To this end, we use gene expression microarray data from 149 human ventricle samples collected from public microarray sources, including Gene Expression Omnibus and Harvard's CardioGenomics website (see Methods).
Primary data sources included: Key informant interviews.
In this article, we explore the relationship between histone deacetylation sites and gene expression patterns on the genome scale using different data sources, including microarray data measuring gene expression levels, microarray data measuring histone deacetylation sites, and information on regulatory targets of transcription factors.
To do this we used data from various sources including published microarray studies, PubMed searches and an online in situ hybridization database, the Allen Brain Atlas (ABA) [ 24].
A total of 33,045 lncRNA probes were generated for our microarray based on authoritative data sources, including RefSeq, UCSC Knowngenes, Ensembl, and related literature.
To determine the biological processes affected by exposure, we compared this list of differentially expressed genes to lists of genes or probe sets from several data sources including Gene Ontology Terms and Microarray Expression Clusters from WormBase, and transcription factor binding sites from the literature [ 24] (See Additional files 2, 3, 4).
Interestingly, the method by Gustafsson and Hörnquist actually attempted to combine multiple data sources, including ChIP-chip data and public microarray data [22].
Ultimately although, for useful target identification within c-MYC-regulated networks in human tumors and eventual clinical utility, it might be necessary to combine data from many different sources, including patient survival outcomes, histology, gene microarray, proteomic, and network data.
These gene sets were curated from multiple sources including online pathway databases, biomedical literature, and mammalian microarray studies.
We examined expression data from a multitude of sources, including cDNAs and mRNAs information as well as microarray data [ 47].
