Identification of bimodally expressed genes is an important task, since genes with bimodal expression play important roles in cell differentiation, signaling, and disease progression. Several useful algorithms have been developed to identify bimodal genes from microarray data. Currently, no method can deal with data from next generation sequencing, which is emerging as a replacement technology for microarrays.
A team led by scientists at M. D. Anderson Cancer Center have developed SIBER (Systematic Identification of Bimodally Expressed genes using RNAseq data) for effectively identifying bimodally expressed genes from next generation RNAseq data. They evaluate several candidate methods for modeling RNAseq count data and compare their performance in identifying bimodal genes through both simulation and real data analysis. They show that the lognormal mixture model performs best in terms of power and robustness under various scenarios. The scientists also compare our method with alternative approaches including PACK and COPA. This method is robust, powerful, invariant to shifting and scaling, has no blind spots, and has a sample-size-free interpretation.
Availability: The R package SIBER is available at the web site http://bioinformatics.mdanderson.org/main/OOMPA:Overview
Tong P, Chen Y, Su X, Coombes KR. (2013) SIBER: Systematic Identification of Bimodally Expressed Genes Using RNAseq Data. Bioinformatics [Epub ahead of print]. [abstract]