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Title: Discovery of biomarker genes form earthworm microarray data      
dateReleased:
10-08-2010
description:
Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. Results: We developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset. First, a total of 869 significantly changed genes in response to TNT or RDX exposure were inferred by class comparison statistical algorithms. Then, nine decision tree-based algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance, and were used to build support vector machines (SVMs). An SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). An unsupervised clustering method was used to cluster the worm samples and results show that the use of the top 100 classifier genes can assign the largest number of worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes. Adult earthworms (E. fetida) were exposed in soil spiked with TNT (0, 6, 12, 24, 48, or 96 mg/kg) or RDX (8, 16, 32, 64, or 128 mg/kg) for 0, 4 or 14 days. The 4-day treatment was repeated with RDX concentration being 2, 4, 8, 16 or 32 mg/kg soil. Each treatment originally had 10 replicate worms with 8-10 survivors at the end of exposure. Total RNA was isolated from the surviving worms. A total of 248 worm RNA samples were hybridized to a custom-designed oligo array using Agilent’s one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript (Gong et al. 2009). After hybridization and scanning, gene expression data were acquired using Agilent’s Feature Extraction Software (v.9.1.3). The 248-array dataset consists of three worm groups: 32 untreated controls, 96 TNT-treated, and 120 RDX-treated.
privacy:
not applicable
aggregation:
instance of dataset
ID:
E-GEOD-18495
refinement:
raw
alternateIdentifiers:
18495
dateSubmitted:
10-08-2009
keywords:
functional genomics
dateModified:
06-10-2011
availability:
available
types:
gene expression
name:
Eisenia fetida
ID:
A-GEOD-9420
name:
Agilent-021219 New15K60mer-EfetidaArray
accessURL: https://www.ebi.ac.uk/arrayexpress/files/E-GEOD-18495/E-GEOD-18495.raw.1.zip
storedIn:
ArrayExpress
qualifier:
gzip compressed
format:
TXT
accessType:
download
authentication:
none
authorization:
none
accessURL: https://www.ebi.ac.uk/arrayexpress/files/E-GEOD-18495/E-GEOD-18495.processed.1.zip
storedIn:
ArrayExpress
qualifier:
gzip compressed
format:
TXT
accessType:
download
authentication:
none
authorization:
none
accessURL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18495
storedIn:
Gene Expression Omnibus
qualifier:
not compressed
format:
HTML
accessType:
landing page
primary:
true
authentication:
none
authorization:
none
abbreviation:
EBI
homePage: http://www.ebi.ac.uk/
ID:
SCR:004727
name:
European Bioinformatics Institute
homePage: https://www.ebi.ac.uk/arrayexpress/
ID:
SCR:002964
name:
ArrayExpress

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