Study Sessions on Bioinformatics and Related Topics
Alexis Vandenbon
Dear All,
Starting this year, we are organizing a series of presentations on topics related to bioinformatics, called “Study Sessions on Bioinformatics and Related Topics” (“大阪大学・バイオインフォマティクス検討会” in Japanese). Information about these presentations can be found here: http://sysimg.ifrec.osaka-u.ac.jp/bioinfo/ .
These study sessions will be conducted in English.
The first session will be held next week, and we are happy to announce that our first speaker will be Prof. Jun Sese, from AIST. Below are the title and abstract of his presentation, as well as the date and place.
Anyone who is interested is welcome to join us.
Best regards,
Alexis Vandenbon
Time: July 8th, from 10:00 AM
Place: Meeting Room 1 on the 2nd floor of the IFReC Building, Suita Campus of Osaka University.
Language: English
Title: Statistical significance for untangling complex genotype-phenotype connections
Genome-wide analyses such as genome-wide association studies (GWAS) or transcriptome analyses have recently widely performed. To understand complex associations between genotypes and phenotypes from the data, the first analysis step is to list up statistically significant combinations of the features. However, the discovery is not only computationally non-trivial but also extremely unlikely due to multiple testing correction. The exponential growth of the number of tests forces us to set a strict limit to the maximum arity. In this talk, we introduce an efficient branch-and-bound algorithm named Limitless Arity Multiple testing Procedure (LAMP) to count the exact number of testable combinations and calibrate the Bonferroni factor to the smallest possible value. LAMP enumerates significant combinations without any limit, while the family-wise error rate is rigorously controlled under the threshold. We applied LAMP to the discovery of combinatorial regulations of transcription factors. From human breast cancer transcriptome, LAMP discovered statistically significant combinations of as many as eight binding motifs. This method may contribute to uncover pathways regulated in a coordinated fashion and solve so-called missing-heritability problem in GWAS.