bluuebunny/crossref_metadata_2025_split huggingface dataset:
ARROGANT: an application to manipulate large gene collections
Authors: Kulkarni Amit V. , Williams Noelle Sevilir , Lian Yun , Wren Jonathan D. , Mittelman David , Pertsemlidis Alexander , Garner Harold R. 
Published: November 2002

Abstract
Summary: ARROGANT (ARRay OrGANizing Tool) is a software tool developed to facilitate the identification, annotation and comparison of large collections of genes or clones. The objective is to enable users to compile gene/clone collections from different databases, allowing them to design experiments and analyze the collections as well as associated experimental data efficiently. ARROGANT can relate different sequence identifiers to their common reference sequence using the UniGene database, allowing for the comparison of data from two different microarray experiments. ARROGANT has been successfully used to analyze microarray expression data for colon cancer, to compile genes potentially related to cardiac diseases for subsequent resequencing (to identify single nucleotide polymorphisms, SNPs), to design a new comprehensive human cDNA microarray for cancer, to combine and compare expression data generated by different microarrays and to provide annotation for genes on custom and Affymetrix chips.
Availability: ARROGANT is freely available for use on the web at http://lethargy.swmed.edu/
Contact: harold.garner@utsouthwestern.edu
* To whom correspondence should be addressed.
Identification of mixups among DNA sequencing plates
Authors: Stojanovic Nikola , Chang Jean L. , Lehoczky Jessica , Zody Michael C. , Dewar Ken 
Published: November 2002

Abstract
Motivation: During the process of high-throughput genome sequencing there are opportunities for mixups of reagents and data associated with particular projects. The sequencing templates or sequence data generated for an assembly may become contaminated with reagents or sequences from another project, resulting in poorer quality and inaccurate assemblies.
Results: We have developed a system to assess sequence assemblies and monitor for laboratory mixups. We describe several methods for testing the consistency of assemblies and resolving mixed ones. We use statistical tests to evaluate the distribution of sequencing reads from different plates into contigs, and a graph-based approach to resolve situations where data has been inappropriately combined. While these methods have been designed for use in a high-throughput DNA sequencing environment processing thousands of clones, they can be applied in any situation where distinct sequencing projects are performed at redundant coverage.
Availability: Our software is available for downloading from: ftp-genome.wi.mit.edu/distribution/mixups_detection
Contact: nick@genome.wi.mit.edu
* To whom correspondence should be addressed.
Comparison of microarray designs for class comparison and
class discovery
Authors: Dobbin K. , Simon R. 
Published: November 2002

Abstract
Motivation: Two-color microarray experiments in which an aliquot derived from a common RNA sample is placed on each array are called reference designs. Traditionally, microarray experiments have used reference designs, but designs without a reference have recently been proposed as alternatives.
Results: We develop a statistical model that distinguishes the different levels of variation typically present in cancer data, including biological variation among RNA samples, experimental error and variation attributable to phenotype. Within the context of this model, we examine the reference design and two designs which do not use a reference, the balanced block design and the loop design, focusing particularly on efficiency of estimates and the performance of cluster analysis. We calculate the relative efficiency of designs when there are a fixed number of arrays available, and when there are a fixed number of samples available. Monte Carlo simulation is used to compare the designs when the objective is class discovery based on cluster analysis of the samples. The number of discrepancies between the estimated clusters and the true clusters were significantly smaller for the reference design than for the loop design. The efficiency of the reference design relative to the loop and block designs depends on the relation between inter- and intra-sample variance. These results suggest that if cluster analysis is a major goal of the experiment, then a reference design is preferable. If identification of differentially expressed genes is the main concern, then design selection may involve a consideration of several factors.
Contact: dobbinke@mail.nih.gov
Supplementary Information: Supplementary material referenced in the text can be found at http://linus.nci.nih.gov/~brb/TechReport.htm
* To whom correspondence should be addressed.
Gene expression data analysis with a dynamically extended
self-organized map that exploits class information
Authors: Mavroudi Seferina , Papadimitriou Stergios , Bezerianos Anastasios 
Published: November 2002

Abstract
Motivation: Currently the most popular approach to analyze genome-wide expression data is clustering. One of the major drawbacks of most of the existing clustering methods is that the number of clusters has to be specified a priori. Furthermore, by using pure unsupervised algorithms prior biological knowledge is totally ignored Moreover, most current tools lack an effective framework for tight integration of unsupervised and supervised learning for the analysis of high-dimensional expression data and only very few multi-class supervised approaches are designed with the provision for effectively utilizing multiple functional class labeling.
Results: The paper adapts a novel Self-Organizing map called supervised Network Self-Organized Map (sNet-SOM) to the peculiarities of multi-labeled gene expression data. The sNet-SOM determines adaptively the number of clusters with a dynamic extension process. This process is driven by an inhomogeneous measure that tries to balance unsupervised, supervised and model complexity criteria. Nodes within a rectangular grid are grown at the boundary nodes, weights rippled from the internal nodes towards the outer nodes of the grid, and whole columns inserted within the map The appropriate level of expansion is determined automatically. Multiple sNet-SOM models are constructed dynamically each for a different unsupervised/supervised balance and model selection criteria are used to select the one optimum one. The results indicate that sNet-SOM yields competitive performance to other recently proposed approaches for supervised classification at a significantly reduced computational cost and it provides extensive exploratory analysis potentiality within the analysis framework. Furthermore, it explores simple design decisions that are easier to comprehend and computationally efficient.
Availability: The source code of the algorithms presented in the paper can be downloaded from http://heart.med.upatras.gr. The implementation is in Borland C++ Builder 5.0.
Contact: severina@heart.med.upatras.gr
* To whom correspondence should be addressed.
Methods for assessing reproducibility of clustering patterns observed in analyses of microarray data
Authors: McShane Lisa M. , Radmacher Michael D. , Freidlin Boris , Yu Ren , Li Ming-Chung , Simon Richard 
Published: November 2002

Abstract
Motivation: Recent technological advances such as cDNA microarray technology have made it possible to simultaneously interrogate thousands of genes in a biological specimen. A cDNA microarray experiment produces a gene expression ‘profile’. Often interest lies in discovering novel subgroupings, or ‘clusters’, of specimens based on their profiles, for example identification of new tumor taxonomies. Cluster analysis techniques such as hierarchical clustering and self-organizing maps have frequently been used for investigating structure in microarray data. However, clustering algorithms always detect clusters, even on random data, and it is easy to misinterpret the results without some objective measure of the reproducibility of the clusters.
Results: We present statistical methods for testing for overall clustering of gene expression profiles, and we define easily interpretable measures of cluster-specific reproducibility that facilitate understanding of the clustering structure. We apply these methods to elucidate structure in cDNA microarray gene expression profiles obtained on melanoma tumors and on prostate specimens.
Availability: Software to implement these methods is contained in BRB ArrayTools microarray analysis package available from http://linus.nci.nih.gov./BRB-ArrayTools.html
Contact: lm5h@nih.gov
* To whom correspondence should be addressed.
† Present address: Human Genome Sciences Inc., Rockville, MD 20850, USA
Theoretical and experimental comparisons of gene
expression indexes for oligonucleotide arrays
Authors: Lemon William J. , Palatini Jeffrey J.T. , Krahe Ralf , Wright Fred A. 
Published: November 2002

Abstract
Motivation: Oligonucleotide expression arrays exhibit systematic and reproducible variation produced by the multiple distinct probes used to represent a gene. Recently, a gene expression index has been proposed that explicitly models probe effects, and provides improved fits of hybridization intensity for arrays containing perfect match (PM) and mismatch (MM) probe pairs.
Results: Here we use a combination of analytical arguments and empirical data to show directly that the estimates provided by model-based expression indexes are superior to those provided by commercial software. The improvement is greatest for genes in which probe effects vary substantially, and modeling the PM and MM intensities separately is superior to using the PM–MM differences. To empirically compare expression indexes, we designed a mixing experiment involving three groups of human fibroblast cells (serum starved, serum stimulated, and a 50:50 mixture of starved/stimulated), with six replicate HuGeneFL arrays in each group. Careful spiking of control genes provides evidence that 88–98% of the genes on the array are detectably transcribed, and that the model-based estimates can accurately detect the presence versus absence of a gene. The use of extensive replication from single RNA sources enables exploration of the technical variability of the array.
Availability: Scripts for computing the Li–Wong reduced and full models are available in C, Splus and Perl in the supplementary information.
Contact: fwright@bios.unc.edu
Supplementary information: http://thinker.med.ohio-state.edu
* To whom correspondence should be addressed at Present address: Department of Biostatistics UNC-CH, 3107B McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC 27599-7420, USA
Statistical analysis of a small set of time-ordered gene
expression data using linear splines
Authors: de Hoon M.J.L. , Imoto S. , Miyano S. 
Published: November 2002

Abstract
Motivation: Recently, the temporal response of genes to changes in their environment has been investigated using cDNA microarray technology by measuring the gene expression levels at a small number of time points. Conventional techniques for time series analysis are not suitable for such a short series of time-ordered data. The analysis of gene expression data has therefore usually been limited to a fold-change analysis, instead of a systematic statistical approach.
Methods: We use the maximum likelihood method together with Akaike's Information Criterion to fit linear splines to a small set of time-ordered gene expression data in order to infer statistically meaningful information from the measurements. The significance of measured gene expression data is assessed using Student's t-test.
Results: Previous gene expression measurements of the cyanobacterium Synechocystis sp. PCC6803 were reanalyzed using linear splines. The temporal response was identified of many genes that had been missed by a fold-change analysis. Based on our statistical analysis, we found that about four gene expression measurements or more are needed at each time point.
Availability: An extension module for Python to calculate linear spline functions is available at http://bonsai.ims.u-tokyo.ac.jp/~mdehoon. This software package (with patent pending) is free of charge for academic use only.
Contact: mdehoon@ims.u-tokyo.ac.jp
* To whom correspondence should be addressed.
A duplication growth model of gene expression networks
Authors: Bhan Ashish , Galas David J. , Dewey T. Gregory 
Published: November 2002

Abstract
Motivation: There has been considerable interest in developing computational techniques for inferring genetic regulatory networks from whole-genome expression profiles. When expression time series data sets are available, dynamic models can, in principle, be used to infer correlative relationships between gene expression levels, which may be causal. However, because of the range of detectable expression levels and the current quality of the data, the predictive nature of such inferred, quantitative models is questionable. Network models derived from simple rate laws offer an intermediate level analysis, going beyond simple statistical analysis, but falling short of a fully quantitative description. This work shows how such network models can be constructed and describes the global properties of the networks derived from such a model. These global properties are statistically robust and provide insights into the design of the underlying network.
Results: Several whole-genome expression time series data sets from yeast microarray experiments were analyzed using a Markov-modeling method (Dewey and Galas, Func. Integr. Genomics, 1, 269–278, 2001) to infer an approximation to the underlying genetic network. We found that the global statistical properties of all the resulting networks are similar. The overall structure of these biological networks is distinctly different from that of other recently studied networks such as the Internet or social networks. These biological networks show hierarchical, hub-like structures that have some properties similar to a class of graphs known as small world graphs. Small world networks exhibit local cliquishness while exhibiting strong global connectivity. In addition to the small world properties, the biological networks show a power law or scale free distribution of connectivities. An inverse power law, N(k)∼k-3/2, for the number of vertices (genes) with k connections was observed for three different data sets from yeast. We propose network growth models based on gene duplication events. Simulations of these models yield networks with the same combination of global graphical properties that we inferred from the expression data.
Contact: Ashish_Bhan@kgi.eduDavid_Galas@kgi.eduGreg_Dewey@kgi.edu
Supplementary Information: http://www.kgi.edu/html/noncore/faculty/dewey/bioinf.pdf
* To whom correspondence should be addressed.
A simulated annealing algorithm for finding consensus sequences
Authors: Keith Jonathan M. , Adams Peter , Bryant Darryn , Kroese Dirk P. , Mitchelson Keith R. , Cochran Duncan A.E. , Lala Gita H. 
Published: November 2002

Abstract
Motivation: A consensus sequence for a family of related sequences is, as the name suggests, a sequence that captures the features common to most members of the family. Consensus sequences are important in various DNA sequencing applications and are a convenient way to characterize a family of molecules.
Results: This paper describes a new algorithm for finding a consensus sequence, using the popular optimization method known as simulated annealing. Unlike the conventional approach of finding a consensus sequence by first forming a multiple sequence alignment, this algorithm searches for a sequence that minimises the sum of pairwise distances to each of the input sequences. The resulting consensus sequence can then be used to induce a multiple sequence alignment. The time required by the algorithm scales linearly with the number of input sequences and quadratically with the length of the consensus sequence. We present results demonstrating the high quality of the consensus sequences and alignments produced by the new algorithm. For comparison, we also present similar results obtained using ClustalW. The new algorithm outperforms ClustalW in many cases.
Availability: The software is made available upon request.
Contact: jonathan@maths.uq.edu.au.
* To whom correspondence should be addressed.
Empirical determination of effective gap penalties for sequence comparison
Authors: Reese J.T. , Pearson W.R. 
Published: November 2002

Abstract
Motivation: No general theory guides the selection of gap penalties for local sequence alignment. We empirically determined the most effective gap penalties for protein sequence similarity searches with substitution matrices over a range of target evolutionary distances from 20 to 200 Point Accepted Mutations (PAMs).
Results: We embedded real and simulated homologs of protein sequences into a database and searched the database to determine the gap penalties that produced the best statistical significance for the distant homologs. The most effective penalty for the first residue in a gap (q+r) changes as a function of evolutionary distance, while the gap extension penalty for additional residues (r) does not. For these data, the optimal gap penalties for a given matrix scaled in 1/3 bit units (e.g. BLOSUM50, PAM200) are q=25−0.1 • (target PAM distance), r=5. Our results provide an empirical basis for selection of gap penalties and demonstrate how optimal gap penalties behave as a function of the target evolutionary distance of the substitution matrix. These gap penalties can improve expectation values by at least one order of magnitude when searching with short sequences, and improve the alignment of proteins containing short sequences repeated in tandem.
Contact: wrp@virginia.edu
* To whom correspondence should be addressed.
Finding relevant references to genes and proteins in Medline
using a Bayesian approach
Authors: Leonard Julie E. , Colombe Jeffrey B. , Levy Joshua L. 
Published: November 2002

Abstract
Motivation: Mining the biomedical literature for references to genes and proteins always involves a tradeoff between high precision with false negatives, and high recall with false positives. Having a reliable method for assessing the relevance of literature mining results is crucial to finding ways to balance precision and recall, and for subsequently building automated systems to analyze these results. We hypothesize that abstracts and titles that discuss the same gene or protein use similar words. To validate this hypothesis, we built a dictionary- and rule-based system to mine Medline for references to genes and proteins, and used a Bayesian metric for scoring the relevance of each reference assignment.
Results: We analyzed the entire set of Medline records from 1966 to late 2001, and scored each gene and protein reference using a Bayesian estimated probability (EP) based on word frequency in a training set of 137837 known assignments from 30594 articles to 36197 gene and protein symbols. Two test sets of 148 and 150 randomly chosen assignments, respectively, were hand-validated and categorized as either good or bad. The distributions of EP values, when plotted on a log-scale histogram, are shown to markedly differ between good and bad assignments. Using EP values, recall was 100% at 61% precision (EP=2×10−5), 63% at 88% precision (EP=0.008), and 10% at 100% precision (EP=0.1). These results show that Medline entries discussing the same gene or protein have similar word usage, and that our method of assessing this similarity using EP values is valid, and enables an EP cutoff value to be determined that accurately and reproducibly balances precision and recall, allowing automated analysis of literature mining results.
Contact: jlevy@incellico.com; jleonard@incellico.com; jeffcolombe@hotmail.com.
* To whom correspondence should be addressed.
AcePrimer: automation of PCR primer design based on gene
structure
Authors: McKay Sheldon J. , Jones Steven J.M. 
Published: November 2002

Abstract
Summary: AcePrimer is an internet-accessed application based on CGI/Perl programming that designs PCR primers to search for deletion alleles in Caenorhabditis elegans gene knockout experiments and uses electronic PCR to search the entire genomic DNA sequence for potential false priming or multiple PCR amplification targets. Features such as the ability to target specific exons with the ‘poison primer’ approach and evaluation of primers with electronic PCR provide a flexible, web-based approach to design effective primers whilst minimizing the need for empirical optimization of PCR experiments.
Availability: Web access to this program is provided at http://elegans.bcgsc.bc.ca/gko/aceprimer.shtml.
* To whom correspondence should be addressed.
SNOMAD (Standardization and NOrmalization of MicroArray
Data): web-accessible gene expression data analysis
Authors: Colantuoni Carlo , Henry George , Zeger Scott , Pevsner Jonathan 
Published: November 2002

Abstract
Summary: SNOMAD is a collection of algorithms for the normalization and standardization of gene expression datasets derived from diverse biological and technological sources. In addition to conventional transformations and visualization tools, SNOMAD includes two non-linear transformations which correct for bias and variance which are non-uniformly distributed across the range of microarray element signal intensities: (1) Local mean normalization; and (2) Local variance correction (Z-score generation using a locally calculated standard deviation).
Availability: The SNOMAD tools were developed in the R statistical language http://www.r-project.org/. SNOMAD is an interactive, user-friendly web-application which can be accessed freely via the internet with any standard HTML browser: http://pevsnerlab.kennedykrieger.org/snomad.htm.
Contact: ccolantu@jhmi.edu or pevsner@jhmi.edu
* To whom correspondence should be addressed.
† Present address: Wadham College, Oxford University, UK.
GeneCards™ 2002: towards a complete, object-oriented, human gene compendium
Authors: Safran Marilyn , Solomon Irina , Shmueli Orit , Lapidot Michal , Shen-Orr Shai , Adato Avital , Ben-Dor Uri , Esterman Nir , Rosen Naomi , Peter Inga , Olender Tsviya , Chalifa-Caspi Vered , Lancet Doron 
Published: November 2002

Abstract
Motivation: In the post-genomic era, functional analysis of genes requires a sophisticated interdisciplinary arsenal. Comprehensive resources are challenged to provide consistently improving, state-of-the-art tools.
Results: GeneCards (Rebhan et al., 1998) has made innovative strides: (a) regular updates and enhancements incorporating new genes enriched with sequences, genomic locations, cDNA assemblies, orthologies, medical information, 3D protein structures, gene expression, and focused SNP summaries; (b) restructured software using object-oriented Perl, migration to schema-driven XML, and (c) pilot studies, introducing methods to produce cards for novel and predicted genes.
Availability: Freely available for educational and research purposes by non-profit institutions at http://bioinfo.weizmann.ac.il/cards and academic mirror sites. Commercial usage requires a license.
Contact: marilyn.safran@weizmann.ac.il
Supplementary Information: http://bioinfo.weizmann.ac.il/cards/9pageGC2002Bioinformatics.doc; http://bioinfo.weizmann.ac.il/cards/GeneCardByResource.xsd and http://bioinfo.weizmann.ac.il/cards/GeneCardByFunction.xsd.
* To whom correspondence should be addressed.
QuickTree: building huge Neighbour-Joining trees of
protein sequences
Authors: Howe Kevin , Bateman Alex , Durbin Richard 
Published: November 2002

Abstract
Summary: We have written a fast implementation of the popular Neighbor-Joining tree building algorithm. QuickTree allows the reconstruction of phylogenies for very large protein families (including the largest Pfam alignment containing 27000 HIV GP120 glycoprotein sequences) that would be infeasible using other popular methods.
Availability: The source-code for QuickTree, written in ANSI~C, is freely available via the world wide web at http://www.sanger.ac.uk/Software/analysis/quicktree
Contact: klh@sanger.ac.uk
* To whom correspondence should be addressed.