Using methylation data to improve transcription factor binding prediction.

Morgan, Daniel; DeMeo, Dawn L; Glass, Kimberly

Morgan, Daniel; DeMeo, Dawn L; Glass, Kimberly.

Afiliación

Morgan D; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
DeMeo DL; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Glass K; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Epigenetics ; 19(1): 2309826, 2024 Dec.

Article en En | MEDLINE | ID: mdl-38300850

ABSTRACT

ABSTRACT

Modelling the regulatory mechanisms that determine cell fate, response to external perturbation, and disease state depends on measuring many factors, a task made more difficult by the plasticity of the epigenome. Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) can be used to estimate transcription factor (TF) binding locations. However, this approach does not incorporate information regarding the epigenetic context necessary for TF binding. CpG methylation is an epigenetic mark influenced by environmental factors that is commonly assayed in human cohort studies. We developed a framework to score inferred TF binding locations using methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole-genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that for most TFs, binding prediction improves using methylation-based scoring compared to standard PWM-scores. We also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context-specific TF binding using methylation data. Importantly, the availability of DNA methylation data in existing patient populations provides an opportunity to use our approach to understand the impact of methylation on gene regulatory processes in the context of human disease.

Asunto(s)

Metilación de ADN; Factores de Transcripción; Humanos; Sitios de Unión; Factores de Transcripción/genética; Factores de Transcripción/metabolismo; Unión Proteica; Regulación de la Expresión Génica

Palabras clave

DNA methylation; Transcription factor binding prediction

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Factores de Transcripción / Metilación de ADN Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Epigenetics Asunto de la revista: GENETICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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