FULL-LENGTHERNEXT is a tool adapted to NGS technologies, able to work in parallel and in a distributed way to minimise computing time. It is able to classify unigenes to full-length, 5’-end, 3’-end and internal, suggesting which unknown genes are coding or not. It will be also shown that FULL-LENGTHERNEXT fixes frame shifts, one of the main mistake found in wrong entries of full-length sequences databases, and it is a fast tool to compare different transcriptome assemblies.

DEPRECATED PROJECT. MIGRATED TO PYTHON: https://github.com/seoanezonjic/pets. PETS suite includes three different tools. CohortAnalyzer performs the calculation of several statistics that gives an overview of a cohort of patients to analyse. Reg2Phen uses associations between pathological phenotypes and regions of the genome (these associations can be calculated from the cohort of patients if they include genotypic & phenotypic information using NetAnalyzer, another Ruby gem) to find, for a given genomic region, which pathological phenotypes have been associated with that region. The third tool, Phen2Reg, is a predictor that using the same associations as Reg2Phen, predicts which genomic regions can be the cause of a list of pathological phenotypes observed in a patient.