ProtFeat is protein feature extraction tool that utilizes POSSUM and iFeature.

Overview

Linux version made-with-python Python GitHub license Open Source Love svg1

Description:

ProtFeat is designed to extract the protein features by employing POSSUM and iFeature python-based tools. ProtFeat includes a total of 39 distinct protein feature extraction methods (protein descriptors) using 21 PSSM-based protein descriptors from POSSUM and 18 protein descriptors from iFeature.

POSSUM (Position-Specific Scoring matrix-based feature generator for machine learning), a versatile toolkit with an online web server that can generate 21 types of PSSM-based feature descriptors, thereby addressing a crucial need for bioinformaticians and computational biologists.

iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors.

Installation

ProtFeat is a python package for feature extracting from protein sequences written in Python 3.9. ProtFeat was developed and tested in Ubuntu 20.04 LTS. Please make sure that you have Anaconda installed on your computer and run the below commands to install requirements. Dependencies are available in requirements.txt file.

conda create -n protFeat_env python=3.9
conda activate protFeat_env

How to run ProtFeat to extract the protein features

Run the following commands in the given order:

To use ProtFeat as a python package:

pip install protFeat

Then, you may use protFeat as the following in python:

import protFeat
from protFeat.feature_extracter import extract_protein_feature, usage
usage()
extract_protein_feature(protein_feature, place_protein_id, input_folder, fasta_file_name)

For example,

extract_protein_feature("AAC", 1, "input_folder", "sample")

To use ProtFeat from terminal:

Clone the Git Repository.

git clone https://github.com/gozsari/ProtFeat

In terminal or command line navigate into protFeat folder.

cd ProtFeat

Install the requirements by the running the following command.

pip install -r requirements.txt

Altenatively you may run ProtFeat from the terminal as the following:

cd src
python protFeat_command_line.py --pf protein_feature --ppid place_protein_id --inpf input_folder --fname fasta_file_name

For example,

python protFeat_command_line.py --pf AAC --ppid 1 --inpf input_folder --fname sample

Explanation of Parameters

protein_feature: {string}, (default = 'aac_pssm'): one of the protein descriptors in POSSUM and iFeature.

POSSUM descriptors:

aac_pssm, d_fpssm, smoothed_pssm, ab_pssm, pssm_composition, rpm_pssm,
s_fpssm, dpc_pssm, k_separated_bigrams_pssm, eedp, tpc, edp, rpssm,
pse_pssm, dp_pssm, pssm_ac, pssm_cc, aadp_pssm, aatp, medp , or all_POSSUM

Note: all_POSSUM extracts the features of all (21) POSSUM protein descriptors.

iFeature descriptors:

AAC, PAAC, APAAC, DPC, GAAC, CKSAAP, CKSAAGP, GDPC, Moran, Geary,
NMBroto, CTDC, CTDD, CTDT, CTriad, KSCTriad, SOCNumber, QSOrder, or all_iFeature

Note: all_iFeature extracts the features of all (18) iFeature protein descriptors.

place_protein_id: {int}, (default = 1): It indicates the place of protein id in fasta header. e.g. fasta header: >sp|O27002|....|....|...., seperate the header wrt. '|' then >sp is in the zeroth position, protein id in the first(1) position.

input_folder: {string}, (default = 'input_folder'}: it is the path to the folder that contains the fasta file.

fasta_file_name: {string}, (default ='sample'): it is the name of the fasta file exclude the '.fasta' extension.

Input file

It must be in fasta format.

Output file

The extracted feature files will be located under feature_extraction_output folder with the name: fasta_file_name_protein_feature.txt (e.g. sample_AAC.txt).

The content of the output files:

  • The output file is tab-seperated.
  • Each row corresponds to the extracted features of the protein sequence.
  • The first column of each row is UniProtKB id of the proteins, the rest is extracted features of the protein sequence.

Tables of the available protein descriptors

Table 1: Protein descriptors obtained from the POSSUM tool.

Descriptor group Protein descriptor Number of dimensions
Row Transformations AAC-PSSM
D-FPSSM
smoothed-PSMM
AB-PSSM
PSSM-composition
RPM-PSSM
S-FPSSM
20
20
1000
400
400
400
400
Column Transformation DPC-PSSM
k-seperated-bigrams-PSSM                    
tri-gram-PSSM
EEDP
TPC
400
400
8000
4000
400
Mixture of row and column transformation EDP
RPSSM
Pre-PSSM
DP-PSSM
PSSM-AC
PSSM-CC
20
110
40
240
200
3800
Combination of above descriptors AADP-PSSSM
AATP
MEDP
420
420
420

Table 2: Protein descriptors obtained from the iFeature tool.
Descriptor group Protein descriptor Number of dimensions
Amino acid composition Amino acid composition (AAC)
Composition of k-spaced amino acid pairs (CKSAAP)
Dipeptide composition (DPC)
20
2400
400
Grouped amino acid composition Grouped amino acid composition (GAAC)
Composition of k-spaced amino acid group pairs (CKSAAGP)
Grouped dipeptide composition (GDPC)
5
150
25
Autocorrelation Moran (Moran)
Geary (Geary)
Normalized Moreau-Broto (NMBroto)
240
240
240
C/T/D Composition (CTDC)
Transition (CTDT)
Distribution (CTDD)
39
39
195
Conjoint triad Conjoint triad (CTriad)
Conjoint k-spaced triad (KSCTriad)
343
343*(k+1)
Quasi-sequence-order Sequence-order-coupling number (SOCNumber)
Quasi-sequence-order descriptors (QSOrder)
60
100
Pseudo-amino acid composition Pseudo-amino acid composition (PAAC)
Amphiphilic PAAC (APAAC)
50
80

License

MIT License

ProtFeat Copyright (C) 2020 CanSyL

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

You might also like...
An Open-Source Package for Neural Relation Extraction (NRE)

OpenNRE We have a DEMO website (http://opennre.thunlp.ai/). Try it out! OpenNRE is an open-source and extensible toolkit that provides a unified frame

SpikeX - SpaCy Pipes for Knowledge Extraction

SpikeX is a collection of pipes ready to be plugged in a spaCy pipeline. It aims to help in building knowledge extraction tools with almost-zero effort.

Datasets of Automatic Keyphrase Extraction

This repository contains 20 annotated datasets of Automatic Keyphrase Extraction made available by the research community. Following are the datasets and the original papers that proposed them. If you know more datasets, and want to contribute, please, notify me.

open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

中文开放信息抽取系统, open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

Code for "Generating Disentangled Arguments with Prompts: a Simple Event Extraction Framework that Works"

GDAP The code of paper "Code for "Generating Disentangled Arguments with Prompts: a Simple Event Extraction Framework that Works"" Event Datasets Prep

Utilizing RBERT model for KLUE Relation Extraction task

RBERT for Relation Extraction task for KLUE Project Description Relation Extraction task is one of the task of Korean Language Understanding Evaluatio

Spert NLP Relation Extraction API deployed with torchserve for inference

SpERT torchserve Spert_torchserve is the Relation Extraction model (SpERT)Span-based Entity and Relation Transformer API deployed with pytorch/serve.

Code to reproduce the results of the paper 'Towards Realistic Few-Shot Relation Extraction' (EMNLP 2021)

Realistic Few-Shot Relation Extraction This repository contains code to reproduce the results in the paper "Towards Realistic Few-Shot Relation Extrac

Contact Extraction with Question Answering.

contactsQA Extraction of contact entities from address blocks and imprints with Extractive Question Answering. Goal Input: Dr. Max Mustermann Hauptstr

Releases(protein-feature-extraction)
  • protein-feature-extraction(Apr 12, 2022)

    ProtFeat is designed to extract the protein features by employing POSSUM and iFeature python-based tools. ProtFeat includes 39 distinct protein feature extraction methods using 21 PSSM-based protein descriptors from POSSUM and 18 protein descriptors from iFeature.

    Source code(tar.gz)
    Source code(zip)
Owner
GOKHAN OZSARI
Research and Teaching Assistant, at CEng, METU
GOKHAN OZSARI
ProteinBERT is a universal protein language model pretrained on ~106M proteins from the UniRef90 dataset.

ProteinBERT is a universal protein language model pretrained on ~106M proteins from the UniRef90 dataset. Through its Python API, the pretrained model can be fine-tuned on any protein-related task in a matter of minutes. Based on our experiments with a wide range of benchmarks, ProteinBERT usually achieves state-of-the-art performance. ProteinBERT is built on TenforFlow/Keras.

null 241 Jan 4, 2023
Protein Language Model

ProteinLM We pretrain protein language model based on Megatron-LM framework, and then evaluate the pretrained model results on TAPE (Tasks Assessing P

THUDM 77 Dec 27, 2022
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

TextBlob: Simplified Text Processing Homepage: https://textblob.readthedocs.io/ TextBlob is a Python (2 and 3) library for processing textual data. It

Steven Loria 8.4k Dec 26, 2022
Python implementation of TextRank for phrase extraction and summarization of text documents

PyTextRank PyTextRank is a Python implementation of TextRank as a spaCy pipeline extension, used to: extract the top-ranked phrases from text document

derwen.ai 1.9k Jan 6, 2023
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

TextBlob: Simplified Text Processing Homepage: https://textblob.readthedocs.io/ TextBlob is a Python (2 and 3) library for processing textual data. It

Steven Loria 7.5k Feb 17, 2021
Python implementation of TextRank for phrase extraction and summarization of text documents

PyTextRank PyTextRank is a Python implementation of TextRank as a spaCy pipeline extension, used to: extract the top-ranked phrases from text document

derwen.ai 1.4k Feb 17, 2021
Blue Brain text mining toolbox for semantic search and structured information extraction

Blue Brain Search Source Code DOI Data & Models DOI Documentation Latest Release Python Versions License Build Status Static Typing Code Style Securit

The Blue Brain Project 29 Dec 1, 2022
Perform sentiment analysis and keyword extraction on Craigslist listings

craiglist-helper synopsis Perform sentiment analysis and keyword extraction on Craigslist listings Background I love Craigslist. I've found most of my

Mark Musil 1 Nov 8, 2021
BERT, LDA, and TFIDF based keyword extraction in Python

BERT, LDA, and TFIDF based keyword extraction in Python kwx is a toolkit for multilingual keyword extraction based on Google's BERT and Latent Dirichl

Andrew Tavis McAllister 41 Dec 27, 2022
An Open-Source Package for Neural Relation Extraction (NRE)

OpenNRE We have a DEMO website (http://opennre.thunlp.ai/). Try it out! OpenNRE is an open-source and extensible toolkit that provides a unified frame

THUNLP 3.9k Jan 3, 2023