bioconductor-findit2

name

bioconductor-findit2

description

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

FindIT2

Workflows

Standard Workflow

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.

When to Use

• Annotating ChIP-seq or ATAC-seq peaks to nearest genes using mm_nearestGene().
• Finding all peaks within a specific genomic window around the TSS of genes using mm_geneScan().
• Calculating gene-level regulatory potential (RP) from peak-level data using calcRP_TFHit() or from bigwig coverage files using calcRP_coverage().
• Identifying influential transcription factors and target genes by integrating multi-omics data.

When NOT to Use

• For differential binding analysis of ChIP-seq data, use DiffBind because FindIT2 focuses on peak-to-gene annotation and regulatory potential calculation.
• For standard peak calling from raw FASTQ/BAM files, use MACS2 because FindIT2 requires pre-called peak files (e.g., BED format).

Data Requirements

• Peak files in BED format, loaded into a GRanges object using loadPeakFile().
• A TxDb object containing genomic coordinates and gene models (e.g., TxDb.Athaliana.BioMart.plantsmart28).
• The GRanges object must contain a metadata column named feature_id representing peak names.

Key Parameters

• peak_GR: A GRanges object representing genomic peaks.
• Txdb: A TxDb object for genomic annotations.
• upstream (2e4): Upstream distance from TSS for gene scan mode.
• downstream (2e4): Downstream distance from TSS for gene scan mode.
• decay_dist (1000): Half-decay distance controlling the weight of peaks based on distance to TSS.
• report_fullInfo (TRUE): Logical indicating whether to return full GRanges annotation details.

Best Practices

• Ensure the chromosome naming style (seqlevels) of the peak GRanges object matches the TxDb object exactly using seqlevels().
• Use plot_annoDistance() to inspect the distribution of peak distances to TSS to determine if the TF is promoter-type or enhancer-type.
• Set decay_dist to 1000 for promoter-type TFs and 10,000 (10 kb) for enhancer-type TFs when calculating regulatory potential.

Common Pitfalls

• Mismatched Chromosome Names: If seqlevels do not match between peak GRanges and TxDb, annotations will fail; fix by aligning seqlevels using seqlevels(Txdb) <- ....
• Missing feature_id Column: FindIT2 functions rely on feature_id to link peaks; fix by loading peaks with loadPeakFile() or manually assigning yourGR$feature_id <- ....
• Excessive Gene Scan Window: Setting upstream/downstream parameters too large on high-density genomes can make results messy; fix by using mm_nearestGene() or reducing the scan window.

Alternatives

• ChIPseeker for general ChIP-seq peak annotation and visualization.
• DiffBind for quantitative differential binding analysis.

Citations

• Guandong Shang, Zhougeng Xu, Muchun Wan, Fuxiang Wang, Jiawei Wang (2022). FindIT2: an R/Bioconductor package to identify influential transcription factor and targets based on multi-omics data. BMC Genomics, 23, 272.

References

• Homepage: bioconductor.org/packages/FindIT2
• Vignette: bioconductor.org/packages/release/bioc/vignettes/FindIT2/inst/doc/FindIT2.html