This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function lik
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Annotate ChIP-seq or ATAC-seq peaks to nearest genes and calculate gene-level regulatory potential (RP) based on peak distance to transcription start sites (TSS).
library(FindIT2)
library(TxDb.Athaliana.BioMart.plantsmart28)
# 1. Load peak file and TxDb annotation
Txdb <- TxDb.Athaliana.BioMart.plantsmart28
seqlevels(Txdb) <- c(paste0("Chr", 1:5), "M", "C")
ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
# 2. Annotate peaks using nearest gene mode
mmAnno_nearestgene <- mm_nearestGene(peak_GR = ChIP_peak_GR, Txdb = Txdb)
# 3. Find related peaks using gene scan mode
mmAnno_geneScan <- mm_geneScan(peak_GR = ChIP_peak_GR, Txdb = Txdb, upstream = 2e4, downstream = 2e4)
# 4. Calculate regulatory potential (RP)
fullRP_hit <- calcRP_TFHit(mmAnno = mmAnno_geneScan, Txdb = Txdb, decay_dist = 1000, report_fullInfo = TRUE)
Input: A peak BED file path and a TxDb object. Output: A GRanges object containing annotated peaks and calculated regulatory potentials.
mm_nearestGene().mm_geneScan().calcRP_TFHit() or from bigwig coverage files using calcRP_coverage().DiffBind because FindIT2 focuses on peak-to-gene annotation and regulatory potential calculation.MACS2 because FindIT2 requires pre-called peak files (e.g., BED format).loadPeakFile().TxDb object containing genomic coordinates and gene models (e.g., TxDb.Athaliana.BioMart.plantsmart28).feature_id representing peak names.seqlevels().plot_annoDistance() to inspect the distribution of peak distances to TSS to determine if the TF is promoter-type or enhancer-type.decay_dist to 1000 for promoter-type TFs and 10,000 (10 kb) for enhancer-type TFs when calculating regulatory potential.seqlevels(Txdb) <- ....feature_id to link peaks; fix by loading peaks with loadPeakFile() or manually assigning yourGR$feature_id <- ....mm_nearestGene() or reducing the scan window.ChIPseeker for general ChIP-seq peak annotation and visualization.DiffBind for quantitative differential binding analysis.No reviews yet. Tell others what you thought.
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