bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 < 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 < 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

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

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

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

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/
SPI: a tool for incorporating gene expression data into a four-dimensional database of Caenorhabditis elegans embryogenesis
Authors: Minakuchi Yohei , Ito Masahiro , Kohara Yuji
Published: March 2004

Motivation: A comprehensive gene expression database is essential for computer modeling and simulation of biological phenomena, including development. Development is a four-dimensional (4D; 3D structure and time course) phenomenon. We are constructing a 4D database of gene expression for the early embryogenesis of the nematode Caenorhabditis elegans. As a framework of the 4D database, we have constructed computer graphics (CG), into which we will incorporate the expression data of a number of genes at the subcellular level. However, the assignment of 3D distribution of gene products (protein, mRNA), of embryos at various developmental stages, is both difficult and tedious. We need to automate this process. For this purpose, we developed a new system, named SPI after superimposing fluorescent confocal microscopic data onto a CG framework.

Results: The scheme of this system comprises the following: (1) acquirement of serial sections (40 slices) of fluorescent confocal images of three colors (4′,6′-diamino-2-phenylindole (DAPI) for nuclei, indodicarbocyanine (Cy-3) for the internal marker, which is a germline-specific protein POS-1 and indocarbocyanine (Cy-5) for the gene product to be examined); (2) identification of several features of the stained embryos, such as contour, developmental stage and position of the internal marker; (3) selection of CG images of the corresponding stage for template matching; (4) superimposition of serial sections onto the CG; (5) assignment of the position of superimposed gene products. The Snakes algorithm identified the embryo contour. The detection accuracy of embryo contours was 92.1% when applied to 2- to 28-cell-stage embryos. The accuracy of the developmental stage prediction method was 81.2% for 2- to 8-cell-stage embryos. We manually judged only the later stage embryos because the accuracy for embryos at the later stages was unsatisfactory due to experimental noise effects. Finally, our system chose the optimal CG and performed the superposition and assignment of gene product distribution. We established an initial 4D gene expression database with 56 maternal gene products.

Availability: This system is available at http://anti.lab.nig.ac.jp/spi/ and http://anti.lab.nig.ac.jp/4ddb/
Confirmation of data mining based predictions of protein function
Authors: King Ross D. , Wise Paul H. , Clare Amanda
Published: March 2004

Motivation: A central problem in bioinformatics is the assignment of function to sequenced open reading frames (ORFs). The most common approach is based on inferred homology using a statistically based sequence similarity (SIM) method, e.g. PSI-BLAST. Alternative non-SIM based bioinformatic methods are becoming popular. One such method is Data Mining Prediction (DMP). This is based on combining evidence from amino-acid attributes, predicted structure and phylogenic patterns; and uses a combination of Inductive Logic Programming data mining, and decision trees to produce prediction rules for functional class. DMP predictions are more general than is possible using homology. In 2000/1, DMP was used to make public predictions of the function of 1309 Escherichia coli ORFs. Since then biological knowledge has advanced allowing us to test our predictions.

Results: We examined the updated (20.02.02) Riley group genome annotation, and examined the scientific literature for direct experimental derivations of ORF function. Both tests confirmed the DMP predictions. Accuracy varied between rules, and with the detail of prediction, but they were generally significantly better than random. For voting rules, accuracies of 75–100% were obtained. Twenty-one of these DMP predictions have been confirmed by direct experimentation. The DMP rules also have interesting biological explanations. DMP is, to the best of our knowledge, the first non-SIM based prediction method to have been tested directly on new data.

Availability: We have designed the ‘Genepredictions’ database for protein functional predictions. This is intended to act as an open repository for predictions for any organism and can be accessed at http://www.genepredictions.org
PDBSprotEC: a Web-accessible database linking PDB chains to EC numbers via SwissProt
Authors: Martin Andrew C.R.
Published: March 2004

Summary: A mapping between chains in the Protein Databank and Enzyme Classification numbers is invaluable for research into structure–function relationships. Mapping at the chain level is a non-trivial problem and we present an automatically updated Web-server, which provides this link in a queryable form and as a downloadable XML or flat file.

Availability: The query interface and downloadable files may be accessed at http://www.bioinf.org.uk/pdbsprotec/

Supplementary information: Further details of the methods used are available at http://www.bioinf.org.uk/pdbsprotec/methods/
Predicting rules on organization of cis-regulatory elements, taking the order of elements into account
Authors: Terai Goro , Takagi Toshihisa
Published: March 2004

Motivation: In eukaryotes, rules regarding organization of cis-regulatory elements are complex. They sometimes govern multiple kinds of elements and positional restrictions on elements.

Results: We propose a method for detecting rules, by which the order of elements is restricted. The order restriction is expressed as element patterns. We extract all the element patterns that occur in promoter regions of at least the specified number of genes. Then, we find significant patterns based on the expression similarity of genes with promoter regions containing each of the extracted patterns. When we applied our method to Saccharomyces cerevisiae, we detected significant patterns overlooked by previous methods, thus demonstrating the utility of our method for analyses of eukaryotic gene regulation. We also suggest that several types of element organization exist: (i) those in which only the order of elements is important, (ii) order and distance both are important and (iii) only the combination of elements is important.

Availability: The program for extracting element patterns is available upon request.

Supplementary information:  http://elpa.hgc.jp/ElementPatterns