bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adplot: detection and visualization of repetitive patterns in complete genomes
Authors: Taneda Akito 
Published: March 2004

Abstract
Motivation: Repetitive DNA sequences are abundant in genomes and efficient mining of significant repeats is important as the first step of repetitive sequence research. Although many computational tools for the purpose, either automatic or visualization ones, have been developed, detection and analysis of approximate repeats are still non-trivial task.
Results: Auto Dot PLOT (Adplot), a dotplot-like repetitive pattern visualization program with a window filtering based on iid Bernoulli trials, is developed and applied to yeast chromosomes and human T cell receptor locus sequence. Typical examples found in yeast chromosomes 1 and 10 and a tandem repeat of periods longer than 10 000 bp in human T cell receptor locus are presented. A complex structure composed of both direct and palindromic repeats found in yeast chromosome 10 is also visualized as specific dot pattern. Computational time measured by a Pentium 3 PC for each yeast auto chromosome with a standard parameter setting is linearly scaled and below 10 s per one chromosome, indicating efficiency of the program. From the examples, it is shown that Adplot can visualize approximate local repeat structures and give us a diagnosis power for inferring a duplicational history of repeats.
Availability: Adplot can be obtained by an e-mail request.
Using functional and organizational information to improve genome-wide computational prediction of transcription units on pathway-genome databases
Authors: Romero P. R. , Karp P. D. 
Published: March 2004

Abstract
Motivation: The prediction of transcription units (TUs, which are similar to operons) is an important problem that has been tackled using many different approaches. The availability of complete microbial genomes has made genome-wide TU predictions possible. Pathway-genome databases (PGDBs) add metabolic and other organizational (i.e. protein complexes) information to the annotated genome, and are able to capture TU organization information. These characteristics of PGDBs make them a suitable framework for the development and implementation of TU predictors.
Results: We implemented a TU predictor that uses only intergenic distance and functional classification of genes to predict TU boundaries, and applied it to EcoCyc, our PGDB of Escherichia coli. To this original predictor, we added information on metabolic pathways, protein complexes and transporters, all readily available in EcoCyc, in order to generate an enhanced predictor. The enhanced predictor correctly predicted 80% of the known E.coli TUs (69% of the known operons), a moderate improvement over the original predictor's performance (75% of TUs and 65% of operons correctly predicted), demonstrating that the extra information available in the PGDB does indeed improve prediction performance. Performance of this E.coli-based predictor on a genome other than that of E.coli was tested on BsubCyc, our computationally generated PGDB for Bacillus subtilis, for which a set of 100 known operons is available. Prediction accuracy decreased substantially (46% of the known operons correctly predicted). This was due in part to missing information in BsubCyc, which prevented full use of the predictor's features. The augmented predictor has been implemented as part of our Pathway Tools software suite, and can be used to populate a PGDB with predicted TUs.
Availability: The TU predictor is included in version 7.0 of the Pathway Tools software suite. Pathway Tools 7.0 is available free of charge to academic institutions and for a fee to commercial enterprises. It runs on Sun Solaris 8, Linux and Windows. TUs predicted on the Caulobacter crescentus and Mycobacterium tuberculosis (H37Rv) genomes are available in our CauloCyc and MtbrvCyc databases, available at the BioCyc web site (http://biocyc.org). To obtain version 7.0 of Pathway Tools, follow the directions in our web site, http://biocyc.org/download.shtml.
Genotyping of single nucleotide polymorphism using model-based clustering
Authors: Fujisawa H. , Eguchi S. , Ushijima M. , Miyata S. , Miki Y. , Muto T. , Matsuura M. 
Published: March 2004

Abstract
Motivation: Single nucleotide polymorphisms have been investigated as biological markers and the representative high-throughput genotyping method is a combination of the Invader assay and a statistical clustering method. A typical statistical clustering method is the k-means method, but it often fails because of the lack of flexibility. An alternative fast and reliable method is therefore desirable.
Results: This paper proposes a model-based clustering method using a normal mixture model and a well-conceived penalized likelihood. The proposed method can judge unclear genotypings to be re-examined and also work well even when the number of clusters is unknown. Some results are illustrated and then satisfactory genotypings are shown. Even when the conventional maximum likelihood method and the typical k-means clustering method failed, the proposed method succeeded.
Bio-support vector machines for computational proteomics
Authors: Yang Zheng Rong , Chou Kuo-Chen 
Published: March 2004

Abstract
Motivation: One of the most important issues in computational proteomics is to produce a prediction model for the classification or annotation of biological function of novel protein sequences. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, few is for solving the fundamental issue, namely, amino acid encoding as most existing pattern recognition algorithms are unable to recognize amino acids in protein sequences. Importantly, the most commonly used amino acid encoding method has the flaw that leads to large computational cost and recognition bias.
Results: By replacing kernel functions of support vector machines (SVMs) with amino acid similarity measurement matrices, we have modified SVMs, a new type of pattern recognition algorithm for analysing protein sequences, particularly for proteolytic cleavage site prediction. We refer to the modified SVMs as bio-support vector machine. When applied to the prediction of HIV protease cleavage sites, the new method has shown a remarkable advantage in reducing the model complexity and enhancing the model robustness.
Dominant spectral component analysis for transcriptional regulations using microarray time-series data
Authors: Yeung Lap Kun , Szeto Lap Keung , Liew Alan Wee-Chung , Yan Hong 
Published: March 2004

Abstract
Motivation: Microarray time-series data provides us a possible means for identification of transcriptional regulation relationships among genes. Currently, the most commonly used method in determining whether or not two genes have a potential regulatory relationship is to measure their expressional similarity using Pearson's correlation coefficient. Although this traditional correlation method has been successfully applied to find functionally correlated genes, it does have many limitations. In the hope of overcoming such circumstances and getting more insights into the transcriptional regulatory issue, we propose an autoregressive (AR)-based technique for detection of potential regulated gene pairs from time-series microarray measurements.
Results: We use the well-known AR modeling technique to characterize temporal gene expression data from the Spellman's α-synchronized yeast cell-cycle experiment. In this method, time-series expression profiles are decomposed into spectral components and correlations between profiles are then computed in a component-wise sense. We show how these component-wise correlations reveal possible regulatory relationships. Our technique is applied on known transcriptional regulations and is able to identify many of those missed by the traditional correlation method.
Regression trees for regulatory element identification
Authors: Phuong Tu Minh , Lee Doheon , Lee Kwang Hyung 
Published: March 2004

Abstract
Motivation: The transcription of a gene is largely determined by short sequence motifs that serve as binding sites for transcription factors. Recent findings suggest direct relationships between the motifs and gene expression levels. In this work, we present a method for identifying regulatory motifs. Our method makes use of tree-based techniques for recovering the relationships between motifs and gene expression levels.
Results: We treat regulatory motifs and gene expression levels as predictor variables and responses, respectively, and use a regression tree model to identify the structural relationships between them. The regression tree methodology is extended to handle responses from multiple experiments by modifying the split function. The significance of regulatory elements is determined by analyzing tree structures and using a variable importance measure. When applied to two data sets of the yeast Saccharomyces cerevisiae, the method successfully identifies most of the regulatory motifs that are known to control gene transcription under the given experimental conditions, and suggests several new putative motifs. Analysis of the tree structures also reconfirms several pairs of motifs that are known to regulate gene transcription in combination.
Availability: http://if.kaist.ac.kr/~phuong/RegTree
Plotting haplotype-specific linkage disequilibrium patterns by extended haplotype homozygosity
Authors: Mueller Jakob C. , Andreoli Christophe 
Published: March 2004

Abstract
Summary: Association studies may request more details of a specific haplotype. Haplotype-specific decay of linkage disequilibrium is such a crucial and versatile characteristic. It may be used, e.g. to search for signals of natural selection in a risk haplotype. Here, we present a web-based tool to explore the relationship between population frequency and extended linkage disequilibrium measured as haplotype homozygosity of observed haplotypes within a specified candidate region.
Availability: The web-tool is available at http://ihg.gsf.de/cgi-bin/mueller/webehh.pl
STAM: simple Transmembrane Alignment Method
Authors: Shafrir Yinon , Guy H. Robert 
Published: March 2004

Abstract
Motivation: The database of transmembrane protein (TMP) structures is still very small. At the same time, more and more TMP sequences are being determined. Molecular modeling is an interim answer that may bridge the gap between the two databases.
The first step in homology modeling is to achieve a good alignment between the target sequences and the template structure. However, since most algorithms to obtain the alignments were constructed with data derived from globular proteins, they perform poorly when applied to TMPs.
In our application, we automate the alignment procedure and design it specifically for TMP. We first identify segments likely to form transmembrane α-helices. We then apply different sets of criteria for transmembrane and non-transmembrane segments. For example, the penalty for insertion/deletions in the transmembrane segments is much higher than that of a penalty in the loop region. Different substitution matrices are used since the frequencies of occurrence of the various amino acids differ for transmembrane segments and water-soluble domains.
Results: This program leads to better models since it does not treat the protein as a single entity with the same properties, but accounts for the different physical properties of the various segments. STAM is the first multisequence alignment program that is directly targeted at transmembrane proteins.
Availability: Source code and installation package are available on request from the authors. Web access is currently implemented.
Predicting protein structure classes from function predictions
Authors: Sommer I. , Rahnenführer J. , Domingues F.S. , de Lichtenberg U. , Lengauer T. 
Published: March 2004

Abstract
Motivation: We introduce a new approach to using the information contained in sequence-to-function prediction data in order to recognize protein template classes, a critical step in predicting protein structure. The data on which our method is based comprise probabilities of functional categories; for given query sequences these probabilities are obtained by a neural net that has previously been trained on a variety of functionally important features. On a training set of sequences we assess the relevance of individual functional categories for identifying a given structural family. Using a combination of the most relevant categories, the likelihood of a query sequence to belong to a specific family can be estimated.
Results: The performance of the method is evaluated using cross-validation. For a fixed structural family and for every sequence, a score is calculated that measures the evidence for family membership. Even for structural families of small size, family members receive significantly higher scores. For some examples, we show that the relevant functional features identified by this method are biologically meaningful. The proposed approach can be used to improve existing sequence-to-structure prediction methods.
Availability: Matlab code is available on request from the authors. The data are available at http://www.mpi-sb.mpg.de/~sommer/Fun2Struc/
Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments
Authors: Baggerly Keith A. , Morris Jeffrey S. , Coombes Kevin R. 
Published: March 2004

Abstract
Motivation: There has been much interest in using patterns derived from surface-enhanced laser desorption and ionization (SELDI) protein mass spectra from serum to differentiate samples from patients both with and without disease. Such patterns have been used without identification of the underlying proteins responsible. However, there are questions as to the stability of this procedure over multiple experiments.
Results: We compared SELDI proteomic spectra from serum from three experiments by the same group on separating ovarian cancer from normal tissue. These spectra are available on the web at http://clinicalproteomics.steem.com. In general, the results were not reproducible across experiments. Baseline correction prevents reproduction of the results for two of the experiments. In one experiment, there is evidence of a major shift in protocol mid-experiment which could bias the results. In another, structure in the noise regions of the spectra allows us to distinguish normal from cancer, suggesting that the normals and cancers were processed differently. Sets of features found to discriminate well in one experiment do not generalize to other experiments. Finally, the mass calibration in all three experiments appears suspect. Taken together, these and other concerns suggest that much of the structure uncovered in these experiments could be due to artifacts of sample processing, not to the underlying biology of cancer. We provide some guidelines for design and analysis in experiments like these to ensure better reproducible, biologically meaningfully results.
Availability: The MATLAB and Perl code used in our analyses is available at http://bioinformatics.mdanderson.org
Quality of alignment comparison by COMPASS improves with inclusion of diverse confident homologs
Authors: Sadreyev Ruslan I. , Grishin Nick V. 
Published: March 2004

Abstract
Motivation: Adding more distant homologs to a multiple alignment and thus increasing its diversity may eventually deteriorate the numerical profile constructed from this alignment. Here, we addressed the question whether such a diversity limit can be reached in the alignments of confident homologs found by PSI-BLAST, and we analyzed the dependence of the quality of the profile–profile comparison made by COMPASS on the sequence diversity within these alignments.
Results: Protein families that have a greater number of diverse confident homologs in the current sequence databases provide an increased quality of similarity detection in profile databases, but produce on average less accurate profile–profile alignments with their remote relatives. This lower alignment accuracy cannot be improved when the most distant members of these families are excluded from their profiles. On the contrary, the presence of more diverse members results in more accurate alignments. For families with a high diversity of confident homologs, the lower quality of profile alignments with their remote relatives seems to be an attribute of these families or their alignments, rather than to be caused by the large number of diverse sequences itself. Our results suggest that at any level of profile diversity, one should include in the multiple alignment as many confident sequence homologs as possible in order to produce the most accurate results.
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays
Authors: Barash Yoseph , Dehan Elinor , Krupsky Meir , Franklin Wilbur , Geraci Marc , Friedman Nir , Kaminski Naftali 
Published: March 2004

Abstract
Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a ‘best’ algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests.
Results: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.
Availability: The algorithms used to evaluate differentially expressed genes and their statistical overabundance can be found at http://www.cs.huji.ac.il/labs/compbio/scoregenes/
Fold-specific substitution matrices for protein classification
Authors: Vilim R. B. , Cunningham R. M. , Lu B. , Kheradpour P. , Stevens F. J. 
Published: March 2004

Abstract
Motivation: Methods that focus on secondary structures, such as Position Specific Scoring Matrices and Hidden Markov Models, have proved useful for assigning proteins to families. However, for assigning proteins to an attribute class within a family these methods may introduce more free parameters than are needed. There are fewer members and there is less variability among sequences within a family. We describe a method for organizing proteins in a family that exhibits up to an order of magnitude reduction in the number of parameters. The basis is the log odds ratio commonly used to measure similarity. We adapt this to characterize the sequence dissimilarities that give rise to attribute differentiation. This leads to the definition of Class Attribute Substitution Matrices (CLASSUM), a dual of the BLOSUM.
Results: The method was applied to classify sequences hierarchically in the lambda and kappa subgroups of the immunoglobulin superfamily. Positions conferring class were identified based on the degree of amino acid variability at a position. The CLASSUM computed for these positions classified better than 90% of test data correctly compared with 35–50% for BLOSUM-62. The expected value for a random matrix is 14%. The results suggest that family-specific data-derived substitution matrices can improve the resolution of automated methods that use generic substitution matrices for searching for and classifying proteins.
Approximate geodesic distances reveal biologically relevant structures in microarray data
Authors: Nilsson Jens , Fioretos Thoas , Höglund Mattias , Fontes Magnus 
Published: March 2004

Abstract
Motivation: Genome-wide gene expression measurements, as currently determined by the microarray technology, can be represented mathematically as points in a high-dimensional gene expression space. Genes interact with each other in regulatory networks, restricting the cellular gene expression profiles to a certain manifold, or surface, in gene expression space. To obtain knowledge about this manifold, various dimensionality reduction methods and distance metrics are used. For data points distributed on curved manifolds, a sensible distance measure would be the geodesic distance along the manifold. In this work, we examine whether an approximate geodesic distance measure captures biological similarities better than the traditionally used Euclidean distance.
Results: We computed approximate geodesic distances, determined by the Isomap algorithm, for one set of lymphoma and one set of lung cancer microarray samples. Compared with the ordinary Euclidean distance metric, this distance measure produced more instructive, biologically relevant, visualizations when applying multidimensional scaling. This suggests the Isomap algorithm as a promising tool for the interpretation of microarray data. Furthermore, the results demonstrate the benefit and importance of taking nonlinearities in gene expression data into account.
Mapping gene ontology to proteins based on protein–protein interaction data
Authors: Deng Minghua , Tu Zhidong , Sun Fengzhu , Chen Ting 
Published: March 2004

Abstract
Motivation: Gene Ontology (GO) consortium provides structural description of protein function that is used as a common language for gene annotation in many organisms. Large-scale techniques have generated many valuable protein–protein interaction datasets that are useful for the study of protein function. Combining both GO and protein–protein interaction data allows the prediction of function for unknown proteins.
Result: We apply a Markov random field method to the prediction of yeast protein function based on multiple protein–protein interaction datasets. We assign function to unknown proteins with a probability representing the confidence of this prediction. The functions are based on three general categories of cellular component, molecular function and biological process defined in GO. The yeast proteins are defined in the Saccharomyces Genome Database (SGD). The protein–protein interaction datasets are obtained from the Munich Information Center for Protein Sequences (MIPS), including physical interactions and genetic interactions. The efficiency of our prediction is measured by applying the leave-one-out validation procedure to a functional path matching scheme, which compares the prediction with the GO description of a protein's function from the abstract level to the detailed level along the GO structure. For biological process, the leave-one-out validation procedure shows 52% precision and recall of our method, much better than that of the simple guilty-by-association methods.
Supplementary material: http://www.cmb.usc.edu/~msms/gomapping