python module to plot beautiful and highly customizable genome browser tracks
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README.md

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pyGenomeTracks

Standalone program and library to plot beautiful genome browser tracks

pyGenomeTracks aims to produce high-quality genome browser tracks that are highly customizable. Currently, it is possible to plot:

  • bigwig
  • bed (many options)
  • bedgraph
  • links (represented as arcs)
  • Hi-C matrices (if HiCExplorer is installed)

pyGenomeTracks can make plots with or without Hi-C data. The following is an example output of pyGenomeTracks from Ramírez et al. 2017

pyGenomeTracks example

Installation

pyGenomeTracks works with python 2.7 and python 3.6.

Currently, the best way to install pyGenomeTracks is with anaconda

$ conda install -c bioconda pygenometracks

Also, pyGenomeTracks can be installed using pip

$ pip install pyGenomeTracks

If the latest version wants to be installed use:

$ pip install  git+https://github.com/maxplanck-ie/pyGenomeTracks.git

Usage

To run pyGenomeTracks a configuration file describing the tracks is required. The easiest way to create this file is using the program make_tracks_file which creates a configuration file with defaults that can be easily changed. The format is:

$ make_tracks_file --trackFiles <file1.bed> <file2.bw> ... -o tracks.ini

make_tracks_file uses the file ending to guess the file type.

Then, a region can be plotted using:

$ pyGenomeTracks --tracks tracks.ini --region chr2:10,000,000-11,000,000 --outFileName nice_image.pdf

The ending --outFileName defines the image format. If .pdf is used, then the resulting image is a pdf. The options are pdf, png and svg.

Citation

If you use pyGenomeTracks in your analysis, you can cite the following paper :

Fidel Ramírez, Vivek Bhardwaj, Laura Arrigoni, Kin Chung Lam, Björn A. Grüning, José Villaveces, Bianca Habermann, Asifa Akhtar & Thomas Manke. High-resolution TADs reveal DNA sequences underlying genome organization in flies. Nature Communications (2018) doi:10.1038/s41467-017-02525-w

Examples

(These examples are found in the examples/ folder)

A minimal example of a configuration file with a single bigwig track looks like this:

[bigwig file test]
file = bigwig.bw
# height of the track in cm (optional value)
height = 4
title = bigwig
min_value = 0
max_value = 30
$ pyGenomeTracks --tracks bigwig_track.ini --region X:2,500,000-3,000,000 -o bigwig.png

pyGenomeTracks bigwig example

Now, let's add the genomic location and some genes:

[bigwig file test]
file = bigwig.bw
# height of the track in cm (optional value)
height = 4
title = bigwig
min_value = 0
max_value = 30

[spacer]
# this simply adds an small space between the two tracks.

[genes]
file = genes.bed.gz
height = 7
title = genes
fontsize = 10
file_type = bed
gene rows = 10

[x-axis]
fontsize=10
$ pyGenomeTracks --tracks bigwig_with_genes.ini --region X:2,800,000-3,100,000 -o bigwig_with_genes.png

pyGenomeTracks bigwig example

Now, we will add some vertical lines across all tracks. The vertical lines should be in a bed format.

[bigwig file test]
file = bigwig.bw
# height of the track in cm (optional value)
height = 4
title = bigwig
min_value = 0
max_value = 30

[spacer]
# this simply adds an small space between the two tracks.

[genes]
file = genes.bed.gz
height = 7
title = genes
fontsize = 10
file_type = bed
gene rows = 10

[x-axis]
fontsize=10

[vlines]
file = domains.bed
type = vlines
$ pyGenomeTracks --tracks bigwig_with_genes_and_vlines.ini --region X:2,800,000-3,100,000 -o bigwig_with_genes_and_vlines.png

pyGenomeTracks bigwig example

Examples with peaks

pyGenomeTracks has an option to plot peaks using MACS2 narrowPeak format.

The following is an example of the output in which the peak shape is drawn based on the start, end, summit and height of the peak.

[narrow]
file = test.narrowPeak
height = 4
max_value = 40
title = max_value=40

[narrow 2]
file = test.narrowPeak
height = 2
show labels = no
show data range =  no
color = #00FF0080
use summit = no
title = show labels=no; show data range=no; use summit=no;color=#00FF0080
[spacer]

[narrow 3]
file = test.narrowPeak
height = 2
show labels = no
color = #0000FF80
use summit = no
width adjust = 4
title = show labels=no;width adjust=3

[spacer]

[narrow 4]
file = test.narrowPeak
height = 3
type = box
color = blue
title = type=box;color=blue;

[x-axis]

pyGenomeTracks bigwig example

Examples with Hi-C data

The following is an example with Hi-C data overlay with topologically associating domains (TADs) and a bigwig file.

[x-axis]
where = top

[hic matrix]
file = hic_data.h5
title = Hi-C data
# depth is the maximum distance plotted in bp. In Hi-C tracks
# the height of the track is calculated based on the depth such
# that the matrix does not look deformated
depth = 300000
transform = log1p
file_type = hic_matrix

[tads]
file = domains.bed
display = triangles
border color = black
color = none
# the tads are overlay over the hic-matrix
# the share-y options sets the y-axis to be shared
# between the Hi-C matrix and the TADs.
overlay previous = share-y

[spacer]

[bigwig file test]
file = bigwig.bw
# height of the track in cm (optional value)
height = 4
title = ChIP-seq
min_value = 0
max_value = 30
$ pyGenomeTracks  --tracks hic_track.ini -o hic_track.png --region chrX:2500000-3500000

pyGenomeTracks bigwig example

Examples with Epilogos

pyGenomeTracks can be used to visualize epigenetic states (for example from chromHMM) as epilogos. For more information see: https://epilogos.altiusinstitute.org/

To plot epilogos a qcat file is needed. This file can be crated using the epilogos software (https://github.com/Altius/epilogos).

An example track file for epilogos looks like:

[epilogos]
file = epilog.qcat.bgz
height = 5
title = epilogos

[x-axis]

epilogos example

The color of the bars can be set by using a json file. The structure of the file is like this

{
"categories":{
          "1":["Active TSS","#ff0000"],
          "2":["Flanking Active TSS","#ff4500"],
          "3":["Transcr at gene 5\" and 3\"","#32cd32"],
          "4":["Strong transcription","#008000"]
          }
}

In the following examples the top epilogo has the custom colors and the one below is shown inverted.

[epilogos]
file = epilog.qcat.bgz
height = 5
title = epilogos with custom colors
categories_file = epilog_cats.json

[epilogos inverted]
file = epilog.qcat.bgz
height = 5
title = epilogos inverted
orientation = inverted

[x-axis]

epilogos example

Examples with multiple options

A comprehensive example of pyGenomeTracks can be found as part of our automatic testing. Note, that pyGenome tracks also allows the combination of multiple tracks into one using the parameter: overlay previous=yes or overlay previous=share-y. In the second option the y-axis of the tracks that overlays is the same as the track being overlay. Multiple tracks can be overlay together.

pyGenomeTracks example

The configuration file for this image is here

Examples with multiple options for bigwig tracks

pyGenomeTracks example

The configuration file for this image is here

Examples with Hi-C data

In these examples is where the overlay tracks are more useful. Notice that any track can be overlay over a Hi-C matrix. Most useful is to overlay TADs or to overlay links using the triangles option that will point in the Hi-C matrix the pixel with the link contact. When overlaying links and TADs is useful to set overlay previous=share-y such that the two tracks match the positions. This is not required when overlying other type of data like a bigwig file that has a different y-scale.

pyGenomeTracks example

The configuration file for this image is here

Adding new tracks

Adding new tracks to pyGenomeTracks only requires adding a new class to the pygenometracks/tracks folder. The class should inherit the the GenomeTrack (or other track class available) and should have a plot method. Additionally, some basic description should be added.

For example, to make a track that prints 'hello world' at a given location looks like this:

class TextTrack(GenomeTrack):
    SUPPORTED_ENDINGS = ['.txt']  # this is used by make_tracks_file to guess the type of track based on file name
    TRACK_TYPE = 'text'
    OPTIONS_TXT = """
height = 3
title =
text =
# x position of text in the plot (in bp)
x position =
"""
    def plot(self, ax, chrom, region_start, region_end):
        """
        This example simply plots the given title at a fixed
        location in the axis. The chrom, region_start and region_end
        variables are not used.
        Args:
            ax: matplotlib axis to plot
            chrom_region: chromosome name
            start_region: start coordinate of genomic position
            end_region: end coordinate
        """
        # print text at position x = self.properties['x position'] and y = 0.5 (center of the plot)
        ax.text(float(self.properties['x position']), 0.5, self.properties['text'])

The OPTIONS_TXT should contain the text to build a default configuration file. This information, together with the information about SUPPORTED_ENDINGS is used by the program make_tracks_file to create a default configuration file based on the endings of the files given.

The configuration file is:

[x-axis]
where = top

[new track]
file =
height = 4
title = new pyGenomeTrack
file_type = text
text = hello world
x position = 3100000
# pgt is short for `pyGenomeTracks`
pgt --tracks new_track.ini --region X:3000000-3200000 -o new_track.png

pyGenomeTracks example

Notice that the resulting track already includes a y-axis (to the left) and a label to the right. This are the defaults that can be changed by adding a plot_y_axis and plot_label methods.

Another more complex example is the plotting of multiple bedgraph data as matrices. The output of HiCExplorer hicFindTADs produces a data format that is similar to a bedgraph but with more value columns. We call this a bedgraph matrix. The following track plot this bedgraph matrix:

import numpy as np
from pygenometracks.tracksClass import BedGraphTrack
from pygenometracks.tracksClass import GenomeTrack

class BedGraphMatrixTrack(BedGraphTrack):
   # this track class extends a BedGraphTrack that is already part of 
   # pyGenomeTracks. The advantage of extending this class is that
   # we can re-use the code for reading a bedgraph file
   SUPPORTED_ENDINGS = ['.bm', '.bm.gz']
   TRACK_TYPE = 'bedgraph_matrix'
   OPTIONS_TXT = GenomeTrack.OPTIONS_TXT + """
       # a bedgraph matrix file is like a bedgraph, except that per bin there
       # are more than one value (separated by tab). This file type is 
       # produced by the HiCExplorer tool hicFindTads and contains
       # the TAD-separation score at different window sizes. 
       # E.g.
       # chrX	18279	40131	0.399113	0.364118	0.320857	0.274307
       # chrX	40132	54262	0.479340	0.425471	0.366541	0.324736
       #min_value = 0.10
       #max_value = 0.70
       file_type = {}
       """.format(TRACK_TYPE)

   def plot(self, ax, chrom_region, start_region, end_region):
       """
       Args:
           ax: matplotlib axis to plot
           chrom_region: chromosome name
           start_region: start coordinate of genomic position
           end_region: end coordinate
       """

       # the BedGraphTrack already has methods to read files
       # in which the first three columns are chrom, start,end
       # here we used the get_scores method inherited from the
       # BedGraphTrack class
       values_list, start_pos = self.get_scores(chrom_region, start_region, end_region)
       
       # the values_list is a python list, containing one element
       # per row in the selected genomic range.
       # In this case, the bedgraph matrix contains per row a list
       # of values, thus, each element of the values_list is itself
       # a list. 
       # In the following code, such list is converted to floats and
       # appended to a new list. 
       matrix_rows = []
       for values in values_list:
           values = map(float, values)
           matrix_rows.append(values)

       # using numpy, the list of values per line in the bedgraph file
       # is converted into a matrix whose columns contain 
       # the bedgraph values for the same line (notice that
       # the matrix is transposed to achieve this)
       matrix = np.vstack(matrix_rows).T

       # using meshgrid we get x and y positions to plot the matrix at
       # corresponding positions given in the bedgraph file.
       x, y = np.meshgrid(start_pos, np.arange(matrix.shape[0]))

       # shading adds some smoothing to the pllot
       shading = 'gouraud'
       vmax = self.properties['max_value']
       vmin = self.properties['min_value']

       img = ax.pcolormesh(x, y, matrix, vmin=vmin, vmax=vmax, shading=shading)
       img.set_rasterized(True)


   def plot_y_axis(self, ax, plot_axis):
       """turn off y_axis plot"
       pass

Let's create a track for this:

[bedgraph matrix]
file = tad_separation_score.bm.gz
title = bedgraph matrix
height = 8
file_type = bedgraph_matrix

[spacer]

[x-axis]
pgt --tracks bedgraph_matrix.ini --region X:2000000-3500000 -o bedgraph_matrix.png

pyGenomeTracks example

Although this image looks interesting another way to plot the data is a overlapping lines with the mean value highlighted. Using the bedgraph version of pyGenomeTracks the following image can be obtained:

pyGenomeTracks example

pyGenomeTracks is used by HiCExporer and HiCBrowser (See e.g. Chorogenome navigator which is made with HiCBrowser)