ReviewBriefings in bioinformatics2024
Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets.
Review in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- CCAAT-enhancer binding protein delta functions as a tumor suppressor gene in acute myeloid leukemia.Neoplasia (New York, N.Y.) · 2026Article
- BARTsc identifies key transcriptional regulators from single-cell omics data.bioRxiv : the preprint server for biology · 2026Article
- The LMO2-LDB1-TAL1 complex regulates transcription networks in acute myeloid leukemia.Blood neoplasia · 2026Article
- Enhancing TFEA.ChIP with ENCODE regulatory maps for generalizable transcription factor enrichment.Briefings in bioinformatics · 2026Article
- BIT: Bayesian Identification of Transcriptional regulators from epigenomics-based query region sets.Nature communications · 2025Article
- Impact of C-FOS/C-JUN Transcriptional Factors Co-Expression in Non-small Cell Lung Carcinoma.Cancer diagnosis & prognosisArticle
- Comprehensive Analysis of FOXO1 as a Tumour Suppressor Biomarker Related to Immune Infiltration and Cell Proliferation of Hepatocellular Carcinoma.IET systems biologyArticle
Corrections and comments
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5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.