ArticleFrontiers in cell and developmental biology2014
Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems.
Article in Frontiers in cell and developmental biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- SC-MO-GRN-DB: A comprehensive repository for single-cell multiomic gene regulatory networks.iScience · 2026Article
- From binding to networks: methods for identifying transcription factor targets in plant systems.Frontiers in plant science · 2026Review
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- High expression ofBiochemistry and biophysics reports · 2025Article
- A transcription network underlies the dual genomic coordination of mitochondrial biogenesis.eLife · 2024Article
- A transcription network underlies the dual genomic coordination of mitochondrial biogenesis.bioRxiv : the preprint server for biology · 2024Article
- The regulatory landscape of neurite development inFrontiers in molecular neuroscience · 2022Review
- Joint changes in RNA, RNA polymerase II, and promoter activity through the cell cycle identify non-coding RNAs involved in proliferation.Scientific reports · 2021Article
- Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks.PLoS computational biology · 2020Article
- Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types.Nucleic acids research · 2020Article
- The single-cell eQTLGen consortium.eLife · 2020Article
- Genome-Wide Histone Modifications and CTCF Enrichment Predict Gene Expression in Sheep Macrophages.Frontiers in genetics · 2020Article
- Assessment of transcriptional importance of cell line-specific features based on GTRD and FANTOM5 data.PloS one · 2020Article
- BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus.BMC bioinformatics · 2019Article
- Integrating Bacterial ChIP-seq and RNA-seq Data With SnakeChunks.Current protocols in bioinformatics · 2019Article
- CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data.Bioinformatics (Oxford, England) · 2018Article
- Integrating ChIP-seq with other functional genomics data.Briefings in functional genomics · 2018Review
- Genome-wide analyses of direct target genes of four rice NAC-domain transcription factors involved in drought tolerance.BMC genomics · 2018Article
- Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods.Frontiers in genetics · 2018Article
- Pan-Cancer Mutational and Transcriptional Analysis of the Integrator Complex.International journal of molecular sciences · 2017Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of omic data produced from international consortia, as well as from worldwide laboratories, is offering the possibility both to answer long-standing questions in biomedicine/molecular biology and to formulate novel hypotheses to test. However, the impact of such data is not fully exploited due to a limited availability of multi-omic data integration tools and methods. In this paper, we discuss the interplay between gene expression and epigenetic markers/transcription factors. We show how integrating ChIP-seq and RNA-seq data can help to elucidate gene regulatory mechanisms. In particular, we discuss the two following questions: (i) Can transcription factor occupancies or histone modification data predict gene expression? (ii) Can ChIP-seq and RNA-seq data be used to infer gene regulatory networks? We propose potential directions for statistical data integration. We discuss the importance of incorporating underestimated aspects (such as alternative splicing and long-range chromatin interactions). We also highlight the lack of data benchmarks and the need to develop tools for data integration from a statistical viewpoint, designed in the spirit of reproducible research.
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