The steps to analyze a microarray or other functional genomics dataset are: 1. Enter the list of accession numbers into GORetriever to return all existing GO annotations available for that dataset.
Our study first involves integration of human genome-wide functional microarray or RNA-seq gene expression data with protein-DNA binding data from ChIP-chip, ChIP-seq, or ChIP-PET platforms to find direct transcriptional targets of the seven well known transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.
For microarray data, functional enrichment analysis of Gene Ontology terms was performed by Fisher's exact test using the FatiGO tool within the Babelomics suite [26].
Gene expression was assessed by microarray and functional module analysis was performed.
Specific assays were chosen based on differential expression by microarray analysis, functional category, and housekeeping genes (Table 2).
Using the functional annotation strategies that are usually applied to microarray experiments functional annotation, we examined the genes identified by deep sequencing.
These genome resources constitute the basis to develop a microarray for functional genomics studies and marker validation for genetic linkage and QTL analysis in turbot.
Sleipnir allows microarray processing, functional ontology mining, clustering, Bayesian learning and inference and support vector machine tasks to be performed for heterogeneous data on scales not previously practical.
The present research aims at studying the transcriptome of Enchytraeus crypticus, sampled from multiple test conditions, and the construction of a high-density microarray for functional genomic studies.
In contrast, the CD1c+ PB DC subset was shown by microarray and functional analysis to be an equivalent human population to the murine CD103-CD11b+ myeloid (possibly monocyte-derived) DCs [ 3].
