bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting the linkage sites in glycoproteins using bio-basis function neural network
Authors: Yang Zheng Rong , Chou Kuo-Chen 
Published: March 2004

Abstract
Motivation: Although, it is known that O-glycosidically linked oligosaccharides are commonly conjugated to a serine, threonine or hydroxylysine residue of the polypeptide, the chemical nature of the anchoring monosaccharide and the size of the oligosaccharide unit varies. Among different types, O-linked or mucin-type oligosaccharides are intimately involved in the secretion of proteins, be they enzymes, hormones or structural glycoproteins. Knowledge of the linkage sites in glycoproteins is critical to the design of specific and efficient inhibitors against the enzyme to catalyse the formation of the carbohydrate–peptide linkage.
Results: We present a method for predicting the linkage sites in O-linked glycoproteins using bio-basis function neural networks. The mean prediction accuracy of this method is 91.15 ± 2.75% while it is 82.28 ± 6.45% using back-propagation neural networks. Importantly, this method has significantly reduced the CPU time for modelling.
Availability: The software and the data used in this study can be downloaded from http://www.dcs.ex.ac.uk/~zryang for free academic use.
Gene Unscrambler for detangling scrambled genes in ciliates
Authors: Cavalcanti Andre R. O. , Landweber Laura F. 
Published: March 2004

Abstract
Summary: Scrambled genes are surprisingly common in some species of ciliates. Until now there was no software available to analyze automatically these genes. We present here a program that can automatically align the macronuclear and micronuclear forms of a gene, outputting the location of the macronuclear destined segments and pointer sequences.
Availability: A web version of the program is available free of charge and can be accessed at http://oxytricha.princeton.edu/GeneUnscrambler.htm
Modeling within-motif dependence for transcription factor binding site predictions
Authors: Zhou Qing , Liu Jun S. 
Published: March 2004

Abstract
Motivation: The position-specific weight matrix (PWM) model, which assumes that each position in the DNA site contributes independently to the overall protein–DNA interaction, has been the primary means to describe transcription factor binding site motifs. Recent biological experiments, however, suggest that there exists interdependence among positions in the binding sites. In order to exploit this interdependence to aid motif discovery, we extend the PWM model to include pairs of correlated positions and design a Markov chain Monte Carlo algorithm to sample in the model space. We then combine the model sampling step with the Gibbs sampling framework for de novo motif discoveries.
Results: Testing on experimentally validated binding sites, we find that about 25% of the transcription factor binding motifs show significant within-site position correlations, and 80% of these motif models can be improved by considering the correlated positions. Using both simulated data and real promoter sequences, we show that the new de novo motif-finding algorithm can infer the true correlated position pairs accurately and is more precise in finding putative transcription factor binding sites than the standard Gibbs sampling algorithms.
Availability: The program is available at http://www.people.fas.harvard.edu/~junliu/
Gaussian mixture clustering and imputation of microarray data
Authors: Ouyang Ming , Welsh William J. , Georgopoulos Panos 
Published: March 2004

Abstract
Motivation: In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation.
Results: Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.
Availability: Matlab code is available on request from the authors.
External control in Markovian genetic regulatory networks: the imperfect information case
Authors: Datta Aniruddha , Choudhary Ashish , Bittner Michael L. , Dougherty Edward R. 
Published: March 2004

Abstract
Probabilistic Boolean Networks, which form a subclass of Markovian Genetic Regulatory Networks, have been recently introduced as a rule-based paradigm for modeling gene regulatory networks. In an earlier paper, we introduced external control into Markovian Genetic Regulatory networks. More precisely, given a Markovian genetic regulatory network whose state transition probabilities depend on an external (control) variable, a Dynamic Programming-based procedure was developed by which one could choose the sequence of control actions that minimized a given performance index over a finite number of steps. The control algorithm of that paper, however, could be implemented only when one had perfect knowledge of the states of the Markov Chain. This paper presents a control strategy that can be implemented in the imperfect information case, and makes use of the available measurements which are assumed to be probabilistically related to the states of the underlying Markov Chain.
Sequence analysis and membrane partitioning energies of α-helical antimicrobial peptides
Authors: Han Xing , Kang Wenjun 
Published: March 2004

Abstract
Sequences of 221 α-helical antimicrobial peptides (αAMPs) were compared and 63–166 of them were selected and analyzed using Perl programs. The results showed that aliphatic amino acids Gly, Leu, Ala, Ile and two positively charged amino acids Lys and Arg were composed of more than 63% of the first 20 residues of αAMPs. The weighed mean membrane partitioning energies at positions from 1 to 25 of αAMPs were calculated. Profile of the partitioning energies suggests oblique membrane insertion and an amphipathic α-helical structure of the N-terminus of αAMP (residues from 1 to 13), a bend structure at positions 13 and 14, and a less structured C-terminus that parallels the surface of the membrane. These structural features are in good agreement with the experimentally determined membrane structure of hemagglutinin fusion peptide from influenza virus. We hypothesize that this (N-terminal oblique α-helix)—central bend—(C-terminus) could be a common structural motif of membrane-disruptive peptides.
Effects of choice of DNA sequence model structure on gene identification accuracy
Authors: Azad Rajeev K. , Borodovsky Mark 
Published: March 2004

Abstract
Motivation: Markov chain models of DNA sequences have frequently been used in gene finding algorithms. Performance of the algorithm critically depends on the model structure and parameters. Still, the issue of choosing the model structure has not been studied with sufficient attention.
Results: We have assessed performance of several types of Markov chain models, both fixed order (FO) models and models with interpolation, within the framework of the GeneMark algorithm. The performance was measured in two ways: (i) the accuracy of detection of protein-coding potential in artificial DNA sequences and (ii) the accuracy of identifying genes in real prokaryotic genomes. We observed that the models built by deleted interpolation (DI) slightly outperformed other models in detecting protein-coding potential in artificial DNA sequences with GC content in the medium range and also in detecting genes in real genomes with medium GC content. For artificial and real genomic DNA with high or low GC content, we observed that the models built by DI were in some cases slightly outperformed by FO models.
Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms
Authors: Clifford Robert J. , Edmonson Michael N. , Nguyen Cu , Buetow Kenneth H. 
Published: March 2004

Abstract
Motivation: Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans. SNPs causing amino acid substitutions are of particular interest as candidates for loci affecting susceptibility to complex diseases, such as diabetes and hypertension. To efficiently screen SNPs for disease association, it is important to distinguish neutral variants from deleterious ones.
Results: We describe the use of Pfam protein motif models and the HMMER program to predict whether amino acid changes in conserved domains are likely to affect protein function. We find that the magnitude of the change in the HMMER E-value caused by an amino acid substitution is a good predictor of whether it is deleterious. We provide internet-accessible display tools for a genomewide collection of SNPs, including 7391 distinct non-synonymous coding region SNPs in 2683 genes.
Availability: http://lpgws.nci.nih.gov/cgi-bin/GeneViewer.cgi
SQUARE—determining reliable regions in sequence alignments
Authors: Tress Michael L. , Graña Osvaldo , Valencia Alfonso 
Published: March 2004

Abstract
Summary: The Server for Quick Alignment Reliability Evaluation (SQUARE) is a Web-based version of the method we developed to predict regions of reliably aligned residues in sequence alignments. Given an alignment between a query sequence and a sequence of known structure, SQUARE is able to predict which residues are reliably aligned. The server accesses a database of profiles of sequences of known three-dimensional structures in order to calculate the scores for each residue in the alignment. SQUARE produces a graphical output of the residue profile-derived alignment scores along with an indication of the reliability of the alignment. In addition, the scores can be compared against template secondary structure, conserved residues and important sites.
Availability: http://www.pdg.cnb.uam.es/servers/square
GelScape: a web-based server for interactively annotating, manipulating, comparing and archiving 1D and 2D gel images
Authors: Young Nelson , Chang Zhan , Wishart David S. 
Published: March 2004

Abstract
Summary: GelScape is a web-based tool that permits facile, interactive annotation, comparison, manipulation and storage of protein gel images. It uses Java applet–servlet technology to allow rapid, remote image handling and image processing in a platform-independent manner. It supports many of the features found in commercial, stand-alone gel analysis software including spot annotation, spot integration, gel warping, image resizing, HTML image mapping, image overlaying as well as the storage of gel image and gel annotation data in compliance with Federated Gel Database requirements.
Availability: GelScape is freely accessible as a web server at http://www.gelscape.ualberta.ca. Local installation packages, instructions as well as a complete tutorial on GelScape are also available at the same website.
Discrepancies in dbSNP confirmation rates and allele frequency distributions from varying genotyping error rates and patterns
Authors: Mitchell Adele A. , Zwick Michael E. , Chakravarti Aravinda , Cutler David J. 
Published: March 2004

Abstract
Summary: Three recent publications have examined the quality and completeness of public database single nucleotide polymorphism (dbSNP) and have come to dramatically different conclusions regarding dbSNPs false positive rate and the proportion of dbSNPs that are expected to be common. These studies employed different genotyping technologies and different protocols in determining minimum acceptable genotyping quality thresholds. Because heterozygous sites typically have lower quality scores than homozygous sites, a higher minimum quality threshold reduces the number of false positive SNPs, but yields fewer heterozygotes and leads to fewer confirmed SNPs. To account for the different confirmation rates and distributions of minor allele frequencies, we propose that the three confirmation studies have different false positive and false negative rates. We developed a mathematical model to predict SNP confirmation rates and the apparent distribution of minor allele frequencies under user-specified false positive and false negative rates. We applied this model to the three published studies and to our own resequencing effort. We conclude that the dbSNP false positive rate is ∼15–17% and that the reported confirmation studies have vastly different genotyping error rates and patterns.
Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data
Authors: Lepre Jorge , Rice J. Jeremy , Tu Yuhai , Stolovitzky Gustavo 
Published: March 2004

Abstract
Motivation: Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes.
Results: We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods.
Availability: Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).
Rapid 3D protein structure database searching using information retrieval techniques
Authors: Aung Zeyar , Tan Kian-Lee 
Published: March 2004

Abstract
Motivation: As the sizes of three-dimensional (3D) protein structure databases are growing rapidly nowadays, exhaustive database searching, in which a 3D query structure is compared to each and every structure in the database, becomes inefficient. We propose a rapid 3D protein structure retrieval system named ‘ProtDex2’, in which we adopt the techniques used in information retrieval systems in order to perform rapid database searching without having access to every 3D structure in the database. The retrieval process is based on the inverted-file index constructed on the feature vectors of the relationships between the secondary structure elements (SSEs) of all the 3D protein structures in the database. ProtDex2 is a significant improvement, both in terms of speed and accuracy, upon its predecessor system, ProtDex.
Results: The experimental results show that ProtDex2 is very much faster than two well-known protein structure comparison methods, DALI and CE, yet not sacrificing on the accuracy of the comparison. When comparing with a similar SSE-based method, namely TopScan, ProtDex2 is much faster with comparable degree of accuracy.
Availability: The software is available at: http://xena1.ddns.comp.nus.edu.sg/~genesis/PD2.htm
Supplementary information: Supplementary tables and figures for this paper can also be found at: http://xena1.ddns.comp.nus.edu.sg/~genesis/PD2.htm
Good spaced seeds for homology search
Authors: Choi Kwok Pui , Zeng Fanfan , Zhang Louxin 
Published: March 2004

Abstract
Motivation: Filtration is an important technique used to speed up local alignment as exemplified in the BLAST programs. Recently, Ma et al. discovered that better filtering can be achieved by spacing out the matching positions according to a certain pattern, instead of contiguous positions to trigger a local alignment in their PatternHunter program. Such a match pattern is called a spaced seed.
Results: Our numerical computation shows that the ranks of spaced seeds (based on sensitivity) change with the sequences similarity. Since homologous sequences may have diverse similarity, we assess the sensitivity of spaced seeds over a range of similarity levels and present a list of good spaced seeds for facilitating homology search in DNA genomic sequences. We validate that the listed spaced seeds are indeed more sensitive using three arbitrarily chosen pairs of DNA genomic sequences.
Sensitivity and specificity of five abundance estimators for high-density oligonucleotide microarrays
Authors: James Andrew C. , Veitch Jim G. , Zareh Ali R. , Triche Timothy 
Published: March 2004

Abstract
Motivation: A number of algorithms have been proposed for the processing of feature-level data from high-density oligonucleotide microarrays to give estimates of transcript abundance. Performance in the common task of detecting differential expression between samples can be quantified by the statistical concepts of sensitivity and specificity, and represented by the use of receiver operating characteristic curves. These have been previously presented for small numbers of genes known to be differentially present in spiked-in samples. We present here a study of performance over a large number (thousands) of transcripts for which there is strong evidence of differential expression, with corresponding false positive rates controlled by comparisons between replicates.
Results: The straight-line regression analysis of a mixture series with replicates by five estimation algorithms produces a consensus set of 4462 transcripts with differential expression of agreed direction and high significance (p &lt; 0.01) according to all algorithms. The more difficult task of two-sample tests between adjacent mixture levels produces performance curves of fraction true positive detected against significance level. Performance varies significantly between algorithms: at the p &lt; 0.01 level, the detection rate varies between 41 and 66%. A control using comparisons between replicates at the same levels indicates that the tests produce empirical false positive rates closely matching the nominal p-values.
Statistically rigorous automated protein annotation
Authors: Krebs Werner G. , Bourne Philip E. 
Published: March 2004

Abstract
Motivation: Assignment of putative protein functional annotation by comparative analysis using pre-defined experimental annotations is performed routinely by molecular biologists. The number and statistical significance of these assignments remains a challenge in this era of high-throughput proteomics. A combined statistical method that enables robust, automated protein annotation by reliably expanding existing annotation sets is described. An existing clustering scheme, based on relevant experimental information (e.g. sequence identity, keywords or gene expression data) is required. The method assigns new proteins to these clusters with a measure of reliability. It can also provide human reviewers with a reliability score for both new and previously classified proteins.
Results: A dataset of 27 000 annotated Protein Data Bank (PDB) polypeptide chains (of 36 000 chains currently in the PDB) was generated from 23 000 chains classified a priori.
Availability: PDB annotations and sample software implementation are freely accessible on the Web at http://pmr.sdsc.edu/go
Recognition and analysis of protein-coding genes in severe acute respiratory syndrome associated coronavirus
Authors: Sharma Ramakant , Maheshwari Jitendra Kumar , Prakash Tulika , Dash Debasis , Brahmachari Samir K. 
Published: March 2004

Abstract
Motivation: The recent outbreak of severe acute respiratory syndrome (SARS) caused by SARS coronavirus (SARS-CoV) has necessitated an in-depth molecular understanding of the virus to identify new drug targets. The availability of complete genome sequence of several strains of SARS virus provides the possibility of identification of protein-coding genes and defining their functions. Computational approach to identify protein-coding genes and their putative functions will help in designing experimental protocols.
Results: In this paper, a novel analysis of SARS genome using gene prediction method GeneDecipher developed in our laboratory has been presented. Each of the 18 newly sequenced SARS-CoV genomes has been analyzed using GeneDecipher. In addition to polyprotein 1ab1, polyprotein 1a and the four genes coding for major structural proteins spike (S), small envelope (E), membrane (M) and nucleocapsid (N), six to eight additional proteins have been predicted depending upon the strain analyzed. Their lengths range between 61 and 274 amino acids. Our method also suggests that polyprotein 1ab, polyprotein 1a, S, M and N are proteins of viral origin and others are of prokaryotic. Putative functions of all predicted protein-coding genes have been suggested using conserved peptides present in their open reading frames.
Availability: Detailed results of GeneDecipher analysis of all the 18 strains of SARS-CoV genomes are available at http://www.igib.res.in/sarsanalysis.html
A rapid method for illustrating features in both coding and non-coding regions of a genome
Authors: Hall R. , Stern L. 
Published: March 2004

Abstract
Summary: A bitmap display of the Fourier spectra has been developed which allows convenient whole chromosome scanning for genes and other features. Use of a limited sliding window gives rapid visualization and localization of coding regions in the chromosomes, as well as non-coding features such as repetitive DNA. The method works particularly well on organisms with a skewed base composition, to provide an overview of genomic features.
Availability: Linux Intel and Solaris 8 Sparc binaries available on request.
Supplementary information: Further Fourier mappings can be viewed at www.student.unimelb.edu.au/~hallrs/
Large-scale assessment of the utility of low-resolution protein structures for biochemical function assignment
Authors: Arakaki Adrian K. , Zhang Yang , Skolnick Jeffrey 
Published: March 2004

Abstract
Motivation: Several protein function prediction methods employ structural features captured in three-dimensional (3D) descriptors of biologically relevant sites. These methods are successful when applied to high-resolution structures, but their detection ability in lower resolution predicted structures has only been tested for a few cases.
Results: A method that automatically generates a library of 3D functional descriptors for the structure-based prediction of enzyme active sites (automated functional templates, 593 in total for 162 different enzymes), based on functional and structural information automatically extracted from public databases, has been developed and evaluated using decoy structures. The applicability to predicted structures was investigated by analyzing decoys of varying quality, derived from enzyme native structures. For 35% of decoy structures, our method identifies the active site in models having 3–4 Å coordinate root mean square deviation from the native structure, a quality that is reachable using state of the art protein structure prediction algorithms.
Availability: See http://www.bioinformatics.buffalo.edu/resources/aft/