bedgraph

Description

A track for bedgraph files.

Parameters

Necessary:

  • file

Optional:

  • title: Put here a title which will apprear on the right.

  • height: 0.5 (default) or float above 0.

  • overlay_previous: no (default) or yes or share-y.

  • orientation: by default this option is not set but you can also put: inverted.

  • color: #a6cee3 (default)

  • alpha: 1 (default) or any float above 0 below 1

  • max_value: by default this option is not set but you can also put: any float

  • min_value: by default this option is not set but you can also put: any float

  • use_middle: false (default) or true.

  • show_data_range: true (default) or false.

  • type: fill (default)

  • negative_color: by default this option is not set

  • nans_to_zeros: false (default) or true.

  • summary_method: by default this option is not set but you can also put: mean, average, max, min, stdev, dev, coverage, cov or sum.

  • number_of_bins: 700 (default) or any integer above 1

  • transform: no (default) or log, log1p, -log, log2 or log10.

  • log_pseudocount: 0 (default) or any float

  • y_axis_values: transformed (default) or original.

  • second_file*: by default this option is not set

  • operation*: file (default)

  • grid: false (default) or true.

  • rasterize: false (default) or true.

* While pyGenomeTracks can convert coverage tracks on the fly, this might be a time-consuming step, especially on large files and if you want to replot many times. In this situation, we recommend using the deepTools suite to convert your files in advance. For example bamCoverage or bamCompare

Output of make_tracks_file:

# title of track (plotted on the right side)
title =
# height of track in cm (ignored if the track is overlay on top the previous track)
height = 2
# if you want to plot the track upside-down:
# orientation = inverted
# if you want to plot the track on top of the previous track. Options are 'yes' or 'share-y'.
# For the 'share-y' option the y axis values is shared between this plot and the overlay plot.
# Otherwise, each plot use its own scale
#overlay_previous = yes

color = green
# To use a different color for negative values
#negative_color = red
# To use transparency, you can use alpha
# default is 1
# alpha = 0.5
# the default for min_value and max_value is 'auto' which means that the scale will go
# roughly from the minimum value found in the region plotted to the maximum value found.
min_value = 0
#max_value = auto
# to convert missing data (NaNs) into zeros. Otherwise, missing data is not plotted.
nans_to_zeros = true
# for type, the options are: line, points, fill. Default is fill
# to add the preferred line width or point size use:
# type = line:lw where lw (linewidth) is float
# similarly points:ms sets the point size (markersize (ms) to the given float
# type = line:0.5
# type = points:0.5
# If you want to plot a 4C track where you want to link
# the non-missing data (NaNs) together and only use the
# middle of the region instead of the region itself:
# Default is false.
# use_middle = true
# By default the bedgraph is plotted at the base pair
# resolution. This can lead to very large pdf/svg files
# If plotting large regions.
# If you want to decrase the size of your file.
# You can either rasterize the bedgraph profile by using:
# rasterize = true
# Or use a summary method on a given number of bin:
# The possible summary methods are given by pyBigWig:
# mean/average/stdev/dev/max/min/cov/coverage/sum
# summary_method = mean
# number_of_bins = 700
# set show_data_range to false to hide the text on the left showing the data range
show_data_range = true
# to compute operations on the fly on the file
# or between 2 bedgraph files
# operation will be evaluated, it should contains file or
# file and second_file,
# we advice to use nans_to_zeros = true to avoid unexpected nan values
#operation = 0.89 * file
#operation = - file
#operation = file - second_file
#operation = log2((1 + file) / (1 + second_file))
#operation = max(file, second_file)
#second_file = path for the second file
# To log transform your data you can also use transform and log_pseudocount:
# For the transform values:
# 'log1p': transformed_values = log(1 + initial_values)
# 'log': transformed_values = log(log_pseudocount + initial_values)
# 'log2': transformed_values = log2(log_pseudocount + initial_values)
# 'log10': transformed_values = log10(log_pseudocount + initial_values)
# '-log': transformed_values = log(log_pseudocount + initial_values)
# For example:
#tranform = log
#log_pseudocount = 2
# When a transformation is applied, by default the y axis
# gives the transformed values, if you prefer to see
# the original values:
#y_axis_values = original
# If you want to have a grid on the y-axis
#grid = true
file_type = bedgraph