ArticlePLoS computational biology2020
MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 62 citations in OpenAlex.
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- ChEA-KG and ChEA-KG-TS: a network-based transcription factor enrichment analysis tool with an accompanying time-series workflow.Nucleic acids research · 2026Article
- Probing transcription factor subsets in gene regulatory networks.Algorithms for molecular biology : AMB · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Dietary isoleucine content modulates the metabolic and molecular response to a Western diet in mice.Molecular metabolism · 2025Article
- Transcriptomic Convergence and the Female Protective Effect in Autism.bioRxiv : the preprint server for biology · 2025Article
- Loss of negative regulation by HDAC1 and REST contributes to MAD1 overexpression in breast cancer.Molecular biology of the cell · 2025Article
- TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.Nature communications · 2025Article
- Disease modification upon 2 weeks of tofacitinib treatment in a mouse model of chronic epilepsy.Science translational medicine · 2025Article
- Late-life protein or isoleucine restriction impacts physiological and molecular signatures of aging.Nature aging · 2024Article
- DEK regulates B-cell proliferative capacity and is associated with aggressive disease in low-grade B-cell lymphomas.Blood cancer journal · 2024Article
- Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets.Briefings in bioinformatics · 2024Review
- Ki-67 is necessary during DNA replication for fork protection and genome stability.Genome biology · 2024Article
- Silencing Apoe with divalent-siRNAs improves amyloid burden and activates immune response pathways in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- The calcium channel TRPC6 promotes chemotherapy-induced persistence by regulating integrin α6 mRNA splicing.Cell reports · 2023Article
- HAPLN1 matrikine: a bone marrow homing factor linked to poor outcomes in patients with MM.Blood advances · 2023Article
- The YAP-TEAD complex promotes senescent cell survival by lowering endoplasmic reticulum stress.Nature aging · 2023Article
- In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer.British journal of cancer · 2023Article
- Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes.Computational and structural biotechnology journal · 2023Article
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1 author at 1 institution in 1 country.
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Abstract
Transcriptomic profiling is an immensely powerful hypothesis generating tool. However, accurately predicting the transcription factors (TFs) and cofactors that drive transcriptomic differences between samples is challenging. A number of algorithms draw on ChIP-seq tracks to define TFs and cofactors behind gene changes. These approaches assign TFs and cofactors to genes via a binary designation of 'target', or 'non-target' followed by Fisher Exact Tests to assess enrichment of TFs and cofactors. ENCODE archives 2314 ChIP-seq tracks of 684 TFs and cofactors assayed across a 117 human cell lines under a multitude of growth and maintenance conditions. The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an a priori binary classification of genes as targets or non-targets. When compared to other TF mining resources, MAGIC displayed favourable performance in predicting TFs and cofactors that drive gene changes in 4 settings: 1) A cell line expressing or lacking single TF, 2) Breast tumors divided along PAM50 designations 3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype 4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels. In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.
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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 OpenQuestion graph.