Chromatin Landscape Exploration, Analysis, and Research Tools
Faster version of APA specifically for larger loop lists (>10k loops)
Aggregate Track Analysis (ATA) is designed for analyzing genomic signal data around defined DNA loci/peaks. This tool reads signal data from a BigWig file, narrow peak data from a BED file, and aggregates the signal values (e.g., ChIP-seq) around the midpoints of peaks to generate a normalized output. The output is saved in a .npy format for downstream analysis.
ata [--res int] <signal.bw> <peaks.bed> <outfile> <genome>
java -jar clear-tools.jar ata --window 1000 --res 1 signal.bw peaks.bed output.npy hg38
This aggregates the signal data from signal.bw around peaks in peaks.bed and saves the results as output.npy,
using the hg38 genome.
--res int: (Optional) Resolution of signal aggregation. Defaults to 1 (and primarily designed and tested for 1 bp
analysis)
signal.bw: input BigWig file containing signal data.
peaks.bed: input BED file containing peak regions.
outfile: Output file prefix for aggregated results (saved as .npy).
genome: Genome assembly (e.g., hg19, hg38) used to map chromosomes.
Combines multiple bedpe files into one. Flags available for duplicate removal, NMS, combining, and more. Attributes do not need to be in the same order between the files. Simplest usage is just to combine multiple bedpe files together without worrying about order of attributes etc.
Split up a bedpe into multiple lists. E.g. will split a file into 10 by randomly assigning features to 1 of the 10 outputs. Useful when you want to parallelize a task on a cluster but need to split up the bedpe to do it. Can use FUSE to combine the results into a final file. Specifying a number <= 0 will split the bedpe by chromosome (i.e. will make a new bedpe for each chromosome, and all features of that chromosome will be together in the split file)
Intersect two bedpe files. Flag available to subtract the files instead of intersecting them. Also has flags for using exact matches, overlapping boundaries, etc.
Expands the size of anchors for each loop. Flags available for whether to explicitly set a size or only expand if the width is currently smaller than the requested width.
Peak Intensity (or Identification) Near Points Of INTeraction
Focal Loop Aggregation via Grid Search
Examining Neighborhoods at High-resolution by Aggregating Nearby Contact Events
Aggregating Mixed Peaks Leading to Improved Focal Intensity
Ratios and Enrichments Compared Across Phenotypes
COMParing Interactions from Loops across Experiments
Highly Observable Transitions in Selective Pixels Of Tissues
Search and Identify Focal Targets Simple Identification of Focal Targets
Selecting Interactions Enriched Versus Environment
Simple Identification of Maximum Point of Local Enrichment
probability
clean
CAMP Comparisons Across Multiple Phenotypes
ADAM analyze differences across maps
SLASH Summarizing Loop Aggregate Statistics and Hierarchies
TODO
ACE Annotating Cliques of Enrichment
CORE Cliques Of Regional Enrichments