{
    "info": {
        "author": "Fidel Ram\u00edrez, Vivek Bhardwaj, Joachim Wolf, Bj\u00f6rn Gr\u00fcning",
        "author_email": "deeptools@googlegroups.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Science/Research",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "[![PyPI Version](https://img.shields.io/pypi/v/pyGenomeTracks.svg?style=plastic)](https://pypi.org/project/pyGenomeTracks/) [![bioconda-badge](https://img.shields.io/conda/vn/bioconda/pyGenomeTracks.svg?style=plastic)](https://anaconda.org/bioconda/pygenometracks)\n[![bioconda-badge](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=plastic)](http://bioconda.github.io)\n[![Build Status](https://travis-ci.org/deeptools/pyGenomeTracks.svg?branch=master)](https://travis-ci.org/deeptools/pyGenomeTracks)\n\npyGenomeTracks\n==============\n\nStandalone program and library to plot beautiful genome browser tracks\n----------------------------------------------------------------------\n\npyGenomeTracks aims to produce high-quality genome browser tracks that\nare highly customizable. Currently, it is possible to plot:\n\n * bigwig\n * bed/gtf (many options)\n * bedgraph\n * epilogos\n * narrow peaks\n * links (represented as arcs)\n * Hi-C matrices\n\npyGenomeTracks can make plots with or without Hi-C data. The following is an example output of pyGenomeTracks from [Ram\u00edrez et al. 2017](https://www.nature.com/articles/s41467-017-02525-w)\n\n![pyGenomeTracks example](./docs/content/images/hic_example_nat_comm_small.png)\n\n\nInstallation\n------------\npyGenomeTracks works with python >=3.6.\n\nCurrently, the best way to install pyGenomeTracks is with anaconda\n\n```bash\n$ conda install -c bioconda pygenometracks\n```\n\nAlso, pyGenomeTracks can be installed using pip\n\n```bash\n$ pip install pyGenomeTracks\n```\n\nIf the latest version wants to be installed use:\n\n```bash\n$ pip install  git+https://github.com/deeptools/pyGenomeTracks.git\n```\n\n\nUsage\n-----\nTo 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\ndefaults that can be easily changed. The format is:\n\n```bash\n$ make_tracks_file --trackFiles <file1.bed> <file2.bw> ... -o tracks.ini\n```\n\n`make_tracks_file` uses the file ending to guess the file type.\n\nThen, a region can be plotted using:\n\n```bash\n$ pyGenomeTracks --tracks tracks.ini --region chr2:10,000,000-11,000,000 --outFileName nice_image.pdf\n```\n\nThe ending `--outFileName` defines the image format. If `.pdf` is used, then the resulting image is a pdf. The options are pdf, png and svg.\n\nCitation\n---------\nIf you use pyGenomeTracks in your analysis, you can cite the following paper :\n\nFidel Ram\u00edrez, Vivek Bhardwaj, Laura Arrigoni, Kin Chung Lam, Bj\u00f6rn A. Gr\u00fcning, Jos\u00e9 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](https://www.nature.com/articles/s41467-017-02525-w)\n\nExamples\n--------\n\n(These examples are found in the `examples/` folder)\n\nA minimal example of a configuration file with a single bigwig track looks like this:\n\n```INI\n[bigwig file test]\nfile = bigwig.bw\n# height of the track in cm (optional value)\nheight = 4\ntitle = bigwig\nmin_value = 0\nmax_value = 30\n```\n\n\n```bash\n$ pyGenomeTracks --tracks bigwig_track.ini --region X:2,500,000-3,000,000 -o bigwig.png\n```\n\n![pyGenomeTracks bigwig example](./examples/bigwig.png)\n\n\nNow, let's add the genomic location and some genes:\n```INI\n[bigwig file test]\nfile = bigwig.bw\n# height of the track in cm (optional value)\nheight = 4\ntitle = bigwig\nmin_value = 0\nmax_value = 30\n\n[spacer]\n# this simply adds an small space between the two tracks.\n\n[genes]\nfile = genes.bed.gz\nheight = 7\ntitle = genes\nfontsize = 10\nfile_type = bed\ngene rows = 10\n\n[x-axis]\nfontsize=10\n```\n\n```bash\n$ pyGenomeTracks --tracks bigwig_with_genes.ini --region X:2,800,000-3,100,000 -o bigwig_with_genes.png\n```\n\n![pyGenomeTracks bigwig example](./examples/bigwig_with_genes.png)\n\nNow, we will add some vertical lines across all tracks. The vertical lines should be in a bed format.\n\n```INI\n[bigwig file test]\nfile = bigwig.bw\n# height of the track in cm (optional value)\nheight = 4\ntitle = bigwig\nmin_value = 0\nmax_value = 30\n\n[spacer]\n# this simply adds an small space between the two tracks.\n\n[genes]\nfile = genes.bed.gz\nheight = 7\ntitle = genes\nfontsize = 10\nfile_type = bed\ngene rows = 10\n\n[x-axis]\nfontsize=10\n\n[vlines]\nfile = domains.bed\ntype = vlines\n```\n\n\n```bash\n$ pyGenomeTracks --tracks bigwig_with_genes_and_vlines.ini --region X:2,800,000-3,100,000 -o bigwig_with_genes_and_vlines.png\n```\n\n\n![pyGenomeTracks bigwig example](./examples/bigwig_with_genes_and_vlines.png)\n\nYou can also overlay bigwig with or without transparency.\n```INI\n[test bigwig]\nfile = bigwig2_X_2.5e6_3.5e6.bw\ncolor = blue\nheight = 7\ntitle = (bigwig color=blue 2000 bins) overlayed with (bigwig mean color=red alpha = 0.5 max over 300 bins) overlayed with (bigwig mean color=red alpha=0.5 200 bins)\nnumber of bins = 2000\n\n[test bigwig max]\nfile = bigwig2_X_2.5e6_3.5e6.bw\ncolor = red\nalpha = 0.5\nsummary method = max\nnumber of bins = 300\noverlay previous = share-y\n\n[test bigwig mean]\nfile = bigwig2_X_2.5e6_3.5e6.bw\ncolor = green\nalpha = 0.5\ntype = fill\nnumber of bins = 200\noverlay previous = share-y\n\n[spacer]\n\n\n[test bigwig]\nfile = bigwig2_X_2.5e6_3.5e6.bw\ncolor = blue\nheight = 7\ntitle = (bigwig color=blue 2000 bins) overlayed with (bigwig mean color=redmax over 300 bins) overlayed with (bigwig mean color=red 200 bins)\nnumber of bins = 2000\n\n[test bigwig max]\nfile = bigwig2_X_2.5e6_3.5e6.bw\ncolor = red\nsummary method = max\nnumber of bins = 300\noverlay previous = share-y\n\n[test bigwig mean]\nfile = bigwig2_X_2.5e6_3.5e6.bw\ncolor = green\ntype = fill\nnumber of bins = 200\noverlay previous = share-y\n\n\n[x-axis]\n```\n\n```bash\n$ pyGenomeTracks --tracks alpha.ini --region X:2700000-3100000 -o master_alpha.png\n```\n\n![pyGenomeTracks bigwig example with transparency](./pygenometracks/tests/test_data/master_alpha.png)\n\nExamples with peaks\n-------------------\n\npyGenomeTracks has an option to plot peaks using MACS2 narrowPeak format.\n\nThe following is an example of the output in which the peak shape is\ndrawn based on the start, end, summit and height of the peak.\n\n```INI\n[narrow]\nfile = test.narrowPeak\nheight = 4\nmax_value = 40\ntitle = max_value=40\n\n[narrow 2]\nfile = test.narrowPeak\nheight = 2\nshow labels = no\nshow data range =  no\ncolor = #00FF0080\nuse summit = no\ntitle = show labels=no; show data range=no; use summit=no;color=#00FF0080\n[spacer]\n\n[narrow 3]\nfile = test.narrowPeak\nheight = 2\nshow labels = no\ncolor = #0000FF80\nuse summit = no\nwidth adjust = 4\ntitle = show labels=no;width adjust=3\n\n[spacer]\n\n[narrow 4]\nfile = test.narrowPeak\nheight = 3\ntype = box\ncolor = blue\ntitle = type=box;color=blue;\n\n[x-axis]\n```\n![pyGenomeTracks bigwig example](./pygenometracks/tests/test_data/master_narrowPeak.png)\n\nExamples with Hi-C data\n-----------------------\n\nThe following is an example with Hi-C data overlay with topologically associating domains (TADs) and a bigwig file.\n\n```INI\n[x-axis]\nwhere = top\n\n[hic matrix]\nfile = hic_data.h5\ntitle = Hi-C data\n# depth is the maximum distance plotted in bp. In Hi-C tracks\n# the height of the track is calculated based on the depth such\n# that the matrix does not look deformated\ndepth = 300000\ntransform = log1p\nfile_type = hic_matrix\n\n[tads]\nfile = domains.bed\ndisplay = triangles\nborder color = black\ncolor = none\n# the tads are overlay over the hic-matrix\n# the share-y options sets the y-axis to be shared\n# between the Hi-C matrix and the TADs.\noverlay previous = share-y\n\n[spacer]\n\n[bigwig file test]\nfile = bigwig.bw\n# height of the track in cm (optional value)\nheight = 4\ntitle = ChIP-seq\nmin_value = 0\nmax_value = 30\n\n```\n\n```bash\n$ pyGenomeTracks  --tracks hic_track.ini -o hic_track.png --region chrX:2500000-3500000\n```\n\n![pyGenomeTracks bigwig example](./examples/hic_track.png)\n\nExamples with Epilogos\n----------------------\n\npyGenomeTracks can be used to visualize epigenetic states (for example from chromHMM) as epilogos. For more information see: https://epilogos.altiusinstitute.org/\n\nTo plot epilogos a `qcat` file is needed. This file can be crated using the epilogos software (https://github.com/Altius/epilogos).\n\nAn example track file for epilogos looks like:\n\n```INI\n\n[epilogos]\nfile = epilog.qcat.bgz\nheight = 5\ntitle = epilogos\n\n[x-axis]\n```\n\n![epilogos example](./examples/epilogos_track.png)\n\n\nThe color of the bars can be set by using a `json` file. The structure of the file is like this\n\n```JSON\n{\n\"categories\":{\n          \"1\":[\"Active TSS\",\"#ff0000\"],\n          \"2\":[\"Flanking Active TSS\",\"#ff4500\"],\n          \"3\":[\"Transcr at gene 5\\\" and 3\\\"\",\"#32cd32\"],\n          \"4\":[\"Strong transcription\",\"#008000\"]\n          }\n}\n```\n\nIn the following examples the top epilogo has the custom colors and the one below is shown inverted.\n\n```INI\n[epilogos]\nfile = epilog.qcat.bgz\nheight = 5\ntitle = epilogos with custom colors\ncategories_file = epilog_cats.json\n\n[epilogos inverted]\nfile = epilog.qcat.bgz\nheight = 5\ntitle = epilogos inverted\norientation = inverted\n\n[x-axis]\n```\n\n![epilogos example](./examples/epilogos_track2.png)\n\nExamples with multiple options\n------------------------------\n\nA comprehensive example of pyGenomeTracks can be found as part of our automatic testing.\nNote, that pyGenome tracks also allows the combination of multiple tracks into one using the parameter: `overlay previous=yes` or `overlay previous=share-y`.\nIn 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.\n\n![pyGenomeTracks example](./pygenometracks/tests/test_data/master_plot.png)\n\nThe configuration file for this image is [here](./pygenometracks/tests/test_data/browser_tracks.ini)\n\n\nExamples with multiple options for bigwig tracks\n------------------------------------------------\n\n![pyGenomeTracks example](./pygenometracks/tests/test_data/master_bigwig.png)\n\nThe configuration file for this image is [here](./pygenometracks/tests/test_data/bigwig.ini)\n\n\nExamples with Hi-C data\n-----------------------\n\nIn 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\nthat 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\nrequired when overlying other type of data like a bigwig file that has a different y-scale.\n\n![pyGenomeTracks example](./pygenometracks/tests/test_data/master_plot_hic.png)\n\nThe configuration file for this image is [here](./pygenometracks/tests/test_data/browser_tracks_hic.ini)\n\n\nAdding new tracks\n-----------------\nAdding new tracks to pyGenomeTracks only requires adding a new class to the `pygenometracks/tracks` folder.\nThe class should inherit the the `GenomeTrack` (or other track class available) and should have a `plot` method.\nAdditionally, some basic description should be added.\n\nFor example, to make a track that prints 'hello world' at a given location looks like this:\n\n```python\nclass TextTrack(GenomeTrack):\n    SUPPORTED_ENDINGS = ['.txt']  # this is used by make_tracks_file to guess the type of track based on file name\n    TRACK_TYPE = 'text'\n    OPTIONS_TXT = \"\"\"\nheight = 3\ntitle =\ntext =\n# x position of text in the plot (in bp)\nx position =\n\"\"\"\n    def plot(self, ax, chrom, region_start, region_end):\n        \"\"\"\n        This example simply plots the given title at a fixed\n        location in the axis. The chrom, region_start and region_end\n        variables are not used.\n        Args:\n            ax: matplotlib axis to plot\n            chrom_region: chromosome name\n            start_region: start coordinate of genomic position\n            end_region: end coordinate\n        \"\"\"\n        # print text at position x = self.properties['x position'] and y = 0.5 (center of the plot)\n        ax.text(float(self.properties['x position']), 0.5, self.properties['text'])\n\n```\n\nThe OPTIONS_TXT should contain the text to build a default configuration file.\nThis information, together with the information about SUPPORTED_ENDINGS is used\nby the program `make_tracks_file` to create a default configuration file\nbased on the endings of the files given.\n\nThe configuration file is:\n\n```INI\n[x-axis]\nwhere = top\n\n[new track]\nfile =\nheight = 4\ntitle = new pyGenomeTrack\nfile_type = text\ntext = hello world\nx position = 3100000\n```\n\n```bash\n# pgt is short for `pyGenomeTracks`\npgt --tracks new_track.ini --region X:3000000-3200000 -o new_track.png\n```\n\n![pyGenomeTracks example](./examples/new_track.png)\n\nNotice that the resulting track already includes a y-axis (to the left) and\na label to the right. This are the defaults that can be changed by\nadding a `plot_y_axis` and `plot_label` methods.\n\nAnother more complex example is the plotting of multiple bedgraph data as matrices. The output of `HiCExplorer hicFindTADs` produces a data format that\nis similar to a bedgraph but with more value columns. We call this a bedgraph matrix. The following track plot this bedgraph matrix:\n\n ```python\nimport numpy as np\nfrom . BedGraphTrack import BedGraphTrack\n\n class BedGraphMatrixTrack(BedGraphTrack):\n    # this track class extends a BedGraphTrack that is already part of\n    # pyGenomeTracks. The advantage of extending this class is that\n    # we can re-use the code for reading a bedgraph file\n    SUPPORTED_ENDINGS = ['.bm', '.bm.gz']\n    TRACK_TYPE = 'bedgraph_matrix'\n    OPTIONS_TXT = GenomeTrack.OPTIONS_TXT + \"\"\"\n        # a bedgraph matrix file is like a bedgraph, except that per bin there\n        # are more than one value (separated by tab). This file type is\n        # produced by the HiCExplorer tool hicFindTads and contains\n        # the TAD-separation score at different window sizes.\n        # E.g.\n        # chrX\t18279\t40131\t0.399113\t0.364118\t0.320857\t0.274307\n        # chrX\t40132\t54262\t0.479340\t0.425471\t0.366541\t0.324736\n        #min_value = 0.10\n        #max_value = 0.70\n        file_type = {}\n        \"\"\".format(TRACK_TYPE)\n\n    def plot(self, ax, chrom_region, start_region, end_region):\n        \"\"\"\n        Args:\n            ax: matplotlib axis to plot\n            chrom_region: chromosome name\n            start_region: start coordinate of genomic position\n            end_region: end coordinate\n        \"\"\"\n        start_pos = []\n        matrix_rows = []\n\n        # the BedGraphTrack already has methods to read files\n        # in which the first three columns are chrom, start,end\n        # here we used the interval_tree method inherited from the\n        # BedGraphTrack class\n        for region in sorted(self.interval_tree[chrom_region][start_region - 10000:end_region + 10000]):\n            start_pos.append(region.begin)\n            # the region.data contains all the values for a given region\n            # In the following code, such list is converted to floats and\n            # appended to a new list.\n            values = list(map(float, region.data))\n            matrix_rows.append(values)\n\n        # using numpy, the list of values per line in the bedgraph file\n        # is converted into a matrix whose columns contain\n        # the bedgraph values for the same line (notice that\n        # the matrix is transposed to achieve this)\n        matrix = np.vstack(matrix_rows).T\n\n        # using meshgrid we get x and y positions to plot the matrix at\n        # corresponding positions given in the bedgraph file.\n        x, y = np.meshgrid(start_pos, np.arange(matrix.shape[0]))\n\n        # shading adds some smoothing to the pllot\n        shading = 'gouraud'\n        vmax = self.properties['max_value']\n        vmin = self.properties['min_value']\n\n        img = ax.pcolormesh(x, y, matrix, vmin=vmin, vmax=vmax, shading=shading)\n        img.set_rasterized(True)\n\n\n    def plot_y_axis(self, ax, plot_axis):\n        \"\"\"turn off y_axis plot\"\n        pass\n ```\n\n\nLet's create a track for this:\n\n```INI\n[bedgraph matrix]\nfile = tad_separation_score.bm.gz\ntitle = bedgraph matrix\nheight = 8\nfile_type = bedgraph_matrix\n\n[spacer]\n\n[x-axis]\n```\n\n```bash\npgt --tracks bedgraph_matrix.ini --region X:2000000-3500000 -o bedgraph_matrix.png\n```\n\n![pyGenomeTracks example](./examples/bedgraph_matrix.png)\n\nAlthough this image looks interesting another way to plot\nthe data is a overlapping lines with the mean value highlighted.\nUsing the bedgraph version of `pyGenomeTracks` the following image\ncan be obtained:\n\n![pyGenomeTracks example](./examples/bedgraph_matrix_lines.png)\n\n\npyGenomeTracks is used by [HiCExporer](https://hicexplorer.readthedocs.io/) and [HiCBrowser](https://github.com/maxplanck-ie/HiCBrowser) (See e.g. [Chorogenome navigator](http://chorogenome.ie-freiburg.mpg.de/) which is made with HiCBrowser)\n\n\n",
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