Pathway Analysis for SNP microarray data Pathway Analysis (PA) is a powerful method for data analysis in genomics, most often applied to gene expression analysis, but little used to analyze variants such as Single Nucleotide Polymorphisms (SNPs). PA could allow the interpretation of variants concerning the biological processes in which the affected genes and proteins are involved. Currently, the available PA software tools are not able to automatically perform pathway analysis using SNPs data. PA software tools cannot deal natively with SNPs data, hence several software tools have to be used to put SNPs data in the proper format for the analysis. To overcome these limitations, we present SNP Microarray Pathway Analysis (MPA), a software tool able to discriminate relevant genes from SNP microarrays to use in PA analysis. MPA automatically identifies relevant SNPs using the well known Fisheru0027s test, with which to perform PA. Pathway analysis in MPA is obtained employing the Hypergeometric function. As a result, MPA provides to the user the list of enriched pathways from the identified SNPs. MPA software tool along with the user guide and datasets, are available for download at https:gitlab.comgiuseppeagapitompa under the GPL v3.0 license.

Identifying Common Driver Pathways based on Pancancer Data The investigation of commonalities among different cancers is one of the important problems for deciphering cancers and will be helpful for personalized therapy in cancer treatment. Zhang et al. have presented the ComMDP method to solve this problem in 2017. However, when the number of samples among different cancers varies largely, accumulating the absolute weight value of every cancer, performed by the ComMDP method, may lead to missing some driver pathways. In this paper, an improved mathematical model is proposed by replacing the absolute weight values with the relative ratios of them, and introducing variance to minimize the dispersion of each ratio. By introducing a kind of short chromosome code and a greedy based recombination operator, a pathenogenetic algorithm PGAMDP is put forward for solving this model. Experimental results indicate that the PGAMDP algorithm is indeed able to detect some biologically meaningful gene sets which are missed by the ComMDP one. Hence it may become a useful complementary tool for identifying cancer pathways.