deML
Maximum likelihood demultiplexing
This project is maintained by grenaud
About
deML is a maximum likelihood demultiplexing algorithm that allows probabilistic sample assignments. We compute the likelihood of assignment to various samples and assign to the most likely sample. We then compute the assignment quality to avoid ambiguous assignments. This is similar to how mapping quality solves the problem of ambiguous assignments.
Citing
Our paper describing our algorithm was published in Bioinformatics. To cite us:
deML: robust demultiplexing of Illumina sequences using a likelihood-based approach.
Gabriel Renaud, Udo Stenzel, Tomislav Maricic, Victor Wiebe, Janet Kelso
Bioinformatics. 2015 March 1.
Documentation
Please find the documentation in the README with the package.
Support Contact
Please contact Gabriel Renaud (@grenaud) for further information:
