A MapReduce tool for indepth analysis of KEGG pathways: identification and visualization of therapeutic target candidates Intracellular biochemical reactions emerge from the interaction among multiple extracellular signaling components. Considering the number, type and connections of the signaling components represents a needed step to characterize, identify and describe potential targets for a clinical purpose. However, it is increasingly documented that the presence of subtypes of signaling proteins, branching and crosstalk may lead to very variable outcomes in the same path, which is not always well defined experimentally. For this reason, we present an improved version of the algorithm based on the MapReduce paradigm to facilitate the discovery of new therapeutic targets. Our algorithm allows you to scan and perform a search in depth of biological pathway in order to analyze less recurrent and therefore nontrivial paths. These routes represent a chain of biochemical interactions among different biological actors that can be represented by quite distant nodes along the pathway. This type of analysis can also be performed manually, but with high execution times due to the large amount of pathways and genes present. Thus, our tool performs exhaustive analysis in an automated way, drastically reducing the time required. Our proposal allows us to discover the genes far from the initial target genes, also showing the number of occurrences of a given path found within the set of biological pathways analyzed during the simulation.

Mutational Forks: Inferring Deregulated Flow of Signal Transduction Based on PatientSpecific Mutations The precise mechanism behind treatment resistance in cancer is still not fully understood. Despite advances in precision oncology, there is a lack of tools that help to understand a mechanistic picture of treatment resistance in cancer patients. Existing enrichment methods heavily rely on quantitative data and limited to analysis of differentially expressed genes, ignoring crucial players that might be involved in this process. In order to tackle treatment resistance, the precise identification of deregulated flow of signal transduction is critical. Here, we introduce a bioinformatics framework that is capable of inferring deregulated flow of signal transduction given evidencebased knowledge about pathway topology and patientspecific mutations. While testing the proposed pipeline on a case study, our algorithm was able to confirm findings from biological experiment, where KRAS mutant cells developed treatment resistance to MEK inhibitor. Our model provides a framework for mechanistic understanding of acquired treatment resistance, thus, equipped clinicians with tool for searching more accurate diagnostic clues in patients with nontrivial disease representations.