.. raw:: html

<p align="center">
<img alt="DNA Features Viewer Logo" title="DNA Features Viewer Logo" src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/docs/_static/images/title.png" width="350">
</p>

DNA Features Viewer

.. image:: https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/actions/workflows/build.yml/badge.svg :target: https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/actions/workflows/build.yml :alt: GitHub CI build status

.. image:: https://coveralls.io/repos/github/Edinburgh-Genome-Foundry/DnaFeaturesViewer/badge.svg?branch=master :target: https://coveralls.io/github/Edinburgh-Genome-Foundry/DnaFeaturesViewer?branch=master

DNA Features Viewer (full documentation here <https://edinburgh-genome-foundry.github.io/DnaFeaturesViewer/>_) is a Python library to visualize DNA features, e.g. from GenBank or GFF files, or Biopython SeqRecords:

.. raw:: html

<p align="center">
<img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/graphic_record_defined_by_hand.png" width="500">
</p>

DNA Features Viewer automatically produce simple and clear plots even for sequences with many overlapping features and long labels. The libray plays well with Matplotlib and Biopython, and the plots can be output to many different formats (PNG, JPEG, SVG, PDF), e.g. for report generation, article figures, or LIMS interfaces.

Installation

If you have PIP installed, just type in a terminal:

.. code:: bash

pip install dna_features_viewer

DNA Features Viewer can be installed by unzipping the source code in one directory and using this command:

.. code:: bash

python setup.py install

If you intend to use the bokeh features, you need to also install Bokeh and Pandas:

.. code:: bash

pip install bokeh pandas

To parse GFF files, install the bcbio-gff library:

.. code:: bash

pip install bcbio-gff

Examples of use

Basic plots

In this first example we define features "by hand":

.. code:: python

    from dna_features_viewer import GraphicFeature, GraphicRecord
    features=[
        GraphicFeature(start=0, end=20, strand=+1, color="#ffd700",
                       label="Small feature"),
        GraphicFeature(start=20, end=500, strand=+1, color="#ffcccc",
                       label="Gene 1 with a very long name"),
        GraphicFeature(start=400, end=700, strand=-1, color="#cffccc",
                       label="Gene 2"),
        GraphicFeature(start=600, end=900, strand=+1, color="#ccccff",
                       label="Gene 3")
    ]
    record = GraphicRecord(sequence_length=1000, features=features)
    record.plot(figure_width=5)

.. raw:: html

    <p align="center">
    <img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/graphic_record_defined_by_hand.png" width="500">
    </p>


If we replace `GraphicRecord` by `CircularGraphicRecord` in the code above we obtain
a circular plot of the construct:

.. raw:: html

    <p align="center">
    <img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/graphic_record_defined_by_hand_circular.png" width="443">
    </p>

It is also possible to generate interactive (browser-based) plots by using ``plot_with_bokeh`` instead of ``plot``:

.. raw:: html

    <p align="center">
    <img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/plot_with_bokeh.png" width="800">
    </p>

Nucleotide sequences, translations, and cropping

DNA Features Viewer allows to plot nucleotide or amino acid sequences under the record plot:

.. code:: python

from dna_features_viewer import GraphicFeature, GraphicRecord

sequence = "ATGCATGCATGCATGCATGCATGCATGC"
record = GraphicRecord(sequence=sequence, features=[
    GraphicFeature(start=5, end=10, strand=+1, color='#ffcccc'),
    GraphicFeature(start=8, end=15, strand=+1, color='#ccccff')
])

ax, _ = record.plot(figure_width=5)
record.plot_sequence(ax)
record.plot_translation(ax, (8, 23), fontdict={'weight': 'bold'})
ax.figure.savefig('sequence_and_translation.png', bbox_inches='tight')

.. raw:: html

<p align="center">
<img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/sequence_and_translation.png" width="415">
</p>

This enables for instance to plot an overview of a sequence along with a detailed detail of a sequence subsegment (full code <https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/blob/master/examples/overview_and_detail.py>_)

.. code:: python

...
record.plot(ax=ax1)
cropped_record = record.crop((zoom_start, zoom_end))
cropped_record.plot(ax=ax2)
cropped_record.plot_sequence(ax=ax2)
cropped_record.plot_translation(ax=ax2, location=(408, 423))

.. raw:: html

<p align="center">
<img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/overview_and_detail.png" width="900">
</p>

Reading the features from a GenBank or GFF file

DnaFeaturesViewer plays nice with BioPython. As a result it is super easy to plot the content of a Biopython record, or directly a GenBank (or GFF) file:

.. code:: python

    from dna_features_viewer import BiopythonTranslator
    graphic_record = BiopythonTranslator().translate_record("my_sequence.gb")
    ax, _ = graphic_record.plot(figure_width=10, strand_in_label_threshold=7)

.. raw:: html

    <p align="center">
    <img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/from_genbank.png" width="900">
    </p>

Note 1: the script uses ``strand_in_label_threshold=7`` to indicate the strand with
an arrow in the annotation text for every feature less than ~7 pixels in width.

Note 2: the ``BiopythonTranslator`` class determines how the genbank information is
transformed into graphical features. It enables to chose which categories of
features to plot, the color of the different features.

Note 3: parsing GFF files requires the BCBio library
(``pip install bcbio-gff``). This library also enables to extract Biopython
records from GFF files containing several records (using ``GFF.parse("records.gff")``).



Displaying the features along with other plots
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

As it uses Matplotlib, DNA Features Viewer can display the features on top of
other sequences statistics, such as the local GC content:

.. code:: python

    import matplotlib.pyplot as plt
    from dna_features_viewer import BiopythonTranslator
    from Bio import SeqIO
    import numpy as np

    fig, (ax1, ax2) = plt.subplots(
        2, 1, figsize=(12, 3), sharex=True, gridspec_kw={"height_ratios": [4, 1]}
    )

    # PLOT THE RECORD MAP
    record = SeqIO.read("example_sequence.gb", "genbank")
    graphic_record = BiopythonTranslator().translate_record(record)
    graphic_record.plot(ax=ax1, with_ruler=False, strand_in_label_threshold=4)

    # PLOT THE LOCAL GC CONTENT (we use 50bp windows)
    gc = lambda s: 100.0 * len([c for c in s if c in "GC"]) / 50
    xx = np.arange(len(record.seq) - 50)
    yy = [gc(record.seq[x : x + 50]) for x in xx]
    ax2.fill_between(xx + 25, yy, alpha=0.3)
    ax2.set_ylim(bottom=0)
    ax2.set_ylabel("GC(%)")

.. raw:: html

    <p align="center">
    <img src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/with_gc_plot.png" width="800">
    </p>

Multi-line and multi-page plots
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Since v3.0 it is possible to plot a sequence over multiple lines (using ``record.plot_on_multiple_lines()``) or even on multiple pages (of a PDF):

.. code:: python

    graphic_record.plot_on_multiple_pages(
        "multipage_plot.pdf",
        nucl_per_line=70,
        lines_per_page=7,
        plot_sequence=True
    )

    
.. raw:: html

    <p align="center">
    <img alt="DNA Features Viewer Logo" title="DNA Features Viewer Logo" src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/docs/_static/images/multiline_example.png" width="900">
    </p>

Custom Biopython translators
----------------------------

DNA Features Viewer allows to define "themes" by using custom record translators
instead of the default ``BiopythonTranslator``. Here is an example:

.. code:: python

    from dna_features_viewer import BiopythonTranslator

    class MyCustomTranslator(BiopythonTranslator):
        """Custom translator implementing the following theme:

        - Color terminators in green, CDS in blue, all other features in gold.
        - Do not display features that are restriction sites unless they are BamHI
        - Do not display labels for restriction sites
        - For CDS labels just write "CDS here" instead of the name of the gene.

        """

        def compute_feature_color(self, feature):
            if feature.type == "CDS":
                return "blue"
            elif feature.type == "terminator":
                return "green"
            else:
                return "gold"

        def compute_feature_label(self, feature):
            if feature.type == 'restriction_site':
                return None
            elif feature.type == "CDS":
                return "CDS here"
            else:
                return BiopythonTranslator.compute_feature_label(self, feature)

        def compute_filtered_features(self, features):
            """Do not display promoters. Just because."""
            return [
                feature for feature in features
                if (feature.type != "restriction_site")
                or ("BamHI" in str(feature.qualifiers.get("label", '')))
            ]


    graphic_record = MyCustomTranslator().translate_record("example_sequence.gb")
    ax, _ = graphic_record.plot(figure_width=10)
    ax.figure.tight_layout()
    ax.figure.savefig("custom_bopython_translator.png")

.. figure:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer/master/examples/custom_biopython_translator.png
    :align: center

Examples in other packages:
---------------------------

DNA Chisel
~~~~~~~~~~~

This GIF uses DNA Features Viewer to plot the progress in the optimization of a DNA sequence with `DNA Chisel <https://github.com/Edinburgh-Genome-Foundry/DnaChisel>`_. It also uses `Proglog <https://github.com/Edinburgh-Genome-Foundry/Proglog>`_ to automatically generate a picture at different time points. See the not-so-great python code for this example `on Gist <https://gist.github.com/Zulko/f9aa781aaaab2c4d66ccd968ca85ca1d>`_.

.. raw:: html

    <p align="center">
    <img alt="DNA Chisel algorithm" title="DNA Chisel" src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/DnaChisel/master/docs/_static/images/dnachisel_algorithm.gif" width="800">
    <br />
    </p>

GeneBlocks
~~~~~~~~~~
`GeneBlocks <https://github.com/Edinburgh-Genome-Foundry/Geneblocks>`_ is a Python library which computes "diffs" of constructs two sequences, indicating all the changes (additions, deletions, mutations). It uses DNA Features Viewer to display these changesx along with the other sequence's features.

.. raw:: html

    <p align="center">
    <img alt="DNA Chisel algorithm" title="DNA Chisel" src="https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/GeneBlocks/master/examples/diff_blocks.png" width="800">
    <br />
    </p>



License = MIT
-------------

DNA Features Viewer is an open-source software originally written at the `Edinburgh Genome Foundry
<http://genomefoundry.org>`_ by `Zulko <https://github.com/Zulko>`_
and `released on Github <https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer>`_ under the MIT licence.
Everyone is welcome to contribute !

More biology software
---------------------

.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Edinburgh-Genome-Foundry.github.io/master/static/imgs/logos/egf-codon-horizontal.png
  :target: https://edinburgh-genome-foundry.github.io/

DNA Features Viewer is part of the `EGF Codons <https://edinburgh-genome-foundry.github.io/>`_ synthetic biology software suite for DNA design, manufacturing and validation.