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The completion of “Human Genome Project” which used an approach of sequencing to characterize and map the entire human genome turned the attention of several researchers to investigate diseases and biological mechanisms at the level of molecules which comprise mostly of DNA , RNA and Proteins.
After pinpointing to a few disease related genes the comparative genomics approach which uses evolutionary biology principles to find similar genes in model organisms gave researchers extra degrees of freedom to study and thoroughly gain insights of the underlying biological mechanisms.
This ultimately drove the discovery approach towards functional genomics to quantitatively elicit the patterns associated with diseases or biological mechanisms.
Choose a ranking metric for sorting genes based on their correlation with the phenotype
Compute a running sum statistic (enrichment score) based on the overrepresentation of the genes at the extremes of the rank ordered list.
Estimate the significance of enrichment score relative to null distribution (empirical phenotype based permutation test).
Multiple hypothesis testing is performed on the normalized enrichment score (gene set size into account) by controlling FDR which is the probability of finding false computation of the normalized enrichment score.
“ RNA interference (RNAi), a form of post-transcriptional gene silencing induced by introduction of double-stranded RNA (dsRNA), has become a powerful experimental tool for studying gene function.” 
“ For drug developers, RNAi phenotypes can provide clues about what to assay to screen antagonist drug candidates” .
Bergey’s Manual is based on polyphasic numerical taxonomy and provides information about multiple phenotypic traits. The classification based on Bergey`s Manual is complicated, expensive, and time consuming. In contrast, classification using 16S rRNA phylotypes is more objective, faster, and less expensive.
Axon guidance genes identified in a large-scale RNAi screen using the RNAi-hypersensitive Caenorhabditis elegans strain nre-1(hd20) lin-15b(hd126) Caroline Schmitz, Parag Kinge*, and Harald Hutter
Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3 , No. 1, Article 3.
Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor , R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420