ncdDetect

ncdDetect

Introduction

Here you will get an introduction on how to produce p-values with ncdDetect. ncdDetect is a statistically founded method for detecting non-coding cancer driver regions. With this method we consider the frequency of mutations alongside their functional impact to reveal signs of recurrent positive selection across cancer genomes. In particular, the observed mutation frequency is compared to a sample- and position-specific background mutation rate, which is estimated based on various genomic annotations. ncdDetect is described in detail in Juul et al. (2017) and Juul et al. (2018).

Position- and sample-specific probabilities of mutation

The position- and sample-specific probabilities of mutation used in the publications describing ncdDetect are based on genomic annotations known to correlate with mutation rate. These are replication timing, tissue-specific gene expression level, trinucleotides (the nucleotide under consideration and its left and right flanking bases, thus taking into account the sample-specific mutational signature), genomic segment (3' and 5' untranslated regions (UTRs), splice sites, promoter elements and protein-coding genes) and local mutation rate.

The null model estimates are based on 505 whole genome samples, described in further detail in Juul et al. (2017). They can be downloaded here (compressed .zip file, 3.2GB).

ncdDetect v.1

ncdDetect v.1 is written in R, and the code is freely available at github.

A tutorial on how to obtain p-values with ncdDetect v.1 is available here.

A tutorial on how to use the core ncdDetect functionalities og ncdDetect v.1 is available here (.pdf).

ncdDetect v.2

ncdDetect v.2 takes overdispersion into account, and performs significance evaluation much faster than ncdDetect v.1. It is written in R, and the code is freely available at github.

A tutorial on how to obtain p-values with ncdDetect v.2 using your own position- and sample-specific probabilities of mutation and impact scores is available here (.pdf).

References

Juul, M., Bertl, J., Guo, Q., Nielsen, M. M., Świtnicki, M., Hornshøj, H., Madsen, T., Hobolth, A., and Pedersen, J. S. (2017). Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate. eLife, 6, e21778.

Juul, M., Madsen, T., Guo, Q., Bertl, J., Hobolth, A., Kellis, M., and Pedersen, J. S. (2018). ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation. Bioinformatics.