A new computational method to predict transcriptional activity of a DNA sequence from diverse datasets of massively parallel reporter assays.

ABSTRACT: In recent years, the dramatic increase in the number of applications for massively parallel reporter assay (MPRA) technology has produced a large body of data for various purposes. However, a computational model that can be applied to decipher regulatory codes for diverse MPRAs does not exist yet. Here, we propose a new computational method to predict the transcriptional activity of MPRAs, as well as luciferase reporter assays, based on the TRANScription FACtor database. We employed regression trees and multivariate adaptive regression splines to obtain these predictions and considered a feature redundancy-dependent formula for conventional regression trees to enable adaptation to diverse data. The developed method was applicable to various MPRAs despite the use of different types of trans

SUBMITTER: Liu Y 

PROVIDER: S-EPMC5737609 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature