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

Brief Bioinform. 2024 Jul 25;25(5):bbae366. doi: 10.1093/bib/bbae366.

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.

Keywords: benchmarking; next-generation sequencing; prediction; query gene set; transcriptional regulator.

Publication types

  • Review

MeSH terms

  • Computational Biology* / methods
  • Gene Expression Regulation
  • High-Throughput Nucleotide Sequencing* / methods
  • Humans
  • Transcription Factors / genetics
  • Transcription Factors / metabolism

Substances

  • Transcription Factors