iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

Introduction

iLearnPlus is the first machine-learning platform with both graphical- and web-based user interface that enables the construction of automated machine-learning pipelines for computational analysis and predictions using nucleic acid and protein sequences. iLearnPlus integrates 21 machine-learning algorithms (including 12 conventional classification algorithms, two ensemble-learning frameworks and seven deep-learning approaches) and 19 major sequence encoding schemes (in total 147 feature descriptors), outnumbering all the current web servers and stand-alone tools for biological sequence analysis, to the best of our knowledge. In addition, the friendly GUI (Graphical User Interface) of iLearnPlus is available to biologists to conduct their analyses smoothly, significantly increasing the effectiveness and user experience compared to the existing pipelines. iLearnPlus is an open-source platform for academic purposes and is available at https://github.com/Superzchen/iLearnPlus/. The iLearnPlus-Basic module is online accessible at http://ilearnplus.erc.monash.edu/.

Methods

Four major modules, including iLearnPlus-Basic, iLearnPlus-Estimator, iLearnPlus-AutoML, and iLearnPlus-LoadModel, are provided in iLearnPlus for biologists and bioinformaticians to conduct customizable sequence-based feature engineering and analysis, machine-learning algorithm construction, performance assessment, statistical analysis, and data visualization, without additional programming.

Running environment

iLearnPlus is an open-source Python-based toolkit, which operates in the Python environment (Python version 3.6 or above) and can run on multiple operating systems (such as Windows, Mac and Linux). Prior to installing and running iLearnPlus, all the dependencies should be installed in the Python environment, including sys, os, re, PyQt5, qdarkstyle, numpy (1.18.5), pandas (1.0.5), threading, sip, datetime, platform, pickle, copy, scikit-learn (0.23.1), math, scipy (1.5.0), collections, itertools, torch (≥1.3.1), lightgbm (2.3.1), xgboost (1.0.2), matplotlib (3.1.1), seaborn, joblib, warnings, random, multiprocessing and time. For convenience, we strongly recommended users to install the Anaconda Python environment in your local computer. The software can be freely downloaded from https://www.anaconda.com/.

Installation

Method 1

• Step 1. Download and install the anaconda platform.

Download from: https://www.anaconda.com/products/individual

• Step 2. Install PyTorch:

Please refer to https://pytorch.org/get-started/locally/ for PyTorch installation.

• Step 3. Install iLearnPlus through pip3:

pip3 install ilearnplus

• Step 4. run iLearnPlus:

$ python
>>> from ilearnplus import runiLearnPlus
>>> runiLearnPlus()

Method 2

• Download iLearnPlus by

git clone https://github.com/Superzchen/iLearnPlus

• Step 1. Download and install the anaconda platform.

Download from: https://www.anaconda.com/products/individual

• Step 2. Install PyTorch:

Please refer to https://pytorch.org/get-started/locally/ for PyTorch installation.

• Step 3. Install lightgbm, xgboost and qdarkstyle:

pip3 install lightgbm
pip3 install xgboost
pip3 install qdarkstyle  

• Step 4. run iLearnPlus: cd to the iLearnPlus folder which contains iLearnPlus.py and run the ‘iLearnPlus.py’ script as follows:

python iLearnPlus.py

Guidance to use :

Please refer to iLearnPlus manual for detailed usage.

iLearnPlus interfaces:

iLearnPlus main interface:

iLearnPlus-Basic module interface:

The iLearnPlus-Basic module facilities analysis and prediction using a selected feature-based representation of the input protein/RNA/DNA sequences (sequence descriptors) and a selected machine-learning classifier. This module is particularly instrumental when interrogating the impact of using different sequence feature descriptors and machine-learning algorithms on the predictive performance.

iLearnPlus-Estimator module interface:

The iLearnPlus-Estimator module provides a flexible way to perform feature extraction by allowing users to select multiple feature descriptors.

iLearnPlus-AutoML module interface:

The iLearnPlus-AutoML module focuses on automated benchmarking and maximization of the predictive performance across different machine-learning classifiers that are applied on the same set or combined sets of feature descriptors.

iLearnPlus-LoadModel module interface:

The iLearnPlus-LoadModel module allows users to upload, deploy and test their models.

iLearnPlus Data visualization:

Application example

Identification of lysine crotonylation site

Estimate the performance of multiple feature descriptors using iLearnPlus-Estimator module

Here, the iLearnPlus-Estimator module was used to comparatively assess the performance of different feature sets. We used the iLearnPlus-Estimator module in the standalone GUI version to load the data, produced seven feature sets (AAC, EAAC, EGAAC, DDE, binary, ZScale, and BLOSUM) and selected a machine-learning algorithm, the random forest algorithm (with the default setting of 1000 trees) to construct the classifier via 10-fold cross-validation. This analysis reveals that the model built utilizing the EGAAC feature descriptors achieved the best performance.

Estimate the performance multiple ML algorithms using iLearnPlus-AutoML module

Then, the iLearnPlus-AutoML module to comparatively evaluate the predictive performance across seven machine-learning algorithms: SGD, LR, XGBoost, LightGBM, RF, MLP, and CNN. We used the bootstrap tests to assess statistical significance of the differences between the ROC curves produced by these algorithms.

The result shows that the deep-learning model, CNN, achieved the best predictive performance among all the seven machine-learning algorithms, with Acc=85.4% and AUC=0.823.

Dataset download

The lysine crotonylation datasets can be downloaded here.

iLearnPlus makes it easy and straightforward to design and optimize machine-learning pipelines to achieve a competitive (if not the best) predictive performance.

Citation

If you find iLearnPlus useful, please kindly cite the following paper:

Zhen Chen, Pei Zhao, Fuyi Li, André Leier, Tatiana T Marquez-Lago, Yanan Wang, Geoffrey I Webb, A Ian Smith, Roger J Daly*, Kuo-Chen Chou*, Jiangning Song*, iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics, 2018, 34(14): 2499–2502. https://doi.org/10.1093/bioinformatics/bty140

Zhen Chen, Pei Zhao, Fuyi Li, Tatiana T Marquez-Lago, André Leier, Jerico Revote, Yan Zhu, David R Powell, Tatsuya Akutsu, Geoffrey I Webb, Kuo-Chen Chou, A Ian Smith, Roger J Daly, Jian Li, Jiangning Song*, iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data. Briefings in Bioinformatics, 2020, 21(3): 1047–1057. https://doi.org/10.1093/bib/bbz041

Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey I Webb, Quanzhi Zhao*, Lukasz Kurgan*, Jiangning Song*, iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization. Nucleic Acids Research , 2021;, gkab122, https://doi.org/10.1093/nar/gkab122