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Plot features from DNA sequences (e.g. Genbank) with Python

Project Description

Dna Features Viewer is a Python library to (wait for it…) visualize DNA features, e.g. from GenBank or Gff files, using the plotting library Matplotlib:

Dna Features Viewer is meant to automatically produce simple and clear plots even for sequences with lots of overlapping features and long labels. The plots can be output to many different formats (PNG, JPEG, SVG, PDF), e.g. for report generation or LIMS interfaces.


Dna Features Viewer is an open-source software originally written at the Edinburgh Genome Foundry by Zulko and released on Github under the MIT licence. Everyone is welcome to contribute !


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

(sudo) pip install dna_features_viewer

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

sudo python install

Examples of use

Basic plots

In this first example we define features “by hand”:

from dna_features_viewer import GraphicFeature, GraphicRecord
    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)

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

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

Reading the features from a GenBank 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 file:

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

The class BiopythonTranslator 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.

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:

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=(8, 4), sharex=True)

# Parse the genbank file, plot annotations
record ="", "genbank")
graphic_record = BiopythonTranslator().translate_record(record)
ax, levels = graphic_record.plot()
graphic_record.plot(ax=ax1, with_ruler=False)

# Plot the local GC content
def plot_local_gc_content(record, window_size, ax):
    gc_content = lambda s: 100.0*len([c for c in s if c in "GC"]) / len(s)
    yy = [gc_content(record.seq[i:i+window_size])
          for i in range(len(record.seq)-window_size)]
    xx = np.arange(len(record.seq)-window_size)+25
    ax.fill_between(xx, yy, alpha=0.3)

plot_local_gc_content(record, window_size=50, ax=ax2)

# Resize the figure

Dna Features Viewer is pretty minimal in terms of features but easily extensible since it uses Matplotlib as a backend.

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
dna_features_viewer-0.1.3.tar.gz (168.2 kB) Copy SHA256 Checksum SHA256 Source May 29, 2017

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