Introduction#
InPheRNo-ChIP: Inference of Phenotype-relevant Regulatory Networks with ChIP-seq Integration#
InPheRNo-ChIP is a computational framework designed to reconstruct phenotype-relevant gene regulatory networks (GRNs) by integrating RNA-seq, TF-specific ChIP-seq, and phenotypic labels.
Framework Overview#
InPheRNo-ChIP main steps, details can be found in the accompanying research paper:
RNA-seq Data Processing: Estimating p-values for TF-gene interactions and gene-phenotype associations using three RNA-seq datasets.
ChIP-seq Data Processing: Deriving p-values for TF-gene interactions from ChIP-seq data.
PGM Integration: Merging calculated p-values within a PGM, introducing binary variables to represent TF-gene pairs.
MCMC Sampling: Estimating posterior probabilities for these variables to form an initial regulatory graph.
Normalization and Filtering: Refining the initial graph to produce a precise and phenotype-relevant GRN.
Key Features#
Comprehensive Data Integration: InPheRNo-ChIP extends the foundational InPheRNo algorithm (available on GitHub) by integrating various datasets, including RNA-seq and ChIP-seq data, from human embryonic and endoderm cell lines.
Probabilistic Graphical Model (PGM): The framework employs a PGM to systematically model the influence of transcription factors on target genes.
Advanced Gene-Filtering and Normalization: Incorporating advanced techniques, InPheRNo-ChIP refines gene regulatory network (GRN) inference, enhancing the accuracy and relevance of its findings.
Phenotype-Relevant GRN Inference: The tool is adept at constructing GRNs that are directly relevant to phenotypic variations, particularly focusing on key endoderm markers.
Intended Audience#
InPheRNo-ChIP is tailored for:
Developmental Biologists: Researchers studying embryogenesis, cellular differentiation, and developmental processes will find InPheRNo-ChIP particularly useful for understanding gene regulation during these critical stages.
Bioinformaticians and Computational Biologists: Professionals who specialize in analyzing biological data, especially those with a focus on high-throughput sequencing data, will utilize InPheRNo-ChIP for in-depth analysis and interpretation.
and much more!