iDES is a general framework from the Alizadeh Lab and the Diehn Lab, developed by Newman et al., for error suppression in high throughput sequencing data. A key advance within iDES is the use of computational “background polishing” to model and eliminate stereotypical sequencing artifacts. Background polishing can deliver performance gains that match, and often surpass, molecular barcoding alone. Applied together, the two techniques can improve analytical sensitivity by ~15-fold.
For software and documentation, click here.