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An IgBLAST wrapper and parser
PyIR is a minimally-dependent high-speed wrapper for the IgBLAST immunoglobulin and T-cell analyzer. This is achieved through chunking the input data set and running IgBLAST single-core in parallel to better utilize modern multi-core and hyperthreaded processors.
PyIR has become an essential part of the Vanderbilt Vaccine Center workflow, and the requirements in the past few years has lead to the development of new features including:
PyIR is described at length in the BMC Bioinformatics article: PyIR: a scalable wrapper for processing billions of immunoglobulin and T cell receptor sequences using IgBLAST
Test files used for the BMC Bioinformatics manuscript can be found at: https://clonomatch.accre.vanderbilt.edu/pyirfiles/
Files pertaining to the manuscript High frequency of shared clonotypes in human B cell receptor repertoires and be found at: https://github.com/crowelab/PyIR/wiki/Files-for-Manuscripts
PyIR is installed with the pip software packager, but is not currently a part of the PyPI repository index. It can be manually downloaded and installed as followed:
This repository can be downloaded by selecting "Download ZIP" from the "Clone and Download" menu at the top right of this github page or by using git from command line:
git clone https://github.com/crowelab/PyIR
pip3 install crowelab_pyir
cd PyIR/
pip3 install --user .
cd PyIR/
sudo pip3 install .
pip3 uninstall crowelab_pyir
PyIR requires a set of BLAST germline databases to assign the VDJ germlines.
A snapshot of the IMGT/GENE-DB human immunome repertoire is included with PyIR, but users are recommended to build their own database to keep up with the newest germline definitions. A link to the full instructions from NCBI can be found here, or you can use PyIR's setup script to build the databases automatically:
#Builds databases in pyir library directory
pyir setup
#Builds databases in specified path
pyir setup -o path/
#Builds databases in global pyir library directory (use if installed with sudo pip3)
sudo pyir setup
Locate your local bin folder with PyIR and add it to your PATH variable. ~/.local/bin and /usr/local/bin are good places to start. If using scl or other virtual environments (such as conda) be sure to account for those when searching your directories.
Double-check that you've met all prerequisites to install IgBLAST, including GLIBC > 2.14 (which has caused issues with CentOS 6) and libuv (can be installed with "sudo apt install libuv1.dev")
Ensure that the version of pip used to install pyir is associated with the correct version of python you are attempting to run. This can also be an issue with virtual environments.
Instructions for installing PyIR with a VirtualBox container can be found in the wiki
#Default PyIR
pyir example.fasta
#PyIR with filtering
pyir example.fasta --enable_filter
#PyIR with custom BLAST database
pyir example.fasta -d [path_to_DB]
## Initialize PyIR and set example file for processing
from crowelab_pyir import PyIR
FILE = 'example.fasta'
pyirfiltered = PyIR(query=FILE, args=['--outfmt', 'dict', '--enable_filter'])
result = pyirfiltered.run()
#Prints size of Python returned dictionary
print(len(result))
## Initialize PyIR and set example file for processing
from crowelab_pyir import PyIR
FILE = 'example.fasta'
sv = {}
for key, entry in result.items():
v3j = entry['v_family'] + '_' + entry['j_family'] + '_' + entry['cdr3_aa']
if v3j not in sv:
sv[v3j] = 0
sv[v3j] += 1
for i,item in enumerate(sorted(sv.items(), key=lambda x: x[1], reverse=True)):
if i > 9:
break
v3j = item[0].split('_')
print('v:', v3j[0], 'j:', v3j[1], 'cdr3:', v3j[2], 'count:', item[1])
## Initialize PyIR and set example file for processing
from crowelab_pyir import PyIR
FILE = 'example.fasta'
pyirfile = PyIR(query=FILE)
result = pyirfile.run()
#Prints the output file
print(result)
## Initialize PyIR and set example file for processing
from crowelab_pyir import PyIR
FILE = 'example.fasta'
pyirfile = PyIR(query=FILE, args=['--outfmt', 'tsv'])
result = pyirfile.run()
#Prints the output file
print(result)
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.pyplot import figure
## Initialize PyIR and set example file for processing
from crowelab_pyir import PyIR
FILE = 'example.fasta'
#create PyIR API instance and return Python dictionary
pyirexample = PyIR(query=FILE, args=['--outfmt', 'dict', '--enable_filter'])
result = pyirexample.run()
cdr3lens = {}
total_reads = 0
#iterate over values returned by PyIR
for key, entry in result.items():
clen = entry['cdr3_aa_length']
#if the CDR3 length is not in the dictionary, add it
if int(clen) not in cdr3lens.keys():
cdr3lens[int(clen)] = 0
#increment appropriate dictionary value and total
cdr3lens[int(clen)] += 1
total_reads += 1
x = []
y = []
for xval in sorted(cdr3lens.keys()):
x.append(xval)
y.append(cdr3lens[xval]/total_reads)
fig, ax = plt.subplots(1 , 1, dpi=600, facecolor='None', edgecolor='None')
plt.bar(x, y, color="#a0814b")
fig.savefig("synth01_cdr3length_distribution.svg", bbox_inches='tight', pad_inches=0)
More examples can be found in the Wiki, such as creating a CDR3 Histogram and Installing PyIR in VirtualBox
Email pyir@vvcenter.org with any questions or open an issue on Github and we'll get back to you.
PyIR is distributed under the Creative Commons Attribution 4.0 International License
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