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
Developed for analyzing the covariance for OrcVIO

about This repo is developed for analyzing the covariance for OrcVIO environment setup platform ubuntu 18.04 using conda conda env create --file envir

Sean 1 Dec 08, 2021
MotorcycleParts DataAnalysis python

We work with the accounting department of a company that sells motorcycle parts. The company operates three warehouses in a large metropolitan area.

NASEEM A P 1 Jan 12, 2022
The Spark Challenge Student Check-In/Out Tracking Script

The Spark Challenge Student Check-In/Out Tracking Script This Python Script uses the Student ID Database to match the entries with the ID Card Swipe a

1 Dec 09, 2021
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
Powerful, efficient particle trajectory analysis in scientific Python.

freud Overview The freud Python library provides a simple, flexible, powerful set of tools for analyzing trajectories obtained from molecular dynamics

Glotzer Group 195 Dec 20, 2022
Codes for the collection and predictive processing of bitcoin from the API of coinmarketcap

Codes for the collection and predictive processing of bitcoin from the API of coinmarketcap

Teo Calvo 5 Apr 26, 2022
In this tutorial, raster models of soil depth and soil water holding capacity for the United States will be sampled at random geographic coordinates within the state of Colorado.

Raster_Sampling_Demo (Resulting graph of this demo) Background Sampling values of a raster at specific geographic coordinates can be done with a numbe

2 Dec 13, 2022
Utilize data analytics skills to solve real-world business problems using Humana’s big data

Humana-Mays-2021-HealthCare-Analytics-Case-Competition- The goal of the project is to utilize data analytics skills to solve real-world business probl

Yongxian (Caroline) Lun 1 Dec 27, 2021
Data Analytics on Genomes and Genetics

Data Analytics performed on On genomes and Genetics dataset to predict genetic disorder and disorder subclass. DONE by TEAM SIGMA!

1 Jan 12, 2022
Produces a summary CSV report of an Amber Electric customer's energy consumption and cost data.

Amber Electric Usage Summary This is a command line tool that produces a summary CSV report of an Amber Electric customer's energy consumption and cos

Graham Lea 12 May 26, 2022
Analyzing Earth Observation (EO) data is complex and solutions often require custom tailored algorithms.

eo-grow Earth observation framework for scaled-up processing in Python. Analyzing Earth Observation (EO) data is complex and solutions often require c

Sentinel Hub 18 Dec 23, 2022
TextDescriptives - A Python library for calculating a large variety of statistics from text

A Python library for calculating a large variety of statistics from text(s) using spaCy v.3 pipeline components and extensions. TextDescriptives can be used to calculate several descriptive statistic

150 Dec 30, 2022
track your GitHub statistics

GitHub-Stalker track your github statistics 👀 features find new followers or unfollowers find who got a star on your project or remove stars find who

Bahadır Araz 34 Nov 18, 2022
This program analyzes a DNA sequence and outputs snippets of DNA that are likely to be protein-coding genes.

This program analyzes a DNA sequence and outputs snippets of DNA that are likely to be protein-coding genes.

1 Dec 28, 2021
ped-crash-techvol: Texas Ped Crash Tech Volume Pack

ped-crash-techvol: Texas Ped Crash Tech Volume Pack In conjunction with the Final Report "Identifying Risk Factors that Lead to Increase in Fatal Pede

Network Modeling Center; Center for Transportation Research; The University of Texas at Austin 2 Sep 28, 2022
Evaluation of a Monocular Eye Tracking Set-Up

Evaluation of a Monocular Eye Tracking Set-Up As part of my master thesis, I implemented a new state-of-the-art model that is based on the work of Che

Pascal 19 Dec 17, 2022
Analysis of a dataset of 10000 passwords to find common trends and mistakes people generally make while setting up a password.

Analysis of a dataset of 10000 passwords to find common trends and mistakes people generally make while setting up a password.

Aryan Raj 7 Sep 04, 2022
A distributed block-based data storage and compute engine

Nebula is an extremely-fast end-to-end interactive big data analytics solution. Nebula is designed as a high-performance columnar data storage and tabular OLAP engine.

Columns AI 131 Dec 26, 2022
Building house price data pipelines with Apache Beam and Spark on GCP

This project contains the process from building a web crawler to extract the raw data of house price to create ETL pipelines using Google Could Platform services.

1 Nov 22, 2021
Udacity-api-reporting-pipeline - Udacity api reporting pipeline

udacity-api-reporting-pipeline In this exercise, you'll use portions of each of

Fabio Barbazza 1 Feb 15, 2022