Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets

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Abstract
This article provides an in-depth review of computational methods for predicting transcriptional regulators (TRs) with query gene sets. Identification of TRs is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.
Funding Information
  • Rally Foundation
  • Children’s Cancer Fund
  • Cancer Prevention and Research Institute of Texas (RP180319, RP200103, RP220032, RP170152, RP180805)
  • National Institutes of Health (R21CA259771, P30CA142543, HG011996, R01HL144969, R01CA258584)

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