FANCY: fast estimation of privacy risk in functional genomics data.
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TLDR
A special version of the model is developed, which can make predictions with higher accuracy when the number of leaking variants is low, and provides an estimate of the overall privacy risk before data release.Abstract:
MOTIVATION Functional genomics data are 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 when the number of leaked variants is low. Thus, we develop a special version of the model, which can make predictions with higher accuracy when the number of leaking variants is low. AVAILABILITY AND IMPLEMENTATION 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. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.read more
Citations
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Journal ArticleDOI
Functional genomics data: privacy risk assessment and technological mitigation.
Gamze Gürsoy,Tianxiao Li,Susanna Liu,Eric Ni,Charlotte M. Brannon,Charlotte M. Brannon,Mark Gerstein +6 more
TL;DR: In this article, the authors highlight privacy issues related to the sharing of functional genomics data, including genotype and phenotype information leakage, and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.
Journal ArticleDOI
Genome Privacy and Trust.
TL;DR: An overview of the importance of genomic privacy, the information gleaned from genomics data, the sources of potential private information leakages in genomics, and ways to preserve privacy while utilizing the genetic information in research is provided.
References
More filters
Journal ArticleDOI
The Sequence Alignment/Map format and SAMtools
Heng Li,Bob Handsaker,Alec Wysoker,T. J. Fennell,Jue Ruan,Nils Homer,Gabor T. Marth,Gonçalo R. Abecasis,Richard Durbin +8 more
TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Journal ArticleDOI
Fast and accurate short read alignment with Burrows–Wheeler transform
Heng Li,Richard Durbin +1 more
TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
Journal ArticleDOI
STAR: ultrafast universal RNA-seq aligner
Alexander Dobin,Carrie A. Davis,Felix Schlesinger,Jorg Drenkow,Chris Zaleski,Sonali Jha,Philippe Batut,Mark Chaisson,Thomas R. Gingeras +8 more
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Book
Gaussian Processes for Machine Learning
TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Journal ArticleDOI
A framework for variation discovery and genotyping using next-generation DNA sequencing data
Mark A. DePristo,Eric Banks,Ryan Poplin,Kiran V. Garimella,Jared Maguire,Christopher Hartl,Anthony A. Philippakis,Anthony A. Philippakis,Anthony A. Philippakis,Guillermo del Angel,Manuel A. Rivas,Manuel A. Rivas,Matt Hanna,Aaron McKenna,Timothy Fennell,Andrew Kernytsky,Andrey Sivachenko,Kristian Cibulskis,Stacey Gabriel,David Altshuler,David Altshuler,Mark J. Daly,Mark J. Daly +22 more
TL;DR: A unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs is presented.