quota, for an “unexploited” abalone population
Authors: Mayfield Stephen , McGarvey Richard , Carlson Ian J. , Dixon Cameron
Published: June 2008

Mayfield, S., McGarvey, R., Carlson, I. J., and Dixon, C. 2008. Integrating commercial and research surveys to estimate the harvestable biomass, and establish a quota, for an “unexploited” abalone population. – ICES Journal of Marine Science, 65: 1122–1130. A key challenge facing many fisheries managers is the absence of information on the level of harvestable biomass. We describe an integrated, two-stage survey approach that was used to measure the spatial distribution and harvestable biomass of a largely unexploited metapopulation of greenlip abalone (Haliotis laevigata) over a large area of northwestern Spencer Gulf, South Australia. In stage 1, commercial fishers conducted systematic surveys to identify subareas with abalone at harvestable densities. Cpue measures from these surveys were used to map and stratify a bounded survey subregion, within which leaded-line, research-diver surveys measured absolute density and harvestable biomass (stage 2). Decision tables, showing minimum biomass at various probabilities vs. harvest fraction, were developed to provide a risk-assessment framework for quota setting. Within two years, our approach allowed, first, the mapping of the broad-scale, spatial distribution and abundance of greenlip abalone in an area of 1143 km2, second, the estimation of harvestable biomass in a smaller (16.9 km2) area, and finally, the allocation by State fishery managers of an additional quota inside a newly defined management subzone. The collaborative approach we describe for providing estimates of absolute biomass over large spatial scales affords multiple advantages for the assessment and management of invertebrate dive fisheries.
Effectiveness of a feedback management procedure based on controlling the size of marine protected areas through catch per unit effort
Authors: Kai Mikihiko , Shirakihara Kunio
Published: June 2008

Kai, M., and Shirakihara, K. 2008. Effectiveness of a feedback management procedure based on controlling the size of marine protected areas through catch per unit effort. – ICES Journal of Marine Science, 65: 1216–1226. The effectiveness of a feedback management procedure using marine protected areas (MPAs) was investigated. This procedure does not control fishing effort, but it does increase the size of the MPA when the catch per unit effort (cpue) is below a predetermined target level and reduces the size when the cpue is above this level. Stability analyses of the approach, which consisted of a population dynamics model and a model to control MPA size, suggest that this procedure can lead to recovery of a depleted population and sustain that population at a predetermined target level, even when there is hyperstability (a non-linear relationship between population size and cpue). An alternative procedure using fishing effort instead of MPA size may also function well, although both procedures may need a long time for a depleted population to approach the target level. Performance of both procedures was examined using numerical simulations focused on system dynamics in the short term after management implementation. The procedure using MPA size was more effective at preventing population collapse. Simulations suggest that if this procedure starts from a desirable level of initial MPA size, it has advantages over the alternative procedure through creating speedier population recovery and a higher level of short-term catch.
Comparative genome analysis of Salmonella Enteritidis PT4 and Salmonella Gallinarum 287/91 provides insights into evolutionary and host adaptation pathways
Authors: Thomson Nicholas R. , Clayton Debra J. , Windhorst Daniel , Vernikos Georgios , Davidson Susanne , Churcher Carol , Quail Michael A. , Stevens Mark , Jones Michael A. , Watson Michael , Barron Andy , Layton Abigail , Pickard Derek , Kingsley Robert A. , Bignell Alex , Clark Louise , Harris Barbara , Ormond Doug , Abdellah Zahra , Brooks Karen , Cherevach Inna , Chillingworth Tracey , Woodward John , Norberczak Halina , Lord Angela , Arrowsmith Claire , Jagels Kay , Moule Sharon , Mungall Karen , Sanders Mandy , Whitehead Sally , Chabalgoity Jose A. , Maskell Duncan , Humphrey Tom , Roberts Mark , Barrow Paul A. , Dougan Gordon , Parkhill Julian
Published: June 2008

We have determined the complete genome sequences of a host-promiscuous Salmonella enterica serovar Enteritidis PT4 isolate P125109 and a chicken-restricted Salmonella enterica serovar Gallinarum isolate 287/91. Genome comparisons between these and other Salmonella isolates indicate that S. Gallinarum 287/91 is a recently evolved descendent of S. Enteritidis. Significantly, the genome of S. Gallinarum has undergone extensive degradation through deletion and pseudogene formation. Comparison of the pseudogenes in S. Gallinarum with those identified previously in other host-adapted bacteria reveals the loss of many common functional traits and provides insights into possible mechanisms of host and tissue adaptation. We propose that experimental analysis in chickens and mice of S. Enteritidis–harboring mutations in functional homologs of the pseudogenes present in S. Gallinarum could provide an experimentally tractable route toward unraveling the genetic basis of host adaptation in S. enterica.
An Abscisic Acid-Induced Protein, HVA22, Inhibits Gibberellin-Mediated Programmed Cell Death in Cereal Aleurone Cells
Authors: Guo Woei-Jiun , David Ho Tuan-Hua
Published: June 2008

Plant HVA22 is a unique abscisic acid (ABA)/stress-induced protein first isolated from barley (Hordeum vulgare) aleurone cells. Its yeast homolog, Yop1p, functions in vesicular trafficking and in the endoplasmic reticulum (ER) network in vivo. To examine the roles of plant HVA22, barley HVA22 was ectopically expressed in barley aleurone cells. Overexpression of HVA22 proteins inhibited gibberellin (GA)-induced formation of large digestive vacuoles, which is an important aspect of GA-induced programmed cell death in aleurone cells. The effect of HVA22 was specific, because overexpression of green fluorescent protein or another ABA-induced protein, HVA1, did not lead to the same effect. HVA22 acts downstream of the transcription factor GAMyb, which activates programmed cell death and other GA-mediated processes. Moreover, expression of HVA22:green fluorescent protein fusion proteins showed network and punctate fluorescence patterns, which were colocalized with an ER marker, BiP:RFP, and a Golgi marker, ST:mRFP, respectively. In particular, the transmembrane domain 2 was critical for protein localization and stability. Ectopic expression of the most phylogenetically similar Arabidopsis (Arabidopsis thaliana) homolog, AtHVA22D, also resulted in the inhibition of vacuolation to a similar level as HVA22, indicating function conservation between barley HVA22 and some Arabidopsis homologs. Taken together, we show that HVA22 is an ER- and Golgi-localized protein capable of negatively regulating GA-mediated vacuolation/programmed cell death in barley aleurone cells. We propose that ABA induces the accumulation of HVA22 proteins to inhibit vesicular trafficking involved in nutrient mobilization to delay coalescence of protein storage vacuoles as part of its role in regulating seed germination and seedling growth.
Apoplastic Polyamine Oxidation Plays Different Roles in Local Responses of Tobacco to Infection by the Necrotrophic FungusSclerotinia sclerotiorumand the Biotrophic BacteriumPseudomonas viridiflava
Authors: Marina María , Maiale Santiago Javier , Rossi Franco Rubén , Romero Matías Fernando , Rivas Elisa Isabel , Gárriz Andrés , Ruiz Oscar Adolfo , Pieckenstain Fernando Luis
Published: June 2008

The role of polyamine (PA) metabolism in tobacco (Nicotiana tabacum) defense against pathogens with contrasting pathogenic strategies was evaluated. Infection by the necrotrophic fungus Sclerotinia sclerotiorum resulted in increased arginine decarboxylase expression and activity in host tissues, as well as putrescine and spermine accumulation in leaf apoplast. Enhancement of leaf PA levels, either by using transgenic plants or infiltration with exogenous PAs, led to increased necrosis due to infection by S. sclerotiorum. Specific inhibition of diamine and PA oxidases attenuated the PA-induced enhancement of leaf necrosis during fungal infection. When tobacco responses to infection by the biotrophic bacterium Pseudomonas viridiflava were investigated, an increase of apoplastic spermine levels was detected. Enhancement of host PA levels by the above-described experimental approaches strongly decreased in planta bacterial growth, an effect that was blocked by a PA oxidase inhibitor. It can be concluded that accumulation and further oxidation of free PAs in the leaf apoplast of tobacco plants occurs in a similar, although not identical way during tobacco defense against infection by microorganisms with contrasting pathogenesis strategies. This response affects the pathogen's ability to colonize host tissues and results are detrimental for plant defense against necrotrophic pathogens that feed on necrotic tissue; on the contrary, this response plays a beneficial role in defense against biotrophic pathogens that depend on living tissue for successful host colonization. Thus, apoplastic PAs play important roles in plant-pathogen interactions, and modulation of host PA levels, particularly in the leaf apoplast, may lead to significant changes in host susceptibility to different kinds of pathogens.