/epitools

A suite of tools I developed to automate and simplify common high-throughput epigenomics data analysis techniques

Primary LanguageShellMIT LicenseMIT

epitools

A suite of tools that I developed to analyze high-throughput epigenomics data.

flexProfiler.sh

For a folder full of WIG files and a folder full of BED files this tool will generate a matrix containing the average signal in 50bp bins across a 1.5kb window (defaults) flanking the centre of each region in a BED file.

sh flexProfiler.sh -h

Options:

  • -u <your alias>
  • -o <output directory name (not full path)>
  • -wp <full path to wigs>
  • -bp <full path to beds>
  • -f <flank size; default=1500 centre of region +/- {-f}>
  • -bin <bin size default=50>
  • -suff <suffix for wig files to bed removed in name {default=.q5.F1028.PET.wig.gz}>
  • -g <4 character genome identifier (ex. mm10,hg19 etc.); {default=mm10}>

Note: This tool requires JRE 1.8 or greater and a custom java calculator (available upon request).

flexProfilePlotter.R

This is an R script that is meant to process, normalize and visualize the files outputted by flexProfiler.sh (above). It strips information about library type, treatment group, position and region of interest from each *.profile file in a directory. For each file the program normalizes signal values and calulates the average signal in each X bp in Y kb flanking the centre of each set of regions. The resulting dataframe can quickly and easily be visualized with ggplot2 in R.

regionRandomizer.sh

This tool compares one set of regions (ROIs) to another set of features/regions (Features).

It leverages shuffleBed to create 33 randomized region sets from each real ROIs set, maintaining the same cumulative genomic occupancy and chromosomal distribution.

Then the actual association between each set of ROIs and each set of Features is compared to the average (expected) overlap between its 33 randomized counterparts and the Features to provide both a real vs. expected relationship between two sets of genomic coordinates.

toBamAligner.sh

This tool was created to align fastq files to a genome (fasta) using the Burrows-Wheeler Aligner algorithm.

This tool:

  1. Aligns paired-end fastq files from Illumina sequencing platforms to BAM format using SAMtools.
  2. Marks duplicate reads in the bam file using Picard.
  3. Generates common library QC metrics (*.flagstat).

regionCharacterizer.sh

This tool takes a folder of BED files (*.bed) and CpG coordinates for your genome and provides the following metrics:

  • # of regions
  • genomic occupancy (kilobases)
  • mean size
  • total # of CpGs
  • mean # of CpGs
  • mean CpG density

regionDistributer.sh

This tool calculates the proportion of regions within a BED file that exist within different genomic features (ex. Introns, Promoters). It is useful for making pie charts or stacked bar plots as the output values will sum to 1.

flexCoverageCalculator.sh

This program is a simple for loop that iterates through a folder of bigwig files and a folder of bed files to calculate the average signal in each region using the UCSC utility bigWigAverageOverBed.

bigWigAverageOverBed

bigWigAverageOverBed v2 - Compute average score of big wig over each bed, which may have introns.

usage: bigWigAverageOverBed in.bw in.bed out.tab

The output columns are:

  • name - name field from bed, which should be unique
  • size - size of bed (sum of exon sizes
  • covered - \# bases within exons covered by bigWig
  • sum - sum of values over all bases covered
  • mean0 - average over bases with non-covered bases counting as zeroes
  • mean - average over just covered bases

Options:

  • -stats=stats.ra - Output a collection of overall statistics to stat.ra file
  • -bedOut=out.bed - Make output bed that is echo of input bed but with mean column appended
  • -sampleAroundCenter=N - Take sample at region N bases wide centered around bed item, rather than the usual sample in the bed item.
  • -minMax - include two additional columns containing the min and max observed in the area.

vitC