bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards realistic codon models: among site variability and dependency of synonymous and non-synonymous rates
Authors: Mayrose Itay , Doron-Faigenboim Adi , Bacharach Eran , Pupko Tal 
Published: July 2007

Abstract
Codon evolutionary models are widely used to infer the selection forces acting on a protein. The non-synonymous to synonymous rate ratio (denoted by Ka/Ks) is used to infer specific positions that are under purifying or positive selection. Current evolutionary models usually assume that only the non-synonymous rates vary among sites while the synonymous substitution rates are constant. This assumption ignores the possibility of selection forces acting at the DNA or mRNA levels. Towards a more realistic description of sequence evolution, we present a model that accounts for among-site-variation of both synonymous and non-synonymous substitution rates. Furthermore, we alleviate the widespread assumption that positions evolve independently of each other. Thus, possible sources of bias caused by random fluctuations in either the synonymous or non-synonymous rate estimations at a single site is removed. Our model is based on two hidden Markov models that operate on the spatial dimension: one describes the dependency between adjacent non-synonymous rates while the other describes the dependency between adjacent synonymous rates. The presented model is applied to study the selection pressure across the HIV-1 genome. The new model better describes the evolution of all HIV-1 genes, as compared to current codon models. Using both simulations and real data analyses, we illustrate that accounting for synonymous rate variability and dependency greatly increases the accuracy of Ka/Ks estimation and in particular of positively selected sites. Finally, we discuss the applicability of the developed model to infer the selection forces in regulatory and overlapping regions of the HIV-1 genome.
Contact: talp@post.tau.ac.il
Translation initiation site prediction on a genomic scale: beauty in simplicity
Authors: Saeys Yvan , Abeel Thomas , Degroeve Sven , Van de Peer Yves 
Published: July 2007

Abstract
Motivation: The correct identification of translation initiation sites (TIS) remains a challenging problem for computational methods that automatically try to solve this problem. Furthermore, the lion's share of these computational techniques focuses on the identification of TIS in transcript data. However, in the gene prediction context the identification of TIS occurs on the genomic level, which makes things even harder because at the genome level many more pseudo-TIS occur, resulting in models that achieve a higher number of false positive predictions.
Results: In this article, we evaluate the performance of several ‘simple’ TIS recognition methods at the genomic level, and compare them to state-of-the-art models for TIS prediction in transcript data. We conclude that the simple methods largely outperform the complex ones at the genomic scale, and we propose a new model for TIS recognition at the genome level that combines the strengths of these simple models. The new model obtains a false positive rate of 0.125 at a sensitivity of 0.80 on a well annotated human chromosome (chromosome 21). Detailed analyses show that the model is useful, both on its own and in a simple gene prediction setting.
Availability: Datafiles and a web interface for the StartScan program are available at http://bioinformatics.psb.ugent.be/supplementary_data/
Contact: yvan.saeys@psb.ugent.be
Locomotif: from graphical motif description to RNA motif search
Authors: Reeder Janina , Reeder Jens , Giegerich Robert 
Published: July 2007

Abstract
Motivation and Results: Motivated by the recent rise of interest in small regulatory RNAs, we present Locomotif—a new approach for locating RNA motifs that goes beyond the previous ones in three ways: (1) motif search is based on efficient dynamic programming algorithms, incorporating the established thermodynamic model of RNA secondary structure formation. (2) motifs are described graphically, using a Java-based editor, and search algorithms are derived from the graphics in a fully automatic way. The editor allows us to draw secondary structures, annotated with size and sequence information. They closely resemble the established, but informal way in which RNA motifs are communicated in the literature. Thus, the learning effort for Locomotif users is minimal. (3) Locomotif employs a client-server approach. Motifs are designed by the user locally. Search programs are generated and compiled on a bioinformatics server. They are made available both for execution on the server, and for download as C source code plus an appropriate makefile.
Availability: Locomotif is available at http://bibiserv.techfak.uni-bielefeld.de/locomotif
Contact: robert@techfak.uni-bielefeld.de
Nucleotide variation of regulatory motifs may lead to distinct expression patterns
Authors: Segal Liat , Lapidot Michal , Solan Zach , Ruppin Eytan , Pilpel Yitzhak , Horn David 
Published: July 2007

AbstractMotivation: Current methodologies for the selection of putative transcription factor binding sites (TFBS) rely on various assumptions such as over-representation of motifs occurring on gene promoters, and the use of motif descriptions such as consensus or position-specific scoring matrices (PSSMs). In order to avoid bias introduced by such assumptions, we apply an unsupervised motif extraction (MEX) algorithm to sequences of promoters. The extracted motifs are assessed for their likely cis-regulatory function by calculating the expression coherence (EC) of the corresponding genes, across a set of biological conditions.Results: Applying MEX to all Saccharomyces cerevisiae promoters, followed by EC analysis across 40 biological conditions, we obtained a high percentage of putative cis-regulatory motifs. We clustered motifs that obtained highly significant EC scores, based on both their sequence similarity and similarity in the biological conditions these motifs appear to regulate. We describe 20 clusters, some of which regroup known TFBS. The clusters display different mRNA expression profiles, correlated with typical changes in the nucleotide composition of their relevant motifs. In several cases, a variation of a single nucleotide is shown to lead to distinct differences in expression patterns. These results are confronted with additional information, such as binding of transcription factors to groups of genes. Detailed analysis is presented for clusters related to MCB/SCB, STRE and PAC. In the first two cases, we provide evidence for different binding mechanisms of different clusters of motifs. For PAC-related motifs we uncover a new cluster that has so far been overshadowed by the stronger effects of known PAC motifs.Contact: horn@tau.ac.ilSupplementary information: Supplementary data are available at http://adios.tau.ac.il/regmotifs and at Bioinformatics online.
Negation of protein–protein interactions: analysis and extraction
Authors: Sanchez-Graillet Olivia , Poesio Massimo 
Published: July 2007

Abstract
Motivation: Negative information about protein–protein interactions—from uncertainty about the occurrence of an interaction to knowledge that it did not occur—is often of great use to biologists and could lead to important discoveries. Yet, to our knowledge, no proposals focusing on extracting such information have been proposed in the text mining literature.
Results: In this work, we present an analysis of the types of negative information that is reported, and a heuristic-based system using a full dependency parser to extract such information. We performed a preliminary evaluation study that shows encouraging results of our system. Finally, we have obtained an initial corpus of negative protein–protein interactions as basis for the construction of larger ones.
Availability: The corpus is available by request from the authors.
Contact: osanch@essex.ac.uk or poesio@essex.ac.uk
Probabilistic whole-genome alignments reveal high indel rates in the human and mouse genomes
Authors: Lunter Gerton 
Published: July 2007

Abstract
Motivation: The two mutation processes that have the largest impact on genome evolution at small scales are substitutions, and sequence insertions and deletions (indels). While the former have been studied extensively, indels have received less attention, and in particular, the problem of inferring indel rates between pairs of divergent sequence remains unsolved. Here, I describe a novel and accurate method for estimating neutral indel rates between divergent pairs of genomes.
Results: Simulations suggest that new method for estimating indel rates is accurate to within 2%, at divergences corresponding to that of human and mouse. Applying the method to these species, I show that indel rates are up to twice higher than is apparent from alignments, and depend strongly on the local G + C content. These results indicate that at these evolutionary distances, the contribution of indels to sequence divergence is much larger than hitherto appreciated. In particular, the ratio of substitution to indel rates between human and mouse appears to be around γ = 8, rather than the currently accepted value of about γ = 14.
Contact: Gerton.lunter@dpag.ox.ac.uk
Anisotropic fluctuations of amino acids in protein structures: insights from X-ray crystallography and elastic network models
Authors: Eyal Eran , Chennubhotla Chakra , Yang Lee-Wei , Bahar Ivet 
Published: July 2007

Abstract
Motivation: A common practice in X-ray crystallographic structure refinement has been to model atomic displacements or thermal fluctuations as isotropic motions. Recent high-resolution data reveal, however, significant departures from isotropy, described by anisotropic displacement parameters (ADPs) modeled for individual atoms. Yet, ADPs are currently reported for a limited set of structures, only.
Results: We present a comparative analysis of the experimentally reported ADPs and those theoretically predicted by the anisotropic network model (ANM) for a representative set of structures. The relative sizes of fluctuations along different directions are shown to agree well between experiments and theory, while the cross-correlations between the (x-, y- and z-) components of the fluctuations show considerable deviations. Secondary structure elements and protein cores exhibit more robust anisotropic characteristics compared to disordered or flexible regions. The deviations between experimental and theoretical data are comparable to those between sets of experimental ADPs reported for the same protein in different crystal forms. These results draw attention to the effects of crystal form and refinement procedure on experimental ADPs and highlight the potential utility of ANM calculations for consolidating experimental data or assessing ADPs in the absence of experimental data.
Availability: The ANM server at http://www.ccbb.pitt.edu/anm is upgraded to permit users to compute and visualize the theoretical ADPs for any PDB structure, thus providing insights into the anisotropic motions intrinsically preferred by equilibrium structures.
Contact: bahar@ccbb.pitt.edu
Supplementary information: Two Supplementary Material files can be accessed at the journal website. The first presents the tabulated results from computations (Pearson correlations and KL distances with respect to experimental ADPs) reported for each of the 93 proteins in Set I (the averages over all proteins are presented above in Table 3). The second file consists of three sections: (A) detailed derivation of Equation (7), (B) analysis of the effect of ANM parameters on computed ADPs and identification of parameters that achieve optimal correlation with experiments and (C) description of the method for computing the tangential and radial components of equilibrium fluctuations.
SNP mining porcine ESTs with MAVIANT, a novel tool for SNP evaluation and annotation
Authors: Panitz Frank , Stengaard Henrik , Hornshøj Henrik , Gorodkin Jan , Hedegaard Jakob , Cirera Susanna , Thomsen Bo , Madsen Lone B. , Høj Anette , Vingborg Rikke K. , Zahn Bujie , Wang Xuegang , Wang Xuefei , Wernersson Rasmus , Jørgensen Claus B. , Scheibye-Knudsen Karsten , Arvin Troels , Lumholdt Steen , Sawera Milena , Green Trine , Nielsen Bente J. , Havgaard Jakob H. , Brunak Søren , Fredholm Merete , Bendixen Christian 
Published: July 2007

Abstract
Motivation: Single nucleotide polymorphisms (SNPs) analysis is an important means to study genetic variation. A fast and cost-efficient approach to identify large numbers of novel candidates is the SNP mining of large scale sequencing projects. The increasing availability of sequence trace data in public repositories makes it feasible to evaluate SNP predictions on the DNA chromatogram level. MAVIANT, a platform-independent Multipurpose Alignment VIewing and Annotation Tool, provides DNA chromatogram and alignment views and facilitates evaluation of predictions. In addition, it supports direct manual annotation, which is immediately accessible and can be easily shared with external collaborators.
Results: Large-scale SNP mining of polymorphisms bases on porcine EST sequences yielded more than 7900 candidate SNPs in coding regions (cSNPs), which were annotated relative to the human genome. Non-synonymous SNPs were analyzed for their potential effect on the protein structure/function using the PolyPhen and SIFT prediction programs. Predicted SNPs and annotations are stored in a web-based database. Using MAVIANT SNPs can visually be verified based on the DNA sequencing traces. A subset of candidate SNPs was selected for experimental validation by resequencing and genotyping. This study provides a web-based DNA chromatogram and contig browser that facilitates the evaluation and selection of candidate SNPs, which can be applied as genetic markers for genome wide genetic studies.
Availability: The stand-alone version of MAVIANT program for local use is freely available under GPL license terms at http://snp.agrsci.dk/maviant.
Contact: christian.bendixen@agrsci.dk
Supplementary information: Supplementary data are available at Bioinformatics online.
Using dynamic programming to create isotopic distribution maps from mass spectra
Authors: McIlwain Sean , Page David , Huttlin Edward L. , Sussman Michael R. 
Published: July 2007

Abstract
Motivation: This article presents a method to identify the isotopic distributions within a mass spectrum using a probabilistic classifier supplemented with dynamic programming. Such a system is needed for a variety of purposes, including generating robust and meaningful features from mass spectra to be used in classification.
Results: The primary result of this article is that the dynamic programming approach significantly improves sensitivity, without harming specificity, of a probabilistic classifier for identifying the isotopic distributions. When annotating isotopic distributions where an expert has performed the initial ‘peak-picking’ (removal of noise peaks), the dynamic programming approach gives a true positive rate of 96% and a false positive rate of 0.0%, whereas the classifier alone has a true positive rate of only 47% when the false positive rate is 0.0%. When annotating isotopic distributions in machine peak-picked spectra, which may contain many noise peaks, the dynamic programming approach gives a true positive rate of only 22.0%, but it still keeps a low false positive rate of 1.0% and still outperforms the classifier alone. It is important to note that all these rates are when we require exact matches with the distributions in annotated spectra; in our evaluation a distribution is considered ‘entirely incorrect’ if it is missing even one peak or contains even one extraneous peak. We compared to the THRASH and AID-MS systems using a looser requirement: correctly identifying the distribution that contains the mono-isotopic mass. Under this measure, our dynamic programming approach achieves a true positive rate of 82% and a false positive rate of 1%, which again outperforms the classifier alone. The dynamic programming approach ends up being more conservative than THRASH and AID-MS, yielding both fewer true and false peaks, but the F-score of the dynamic programming approach is significantly better than those of THRASH and AID-MS. All results were obtained with 10-fold cross-validation of 99 sections of mass spectra with a total of 214 hand-annotated isotopic distributions.
Availability: Programs are available via http://www.cs.wisc.edu/~mcilwain/IDM
Contact: mcilwain@cs.wisc.edu
Functional annotation of regulatory pathways
Authors: Pandey Jayesh , Koyutürk Mehmet , Kim Yohan , Szpankowski Wojciech , Subramaniam Shankar , Grama Ananth 
Published: July 2007

Abstract
Motivation: Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level.
Results: We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations.
Availability: Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/
Contact: jpandey@cs.purdue.edu
GPDTI: A Genetic Programming Decision Tree Induction method to find epistatic effects in common complex diseases
Authors: Estrada-Gil Jesús K. , Fernández-López Juan C. , Hernández-Lemus Enrique , Silva-Zolezzi Irma , Hidalgo-Miranda Alfredo , Jiménez-Sánchez Gerardo , Vallejo-Clemente Edgar E. 
Published: July 2007

Abstract
Motivation: The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities.
Results: We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors.
Availability: Information on the generated data sets and executable code is available upon request.
Contact: jestrada@inmegen.gob.mx
Multiway analysis of epilepsy tensors
Authors: Acar Evrim , Aykut-Bingol Canan , Bingol Haluk , Bro Rasmus , Yener Bülent 
Published: July 2007

Abstract
Motivation: The success or failure of an epilepsy surgery depends greatly on the localization of epileptic focus (origin of a seizure). We address the problem of identification of a seizure origin through an analysis of ictal electroencephalogram (EEG), which is proven to be an effective standard in epileptic focus localization.
Summary: With a goal of developing an automated and robust way of visual analysis of large amounts of EEG data, we propose a novel approach based on multiway models to study epilepsy seizure structure. Our contributions are 3-fold. First, we construct an Epilepsy Tensor with three modes, i.e. time samples, scales and electrodes, through wavelet analysis of multi-channel ictal EEG. Second, we demonstrate that multiway analysis techniques, in particular parallel factor analysis (PARAFAC), provide promising results in modeling the complex structure of an epilepsy seizure, localizing a seizure origin and extracting artifacts. Third, we introduce an approach for removing artifacts using multilinear subspace analysis and discuss its merits and drawbacks.
Results: Ictal EEG analysis of 10 seizures from 7 patients are included in this study. Our results for 8 seizures match with clinical observations in terms of seizure origin and extracted artifacts. On the other hand, for 2 of the seizures, seizure localization is not achieved using an initial trial of PARAFAC modeling. In these cases, first, we apply an artifact removal method and subsequently apply the PARAFAC model on the epilepsy tensor from which potential artifacts have been removed. This method successfully identifies the seizure origin in both cases.
Contact: acare@cs.rpi.edu
An ensemble framework for clustering protein–protein interaction networks
Authors: Asur Sitaram , Ucar Duygu , Parthasarathy Srinivasan 
Published: July 2007

Abstract
Motivation: Protein–Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network.
Results: In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins.
Contact: srini@cse.ohio-state.edu
Supplementary information: Supplementary data are available at Bioinformatics online.