# Integrative annotation scores of variants for impact on RNA binding protein activities

## TL;DR

This paper introduces INCA, a new method that uses RNA binding protein data to better understand how genetic variants affect RNA binding protein activities.

## Contribution

The novel contribution is the development of INCA, which integrates ENCODE RBP data with computational approaches to improve variant scoring.

## Key findings

- INCA provides specificity beyond generic scoring for RNA binding protein binding disruption.
- It can augment scoring for 46.2% of candidate variants and their linkage-disequilibrium partners.
- INCA is implemented in R and available for use.

## Abstract

The ENCODE project generated a large collection of eCLIP-seq RNA binding protein (RBP) profiling data with accompanying RNA-seq transcriptomes of shRNA knockdown of RBPs. These data could have utility in understanding the functional impact of genetic variants, however their potential has not been fully exploited. We implement INCA (Integrative annotation scores of variants for impact on RBP activities) as a multi-step genetic variant scoring approach that leverages the ENCODE RBP data together with ClinVar and integrates multiple computational approaches to aggregate evidence.

INCA evaluates variant impacts on RBP activities by leveraging genotypic differences in cell lines used for eCLIP-seq. We show that INCA provides critical specificity, beyond generic scoring for RBP binding disruption, for candidate variants and their linkage-disequilibrium partners. As a result, it can, on average, augment scoring of 46.2% of the candidate variants beyond generic scoring for RBP binding disruption and aid in variant prioritization for follow-up analysis.

INCA is implemented in R and is available at https://github.com/keleslab/INCA.