FANCY: Fast Estimation of Privacy Risk in Functional Genomics Data.

ABSTRACT:

Motivation

Functional genomics data is becoming clinically actionable, raising privacy concerns. However, quantifying privacy leakage via genotyping is difficult due to the heterogeneous nature of sequencing techniques. Thus, we present FANCY, a tool that rapidly estimates the number of leaking variants from raw RNA-Seq, ATAC-Seq and ChIP-Seq reads, without explicit genotyping. FANCY employs supervised regression using overall sequencing statistics as features and provides an estimate of the overall privacy risk before data release.

Results

FANCY can predict the cumulative number of leaking SNVs with an average 0.95 R2 for all independent test sets. We realize the importance of accurate prediction even when the number of leaked variants is low. Thus, we develop a special version of the model, which can make predictions with higher accuracy for only a few leaking variants.

Availability

A python and MATLAB implementation of FANCY, as well as custom scripts to generate the features can be found at https://github.com/gersteinlab/FANCY. We also provide jupyter notebooks so that users can optimize the parameters in the regression model based on their own data. An easy-to-use webserver that takes inputs and displays results can be found at fancy.gersteinlab.org.

SUBMITTER: Gursoy G 

PROVIDER: S-EPMC7850135 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature