bluuebunny/crossref_metadata_2025_split huggingface dataset:
Common structural patterns in human genes
Authors: Hill Aubrey , Sorscher Eric 
Published: March 2004

Abstract
Motivation: A graphical representation of the exon–intron structure of various genes, such as that presented by the National Center for Biotechnology Information Map Viewer, suggests a digital waveform or pattern that varies either in amplitude or frequency. This observation suggests that different genes may have portions of their total exon–intron structure in common. The existence of common structural patterns across unrelated genes suggests the repeated insertion of transposable elements throughout the human genome and/or a common structural function.
Results: We compared the exon–intron size patterns of a number of human genes and discovered numerous conserved arrangements with similarity at a high degree of stringency (&gt;99%) across the otherwise unrelated and diverse genomic landscape. In our experimental analyses, more than 200 patterns of length 2 or greater at 99% stringency were found among the 72 genes we compared.
Mathematical characterization of three-dimensional gene expression patterns
Authors: Costa L. da F. , Barbosa M. S. , Manoel E. T. M. , Streicher J. , Müller G. B. 
Published: March 2004

Abstract
Motivation: The importance of a systematic methodology for the mathematical characterization of three-dimensional gene expression patterns in embryonic development.
Methods: By combining lacunarity and multiscale fractal dimension analyses with computer-based methods of three-dimensional reconstruction, it becomes possible to extract new information from in situ hybridization studies. Lacunarity and fractality are appropriate measures for the cloud-like gene activation signals in embryonic tissues. The newly introduced multiscale method provides a natural extension of the fractal dimension concept, being capable of characterizing the fractality of geometrical patterns in terms of spatial scale. This tool can be systematically applied to three-dimensional patterns of gene expression.
Results: Applications are illustrated using the three-dimensional expression patterns of the myogenic marker gene Myf5 in a series of differentiating somites of a mouse embryo.
Development of joint application strategies for two microbial gene finders
Authors: McHardy Alice C. , Goesmann Alexander , Pühler Alfred , Meyer Folker 
Published: March 2004

Abstract
Motivation: As a starting point in annotation of bacterial genomes, gene finding programs are used for the prediction of functional elements in the DNA sequence. Due to the faster pace and increasing number of genome projects currently underway, it is becoming especially important to have performant methods for this task.
Results: This study describes the development of joint application strategies that combine the strengths of two microbial gene finders to improve the overall gene finding performance. Critica is very specific in the detection of similarity-supported genes as it uses a comparative sequence analysis-based approach. Glimmer employs a very sophisticated model of genomic sequence properties and is sensitive also in the detection of organism-specific genes. Based on a data set of 113 microbial genome sequences, we optimized a combined application approach using different parameters with relevance to the gene finding problem. This results in a significant improvement in specificity while there is similarity in sensitivity to Glimmer. The improvement is especially pronounced for GC rich genomes. The method is currently being applied for the annotation of several microbial genomes.
Availability: The methods described have been implemented within the gene prediction component of the GenDB genome annotation system.
GeneCluster 2.0: an advanced toolset for bioarray analysis
Authors: Reich M. , Ohm K. , Angelo M. , Tamayo P. , Mesirov J. P. 
Published: March 2004

Abstract
Summary: GeneCluster 2.0 is a software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding classification, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting and k-nearest neighbor algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. GeneCluster 2.0 is a standalone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater.
Availability: http://www.broad.mit.edu/cancer/software
Decoupling dynamical systems for pathway identification from metabolic profiles
Authors: Voit Eberhard O. , Almeida Jonas 
Published: March 2004

Abstract
Rationale: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations.
Results: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a ‘universal function’ that is computed directly from the data by cross-validated training of an artificial neural network (ANN).
Conclusions: Without preprocessing, the inverse problem of determining structure from metabolic or proteomic profile data is challenging and computationally expensive. The combination of system decoupling and data fitting with universal functions simplifies this inverse problem very significantly. Examples show successful estimations and current limitations of the method.
Availability: A preliminary Web-based application for ANN smoothing is accessible at http://bioinformatics.musc.edu/webmetabol/. S-systems can be interactively analyzed with the user-friendly freeware PLAS© (http://correio.cc.fc.ul.pt/~aenf/plas.html) or with the MATLAB module BSTLab (http://bioinformatics.musc.edu/bstlab/), which is currently being beta-tested.
A knowledge-based scale for the analysis and prediction of buried and exposed faces of transmembrane domain proteins
Authors: Beuming Thijs , Weinstein Harel 
Published: March 2004

Abstract
Motivation: The dearth of structural data on α-helical membrane proteins (MPs) has hampered thus far the development of reliable knowledge-based potentials that can be used for automatic prediction of transmembrane (TM) protein structure. While algorithms for identifying TM segments are available, modeling of the TM domains of α-helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains.
Results: A recent increase in the number of crystal structures of α-helical MPs has enabled an analysis of the lipid-exposed surfaces and the interiors of such molecules on the basis of structure, rather than sequence alone. Together with a conservation criterion that is based on previous observations that conserved residues are mostly found in the interior of MPs, the bias of certain residue types to be preferably buried or exposed is proposed as a criterion for predicting the lipid-exposed and interior faces of TMs. Applications to known structures demonstrates 80% accuracy of this prediction algorithm.
Availability: The algorithm used for the predictions is implemented in the ProperTM Web server (http://icb.med.cornell.edu/services/propertm/start).
HMMGEP: clustering gene expression data using hidden Markov models
Authors: Ji Xinglai , Yuan Yuan , Li-Ling Jesse , Li Yanda , Sun Zhirong 
Published: March 2004

Abstract
Summary: The package HMMGEP performs cluster analysis on gene expression data using hidden Markov models.
Availability: HMMGEP, including the source code, documentation and sample data files, is available at http://www.bioinfo.tsinghua.edu.cn:8080/~rich/hmmgep_download/index.html.
Structural exon database, SEDB, mapping exon boundaries on multiple protein structures
Authors: Leslin Chesley M. , Abyzov Alex , Ilyin Valentin A. 
Published: March 2004

Abstract
Summary: Comparative analysis of exon/intron organization of genes and their resulting protein structures is important for understanding evolutionary relationships between species, rules of protein organization and protein functionality. We present Structural Exon Database (SEDB), with a Web interface, an application that allows users to retrieve the exon/intron organization of genes and map the location of the exon boundaries and the intron phase onto a multiple structural alignment. SEDB is linked with Friend, an integrated analytical multiple sequence/structure viewer, which allows simultaneous visualization of exon boundaries on structure and sequence alignments. With SEDB researchers can study the correlations of gene structure with the properties of the encoded three-dimensional protein structures across eukaryotic organisms.
Availability: SEDB is publicly available at http://glinka.bio.neu.edu/SEDB/SEDB.html
Supplementary information: On the SEDB Web site.
TOPALi: software for automatic identification of recombinant sequences within DNA multiple alignments
Authors: Milne Iain , Wright Frank , Rowe Glenn , Marshall David F. , Husmeier Dirk , McGuire Gràinne 
Published: March 2004

Abstract
Summary: TOPALi is a new Java graphical analysis application that allows the user to identify recombinant sequences within a DNA multiple alignment (either automatically or via manual investigation). TOPALi allows a choice of three statistical methods to predict the positions of breakpoints due to past recombination. The breakpoint predictions are then used to identify putative recombinant sequences and their relationships to other sequences. In addition to its sophisticated interface, TOPALi can import many sequence formats, estimate and display phylogenetic trees and allow interactive analysis and/or automatic HTML report generation.
Availability: TOPALi is freely available from http://www.bioss.ac.uk/software.html
FunMap: functional mapping of complex traits
Authors: Ma Chang-Xing , Wu Rongling , Casella George 
Published: March 2004

Abstract
Summary: FunMap is a Web-based user interface designed to map quantitative trait loci (QTL) affecting function-valued traits or infinite-dimensional traits in well-structured pedigrees or natural populations. User input includes three files: longitudinal trait data, marker genotypes and/or a linkage map. This software allows for a systematic genome-wide scan and significance test of QTL throughout the map. The dynamic change of QTL effects during the time course of growth is automatically drawn, from which specific biological hypotheses regarding the genetic control mechanisms of growth and development can be tested.
Availability: http://web.biostat.ufl.edu/~cma/genetics/software.html
Second eigenvalue of the Laplacian matrix for predicting RNA conformational switch by mutation
Authors: Barash Danny 
Published: March 2004

Abstract
Motivation: Conformational switching in RNAs is thought to be of fundamental importance in several biological processes, including translational regulation, regulation of self-cleavage in viruses, protein biosynthesis and mRNA splicing. Current methods for detecting bi-stable RNAs that can lead to structural switching when triggered by an outside event rely on kinetics, energetics and properties of the combinatorial structure space of RNAs. Based on these properties, tools have been developed to predict whether a given sequence folds to a structure characterized by a bi-stable conformation, or to design multi-stable RNAs by an iterative algorithm. A useful addition is in developing a local procedure to prescribe, given an initial sequence, the least amount of mutations needed to drive the system into an optimal bi-stable conformation.
Results: We introduce a local procedure for predicting mutations, by generating and analyzing eigenvalue tables, that are capable of transforming the wild-type sequence into a bi-stable conformation. The method is independent of the folding algorithms but relies on their success. It can be used in conjunction with existing tools, as well as being incorporated into more general RNA prediction packages. We apply this procedure on three well-studied structures. First, the method is validated on the mutation leading to a conformational switch in the spliced leader RNA from Leptomonas collosoma, a mutation that has already been confirmed by an experiment. Second, the method is used to predict a mutation that can lead to a novel conformational switch in the P5abc subdomain of the group I intron ribozyme in Tetrahymena thermophila. Third, the method is applied on Hepatitis delta virus to predict mutations that transform the wild-type into a bi-stable conformation, a configuration assessed by calculating the free energies using folding prediction algorithms. The predictions in the final examples need to be verified experimentally, whereas the mutation predicted in the first example complies with the experiment. This supports the use of our proposed method on other known structures, as well as genetically engineered ones.
Availability: An eigenvalue application will be available in the near future attached to one of the existing tools.
Robust PCA and classification in biosciences
Authors: Hubert Mia , Engelen Sanne 
Published: March 2004

Abstract
Motivation: Principal components analysis (PCA) is a very popular dimension reduction technique that is widely used as a first step in the analysis of high-dimensional microarray data. However, the classical approach that is based on the mean and the sample covariance matrix of the data is very sensitive to outliers. Also, classification methods based on this covariance matrix do not give good results in the presence of outlying measurements.
Results: First, we propose a robust PCA (ROBPCA) method for high-dimensional data. It combines projection-pursuit ideas with robust estimation of low-dimensional data. We also propose a diagnostic plot to display and classify the outliers. This ROBPCA method is applied to several bio-chemical datasets. In one example, we also apply a robust discriminant method on the scores obtained with ROBPCA. We show that this combination of robust methods leads to better classifications than classical PCA and quadratic discriminant analysis.
Availability: All the programs are part of the Matlab Toolbox for Robust Calibration, available at http://www.wis.kuleuven.ac.be/stat/robust.html.
Visualization for genomics: the Microbial Genome Viewer
Authors: Kerkhoven Robert , van Enckevort Frank H. J. , Boekhorst Jos , Molenaar Douwe , Siezen Roland J. 
Published: March 2004

Abstract
Summary: A Web-based visualization tool, the Microbial Genome Viewer, is presented that allows the user to combine complex genomic data in a highly interactive way. This Web tool enables the interactive generation of chromosome wheels and linear genome maps from genome annotation data stored in a MySQL database. The generated images are in scalable vector graphics (SVG) format, which is suitable for creating high-quality scalable images and dynamic Web representations. Gene-related data such as transcriptome and time-course microarray experiments can be superimposed on the maps for visual inspection.
Availability: The Microbial Genome Viewer 1.0 is freely available at http://www.cmbi.kun.nl/MGV
Improving false discovery rate estimation
Authors: Pounds Stan , Cheng Cheng 
Published: March 2004

Abstract
Motivation: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such an interpretation of the q-value may be unwarranted considering that the q-value is based on an unstable estimator of the positive FDR (pFDR). Another method proposes estimating the FDR by modeling p-values as arising from a beta-uniform mixture (BUM) distribution. Unfortunately, the BUM approach is reliable only in settings where the assumed model accurately represents the actual distribution of p-values.
Methods: A method called the spacings LOESS histogram (SPLOSH) is proposed for estimating the conditional FDR (cFDR), the expected proportion of false positives conditioned on having k ‘significant’ findings. SPLOSH is designed to be more stable than the q-value and applicable in a wider variety of settings than BUM.
Results: In a simulation study and data analysis example, SPLOSH exhibits the desired characteristics relative to the q-value and BUM.
Availability: The Web site www.stjuderesearch.org/statistics/splosh.html has links to freely available S-plus code to implement the proposed procedure.
Antioxidants and condition‐dependence of arrival date in a migratory passerine
Authors: Ninni Paola , De Lope Florentino , Saino Nicola , Haussy Claudy , Møller Anders Pape 
Published: March 2004

Early arrival at the breeding area after spring migration determines several fitness benefits to migratory birds, including an early start of reproduction and a greater seasonal reproductive success. Environmental conditions determine the onset of migration at the population level, while physiological costs associated with migration and imposed on individuals determine the level of asynchrony in the pattern of arrival. These costs include several physiological changes, suppression of immune response, and an increased risk of oxidation by production of free radicals due to energetically expensive activities. According to the condition‐dependent hypothesis of arrival date, only healthy individuals in prime condition can afford to arrive and thus reproduce early in the season.We investigated condition‐dependence of arrival date in male barn swallow (Hirundo rusticaL.), a migratory passerine, in two years that differed in environmental condition, and the role of carotenoids in mediating arrival date. We show that male tail length, the main secondary sexual character, body condition, and hematocrit were negatively correlated with arrival date in the first year, indicating better quality of early arriving males. In the second year, better environmental conditions advanced arrival of the entire population by weeks. In this season we could show no relationship between arrival and tail length or body condition. Moreover, a lower value of sedimentation rate and a brighter colour of the red throat feathers indicated better health status of the population. Arrival date is a condition‐dependent character in barn swallows, since we found high repeatability of arrival date, lower values of carotenoids in blood and a lower depletion of carotenoids in blood shortly after arrival in early arriving males. This suggests that early arriving males either use fewer antioxidants for free radical scavenging, or they have differential access to antioxidants.
Context and connectivity in plant metapopulations and landscape mosaics: does the matrix matter?
Authors: Murphy Helen T. , Lovett‐Doust Jon 
Published: March 2004

Recent reviews of evidence for plant metapopulation prevalence in nature have concluded that most species appear not to be arranged as metapopulations – hence other frameworks may be necessary for understanding large‐scale, regional dynamics in plants. Separate but related paradigms from the disciplines of landscape ecology and metapopulation ecology exist for understanding patterns of regional population variation. The major models of both paradigms assume a binary landscape mosaic composed of “suitable habitat” and background “matrix.” An important distinction between the two approaches is that metapopulation models essentially ignore features of the matrix. A binary approach to the landscape seems inappropriate for plants for several reasons. First, plants probably do not have a binary perception of the landscape, but rather respond to gradients of resource quality. Thus properties of patches, or the matrix per se, may be less important than the nature of the landscape mosaic, in particular as this is reflected in terms of connectivity. Secondly, many plants rely on a range of other agents for dispersal of pollen and seed, all of which are also affected by their environment in terms of connectivity. Furthermore the various components of the mosaic, including physical, spatial and functional elements can significantly influence plant movements. We review important effects of the matrix – via composition and configuration of habitat patches, extent of edges, patterns of land use, etc., upon plant populations.We describe evidence supporting a general integration of metapopulation and landscape ecological approaches for understanding regional dynamics in plants, emphasizing notions of connectivity (traditionally measured in very different ways by metapopulation and landscape ecologists), and context, an emerging concept describing components of variability in the landscape from a species‐specific perspective. Finally, we describe a functional landscape mosaic approach that treats structural and functional features of the landscape and show how these interact to determine the fate of plant populations.
Measures of stratigraphic fit to phylogeny and their sensitivity to tree size, tree shape, and scale
Authors: Pol Diego , Norell Mark A. , Siddall Mark E. 
Published: March 2004

AbstractMeasures of stratigraphic fit to phylogeny are analyzed to test how they are affected by the shape and size of the phylogenetic trees and by the number of stratigraphic intervals encompassed. Monte Carlo randomizations are used to investigate the sensitivity of three commonly used measures (SCI, GER and MSM*) approximating their distribution of possible values under certain conditions. All are shown to vary in different ways as parameters are varied, although MSM* seems to be the most invariant in the analyzed parameter space. These results suggest that the raw metrics should not be used for comparing the fit of different taxonomic groups or competing phylogenetic trees of the same group that differ in tree size or balance. Tree balance also affects the distributions used in significance tests based on randomization and therefore their results should not be interpreted in terms of the amount of conflict implied by a phylogenetic tree.
Myth of the molecule: DNA barcodes for species cannot replace morphology for identification and classification
Authors: Will Kipling W. , Rubinoff Daniel 
Published: March 2004

AbstractSo‐called DNA barcodes have recently been proposed to answer the problem of specimen identification and to quantify global biodiversity. We show that this proposition is wanting in terms of rationale, methodology and interpretation of results. In addition to falling short of all its stated goals, the method abandons the benefits of morphological studies in favor of a limited molecular identification system that would ultimately impede our understanding of biodiversity.