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GNUMAP / Genomic Next-generation Universal MAPper
Offers a platform dedicated to map next generation sequencing (NGS) data. GNUMAP combines a Needleman–Wunsch method, statistical mapping algorithms and a full-text index in minute space (FM-index). The application leverages additional information to enhance exploitable data. It is able to determine spike regions in the reference genome as well as to manage repetitive regions. The results can be exported towards the UCSC Genome Browser or Affymetrix’s Integrated Genome Browser for further visualization.
RED-ML / RNA Editing Detection based on Machine Learning
Performs genome-wide RED based on human RNA-seq data alone. RED-ML is a highly accurate, speedy and general-purpose tool for RNA editing detection using RNA-seq data. It can take advantage of matching DNA-seq data if available, and integrates well with other common RNA-seq data analysis steps. It can accurately detect novel RNA editing sites without relying on curated RNA editing databases. It can benefit the RNA editing research community and has profound impact to accelerate our understanding of this intriguing post-transcriptional modification process.
Identifies Adenosine-to-Inosine (A-to-I) RNA editing sites into genomic sequences. Once you submit DNA sequences, it will analyze them and will highlight putative A-to-I RNA Editing sites. It is built on top of a knowledge-base integrating information of genes from UCSC, EST of NCBI, SNPs, DARNED, and next-generations sequencing data. The tool is equipped with a user-friendly interface allowing users to analyze genomic sequences in order to identify candidate A-to-I editing sites.

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