bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of disturbed images
Authors: Kauer Gerhard , Blöcker Helmut 
Published: March 2004

Abstract
Motivation: Images in cellular and molecular biology (from microscopy, blots, biochips, etc.) are often disturbed, so that the detection and analysis of the respective relevant geometrical objects may be difficult or error-prone. The disturbances are either caused by the detector, usually a CCD camera, or by the experimental setup. Furthermore, microtechnology experiments often require simultaneous multiple-colour stainings. Therefore, the image analysis of such experiments should be colour-sensitive, and colour shadings should not only be detectable but also quantifiable.
Results: Here, we describe a general solution as applied to the analysis of blots and DNA chips as well as to microscopy images of tissues. We decided to use (i) a stochastic filter as used by Wiener for image segmentation as the starting point for object detection, (ii) chaincodes as described by Freeman for object description, (iii) a novel ‘rolling disc algorithm’ to spot the objects to be analysed and (iv) an HSI instead of an RGB colour model for colour analysis. With this combination we succeeded in performing shape detection and colour-based analysis of disturbed images.
Availability: The corresponding modules (C++) are available on request.
Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach
Authors: Nielsen Morten , Lundegaard Claus , Worning Peder , Hvid Christina Sylvester , Lamberth Kasper , Buus Søren , Brunak Søren , Lund Ole 
Published: March 2004

Abstract
Motivation: Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design.
Results: We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.
GeneSyn: a tool for detecting conserved gene order across genomes
Authors: Pavesi G. , Mauri G. , Iannelli F. , Gissi C. , Pesole G. 
Published: March 2004

Abstract
Summary: GeneSyn is a software tool that allows automatic detection of conserved gene order from annotated genomes.
Availability: Available free of charge for Unix/Linux/Cygwin platforms at ftp://159.149.110.11/pub/GeneSyn_1.0/
Supplementary information: ftp://159.149.110.11/pub/GeneSyn_1.0/
Spectral Repeat Finder (SRF): identification of repetitive sequences using Fourier transformation
Authors: Sharma Deepak , Issac Biju , Raghava G. P. S. , Ramaswamy R. 
Published: March 2004

Abstract
Motivation: Repetitive DNA sequences, besides having a variety of regulatory functions, are one of the principal causes of genomic instability. Understanding their origin and evolution is of fundamental importance for genome studies. The identification of repeats and their units helps in deducing the intra-genomic dynamics as an important feature of comparative genomics. A major difficulty in identification of repeats arises from the fact that the repeat units can be either exact or imperfect, in tandem or dispersed, and of unspecified length.
Results: The Spectral Repeat Finder program circumvents these problems by using a discrete Fourier transformation to identify significant periodicities present in a sequence. The specific regions of the sequence that contribute to a given periodicity are located through a sliding window analysis, and an exact search method is then used to find the repetitive units. Efficient and complete detection of repeats is provided together with interactive and detailed visualization of the spectral analysis of input sequence. We demonstrate the utility of our method with various examples that contain previously unannotated repeats. A Web server has been developed for convenient access to the automated program.
Availability: The Web server is available at http://www.imtech.res.in/raghava/srf and http://www2.imtech.res.in/raghava/srf
Enhanced homology searching through genome reading frame predetermination
Authors: Yuan Jeffrey , Bush Bruce , Elbrecht Alex , Liu Yuan , Zhang Theresa , Zhao Wenqing , Blevins Richard 
Published: March 2004

Abstract
Motivation: Many bioinformatic approaches exist for finding novel genes within genomic sequence data. Traditionally, homology search-based methods are often the first approach employed in determining whether a novel gene exists that is similar to a known gene. Unfortunately, distantly related genes or motifs often are difficult to find using single query-based homology search algorithms against large sequence datasets such as the human genome. Therefore, the motivation behind this work was to develop an approach to enhance the sensitivity of traditional single query-based homology algorithms against genomic data without losing search selectivity.
Results: We demonstrate that by searching against a genome fragmented into all possible reading frames, the sensitivity of homology-based searches is enhanced without degrading its selectivity. Using the ETS-domain, bromodomain and acetyl-CoA acetyltransferase gene as queries, we were able to demonstrate that direct protein–protein searches using BLAST2P or FASTA3 against a human genome segmented among all possible reading frames and translated was substantially more sensitive than traditional protein–DNA searches against a raw genomic sequence using an application such as TBLAST2N. Receiver operating characteristic analysis was employed to demonstrate that the algorithms remained selective, while comparisons of the algorithms showed that the protein–protein searches were more sensitive in identifying hits. Therefore, through the overprediction of reading frames by this method and the increased sensitivity of protein–protein based homology search algorithms, a genome can be deeply mined, potentially finding hits overlooked by protein–DNA searches against raw genomic data.
Analysis of variance components in gene expression data
Authors: Chen James J. , Delongchamp Robert R. , Tsai Chen-An , Hsueh Huey-miin , Sistare Frank , Thompson Karol L. , Desai Varsha G. , Fuscoe James C. 
Published: March 2004

Abstract
Motivation: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and technical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The variance components included between-array, and two nested within-array variances: between-section (the upper- and lower-sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four different weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The variance components included week-to-week, animal-to-animal and between-array and within-array variances.
Results: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between-section variance, which, in turn, is greater than the within-section variance. In the second data set, for the reference samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to-week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within-array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In summary, the largest variation observed is the week-to-week effect. Another important source of variability is the animal-to-animal variation. Finally, we describe the use of variance-component estimates to determine optimal numbers of animals, arrays per animal and sections per array in planning microarray experiments.
galaxie—CGI scripts for sequence identification through automated phylogenetic analysis
Authors: Nilsson R. Henrik , Larsson Karl-Henrik , Ursing Björn M. 
Published: March 2004

Abstract
Motivation: The prevalent use of similarity searches like BLAST to identify sequences and species implicitly assumes the reference database to be of extensive sequence sampling. This is often not the case, restraining the correctness of the outcome as a basis for sequence identification. Phylogenetic inference outperforms similarity searches in retrieving correct phylogenies and consequently sequence identities, and a project was initiated to design a freely available script package for sequence identification through automated Web-based phylogenetic analysis.
Results: Three CGI scripts were designed to facilitate qualified sequence identification from a Web interface. Query sequences are aligned to pre-made alignments or to alignments made by ClustalW with entries retrieved from a BLAST search. The subsequent phylogenetic analysis is based on the PHYLIP package for inferring neighbor-joining and parsimony trees. The scripts are highly configurable.
Availability: A service installation and a version for local use are found at http://andromeda.botany.gu.se/galaxiewelcome.html and http://galaxie.cgb.ki.se
Gap statistics for whole genome shotgun DNA sequencing projects
Authors: Wendl Michael C. , Yang Shiaw-Pyng 
Published: March 2004

Abstract
Motivation: Investigators utilize gap estimates for DNA sequencing projects. Standard theories assume sequences are independently and identically distributed, leading to appreciable under-prediction of gaps.
Results: Using a statistical scaling factor and data from 20 representative whole genome shotgun projects, we construct regression equations that relate coverage to a normalized gap measure. Prokaryotic genomes do not correlate to sequence coverage, while eukaryotes show strong correlation if the chaff is ignored. Gaps decrease at an exponential rate of only about one-third of that predicted via theory alone. Case studies suggest that departure from theory can largely be attributed to assembly difficulties for repeat-rich genomes, but bias and coverage anomalies are also important when repeats are sparse. Such factors cannot be readily characterized a priori, suggesting upper limits on the accuracy of gap prediction. We also find that diminishing coverage probability discussed in other studies is a theoretical artifact that does not arise for the typical project.
Amplicon: software for designing PCR primers on aligned DNA sequences
Authors: Jarman Simon N. 
Published: March 2004

Abstract
Summary: Amplicon is a program for designing PCR primers on aligned groups of DNA sequences. The most important application for Amplicon is the design of ‘group-specific’ PCR primer sets that amplify a DNA region from a given taxonomic group but do not amplify orthologous regions from other taxonomic groups.
Availability: Amplicon is freely available as a script that will run on any platform with Python 2.3 installed (http://www.python.org). It is also available as a Windows executable. Free downloads that do not require registration can be found at http://www.aad.gov.au/amplicon
OBIYagns: a grid-based biochemical simulator with a parameter estimator
Authors: Kimura Shuhei , Kawasaki Takuji , Hatakeyama Mariko , Naka Takashi , Konishi Fumikazu , Konagaya Akihiko 
Published: March 2004

Abstract
Summary: OBIYagns (yet another gene network simulator) is a biochemical system simulator that comprises a multiple-user Web-based graphical interface, an ordinary differential equation solver and a parameter estimators distributed over an open bioinformatics grid (OBIGrid). This grid-based biochemical simulation system can achieve high performance and provide a secure simulation environment for estimating kinetic parameters in an acceptable time period. OBIYagns can be applied to larger system biology-oriented simulation projects.
Availability: OBIYagns example models, methods and user guide are available at https://access.obigrid.org/yagns/
Supplementary information: Please refer to Bioinformatics online.
SeqExpress: desktop analysis and visualization tool for gene expression experiments
Authors: Boyle John 
Published: March 2004

Abstract
Summary: SeqExpress is a stand-alone desktop application for the identification of relevant genes within collections of microarray or SAGE experiments. A number of analysis, filtering and visualization tools are provided to aid in the selection of groups of genes. If R is installed then the application can use this to provide further analysis.
Availability: SeqExpress is available at: http://www.seqexpress.com
A general computational model of mitochondrial metabolism in a whole organelle scale
Authors: Yugi Katsuyuki , Tomita Masaru 
Published: March 2004

Abstract
Summary: A computational tool for mitochondrial systems biology has been developed as a simulation model of E-Cell2, a publicly available simulation system. The general model consists of 58 enzymatic reactions and 117 metabolites, representing the respiratory chain, the TCA cycle, the fatty acid β-oxidation and the inner-membrane transport system. It is based on previously published enzyme kinetics studies in the literature; we have successfully integrated and packaged them into a single large model. The model can be easily extended and modified so that mitochondrial biologists/physiologists can integrate their own models and evaluate them in the context of the whole organelle metabolism.
Availability: The mitochondrial model is bundled up with E-Cell2 simulation system, which can be downloaded from http://www.e-cell.org. CD-ROMs are also available and are distributed at major conferences.
Supplementary information: All the kinetic data are available via http://www.e-cell.org
Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems
Authors: Grasso Catherine , Lee Christopher 
Published: March 2004

Abstract
Motivation: Partial order alignment (POA) has been proposed as a new approach to multiple sequence alignment (MSA), which can be combined with existing methods such as progressive alignment. This is important for addressing problems both in the original version of POA (such as order sensitivity) and in standard progressive alignment programs (such as information loss in complex alignments, especially surrounding gap regions).
Results: We have developed a new Partial Order–Partial Order alignment algorithm that optimally aligns a pair of MSAs and which therefore can be applied directly to progressive alignment methods such as CLUSTAL. Using this algorithm, we show the combined Progressive POA alignment method yields results comparable with the best available MSA programs (CLUSTALW, DIALIGN2, T-COFFEE) but is far faster. For example, depending on the level of sequence similarity, aligning 1000 sequences, each 500 amino acids long, took 15 min (at 90% average identity) to 44 min (at 30% identity) on a standard PC. For large alignments, Progressive POA was 10–30 times faster than the fastest of the three previous methods (CLUSTALW). These data suggest that POA-based methods can scale to much larger alignment problems than possible for previous methods.
Availability: The POA source code is available at http://www.bioinformatics.ucla.edu/poa
BioOptimizer: a Bayesian scoring function approach to motif discovery
Authors: Jensen Shane T. , Liu Jun S. 
Published: March 2004

Abstract
Motivation: Transcription factors (TFs) bind directly to short segments on the genome, often within hundreds to thousands of base pairs upstream of gene transcription start sites, to regulate gene expression. The experimental determination of TFs binding sites is expensive and time-consuming. Many motif-finding programs have been developed, but no program is clearly superior in all situations. Practitioners often find it difficult to judge which of the motifs predicted by these algorithms are more likely to be biologically relevant.
Results: We derive a comprehensive scoring function based on a full Bayesian model that can handle unknown site abundance, unknown motif width and two-block motifs with variable-length gaps. An algorithm called BioOptimizer is proposed to optimize this scoring function so as to reduce noise in the motif signal found by any motif-finding program. The accuracy of BioOptimizer, which can be used in conjunction with several existing programs, is shown to be superior to using any of these motif-finding programs alone when evaluated by both simulation studies and application to sets of co-regulated genes in bacteria. In addition, this scoring function formulation enables us to compare objectively different predicted motifs and select the optimal ones, effectively combining the strengths of existing programs.
Availability: BioOptimizer is available for download at www.fas.harvard.edu/~junliu/BioOptimizer/
Evaluating the predictability of conformational switching in RNA
Authors: Voss Björn , Meyer Carsten , Giegerich Robert 
Published: March 2004

Abstract
Motivation: There are various cases where the biological function of an RNA molecule involves a reversible change of conformation. paRNAss is a software approach to the prediction of such structural switching in RNA. It is based on three hypotheses about the secondary structure space of a switching RNA molecule that can be evaluated by RNA folding and structure comparison. In the positive case, the predicted structural switching must be verified experimentally.
Results: After reviewing the strategy used in paRNAss, we present recent improvements on the algorithmic level of the approach, and the results of an evaluation procedure, comprising 1500 RNA sequences. It could be shown that the paRNAss approach performs well on known examples for conformational switching in RNA. The overall number of positive predictions was small, whereas for human 3′ UTRs, representing regulatory important regions, it was substantially higher than for arbitrary natural and random sequences.
Availability: paRNAss is available as a Web service at http://bibiserv.techfak.uni-bielefeld.de/parnass
Supplementary information: Detailed information on the analyses summarized in Table 1 can be found at http://bibiserv.techfak.uni-bielefeld.de/parnass/examples.html
matchprobes: a Bioconductor package for the sequence-matching of microarray probe elements
Authors: Huber Wolfgang , Gentleman Robert 
Published: March 2004

Abstract
Summary: The nucleotide sequences of the probes on a microarray can be used for a variety of purposes in the analysis of microarray experiments. We describe software and a paradigm for the creation of data packages for curating, distributing and working with probe sequence data in a uniform, across-types-of-microarrays manner. While the implementation is specific to the Bioconductor project, the ideas and general strategies are more general and could be easily adopted by other projects.
Availability: The R package matchprobes is available under LGPL at http://www.bioconductor.org
Supplementary information: The package contains documentation in the form of a vignette and manual pages.
Protein backbone angle prediction with machine learning approaches
Authors: Kuang Rui , Leslie Christina S. , Yang An-Suei 
Published: March 2004

Abstract
Motivation: Protein backbone torsion angle prediction provides useful local structural information that goes beyond conventional three-state (α, β and coil) secondary structure predictions. Accurate prediction of protein backbone torsion angles will substantially improve modeling procedures for local structures of protein sequence segments, especially in modeling loop conformations that do not form regular structures as in α-helices or β-strands.
Results: We have devised two novel automated methods in protein backbone conformational state prediction: one method is based on support vector machines (SVMs); the other method combines a standard feed-forward back-propagation artificial neural network (NN) with a local structure-based sequence profile database (LSBSP1). Extensive benchmark experiments demonstrate that both methods have improved the prediction accuracy rate over the previously published methods for conformation state prediction when using an alphabet of three or four states.
Availability: LSBSP1 and the NN algorithm have been implemented in PrISM.1, which is available from www.columbia.edu/~ay1/.
Supplementary information: Supplementary data for the SVM method can be downloaded from the Website www.cs.columbia.edu/compbio/backbone.
Mixture models for assessing differential expression in complex tissues using microarray data
Authors: Ghosh Debashis 
Published: March 2004

Abstract
Motivation: The use of DNA microarrays has become quite popular in many scientific and medical disciplines, such as in cancer research. One common goal of these studies is to determine which genes are differentially expressed between cancer and healthy tissue, or more generally, between two experimental conditions. A major complication in the molecular profiling of tumors using gene expression data is that the data represent a combination of tumor and normal cells. Much of the methodology developed for assessing differential expression with microarray data has assumed that tissue samples are homogeneous.
Results: In this paper, we outline a general framework for determining differential expression in the presence of mixed cell populations. We consider study designs in which paired tissues and unpaired tissues are available. A hierarchical mixture model is used for modeling the data; a combination of methods of moments procedures and the expectation–maximization algorithm are used to estimate the model parameters. The finite-sample properties of the methods are assessed in simulation studies; they are applied to two microarray datasets from cancer studies. Commands in the R language can be downloaded from the URL http://www.sph.umich.edu/~ghoshd/COMPBIO/COMPMIX/.