bluuebunny/crossref_metadata_2025_split huggingface dataset:
AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets
Authors: Bottegoni Giovanni , Rocchia Walter , Recanatini Maurizio , Cavalli Andrea 
Published: July 2006

Abstract
Motivation: Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing.
Results: We integrated hierarchical agglomerative cluster analysis with a clusterability assessment method and a user independent cutting rule, to form a global protocol that we implemented in a MATLAB metalanguage program (AClAP). We tested it on the conformational space of a quite diverse set of drugs generated via Metropolis Monte Carlo simulation, and on the poses we obtained by reiterated docking runs performed by four widespread programs. In our tests, AClAP proved to remarkably reduce the dimensionality of the original datasets at a negligible computational cost. Moreover, when applied to the outcomes of many docking programs together, it was able to point to the crystallographic pose.
Availability: AClAP is available at the “AClAP” section of the website .
Contact: E-mail: andrea.cavalli@unibo.it.
Supplementary Information: The complete series of AClAP results is available in the “services” section of the website .
Automatic clustering of orthologs and inparalogs shared by multiple proteomes
Authors: Alexeyenko Andrey , Tamas Ivica , Liu Gang , Sonnhammer Erik L.L. 
Published: July 2006

Abstract
Motivation: The complete sequencing of many genomes has made it possible to identify orthologous genes descending from a common ancestor. However, reconstruction of evolutionary history over long time periods faces many challenges due to gene duplications and losses. Identification of orthologous groups shared by multiple proteomes therefore becomes a clustering problem in which an optimal compromise between conflicting evidences needs to be found.
Results: Here we present a new proteome-scale analysis program called MultiParanoid that can automatically find orthology relationships between proteins in multiple proteomes. The software is an extension of the InParanoid program that identifies orthologs and inparalogs in pairwise proteome comparisons. MultiParanoid applies a clustering algorithm to merge multiple pairwise ortholog groups from InParanoid into multi-species ortholog groups. To avoid outparalogs in the same cluster, MultiParanoid only combines species that share the same last ancestor.
To validate the clustering technique, we compared the results to a reference set obtained by manual phylogenetic analysis. We further compared the results to ortholog groups in KOGs and OrthoMCL, which revealed that MultiParanoid produces substantially fewer outparalogs than these resources.
Availability: MultiParanoid is a freely available standalone program that enables efficient orthology analysis much needed in the post-genomic era. A web-based service providing access to the original datasets, the resulting groups of orthologs, and the source code of the program can be found at .
Contact: Erik.Sonnhammer@sbc.su.se
Supplementary information:
Computational inference of the molecular logic for synaptic connectivity in C. elegans
Authors: Varadan Vinay , Miller David M. , Anastassiou Dimitris 
Published: July 2006

Abstract
Motivation: The nematode C. elegans is an ideal model organism in which to investigate the biomolecular mechanisms underlying the connectivity of neurons, because synaptic connections are described in a comprehensive wiring diagram and methods for defining gene expression profiles of individual neurons are now available.
Results: Here we present computational techniques linking these two types of information. A systems-based approach (EMBP: Entropy Minimization and Boolean Parsimony) identifies sets of synergistically interacting genes whose joint expression predicts neural connectivity. We introduce an information theoretic measure of the multivariate synergy, a fundamental concept in systems biology, connecting the members of these gene sets. We present and validate our preliminary results based on publicly available information, and demonstrate that their synergy is exceptionally high indicating joint involvement in pathways. Our strategy provides a robust methodology that will yield increasingly more accurate results as more neuron-specific gene expression data emerge. Ultimately, we expect our approach to provide important clues for universal mechanisms of neural interconnectivity.
Contact: anastas@ee.columbia.edu
Supplementary Information: Expression and connectivity data will be available and maintained in the future as new results become available, together with software and additional clarifying descriptions of our techniques, on
Quantification of transcription factor expression from Arabidopsis images
Authors: Mace Daniel L. , Lee Ji-Young , Twigg Richard W. , Colinas Juliette , Benfey Philip N. , Ohler Uwe 
Published: July 2006

Abstract
Motivation: Confocal microscopy has long provided qualitative information for a variety of applications in molecular biology. Recent advances have led to extensive image datasets, which can now serve as new data sources to obtain quantitative gene expression information. In contrast to microarrays, which usually provide data for many genes at one time point, these image data provide us with expression information for only one gene, but with the advantage of high spatial and/or temporal resolution, which is often lostin microarray samples.
Results: We have developed a prototype for the automatic analysis of Arabidopsis confocal images, which show the expression of a single transcription factor by means of GFP reporter constructs. Using techniques from image registration, we are able to address inherent problems of non-rigid transformation and partial mapping, and obtain relative expression values for 13 different tissues in Arabidopsis roots. This provides quantitative information with high spatial resolution, which accurately represents the underlying expression values within the organism. We validate our approach on a data set of 122 images depicting expression patterns of 30 transcription factors, both in terms of registration accuracy, as well as correlation with cell-sorted microarray data. Approaches like this will be useful to lay the groundwork to reconstruct regulatory networks on the level of tissues or even individual cells.
Contact: uwe.ohler@duke.edu
Availability: Upon request from the authors.
Supplementary Data:
Identifying cycling genes by combining sequence homology and expression data
Authors: Lu Yong , Rosenfeld Roni , Bar-Joseph Ziv 
Published: July 2006

Abstract
Motivation: The expression of genes during the cell division process has now been studied in many different species. An important goal of these studies is to identify the set of cycling genes. To date, this was done independently for each of the species studied. Due to noise and other data analysis problems, accurately deriving a set of cycling genes from expression data is a hard problem. This is especially true for some of the multicellular organisms, including humans.
Results: Here we present the first algorithm that combines microarray expression data from multiple species for identifying cycling genes. Our algorithm represents genes from multiple species as nodes in a graph. Edges between genes represent sequence similarity. Starting with the measured expression values for each species we use Belief Propagation to determine a posterior score for genes. This posterior is used to determine a new set of cycling genes for each species.
We applied our algorithm to improve the identification of the set of cell cycle genes in budding yeast and humans. As we show, by incorporating sequence similarity information we were able to obtain a more accurate set of genes compared to methods that rely on expression data alone. Our method was especially successful for the human dataset indicating that it can use a high quality dataset from one species to overcome noise problems in another.
Availability: C implementation is available from the supporting website:
Contact: zivbj@cs.cmu.edu
Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks
Authors: Gevaert Olivier , Smet Frank De , Timmerman Dirk , Moreau Yves , Moor Bart De 
Published: July 2006

Abstract
Motivation: Clinical data, such as patient history, laboratory analysis, ultrasound parameters—which are the basis of day-to-day clinical decision support—are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable.
Results: We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices.
Contact: olivier.gevaert@esat.kuleuven.be
BNTagger: improved tagging SNP selection using Bayesian networks
Authors: Lee Phil Hyoun , Shatkay Hagit 
Published: July 2006

Abstract
Genetic variation analysis holds much promise as a basis for disease-gene association. However, due to the tremendous number of candidate single nucleotide polymorphisms (SNPs), there is a clear need to expedite genotyping by selecting and considering only a subset of all SNPs. This process is known as tagging SNP selection. Several methods for tagging SNP selection have been proposed, and have shown promising results. However, most of them rely on strong assumptions such as prior block-partitioning, bi-allelic SNPs, or a fixed number or location of tagging SNPs.
We introduce BNTagger, a new method for tagging SNP selection, based on conditional independence among SNPs. Using the formalism of Bayesian networks (BNs), our system aims to select a subset of independent and highly predictive SNPs. Similar to previous prediction-based methods, we aim to maximize the prediction accuracy of tagging SNPs, but unlike them, we neither fix the number nor the location of predictive tagging SNPs, nor require SNPs to be bi-allelic. In addition, for newly-genotyped samples, BNTagger directly uses genotype data as input, while producing as output haplotype data of all SNPs.
Using three public data sets, we compare the prediction performance of our method to that of three state-of-the-art tagging SNP selection methods. The results demonstrate that our method consistently improves upon previous methods in terms of prediction accuracy. Moreover, our method retains its good performance even when a very small number of tagging SNPs are used.
Contact: lee@cs.queensu.ca, shatkay@cs.queensu.ca
Integrating image data into biomedical text categorization
Authors: Shatkay Hagit , Chen Nawei , Blostein Dorothea 
Published: July 2006

Abstract
Categorization of biomedical articles is a central task for supporting various curation efforts. It can also form the basis for effective biomedical text mining. Automatic text classification in the biomedical domain is thus an active research area. Contests organized by the KDD Cup (2002) and the TREC Genomics track (since 2003) defined several annotation tasks that involved document classification, and provided training and test data sets. So far, these efforts focused on analyzing only the text content of documents. However, as was noted in the KDD'02 text mining contest—where figure-captions proved to be an invaluable feature for identifying documents of interest—images often provide curators with critical information. We examine the possibility of using information derived directly from image data, and of integrating it with text-based classification, for biomedical document categorization. We present a method for obtaining features from images and for using them—both alone and in combination with text—to perform the triage task introduced in the TREC Genomics track 2004. The task was to determine which documents are relevant to a given annotation task performed by the Mouse Genome Database curators. We show preliminary results, demonstrating that the method has a strong potential to enhance and complement traditional text-based categorization methods.
Contact: shatkay@cs.queensu.ca
Rapid knot detection and application to protein structure prediction
Authors: Khatib Firas , Weirauch Matthew T. , Rohl Carol A. 
Published: July 2006

Abstract
Motivation: Knots in polypeptide chains have been found in very few proteins, and consequently should be generally avoided in protein structure prediction methods. Most effective structure prediction methods do not model the protein folding process itself, but rather seek only to correctly obtain the final native state. Consequently, the mechanisms that prevent knots from occurring in native proteins are not relevant to the modeling process, and as a result, knots can occur with significantly higher frequency in protein models. Here we describe Knotfind, a simple algorithm for knot detection that is fast enough for structure prediction, where tens or hundreds of thousands of conformations may be sampled during the course of a prediction. We have used this algorithm to characterize knots in large populations of model structures generated for targets in CASP 5 and CASP 6 using the Rosetta homology-based modeling method.
Results: Analysis of CASP5 models suggested several possible avenues for introduction of knots into these models, and these insights were applied to structure prediction in CASP 6, resulting in a significant decrease in the proportion of knotted models generated. Additionally, using the knot detection algorithm on structures in the Protein Data Bank, a previously unreported deep trefoil knot was found in acetylornithine transcarbamylase.
Availability: The Knotfind algorithm is available in the Rosetta structure prediction program at
Contact: bort@soe.ucsc.edu
Efficient identification of DNA hybridization partners in a sequence database
Authors: Mann Tobias P. , Noble William Stafford 
Published: July 2006

Abstract
Motivation: The specific hybridization of complementary DNA molecules underlies many widely used molecular biology assays, including the polymerase chain reaction and various types of microarray analysis. In order for such an assay to work well, the primer or probe must bind to its intended target, without also binding to additional sequences in the reaction mixture. For any given probe or primer, potential non-specific binding partners can be identified using state-of-the-art models of DNA binding stability. Unfortunately, these models rely on dynamic programming algorithms that are too slow to apply on a genomic scale.
Results: We present an algorithm that efficiently scans a DNA database for short (approximately 20–30 base) sequences that will bind to a query sequence. We use a filtering approach, in which a series of increasingly stringent filters is applied to a set of candidate k-mers. The k-mers that pass all filters are then located in the sequence database using a precomputed index, and an accurate model of DNA binding stability is applied to the sequence surrounding each of the k-mer occurrences. This approach reduces the time to identify all binding partners for a given DNA sequence in human genomic DNA by approximately three orders of magnitude, from two days for the ENCODE regions to less than one minute for typical queries. Our approach is scalable to large DNA sequences. Our method can scan the human genome for medium strength binding sites to a candidate PCR primer in an average of 34.5 minutes.
Availability: Software implementing the algorithms described here is available at
Contact: mann@gs.washington.edu
ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains
Authors: Aoki-Kinoshita Kiyoko F. , Ueda Nobuhisa , Mamitsuka Hiroshi , Kanehisa Minoru 
Published: July 2006

Abstract
Motivation: Carbohydrate sugar chains, or glycans, are considered the third major class of biomolecules after DNA and proteins. They consist of branching monosaccharides, starting from a single monosaccharide. They are extremely vital to the development and functioning of multicellular organisms because they are recognized by various proteins to allow them to perform specific functions. Our motivation is to study this recognition mechanism using informatics techniques from the data available. Previously, we introduced a probabilistic sibling-dependent tree Markov model (PSTMM), which we showed could be efficiently trained on sibling-dependent tree structures and return the most likely state paths. However, it had some limitations in that the extra dependency between siblings caused overfitting problems. The retrieval of the patterns from the trained model also involved manually extracting the patterns from the most likely state paths. Thus we introduce a profilePSTMM model which avoids these problems, incorporating a novel concept of different types of state transitions to handle parent-child and sibling dependencies differently.
Results: Our new algorithms are more efficient and able to extract the patterns more easily. We tested the profilePSTMM model on both synthetic (controlled) data as well as glycan data from the KEGG GLYCAN database. Additionally, we tested it on glycans which are known to be recognized and bound to proteins at various binding affinities, and we show that our results correlate with results published in the literature.
Contact: kkiyoko@t.soka.ac.jp
Apples to apples: improving the performance of motif finders and their significance analysis in the Twilight Zone
Authors: Ng Patrick , Nagarajan Niranjan , Jones Neil , Keich Uri 
Published: July 2006

Abstract
Motivation: Effective algorithms for finding relatively weak motifs are an important practical necessity while scanning long DNA sequences for regulatory elements. The success of such an algorithm hinges on the ability of its scoring function combined with a significance analysis test to discern real motifs from random noise.
Results: In the first half of the paper we show that the paradigm of relying on entropy scores and their E-values can lead to undesirable results when searching for weak motifs and we offer alternate approaches to analyzing the significance of motifs. In the second half of the paper we reintroduce a scoring function and present a motif-finder that optimizes it that are more effective in finding relatively weak motifs than other tools.
Availability: The GibbsILR motif finder is available at
Contact: Uri Keich, keich@cs.cornell.edu
Finding novel genes in bacterial communities isolated from the environment
Authors: Krause Lutz , Diaz Naryttza N. , Bartels Daniela , Edwards Robert A. , Pühler Alfred , Rohwer Forest , Meyer Folker , Stoye Jens 
Published: July 2006

Abstract
Motivation: Novel sequencing techniques can give access to organisms that are difficult to cultivate using conventional methods. When applied to environmental samples, the data generated has some drawbacks, e.g. short length of assembled contigs, in-frame stop codons and frame shifts. Unfortunately, current gene finders cannot circumvent these difficulties. At the same time, the automated prediction of genes is a prerequisite for the increasing amount of genomic sequences to ensure progress in metagenomics.
Results: We introduce a novel gene finding algorithm that incorporates features overcoming the short length of the assembled contigs from environmental data, in-frame stop codons as well as frame shifts contained in bacterial sequences. The results show that by searching for sequence similarities in an environmental sample our algorithm is capable of detecting a high fraction of its gene content, depending on the species composition and the overall size of the sample. The method is valuable for hunting novel unknown genes that may be specific for the habitat where the sample is taken. Finally, we show that our algorithm can even exploit the limited information contained in the short reads generated by 454 technology for the prediction of protein coding genes.
Availability: The program is freely available upon request.
Contact: Lutz.Krause@CeBiTec.Uni-Bielefeld.DE
Modelling sequential protein folding under kinetic control
Authors: Huard Fabien P.E. , Deane Charlotte M. , Wood Graham R. 
Published: July 2006

Abstract
Motivation: This study presents a novel investigation of the effect of kinetic control on cotranslational protein folding. We demonstrate the effect using simple HP lattice models and show that the cotranslational folding of proteins under kinetic control has a significant impact on the final conformation. Differences arise if nature is not capable of pushing a partially folded protein back over a large energy barrier. For this reason we argue that such constraints should be incorporated into structure prediction techniques. We introduce a finite surmountable energy barrier which allows partially formed chains to partly unfold, and permits us to enumerate exhaustively all energy pathways.
Results: We compare the ground states obtained sequentially with the global ground states of designing sequences (those with a unique global ground state). We find that the sequential ground states become less numerous and more compact as the surmountable energy barrier increases. We also introduce a probabilistic model to describe the distribution of final folds and allow partial settling to the Boltzmann distribution of states at each stage. As a result, conformations with the highest probability of final occurrence are not necessarily the ones of lowest energy.
Availability: Software available on request
Contact: fhuard@efs.mq.edu.au