CERENKOV is a software pipeline and associated machine-learning framework for identifying regulatory single nucleotide polymorphisms (rSNPs) in the noncoding genome for post-analysis of genetic regions identified in genome-wide association studies (GWAS). CERENKOV was created by Yao Yao, Zheng Liu, Satpreet Singh, Qi Wei, and Stephen Ramsey at Oregon State University.
The March 2017 data files for CERENKOV can be accessed on the Ramsey Lab file server (see README.md files under the subdirectories of the GitHub CERENKOV project area, for more information about which data files are used in which parts of CERENKOV).
Based on the 2017 ACM-BCB version, we further developed CERENKOV2 and submitted a methodology article CERENKOV2: data-space geometric features improve machine learning-based detection of functional noncoding SNPs to BMC Bioinformatics in July 2018.
To reproduce the results reported in this submission, please follow the README file in experiments/CERENKOV2 folder.
We revised our manuscript and code in the following weeks and re-submitted with a new title CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features in Nov. 2018.
To reproduce the results reported in revised submission, please follow the README file in experiments/CERENKOV2_revision folder.
We also provided an R scripts, install_packages.R,
to install all dependencies of reproduction.
We presented the very first version of CERENKOV at the 2017 ACM-BCB conference in Boston in August 2017, with an accompanying full research article CERENKOV: Computational Elucidation of the Regulatory Noncoding Variome in the proceedings, describing CERENKOV and demonstrating its accuracy for discriminating rSNPs from nonfunctional SNPs.
The corresponding code is archived in release v0.1-alpha.
To reproduce the results of our article, please follow the instructions below.
cerenkov_ml_compare_models.R: obtains the comparative machine-learning performance results that were used to make Figure 3 of the article (the scriptcerenkov_analyze_ml_results_compare_models.Ractually generates the plot).
The cerenkov_ml_compare_models.R script will require the R packages PRROC, parallel,
xgboost (version 0.6-4), ranger (version 0.6.0), Matrix, and pbapply. The
cerenkov_analyze_ml_results_compare_models.R script will require the R packages ggplot2
and reshape2.
cerenkov_ml_xgboost_importance.R: obtains the feature importance scores for the CERENKOV method that were used to make Figure 2 in the article.
The script requires the R packages Matrix, parallel, pbapply, and xgboost.
cerenkov_ml_tune_xgboost.R: obtains the grid-search tuning machine-learning performance results that were used to make Figure 1b,c in the article (the scriptcerenkov_analyze_ml_results_tune_xgboost.Ractually generates the plot).
The cerenkov_ml_tune_xgboost.R script will require the R packages PRROC,
parallel, xgboost (version 0.6-4), Matrix, and pbapply. The
cerenkov_analyze_ml_results_tune_xgboost.R script will require the R packages
ggplot2 and reshape2.
The following .Rdata files (the "201703 data supplement" for CERENKOV) accompany
the article CERENKOV: Computational elucidation of the regulatory noncoding
variome by Yao Yao, Zheng Liu, Satpreet Singh, Qi Wei, and Stephen A. Ramsey
(submitted to the ACM-BCB conference, April 2017). The data files are available
at the following links (all are HTTP links to the file server files.cgrb.oregonstate.edu):
features_GWAVA.Rdata(604 kB)features_OSU.Rdata(3.2 MB)features_RSVP.Rdata(77 MB)features_danq.Rdata(206 MB)features_deepsea.Rdata(176 MB)features_deltaSVM.Rdata(22 MB)scores_cadd.Rdata(132 kB)scores_dann.Rdata(151 kB)scores_eigen.Rdata(152 kB)scores_fitcons.Rdata(54 kB)snp_coordinages.Rdata(104 kB)