GeneMerge is used to analyze genomic data for statistical enrichment of particular functions or attributes in a given set of genes. In particular GeneMerge is useful for the analysis of microarray and RNAseq experiments and other genome-scale data, for example, those generated by evolutionary and population genomic studies.
GeneMerge uses the statistically exact hypergeometric probability distribution to calculate over-representation of gene-attribute associations and provides Bonferroni and False Discovery Rate (FDR) correction for multiple tests.
Smoothing Spline Clustering is a method for clustering time-course gene-expression data. In particular, SSC is useful for clustering genes in microarray/RNA-seq experiments performed over several time-points, for example, over the course of development, a drug treatment, or other temporally based experiment.
Smoothing Spline Clustering provides clusters of similarly expressed genes using a statistically rigorous, biologically based, data-driven method. Importantly, SSC provides the number of gene clusters in a given dataset without a priori specification, a mean curve for each cluster describing the average expression profile of each cluster and associated 95% confidence bands. SSC has been implemented in the R package SSClust written by Ping Ma and Wenxuan Zhong.
Shared Motif Method
The SMM is an implementation of the Watterman-Eggert sequence alignment algorithm for discovering shared DNA subsequences or 'motifs' between orthologous noncoding DNA sequences without respect to order, spacing or orientation.
The total fraction of shared motifs is used to calculate shared motif divergence or dSM, a measure of noncoding sequence evolution. The SMM has been implemented in C in the software sharmot written by G. Achaz.