Differentially processed genes

By doing so, one of the major problems intrinsic to tiling array data—the variation of probe affinities—was significantly alleviated. In the second step, RADPRE took the probe ratios as inputs and performed a hierarchy of statistical tests to identify differentially processed genes (DPG) and differentially expressed genes (DEG) (Fig. 2b-d). In the final step, the false discovery rate (FDR) of the DEG and DPG was estimated by using the balanced random combinations of the replicates between two conditions. The output of RADPRE was a few tabular text files with one of them containing the DPG, one the DEG and the others as intermediate files. 

The cellular form of oncogenes (known as c-oncogenes or proto-oncogenes) code for proteins involved in controlling growth and differentiation processes. They only become oncogenes if their sequence has been altered by mutations (see p. 256), deletions, and other processes, or when excessive amounts of the gene products have been produced as a result of overexpression. 

The EST has continued to be a popular test subject to further optimization, especially through the addition of alternative pathways of differentiation and the addition of molecular markers of effects. Besides cardiac muscle differentiation, other cell types such as neurons (29-31), osteoblasts (32), adipocytes (33), and hepato-cytes (34) have been generated in dedicated differentiation protocols, providing additional opportunities for testing the interference of differentiation pathways with chemical exposures. The addition of transcriptomics approaches to measure differential gene expression, both as influenced by the differentiation process as well as due to chemical exposure, has proven informative and may enhance the predictability of EST assays (35). The extension of this concept to human embryonic stem cell lines is still in its infancy, but has opened a new realm of options that bypasses the need for interspecies extrapolation (36). 

While only -10% of microarray datasets address the problem of batch effects (48), the degree of error contributed by batch effects may be significant. Batch effects may include experimental variations introduced due to multiple types of technical bias (e.g., time, laboratory, reagents, handling). Analysis of multiple methods to address batch effects has been addressed for precision, accuracy, and overall performance (48). Once probe set raw intensities have been processed via normalization and possible additional corrective measures, values can be used for downstream analyses in identifying differentially expressed genes and corresponding functional associations. 

Systematic screen for novel genes required in pattern formation, organogenesis and differentiation processes of the mouse embryo... 

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