MetaExploArrays: A Large-Scale Oligonucleotide Probe Design Software for Explorative DNA Microarrays

2012 ◽  
Author(s):  
Faouzi Jaziri ◽  
David R.C. Hill ◽  
Nicolas Parisot ◽  
Jeremie Denonfoux ◽  
Eric Dugat-Bony ◽  
...  
Keyword(s):  
Large Scale ◽  
Dna Microarrays ◽  
Oligonucleotide Probe ◽  
Probe Design ◽  
Design Software

Oligonucleotide probe design — a new approach

Nature ◽  
1994 ◽  
Vol 367 (6465) ◽  
pp. 759-761 ◽  
Author(s):  
Masato Mitsuhashi ◽  
Allan Cooper ◽  
Mieko Ogura ◽  
Tatsuo Shinagawa ◽  
Katsusuke Yano ◽  
...  
Keyword(s):  
Oligonucleotide Probe ◽  
Probe Design ◽  
New Approach

Linking Interactome to Disease

2013 ◽  
pp. 151-172
Author(s):  
Maxime Garcia ◽  
Olivier Stahl ◽  
Pascal Finetti ◽  
Daniel Birnbaum ◽  
François Bertucci ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Large Scale ◽  
Dna Microarrays ◽  
Protein Interaction Data ◽  
Human Interactome ◽  
Protein Protein Interaction ◽  
Metastatic Relapse ◽  
High Throughput Gene Expression ◽  
Data Topology

The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of protein-protein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.

Study on Collision early Alert System of Large-Scale Mechanical Equipment for Hydropower Station Construction

2012 ◽  
Vol 616-618 ◽  
pp. 549-554
Author(s):  
Na Lu ◽  
Zhi Huai Xiao ◽  
Jia Chen ◽  
Tian Fu Cai ◽  
Zhi Qiang Zhang
Keyword(s):  
Virtual Instrument ◽  
Large Scale ◽  
Hydropower Station ◽  
Mechanical Equipment ◽  
Alert System ◽  
Instrument Technology ◽  
Design Software ◽  
Efficiency And Reliability ◽  
Great Damage ◽  
Early Alert

Collision between mechanical equipment during the process of hydropower station construction frequently happens, and always causes great damage. Consequently, a set of collision early alert system, which employs GPS positioning technology, wireless microwave communication technology and virtual instrument technology, was developed. Functions of this system include dynamic picture display of the field model, sound light anti-collision alert of the mechanical equipment, remote monitoring of the running equipment, and fault diagnosis of the GPS equipment. With this system, the collision possibility among the various equipment in the process of operation was reduced. The paper mainly introduces the system’s hardware design, software design, collision calculation and so on. Practicability, efficiency and reliability of this system have been proved through experiment and practice in LONGKAIKOU hydropower station.

scholarly journals An Approach to Inferring Transcriptional Regulation Among Genes From Large-Scale Expression Data

2003 ◽  
Vol 4 (1) ◽  
pp. 148-154 ◽  
Author(s):  
Javier Herrero ◽  
Ramón Díaz-Uriarte ◽  
Joaquín Dopazo
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Dna Microarrays ◽  
Genetic Network ◽  
Genetic Networks ◽  
Gene Interactions ◽  
Expression Data ◽  
Dynamic Correlations ◽  
Common Time ◽  
Data Points

The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.

scholarly journals Tom20 Mediates Localization of mRNAs to Mitochondria in a Translation-Dependent Manner

2009 ◽  
Vol 30 (1) ◽  
pp. 284-294 ◽  
Author(s):  
Erez Eliyahu ◽  
Lilach Pnueli ◽  
Daniel Melamed ◽  
Tanja Scherrer ◽  
André P. Gerber ◽  
...  
Keyword(s):  
Large Scale ◽  
Dna Microarrays ◽  
Protein Transport ◽  
Yeast Cells ◽  
Mitochondrial Proteins ◽  
Mitochondrial Outer Membrane ◽  
Dependent Manner ◽  
Mitochondrial Targeting ◽  
Double Knockout ◽  
Mitochondrial Fractions

ABSTRACT mRNAs encoding mitochondrial proteins are enriched in the vicinity of mitochondria, presumably to facilitate protein transport. A possible mechanism for enrichment may involve interaction of the translocase of the mitochondrial outer membrane (TOM) complex with the precursor protein while it is translated, thereby leading to association of polysomal mRNAs with mitochondria. To test this hypothesis, we isolated mitochondrial fractions from yeast cells lacking the major import receptor, Tom20, and compared their mRNA repertoire to that of wild-type cells by DNA microarrays. Most mRNAs encoding mitochondrial proteins were less associated with mitochondria, yet the extent of decrease varied among genes. Analysis of several mRNAs revealed that optimal association of Tom20 target mRNAs requires both translating ribosomes and features within the encoded mitochondrial targeting signal. Recently, Puf3p was implicated in the association of mRNAs with mitochondria through interaction with untranslated regions. We therefore constructed a tom20Δ puf3Δ double-knockout strain, which demonstrated growth defects under conditions where fully functional mitochondria are required. Mislocalization effects for few tested mRNAs appeared stronger in the double knockout than in the tom20Δ strain. Taken together, our data reveal a large-scale mRNA association mode that involves interaction of Tom20p with the translated mitochondrial targeting sequence and may be assisted by Puf3p.

scholarly journals Empirical Evaluation of Oligonucleotide Probe Selection for DNA Microarrays

PLoS ONE ◽  
2010 ◽  
Vol 5 (3) ◽  
pp. e9921 ◽  
Author(s):  
Jennifer G. Mulle ◽  
Viren C. Patel ◽  
Stephen T. Warren ◽  
Madhuri R. Hegde ◽  
David J. Cutler ◽  
...  
Keyword(s):  
Dna Microarrays ◽  
Oligonucleotide Probe ◽  
Empirical Evaluation ◽  
Probe Selection ◽  
Selection For

scholarly journals RGBM: Regularized Gradient Boosting Machines for the Identification of Transcriptional Regulators of Discrete Glioma Subtypes

2017 ◽  
Author(s):  
Raghvendra Mall ◽  
Luigi Cerulo ◽  
Khalid Kunji ◽  
Halima Bensmail ◽  
Thais S. Sabedot ◽  
...  
Keyword(s):  
Large Scale ◽  
Dna Microarrays ◽  
Target Genes ◽  
Gene Knockout ◽  
Biological Activities ◽  
Expression Profiles ◽  
A Priori ◽  
Gradient Boosting ◽  
Gene Expressions ◽  
Cellular Phenotypes

AbstractThe transcription factors (TF) which regulate gene expressions are key determinants of cellular phenotypes. Reconstructing large-scale genome-wide networks which capture the influence of TFs on target genes are essential for understanding and accurate modelling of living cells. We propose RGBM: a gene regulatory network (GRN) inference algorithm, which can handle data from heterogeneous information sources including dynamic time-series, gene knockout, gene knockdown, DNA microarrays and RNA-Seq expression profiles. RGBM allows to use an a priori mechanistic of active biding network consisting of TFs and corresponding target genes. RGBM is evaluated on the DREAM challenge datasets where it surpasses the winners of the competitions and other established methods for two evaluation metrics by about 10-15%.We use RGBM to identify the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators driving transformation of the G-CIMP-high into the G-CIMP-low subtype of glioma and PA-like into LGm6-GBM, thus, providing a clue to the yet undetermined nature of the transcriptional events driving the evolution among these novel glioma subtypes.RGBM is available for download on CRAN at https://cran.rproject.org/web/packages/RGBM/index.html

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