ArticlePLoS computational biology2022
Identification of upstream transcription factor binding sites in orthologous genes using mixed Student's t-test statistics.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 6 citations in OpenAlex.
- African Swine Fever Virus R238L and R298L Disrupt Lung Cell Collagen Formation and Cell Adhesion Pathway by Targeting Transcription Factors Containing zf-C2H2 Domain.Veterinary sciences · 2026Article
- TransPilot: mining key transcription factors by correlating binding sites with differentially expressed genes.Bioinformatics advances · 2026Article
- Identification of Regulators for Antigen-Specific CD8Veterinary sciences · 2025Article
- Procyanidin-B1-EnrichedAnimals : an open access journal from MDPI · 2025Article
- Ornithine decarboxylase antizyme 2 (OAZ2) in human colon adenocarcinoma: a potent prognostic factor associated with immunity.Scientific reports · 2025Article
- Testing the Significance of Ranked Gene Sets in Genome-wide TranscriptomeCurrent genomics · 2024Article
- Correlating gene expression levels with transcription factor binding sites facilitates identification of key transcription factors from transcriptome data.Frontiers in genetics · 2024Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTranscription factor (TF) regulates the transcription of DNA to messenger RNA by binding to upstream sequence motifs. Identifying the locations of known motifs in whole genomes is computationally intensive. METHODOLOGY/PRINCIPAL
findingsThis study presents a computational tool, named "Grit", for screening TF-binding sites (TFBS) by coordinating transcription factors to their promoter sequences in orthologous genes. This tool employs a newly developed mixed Student's t-test statistical method that detects high-scoring binding sites utilizing conservation information among species. The program performs sequence scanning at a rate of 3.2 Mbp/s on a quad-core Amazon server and has been benchmarked by the well-established ChIP-Seq datasets, putting Grit amongst the top-ranked TFBS predictors. It significantly outperforms the well-known transcription factor motif scanning tools, Pscan (4.8%) and FIMO (17.8%), in analyzing well-documented ChIP-Atlas human genome Chip-Seq datasets. SIGNIFICANCE: Grit is a good alternative to current available motif scanning tools.
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