pip install mhctools==1.8.1

Python interface to running command-line and web-based MHC binding predictors

Source
Among top 50% packages on PyPI.
Over 4.2K downloads in the last 90 days.

Commonly used with mhctools

Based on how often these packages appear together in public requirements.txt files on GitHub.

mhcnames

Python library for MHC nomenclature parsing

varcode

Variant annotation in Python

serializable

Base class with serialization helpers for user-defined Python objects

pyensembl

Python interface to ensembl reference genome metadata

isovar

Determine mutant protein sequences from RN using assembly around variants

typechecks

Helper functions for runtime type checking

datacache

Helpers for transparently downloading datasets

sercol

Rich collection class with grouping and filtering helpers

tinytimer

Tiny Python benchmarking library

cfchecker

The NetCDF Climate Forecast Conventions compliance checker

pyviz

How to solve visualization problems with Python tools.

naive-bayes

Naive Bayes Text Classification

mhcflurry

MHC Binding Predictor

pyanyzip

pyanyzip is a module to help with dealing with compressed files transparently

pandas

Powerful data structures for data analysis, time series, and statistics

abydos

Abydos NLP/IR library

numerapi

Automatically download and upload data for the Numerai machine learning competition

matplotlib-scalebar

Artist for matplotlib to display a scale bar

negspy

Python NGS tools

Version usage of mhctools

Proportion of downloaded versions in the last 3 months (only versions over 1%).

1.8.1

15.13%

1.8.0

2.74%

1.7.1

2.12%

0.3.1

1.98%

1.7.0

1.94%

1.6.20

1.70%

1.6.18

1.70%

1.6.23

1.70%

1.6.19

1.68%

1.6.13

1.68%

1.6.16

1.68%

1.6.22

1.65%

1.6.6

1.65%

1.6.21

1.65%

1.6.17

1.63%

1.4.0

1.63%

1.6.8

1.63%

1.6.10

1.63%

0.1.1

1.60%

0.5.0

1.60%

1.0.2

1.60%

1.6.4

1.60%

1.6.15

1.60%

1.6.2

1.60%

1.1.0

1.58%

1.0.1

1.58%

0.4.0

1.58%

1.3.0

1.58%

1.6.5

1.58%

0.2.2

1.58%

0.2.0

1.56%

1.6.1

1.56%

0.1.8

1.56%

1.6.3

1.56%

1.5.0

1.56%

0.1.0

1.56%

0.0.4

1.56%

0.0.5

1.56%

1.6.0

1.56%

0.1.3

1.56%

0.1.2

1.53%

0.3.0

1.53%

1.2.0

1.53%

0.0.6

1.53%

0.0.11

1.53%

0.2.3

1.53%

0.1.4

1.53%

1.0.0

1.51%

0.4.1

1.51%

0.2.1

1.51%

0.1.7

1.51%

0.1.6

1.51%

0.1.5

1.51%