Authors:
A. T. Milnthorpe
and
Mikhail Soloviev
Affiliation:
Royal Holoway and University of London, United Kingdom
Keyword(s):
mRNA Expression, Transcriptomics, Gene Expression, EST Expression, Quality Control, Tissue Typing, Tissue Identification, Differential Expression, Tissue Specific Markers, Differential Gene Expression in Cancer.
Related
Ontology
Subjects/Areas/Topics:
Algorithms and Software Tools
;
Bioinformatics
;
Biomedical Engineering
;
Databases and Data Management
Abstract:
There are currently a few bioinformatics tools, such as dbEST, DDD, GEPIS, cDNA xProfiler and cDNA DGED to name a few, which have been widely used to retrieve and analyse EST expression data and for comparing gene expression levels e.g. between cancer and normal tissues. The outcome of any such comparison depends on EST libraries' annotations and assumes that the actual expression data (EST counts) are correct. None of the existing tools provide a quality control method for the selection and evaluation of the original EST expression libraries. Here we report the selection, optimisation and evaluation of a minimal gene expression data set using CGAP cDNA DGED. Our approach relies solely on the expression data itself and is independent on the libraries annotations. The reported approach allows tissue typing of expression libraries of different sizes containing between as little as 249 total EST counts and up to 13,929 total EST counts (the highest tested so far).