::: Schedule. Biological (Functional) Databases Threshold-based and threshold free methods Threshold-based example: FatiGO. Threshold free example 1: FatisScan.

Two-steps approach reproduces pre-genomics paradigms annotation pass test experiments no experiments test annotation .... : Context and cooperation between genes is ignored

A previous step of gene selection causes loss of information and makes the test insensitive A B GO1 GO2 - Significantly over-expressed in B Functional Classes expressed as blocks in A and B Very few genes selected to arrive to a significant conclussion on GO1 and GO2 If a threshold based on the experimental values is applied, and the resulting selection of genes compared for enrichment of a functional term, this might not be found t-test with two tails. p<0.05 statistic Significantly over-expressed in A +

Threshold-free approach Including information in the procedure of gene selection Functional label A Functional label B Functional label C A B C Genes are ranked by any biological criteria (expression value, p- value, positive selection, etc). FatiScan searchs for the distribution of the blocks of functionally related genes across the list. To detect significant terms a segmentation test is performed. Properties of groups of genes are considered. + List of genes Blocks of genes with significant functional term A annotated Homogeneously distributed Functional term C Blocks of genes with significant functional term B annotated -

Gene Set Enrichment Just ONE list (ranked)

::: Exercise 3: FatiScan Selected data Mootha et al. Nat Genet 2003

-RunT-rex for differential expression analysis- Files: fatiscan_diabetes_mootha_array.txt fatiscan_diabetes_mootha_classes.txt Results T-Rex Run T-Rex here http://www.gepas.org/

-Examine Results and send to FatiScan- Send to FatiScan here Check the FDR adjusted p-value

-Run Fatiscan with statistic-ranked list In FatiScan input form: select the organism Homo sapiens, Gene Ontology: biological process with the default filtering, 30 partitions, Two tailed Fisher's exact test, and press Run. As the sorted list comes from GEPAS it will automatically detect the sorting and the labels. This could be true or not depending on the relative abundance of this term. If you look to the rest of genes not activated in the experiment and the proportion of them related to metabolism is, let's say 10%, then you are right. Contrarily, if the proportion is, let's say 61%, then the experiment has probably nothing to do with metabolism. The comparison is compulsory to support such assertions.

Significantly altered KEGG Pathways (MGT y DM2/IGT):