Motivation: Next Generation Sequencing (NGS) data provide an unprecedented access to life mechanisms. In particular, these data enable to detect polymorphisms such as SNPs and indels. As these polymorphisms represent a fundamental source of information in agronomy, environment or medicine, their detection in NGS data is now a routine task. The main methods for their prediction usually need a reference genome. However, non-model organisms and highly divergent genomes such as in cancer studies are extensively investigated. Results: We propose DiscoSnp++, in which we revisit the DiscoSnp algorithm. DiscoSnp++ is designed for detecting and ranking all kinds of SNPs and small indels from raw read set(s). It outputs files in fasta and VCF formats. In particular, predicted variants can be automatically localized afterward on a reference genome if available. Its usage is extremely simple and its low resource requirements make it usable on common desktop computers. Results show that DiscoSnp++ performs better than state-of-the-art methods in terms of computational resources and in terms of results quality. An important novelty is the de novo detection of indels, for which we obtained 99% precision when calling indels on simulated human datasets and 90% recall on high confident indels from the Platinum dataset. License: GNU Affero general public license. Availability: https://github.com/GATB/DiscoSnp .
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