DOI: 10.1101/482992Nov 29, 2018Paper

GRAM: A generalized model to predict the molecular effect of a non-coding variant in a cell-type specific manner

BioRxiv : the Preprint Server for Biology
Shaoke LouMark Gerstein

Abstract

There has been much effort to prioritize genomic variants with respect to their impact on "function". However, function is often not precisely defined: Sometimes, it is the disease association of a variant; other times, it reflects a molecular effect on transcription or epigenetics. Here we coupled multiple genomic predictors to build GRAM, a generalized model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant in a cell-specific manner. As a first step, we performed feature engineering: using a LASSO regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other functional-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating SELEX features and expression profiles. The program combines a universal regulatory score for a variant in a non-coding element with a modifier score reflecting the particular cell type. We benchmarked GRAM on a large-scale MPRA dataset in the GM12878 cell line, achieving a...Continue Reading

Related Concepts

Genome
Luciferases
Transcription Factor
Transcription, Genetic
Gram-Positive Bacterial Infections
Cell Line, Tumor
Bacterial Stain, Routine
Genes, Reporter
MCF-7 Cells
K562 Cells

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