bluuebunny/crossref_metadata_2025_split huggingface dataset:
Microarray analysis of toxicogenomic effects of Ortho-phenylphenol in Staphylococcus aureus
Authors: Jang Hyeung-Jin , Nde Chantal , Toghrol Freshteh , Bentley William E 
Published: September 2008

Abstract

Background

Staphylococcus aureus (S. aureus), is responsible for many infectious diseases, ranging from benign skin infections to life-threatening endocarditis and toxic shock syndrome. Ortho-phenylphenol (OPP) is an antimicrobial agent and an active ingredient of EPA-registered disinfectants with wide human exposure in various agricultural, hospital and veterinary disinfectant products. Despite many uses, an understanding of a cellular response to OPP and it's mechanism of action, targeted genes, and the connectivity between targeted genes and the rest of cell metabolism remains obscure.

Results
Herein, we performed a genome-wide transcriptome analysis of the cellular responses of S. aureus when exposed to 0.82 mM of OPP for 20 and 60 min. Our data indicated that OPP downregulated the biosynthesis of many amino acids, which are required for protein synthesis. In particular, the genes encoding the enzymes of the diaminopimelate (DAP) pathway which results in lysine biosynthesis were significantly downregualted. Intriguingly, we revealed that the transcription of genes encoding ribosomal proteins was upregulated by OPP and at the same time, the genes encoding iron acquisition and transport were downregulated. The genes encoding virulence factors were upregulated and genes encoding phospholipids were downregulated upon 20 min exposure to OPP.

Conclusion
By using microarray analysis that enables us to simultaneously and globally examine the complete transcriptome during cellular responses, we have revealed novel information regarding the mode of action of OPP on Staphylococcus: OPP inhibits anabolism of many amino acids and highly downregulates the genes that encode the enzymes involved in the DAP pathway. Lysine and DAP are essential for building up the peptidoglycan cell wall. It was concluded that the mode of action of OPP is similar to the mechanism of action of some antibiotics. The discovery of this phenomenon provides useful information that will benefit further antimicrobial research on S. aureus.
Novel genome polymorphisms in BCG vaccine strains and impact on efficacy
Authors: Leung Andrea S , Tran Vanessa , Wu Zuowei , Yu Xuping , Alexander David C , Gao George Fu , Zhu Baoli , Liu Jun 
Published: September 2008

AbstractBacille Calmette-Guérin (BCG) is an attenuated strain ofMycobacterium boviscurrently used as a vaccine against tuberculosis. Global distribution and propagation of BCG has contributed to thein vitroevolution of the vaccine strain and is thought to partially account for the different outcomes of BCG vaccine trials. Previous efforts by several molecular techniques effectively identified large sequence polymorphisms among BCG daughter strains, but lacked the resolution to identify smaller changes. In this study, we have used a NimbleGen tiling array for whole genome comparison of 13 BCG strains. Using this approach, in tandem with DNA resequencing, we have identified six novel large sequence polymorphisms including four deletions and two duplications in specific BCG strains. Moreover, we have uncovered various polymorphisms in thephoP-phoRlocus. Importantly, these polymorphisms affect genes encoding established virulence factors including cell wall complex lipids, ESX secretion systems, and the PhoP-PhoR two-component system. Our study demonstrates that major virulence factors are different among BCG strains, which provide molecular mechanisms for important vaccine phenotypes including adverse effect profile, tuberculin reactivity and protective efficacy. These findings have important implications for the development of a new generation of vaccines.
BOV – a web-based BLAST output visualization tool
Authors: Gollapudi Rajesh , Revanna Kashi Vishwanath , Hemmerich Chris , Schaack Sarah , Dong Qunfeng 
Published: September 2008

Abstract

Background
The BLAST program is one of the most widely used sequence similarity search tools for genomic research, even by those biologists lacking extensive bioinformatics training. As the availability of sequence data increases, more researchers are downloading the BLAST program for local installation and performing larger and more complex tasks, including batch queries. In order to manage and interpret the results of batch queries, a host of software packages have been developed to assist with data management and post-processing. Among these programs, there is almost a complete lack of visualization tools to provide graphic representation of complex BLAST pair-wise alignments. We have developed a web-based program, B LAST O utput V isualization Tool (BOV), that allows users to interactively visualize the matching regions of query and database hit sequences, thereby allowing the user to quickly and easily dissect complex matching patterns.

Results
Users can upload the standard BLAST output in pair-wise alignment format as input to the web server (including batch queries generated installing and running the stand-alone BLAST program on a local server). The program extracts the alignment coordinates of matching regions between the query and the corresponding database hit sequence. The coordinates are used to plot each matching region as colored lines or trapezoids. Using the straightforward control panels throughout the web site, each plotted matching region can be easily explored in detail by, for example, highlighting the region of interest or examining the raw pair-wise sequence alignment. Tutorials are provided at the website to guide users step-by-step through the functional features of BOV.

Conclusion
BOV provides a user-friendly web interface to visualize the standard BLAST output for investigating wide-ranging genomic problems, including single query and batch query datasets. In particular, this software is valuable to users interested in identifying regions of co-linearity, duplication, translocation, and inversion among sequences. A web server hosting BOV is accessible via http://bioportal.cgb.indiana.edu/cgi-bin/BOV/index.cgi and the software is freely available for local installations.
Transcriptomic analysis of the exit from dormancy of Aspergillus fumigatus conidia
Authors: Lamarre Claude , Sokol Sergueï , Debeaupuis Jean-Paul , Henry Christine , Lacroix Céline , Glaser Philippe , Coppée Jean-Yves , François Jean-Marie , Latgé Jean-Paul 
Published: September 2008

Abstract

Background
Establishment of aspergillosis is depending upon the exit from dormancy and germination of the conidia of Aspergillus fumigatus in the lung. To gain an understanding of the molecular mechanisms underlying the early steps of conidial germination, we undertook a transcriptomic analysis using macroarrays constructed with PCR fragments from > 3,000 genes (around one third of the annotated A. fumigatus genome).

Results
Major results of this analysis are the following: (i) conidia stored pre-packaged mRNAs transcripts (27% of genes have transcripts in the resting conidia; (ii) incubation at 37°C in a nutritive medium induced up- and down-regulation of genes: 19% of the total number of genes deposited on the array were up-regulated whereas 22% of the genes with pre-packaged mRNA in the resting conidia were down-regulated; (iii) most modifications were seen during the first 30 min of germination whereas very little modification of gene expression occurred during the following hour; (iv) one-year old conidia and one-week old conidia behaved similarly at transcriptional level.

Conclusion
Transcriptomic data indicate that the exit from dormancy is associated with a shift from a fermentative metabolism to a respiratory metabolism as well as a trend toward immediate protein synthesis.
A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome
Authors: Hanriot Lucie , Keime Céline , Gay Nadine , Faure Claudine , Dossat Carole , Wincker Patrick , Scoté-Blachon Céline , Peyron Christelle , Gandrillon Olivier 
Published: September 2008

Abstract

Background
"Open" transcriptome analysis methods allow to study gene expression without a priori knowledge of the transcript sequences. As of now, SAGE (Serial Analysis of Gene Expression), LongSAGE and MPSS (Massively Parallel Signature Sequencing) are the mostly used methods for "open" transcriptome analysis. Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript. In contrast to LongSAGE, the high throughput sequencing method used in MPSS enables the rapid sequencing of very large libraries containing several millions of tags, allowing deep transcriptome analysis. However, a bias in the complexity of the transcriptome representation obtained by MPSS was recently uncovered.

Results
In order to make a deep analysis of mouse hypothalamus transcriptome avoiding the limitation introduced by MPSS, we combined LongSAGE with the Solexa sequencing technology and obtained a library of more than 11 millions of tags. We then compared it to a LongSAGE library of mouse hypothalamus sequenced with the Sanger method.

Conclusion
We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis. In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.
Batch Blast Extractor: an automated blastx parser application
Authors: Pirooznia Mehdi , Perkins Edward J , Deng Youping 
Published: September 2008

Abstract

Motivation
BLAST programs are very efficient in finding similarities for sequences. However for large datasets such as ESTs, manual extraction of the information from the batch BLAST output is needed. This can be time consuming, insufficient, and inaccurate. Therefore implementation of a parser application would be extremely useful in extracting information from BLAST outputs.

Results
We have developed a java application, Batch Blast Extractor, with a user friendly graphical interface to extract information from BLAST output. The application generates a tab delimited text file that can be easily imported into any statistical package such as Excel or SPSS for further analysis. For each BLAST hit, the program obtains and saves the essential features from the BLAST output file that would allow further analysis. The program was written in Java and therefore is OS independent. It works on both Windows and Linux OS with java 1.4 and higher. It is freely available from: http://mcbc.usm.edu/BatchBlastExtractor/
ILOOP – a web application for two-channel microarray interwoven loop design
Authors: Pirooznia Mehdi , Gong Ping , Yang Jack Y , Yang Mary Qu , Perkins Edward J , Deng Youping 
Published: September 2008

AbstractMicroarray technology is widely applied to address complex scientific questions. However, there remain fundamental issues on how to design experiments to ensure that the resulting data enables robust statistical analysis. Interwoven loop design has several advantages over other designs. However it suffers in the complexity of design. We have implemented an online web application which allows users to find optimal loop designs for two-color microarray experiments. Given a number of conditions (such as treatments or time points) and replicates, the application will find the best possible design of the experiment and output experimental parameters. It is freely available fromhttp://mcbc.usm.edu/iloop.
Approaches to reduce false positives and false negatives in the analysis of microarray data: applications in type 1 diabetes research
Authors: Wu Jian , Lenchik Nataliya I , Gerling Ivan C 
Published: September 2008

Abstract

Background
As studies of molecular biology system attempt to achieve a comprehensive understanding of a particular system, Type 1 errors may be a significant problem. However, few investigators are inclined to accept the increase in Type 2 errors (false positives) that may result when less stringent statistical cut-off values are used. To address this dilemma, we developed an analysis strategy that used a stringent statistical analysis to create a list of differentially expressed genes that served as "bait" to "fish out" other genes with similar patterns of expression.

Results
Comparing two strains of mice (NOD and C57Bl/6), we identified 93 genes with statistically significant differences in their patterns of expression. Hierarchical clustering identified an additional 39 genes with similar patterns of expression differences between the two strains. Pathway analysis was then employed: 1) identify the central genes and define biological processes that may be regulated by the genes identified, and 2) identify genes on the lists that could not be connected to each other in pathways (potential false positives). For networks created by both gene lists, the most connected (central) genes were interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). These two cytokines are relevant to the biological differences between the two strains of mice. Furthermore, the network created by the list of 39 genes also suggested other biological differences between the strains.

Conclusion
Taken together, these data demonstrate how stringent statistical analysis, combined with hierarchical clustering and pathway analysis may offer deeper insight into the biological processes reflected from a set of expression array data. This approach allows us to 'recapture" false negative genes that otherwise would have been missed by the statistical analysis.
Use artificial neural network to align biological ontologies
Authors: Huang Jingshan , Dang Jiangbo , Huhns Michael N , Zheng W Jim 
Published: September 2008

Abstract

Background
Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics.

Results
In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others.

Conclusion
The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.
A stable iterative method for refining discriminative gene clusters
Authors: Xu Min , Zhu Mengxia , Zhang Louxin 
Published: September 2008

Abstract

Background
Microarray technology is often used to identify the genes that are differentially expressed between two biological conditions. On the other hand, since microarray datasets contain a small number of samples and a large number of genes, it is usually desirable to identify small gene subsets with distinct pattern between sample classes. Such gene subsets are highly discriminative in phenotype classification because of their tightly coupling features. Unfortunately, such identified classifiers usually tend to have poor generalization properties on the test samples due to overfitting problem.

Results
We propose a novel approach combining both supervised learning with unsupervised learning techniques to generate increasingly discriminative gene clusters in an iterative manner. Our experiments on both simulated and real datasets show that our method can produce a series of robust gene clusters with good classification performance compared with existing approaches.

Conclusion
This backward approach for refining a series of highly discriminative gene clusters for classification purpose proves to be very consistent and stable when applied to various types of training samples.
A Poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology
Authors: Feng Weixing , Liu Yunlong , Wu Jiejun , Nephew Kenneth P , Huang Tim HM , Li Lang 
Published: September 2008

Abstract
We present a mixture model-based analysis for identifying differences in the distribution of RNA polymerase II (Pol II) in transcribed regions, measured using ChIP-seq (chromatin immunoprecipitation following massively parallel sequencing technology). The statistical model assumes that the number of Pol II-targeted sequences contained within each genomic region follows a Poisson distribution. A Poisson mixture model was then developed to distinguish Pol II binding changes in transcribed region using an empirical approach and an expectation-maximization (EM) algorithm developed for estimation and inference. In order to achieve a global maximum in the M-step, a particle swarm optimization (PSO) was implemented. We applied this model to Pol II binding data generated from hormone-dependent MCF7 breast cancer cells and antiestrogen-resistant MCF7 breast cancer cells before and after treatment with 17β-estradiol (E2). We determined that in the hormone-dependent cells, ~9.9% (2527) genes showed significant changes in Pol II binding after E2 treatment. However, only ~0.7% (172) genes displayed significant Pol II binding changes in E2-treated antiestrogen-resistant cells. These results show that a Poisson mixture model can be used to analyze ChIP-seq data.
Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis
Authors: Li Guo-Zheng , Bu Hua-Long , Yang Mary Qu , Zeng Xue-Qiang , Yang Jack Y 
Published: September 2008

Abstract

Background
Dimension reduction is a critical issue in the analysis of microarray data, because the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers. It consists of two types of methods, i.e. feature selection and feature extraction. Principle component analysis (PCA) and partial least squares (PLS) are two frequently used feature extraction methods, and in the previous works, the top several components of PCA or PLS are selected for modeling according to the descending order of eigenvalues. While in this paper, we prove that not all the top features are useful, but features should be selected from all the components by feature selection methods.

Results
We demonstrate a framework for selecting feature subsets from all the newly extracted components, leading to reduced classification error rates on the gene expression microarray data. Here we have considered both an unsupervised method PCA and a supervised method PLS for extracting new components, genetic algorithms for feature selection, and support vector machines and k nearest neighbor for classification. Experimental results illustrate that our proposed framework is effective to select feature subsets and to reduce classification error rates.

Conclusion
Not only the top features newly extracted by PCA or PLS are important, therefore, feature selection should be performed to select subsets from new features to improve generalization performance of classifiers.
Gene selection algorithm by combining reliefF and mRMR
Authors: Zhang Yi , Ding Chris , Li Tao 
Published: September 2008

Abstract

Background
Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. In this paper, we present a two-stage selection algorithm by combining ReliefF and mRMR: In the first stage, ReliefF is applied to find a candidate gene set; In the second stage, mRMR method is applied to directly and explicitly reduce redundancy for selecting a compact yet effective gene subset from the candidate set.

Results
We perform comprehensive experiments to compare the mRMR-ReliefF selection algorithm with ReliefF, mRMR and other feature selection methods using two classifiers as SVM and Naive Bayes, on seven different datasets. And we also provide all source codes and datasets for sharing with others.

Conclusion
The experimental results show that the mRMR-ReliefF gene selection algorithm is very effective.
Allele distribution and genetic diversity of VNTR loci in Salmonella entericaserotype Enteritidis isolates from different sources
Authors: Cho Seongbeom , Whittam Thomas S , Boxrud David J , Bartkus Joanne M , Saeed A Mahdi 
Published: September 2008

Abstract

Background

Salmonella enterica serotype Enteritidis (S. Enteritidis) is a zoonotic pathogen, which can be found in many sources including animals and the environment. However, little is known about the molecular relatedness among S. Enteritidis isolates from different sources. We have applied multiple-locus variable number tandem repeat analysis (MLVA) to study the genetic diversity of S. Enteritidis isolates from human and non-human sources.

Results
We identified 38 unique MLVA types using nine VNTR loci markers for discrimination between 145 S. Enteritidis isolates from different sources including humans (n = 41), chickens (n = 45), and eggs (n = 40). There were 20 distinct MLVA types identified from human isolates, 17 distinct MLVA types from chicken isolates, and 5 from egg isolates. We compared allele distribution and frequency for each VNTR marker and measured allelic polymorphism within each VNTR locus of S. Enteritidis isolates from the sources using Nei's diversity index (D). Differences in allele distribution and frequency were detected in most loci of study isolates. Different genetic diversity for certain loci was identified in isolates from different sources. The average of genetic diversity (D) was lower in egg isolates (0.16) compared to human (0.41) and chicken (0.30). However, for loci SE3, SE7, and SE9, human isolates showed significantly higher diversity than both chicken and egg isolates. Whereas for loci SE5 and SE10, chicken isolates had significantly higher diversity than both human and egg isolates. Minimum-spanning tree (MST) comprised one major cluster, a minor cluster, and four clonal expansions. MLVA application enabled a cluster analysis by the MST of the S. Enteritidis isolates by sources, which allows a great insight into the genetic relatedness and the possible flow of these organisms between different reservoirs and humans.

Conclusion
Differences in allele distribution and genetic diversity of VNTR loci in S. Enteritidis isolates from different sources were found. Polymorphism in most of the VNTR loci was more frequent among human S. Enteritidis isolates than isolates from chickens or eggs. Therefore, VNTR profiles of S. Enteritidis isolates from a specific source should be further evaluated as potential markers in epidemiologic investigations to trace S. Enteritidis to their probable source.