peptides.py is a pure-Python package to compute common descriptors for protein sequences

Related tags

Data Analysispeptides.py
Overview

peptides.py Stars

Physicochemical properties and indices for amino-acid sequences.

Actions Coverage PyPI Wheel Python Versions Python Implementations License Source Mirror GitHub issues Changelog Downloads

🗺️ Overview

peptides.py is a pure-Python package to compute common descriptors for protein sequences. It is a port of Peptides, the R package written by Daniel Osorio for the same purpose. This library has no external dependency and is available for all modern Python versions (3.6+).

🔧 Installing

Install the peptides package directly from PyPi which hosts universal wheels that can be installed with pip:

$ pip install peptides

💡 Example

Start by creating a Peptide object from a protein sequence:

>>> import peptides
>>> peptide = peptides.Peptide("MLKKRFLGALAVATLLTLSFGTPVMAQSGSAVFTNEGVTPFAISYPGGGT")

Then use the appropriate methods to compute the descriptors you want:

>>> peptide.aliphatic_index()
89.8...
>>> peptide.boman()
-0.2097...
>>> peptide.charge(pH=7.4)
1.99199...
>>> peptide.isoelectric_point()
10.2436...

Methods that return more than one scalar value (for instance, Peptide.blosum_indices) will return a dedicated named tuple:

>>> peptide.ms_whim_scores()
MSWHIMScores(mswhim1=-0.436399..., mswhim2=0.4916..., mswhim3=-0.49200...)

Use the Peptide.descriptors method to get a dictionary with every available descriptor. This makes it very easy to create a pandas.DataFrame with descriptors for several protein sequences:

>> df = pandas.DataFrame([ peptides.Peptide(s).descriptors() for s in seqs ]) >>> df BLOSUM1 BLOSUM2 BLOSUM3 BLOSUM4 ... Z2 Z3 Z4 Z5 0 0.367000 -0.436000 -0.239 0.014500 ... -0.711000 -0.104500 -1.486500 0.429500 1 -0.697500 -0.372500 -0.493 0.157000 ... -0.307500 -0.627500 -0.450500 0.362000 2 0.479333 -0.001333 0.138 0.228667 ... -0.299333 0.465333 -0.976667 0.023333 [3 rows x 66 columns] "> 
>>> seqs = ["SDKEVDEVDAALSDLEITLE", "ARQQNLFINFCLILIFLLLI", "EGVNDNECEGFFSAR"]
>>> df = pandas.DataFrame([ peptides.Peptide(s).descriptors() for s in seqs ])
>>> df
    BLOSUM1   BLOSUM2  BLOSUM3   BLOSUM4  ...        Z2        Z3        Z4        Z5
0  0.367000 -0.436000   -0.239  0.014500  ... -0.711000 -0.104500 -1.486500  0.429500
1 -0.697500 -0.372500   -0.493  0.157000  ... -0.307500 -0.627500 -0.450500  0.362000
2  0.479333 -0.001333    0.138  0.228667  ... -0.299333  0.465333 -0.976667  0.023333

[3 rows x 66 columns]

💭 Feedback

⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.

🏗️ Contributing

Contributions are more than welcome! See CONTRIBUTING.md for more details.

⚖️ License

This library is provided under the GNU General Public License v3.0. The original R Peptides package was written by Daniel Osorio, Paola Rondón-Villarreal and Rodrigo Torres, and is licensed under the terms of the GPLv2.

This project is in no way not affiliated, sponsored, or otherwise endorsed by the original Peptides authors. It was developed by Martin Larralde during his PhD project at the European Molecular Biology Laboratory in the Zeller team.

You might also like...
Python Package for DataHerb: create, search, and load datasets.
Python Package for DataHerb: create, search, and load datasets.

The Python Package for DataHerb A DataHerb Core Service to Create and Load Datasets.

DataHerb 4 Feb 11, 2022
wikirepo is a Python package that provides a framework to easily source and leverage standardized Wikidata information
wikirepo is a Python package that provides a framework to easily source and leverage standardized Wikidata information

Python based Wikidata framework for easy dataframe extraction wikirepo is a Python package that provides a framework to easily source and leverage sta

Andrew Tavis McAllister 35 Jan 4, 2023
Python package for processing UC module spectral data.

UC Module Python Package How To Install clone repo. cd UC-module pip install . How to Use uc.module.UC(measurment=str, dark=str, reference=str, heade

Nicolai Haaber Junge 1 Oct 20, 2021
sportsdataverse python package
sportsdataverse python package

sportsdataverse-py See CHANGELOG.md for details. The goal of sportsdataverse-py is to provide the community with a python package for working with spo

Saiem Gilani 37 Dec 27, 2022
PyEmits, a python package for easy manipulation in time-series data.
PyEmits, a python package for easy manipulation in time-series data.

PyEmits, a python package for easy manipulation in time-series data. Time-series data is very common in real life. Engineering FSI industry (Financial

Thompson 5 Sep 23, 2022
Retail-Sim is python package to easily create synthetic dataset of retaile store.

Retailer's Sale Data Simulation Retail-Sim is python package to easily create synthetic dataset of retaile store. Simulation Model Simulator consists

Corca AI 7 Sep 30, 2022
A python package which can be pip installed to perform statistics and visualize binomial and gaussian distributions of the dataset

GBiStat package A python package to assist programmers with data analysis. This package could be used to plot : Binomial Distribution of the dataset p

Rishikesh S 4 Oct 17, 2022
VevestaX is an open source Python package for ML Engineers and Data Scientists.
VevestaX is an open source Python package for ML Engineers and Data Scientists.

VevestaX Track failed and successful experiments as well as features. VevestaX is an open source Python package for ML Engineers and Data Scientists.

Vevesta 24 Dec 14, 2022
nrgpy is the Python package for processing NRG Data Files

nrgpy nrgpy is the Python package for processing NRG Data Files Website and source: https://github.com/nrgpy/nrgpy Documentation: https://nrgpy.github

NRG Tech Services 23 Dec 8, 2022
Comments
  • Per-residue data

    Per-residue data

    It seems that the API can only output single statistics for the entire peptide chain, but I'm interested in statistics for each residue individually. I'm wondering if it might be possible to output an array/list from some of these functions instead of always averaging them as is done now.

    enhancement 
    opened by multimeric 1
  • Hydrophobic moment is inconsistent with R version

    Hydrophobic moment is inconsistent with R version

    Computed hydrophobic moment is not the same as the one computed by R. More specifically, it seems that peptides.py always outputs 0 for the hydrophobic moment when peptide length is shorter than the set window. The returned value matches the value from R when peptide length is equal to or greater than the set window length.

    Example in python:

    >>> import peptides`
>>> peptides.Peptide("MLK").hydrophobic_moment(window=5, angle=100)
0.0
>>> peptides.Peptide("AACQ").hydrophobic_moment(window=5, angle=100)
0.0
>>> peptides.Peptide("FGGIQ").hydrophobic_moment(window=5, angle=100)
0.31847187610377536

    Example in R:

    > library(Peptides)
> hmoment(seq="MLK", window=5, angle=100)
[1] 0.8099386
> hmoment(seq="AACQ", window=5, angle=100)
[1] 0.3152961
> hmoment(seq="FGGIQ", window=5, angle=100)
[1] 0.3184719

    I think that it can be easily fixed by internally setting the window length to the length of the peptide if the latter is shorter. What I propose:

    --- a/peptides/__init__.py
+++ b/peptides/__init__.py
@@ -657,6 +657,7 @@ class Peptide(typing.Sequence[str]):
               :doi:`10.1073/pnas.81.1.140`. :pmid:`6582470`.

         """
+        window = min(window, len(self))
         scale = tables.HYDROPHOBICITY["Eisenberg"]
         lut = [scale.get(aa, 0.0) for aa in self._CODE1]
         angles = [(angle * i) % 360 for i in range(window)]
    bug 
    opened by eotovic 1
  • RuntimeWarning in auto_correlation function()

    RuntimeWarning in auto_correlation function()

    Hi, thank you for creating peptides.py.

    Some hydrophobicity tables together with certain proteins cause a runtime warning for in the function auto_correlation():

    import peptides

for hydro in peptides.tables.HYDROPHOBICITY.keys():
    print(hydro)
    table = peptides.tables.HYDROPHOBICITY[hydro]
    peptides.Peptide('MANTQNISIWWWAR').auto_correlation(table)

    Warning (s2 == 0):

    RuntimeWarning: invalid value encountered in double_scalars
  return s1 / s2

    The tables concerned are: octanolScale_pH2, interfaceScale_pH2, oiScale_pH2 Some other proteins causing the same warning: ['MSYGGSCAGFGGGFALLIVLFILLIIIGCSCWGGGGYGY', 'MFILLIIIGASCFGGGGGCGYGGYGGYAGGYGGYCC', 'MSFGGSCAGFGGGFALLIVLFILLIIIGCSCWGGGGGF']

    opened by jhahnfeld 0
Releases(v0.3.1)

  • v0.3.1(Sep 1, 2022)

  • v0.3.0(Sep 1, 2022)

    Added

    • Peptide.linker_preference_profile to build a profile like used in the DomCut method from Suyama & Ohara (2002).
    • Peptide.profile to build a generic per-residue profile from a data table (#3).
    Source code(tar.gz)
    Source code(zip)

  • v0.2.0(Oct 25, 2021)

    Added

    • Peptide.counts method to get the number of occurences of each amino acid in the peptide.
    • Peptide.frequencies to get the frequencies of each amino acid in the peptide.
    • Peptide.pcp_descriptors to compute the PCP descriptors from Mathura & Braun (2001).
    • Peptide.sneath_vectors to compute the descriptors from Sneath (1966).
    • Hydrophilicity descriptors from Barley (2018).
    • Peptide.structural_class to predict the structural class of a protein using one of three reference datasets and one of four distance metrics.

    Changed

    • Peptide.aliphatic_index now supports unknown Leu/Ile residue (code J).
    • Swap order of Peptide.hydrophobic_moment arguments for consistency with profile methods.
    • Some Peptide functions now support vectorized code using numpy if available.
    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(Oct 21, 2021)

Owner
Martin Larralde
PhD candidate in Bioinformatics, passionate about programming, Pythonista, Rustacean. I write poems, and sometimes they are executable.
Martin Larralde
GitHub Repository
Deep universal probabilistic programming with Python and PyTorch

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

7.7k Dec 30, 2022
Galvanalyser is a system for automatically storing data generated by battery cycling machines in a database

Galvanalyser is a system for automatically storing data generated by battery cycling machines in a database, using a set of "harvesters", whose job it

Battery Intelligence Lab 20 Sep 28, 2022
Demonstrate a Dataflow pipeline that saves data from an API into BigQuery table

Overview dataflow-mvp provides a basic example pipeline that pulls data from an API and writes it to a BigQuery table using GCP's Dataflow (i.e., Apac

Chris Carbonell 1 Dec 03, 2021
VevestaX is an open source Python package for ML Engineers and Data Scientists.

VevestaX Track failed and successful experiments as well as features. VevestaX is an open source Python package for ML Engineers and Data Scientists.

Vevesta 24 Dec 14, 2022
First steps with Python in Life Sciences

First steps with Python in Life Sciences This course material is part of the "First Steps with Python in Life Science" three-day course of SIB-trainin

SIB Swiss Institute of Bioinformatics 22 Jan 08, 2023
This creates a ohlc timeseries from downloaded CSV files from NSE India website and makes a SQLite database for your research.

NSE-timeseries-form-CSV-file-creator-and-SQL-appender- This creates a ohlc timeseries from downloaded CSV files from National Stock Exchange India (NS

PILLAI, Amal 1 Oct 02, 2022
Project under the certification "Data Analysis with Python" on FreeCodeCamp

Sea Level Predictor Assignment You will anaylize a dataset of the global average sea level change since 1880. You will use the data to predict the sea

Bhavya Gopal 3 Jan 31, 2022
This is an example of how to automate Ridit Analysis for a dataset with large amount of questions and many item attributes

This is an example of how to automate Ridit Analysis for a dataset with large amount of questions and many item attributes

Ishan Hegde 1 Nov 17, 2021
Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks

The following Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks (MOFs). The training set is extracted from the Cambridge S

1 Jan 09, 2022
This is a python script to navigate and extract the FSD50K dataset

FSD50K navigator This is a script I use to navigate the sound dataset from FSK50K.

sweemeng 2 Nov 23, 2021
BinTuner is a cost-efficient auto-tuning framework, which can deliver a near-optimal binary code that reveals much more differences than -Ox settings.

BinTuner is a cost-efficient auto-tuning framework, which can deliver a near-optimal binary code that reveals much more differences than -Ox settings. it also can assist the binary code analysis rese

BinTuner 42 Dec 16, 2022
Python for Data Analysis, 2nd Edition

Python for Data Analysis, 2nd Edition Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media Buy

Wes McKinney 18.6k Jan 08, 2023
This repo contains a simple but effective tool made using python which can be used for quality control in statistical approach.

📈 Statistical Quality Control 📉 This repo contains a simple but effective tool made using python which can be used for quality control in statistica

SasiVatsal 8 Oct 18, 2022
Exploratory Data Analysis of the 2019 Indian General Elections using a dataset from Kaggle.

2019-indian-election-eda Exploratory Data Analysis of the 2019 Indian General Elections using a dataset from Kaggle. This project is a part of the Cou

Souradeep Banerjee 5 Oct 10, 2022
A Streamlit web-app for a data-science project that aims to evaluate if the answer to a question is helpful.

How useful is the aswer? A Streamlit web-app for a data-science project that aims to evaluate if the answer to a question is helpful. If you want to l

1 Dec 17, 2021
This repo contains a simple but effective tool made using python which can be used for quality control in statistical approach.

This repo contains a powerful tool made using python which is used to visualize, analyse and finally assess the quality of the product depending upon the given observations

SasiVatsal 8 Oct 18, 2022
LynxKite: a complete graph data science platform for very large graphs and other datasets.

LynxKite is a complete graph data science platform for very large graphs and other datasets. It seamlessly combines the benefits of a friendly graphical interface and a powerful Python API.

124 Dec 14, 2022
Single-Cell Analysis in Python. Scales to >1M cells.

Scanpy – Single-Cell Analysis in Python Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It inc

Theis Lab 1.4k Jan 05, 2023
Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.

Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.

2 Nov 20, 2021
A Pythonic introduction to methods for scaling your data science and machine learning work to larger datasets and larger models, using the tools and APIs you know and love from the PyData stack (such as numpy, pandas, and scikit-learn).

This tutorial's purpose is to introduce Pythonistas to methods for scaling their data science and machine learning work to larger datasets and larger models, using the tools and APIs they know and lo

Coiled 102 Nov 10, 2022