PyFeat: A Python-based Effective Feature Generation Tool from DNA, RNA, and Protein Sequences

Authors: Rafsanjani Muhammod, Sajid Ahmed, Dewan Md Farid, Swakkhar Shatabda, Alok Sharma, and Abdollah Dehzangi

1. Download Package

1.1. Direct Download

We can directly download by clicking the link.

Note: The package will download in zip format (.zip) named PyFeat-master.zip.

or,

1.2. Clone a GitHub Repository (Optional)

Cloning a repository syncs it to our local machine (Example for Linux-based OS). After clone, we can add and edit files and then push and pull updates.

Clone over HTTPS: user@machine:~$ git clone https://github.com/mrzResearchArena/PyFeat.git
Clone over SSH: user@machine:~$ git clone git@github.com:mrzResearchArena/PyFeat.git

Note #1: If the clone was successful, a new sub-directory appears on our local drive. This directory has the same name (PyFeat) as the GitHub repository that we cloned.

Note #2: We can run any Linux-based command from any valid location or path, but by default, a command generally runs from /home/user/.

Note #2.1: user is the name of our computer but your computer name can be different (Example: /home/bioinformatics/).

2. Installation Process

2.1. Required Python Packages

Major (Generate Features):

Install: python (version >= 3.5)
Install: numpy (version >= 1.13.0)

Minor (Performance Measures):

Install: sklearn (version >= 0.19.0)
Install: pandas (version >= 0.21.0)
Install: matplotlib (version >= 2.1.0)

2.2. How to download

Using PIP: pip install <package name>

user@machine:~$ pip install scikit-learn

or,

Using anaconda environment: conda install <package name>

user@machine:~$ conda install scikit-learn

3. Working Procedure

Run command on your console or terminal.

3.1. Generate Features

3.1.1. Training Purpose

user@machine:~$ python main.py --sequenceType=DNA --fullDataset=1 --optimumDataset=1 --fasta=/home/user/PyFeat/Datasets/DNA/FASTA.txt --label=/home/user/PyFeat/Datasets/DNA/Labels.txt --kTuple=3 --kGap=5 --pseudoKNC=1 --zCurve=1 --gcContent=1 --cumulativeSkew=1 --atgcRatio=1 --monoMono=1 --monoDi=1 --monoTri=1 --diMono=1 --diDi=1 --diTri=1 --triMono=1 --triDi=1

or,

user@machine:~$ python main.py -seq=DNA -full=1 -optimum=1 -fa=/home/user/PyFeat/Datasets/DNA/FASTA.txt -la=/home/user/PyFeat/Datasets/DNA/Label.txt -ktuple=3 -kgap=5 -pseudo=1 -zcurve=1 -gc=1 -skew=1 -atgc=1 -f11=1 -f12=1 -f13=1 -f21=1 -f22=1 -f23=1 -f31=1 -f32=1

3.1.2. Evaluation Purpose

user@machine:~$ python main.py --sequenceType=Protein --testDataset=1 --fasta=/home/user/PyFeat/Datasets/Protein/independentFASTA.txt --label=/home/user/PyFeat/Datasets/Protein/independentLabel.txt --kTuple=3 --kGap=5 --pseudoKNC=1 --zCurve=1 --gcContent=1 --cumulativeSkew=1 --atgcRatio=1 --monoMono=1 --monoDi=1 --monoTri=1 --diMono=1 --diDi=1 --diTri=1 --triMono=1 --triDi=1

or,

user@machine:~$ python main.py -seq=Protein -test=1 -fa=/home/user/PyFeat/Datasets/Protein/independentFASTA.txt -la=/home/user/PyFeat/Datasets/Protein/independentLabel.txt -ktuple=3 -kgap=5 -pseudo=1 -zcurve=1 -gc=1 -skew=1 -atgc=1 -f11=1 -f12=1 -f13=1 -f21=1 -f22=1 -f23=1 -f31=1 -f32=1

[ Comment: The = sign is optional. ]

Note #1: It will generate a full dataset named fullDataset.csv (if -full=1 or, —fullDataset==1)

Note #2: It will generate a selected features dataset named optimumDataset.csv (if -optimum=1 or, —optimumDataset==1), and It will also track the selected features index.

Note #3: It will generate a full dataset named testDataset.csv (if -test=1 or, —testDataset==1) [ Especially for the independent (testing) dataset purpose. ] [ 3.1.2. ]

Note #4: The process will run smoothly for valid FASTA sequences and row-wise class label.

Table 1: Arguments Details for the Features Generation

Argument	Corresponding Optional Argument	Type	Default	Help
—sequenceType	-seq	string	—sequenceType=DNA	We can use DNA, RNA, and protein or prot as option; Case is not sensitive.
—fasta	-fa	string		Enter a UNIX-like path; Example: /home/user/FASTA.txt
—label	-la	string		Enter a UNIX-like path; Example: /home/user/Label.txt
—kGap	-kgap	integer	—kGap=5	The number of gaps ranging from 1 to 5 inclusive; Example: -kGap=5
—kTuple	-ktuple	integer	—kTuple=3	The number of nucleotides ranging from 1 to 3 inclusive; Example: -kTuple=3
—fullDataset	-full	integer	—fullDataset=0	Set —fullDataset=1, if we don’t want to save full dataset.
—testDataset	-test	integer	—testDataset=0	Set —testDataset=1, if we don’t want to save test dataset.
—optimumDataset	-optimum	integer	—optimumDataset=0	Set —optimumDataset=1, if we don’t want to save optimum dataset.
—pseudoKNC	-pseudo	integer	—pseudoKNC=0	Set —pseudoKNC=1, if we want to generate features.
—zCurve	-zcurve	integer	—zCurve=0	Set —zCurve=1, if we want to generate features.
—gcContent	-gc	integer	—gcContent=0	Set —gcContent=1, if we want to generate features.
—cumulativeSkew	-skew	integer	—cumulativeSkew=0	Set —cumulativeSkew=1, if we want to generate features.
—atgcRatio	-atgc	integer	—atgcRatio=0	Set —atgcRatio=1, if we want to generate features.
—monoMono	-f11	integer	—monoMono=0	Set —monoMono=1, if we want to generate features.
—monoDi	-f12	integer	—monoDi=0	Set —monoDi=1, if we want to generate features.
—monoTri	-f13	integer	—monoTri=0	Set —monoTri=1, if we want to generate features.
—diMono	-f21	integer	—diMono=0	Set —diMono=1, if we want to generate features.
—diDi	-f22	integer	—diDi=0	Set —diDi=1, if we want to generate features.
—diTri	-f23	integer	—diTri=0	Set —diTri=1, if we want to generate features.
—triMono	-f31	integer	—triMono=0	Set —triMono=1, if we want to generate features.
—triDi	-f32	integer	—triDi=0	Set —triDi=1, if we want to generate features.

Table 2: Feature Description

Feature Name	Feature Structure / Formula	Number of Features	Applicable
zCurve	x_axis = (A+G)-(C+T); y_axis = (A+C)-(G+T); z_axis = (A+T)-(G+C)	3 features for DNA/RNA	DNA, RNA
gcContent	( (G+C)/(A+C+G+T) ) x 100 %	1 features for DNA/RNA	DNA, RNA
atgcRatio	(A+T)/(G+C)	1 features for DNA/RNA	DNA, RNA
cumulativeSkew	gcSkew=(G-C)/(G+C); atSkew=(A-T)/(A+T)	2 features for DNA/RNA	DNA, RNA
pseudoKNC	X, XX, XXX	when —kTuple=3, 84 features for DNA/RNA and 8,420 features for protein	DNA, RNA, Protein
monoMonoKGap	X_X	when —kGap=1, 16 features for DNA/RNA and 400 features for protein	DNA, RNA, Protein
monoDiKGap	X_XX	when —kGap=1, 64 features for DNA/RNA and 8,000 features for protein	DNA, RNA, Protein
monoTriKGap	X_XXX	when —kGap=1, 256 features for DNA/RNA and 160,000 features for protein	DNA, RNA, Protein
diMonoKGap	XX_X	when —kGap=1, 64 features for DNA/RNA and 8,000 features for protein	DNA, RNA, Protein
diDiKGap	XX_XX	when —kGap=1, 256 features for DNA/RNA and 160,000 features for protein	DNA, RNA, Protein
diTriKGap	XX_XXX	when —kGap=1, 1024 features for DNA/RNA and 3,200,000 features for protein	DNA, RNA, Protein
triMonoKGap	XXX_X	when —kGap=1, 256 features for DNA/RNA and 160,000 features for protein	DNA, RNA, Protein
triDiKGap	XXX_XX	when —kGap=1, 1024 features for DNA/RNA and 3,200,000 features for protein	DNA, RNA, Protein

Note: When sequence becomes DNA, RNA, and Protein then X = {A,C,G,T}, X = {A,C,G,U}, and
X = {A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y} respectively.

Arguments Details for the Features Generation and Feature Description are provided in Tables 1 and Table 2, respectively.

3.2. Run Machine Learning Classifiers (Optional)

user@machine:~$ python runClassifiers.py --nFCV=10 --dataset=optimumDataset.csv --auROC=1 --boxPlot=1

Note #1: It will provide classification results (evaluationResults.txt) from the user provides binary class dataset (.csv format).

Note #2: Generate a ROC Curve (auROC.png).

Note #3: Generate an accuracy comparison via boxPlot (AccuracyBoxPlot.png).

Table 3: Arguments Details for the Machine Learning Classifiers

Argument	Corresponding Optional Argument	Type	Default	Help
—nFCV	-cv	integer	—nFCV=10	How many numbers of cross-validation?
—dataset	-data	string	—dataset=optimumDataset.csv	Enter a UNIX-like path for a .csv file; Example: /home/User/dataset.csv
—auROC	-roc	integer	—auROC=1	Set —auROC=0, if we didn’t want to generate the ROC Curve.
—boxPlot	-box	integer	—boxPlot=1	Set —boxPlot=0, if we didn’t want to generate the accuracy box-plot.

3.3. Training Model (Optional)

user@machine:~$ python trainModel.py --dataset=optimumDataset.csv --model=LR

Note #1: It will provide a dumpModel.pkl from the user provides binary class dataset (.csv format).

Table 4: Arguments Details for Training Model

Argument	Corresponding Optional Argument	Type	Default	Help
—dataset	-data	string	—dataset=optimumDataset.csv	Enter a UNIX-like path for a .csv file; Example: /home/User/dataset.csv
—model	-m	string	—model=LR	We can use LR, SVM, KNN, DT, SVM, NB, Bagging, RF, AB, GB, and LDA as an option; All options are case sensitive.
—K	-k	integer	—K=5	Only for the KNN classifier; Number of neighbor

Note: LR, SVM, KNN, DT, NB, Bagging, RF, AB, GB, and LDA represents Logistics Regression, Support Vector Machine, k-Nearest Neighbor, Decision Tree, Naive Bayes, Bagging, Random Forest, AdaBoost, Gradient Boosting, Linear Discriminant Analysis classifier respectively.

3.4. Evaluation Model (Optional)

user@machine:~$ python evaluateModel.py --optimumDatasetPath=optimumDataset.csv --testDatasetPath=testDataset.csv

Note #1: Here, optimumDataset.csv, and testDataset.csv using as a traing dataset and test dataset respectively.

Table 5: Arguments Details for Evaluation Model

Argument	Corresponding Optional Argument	Type	Default	Help
—optimumDatasetPath	-optimumPath	string	—optimumDatasetPath=optimumDataset.csv	Enter a UNIX-like path for a .csv file; Example: /home/User/dataset.csv
—testDatasetPath	-testPath	string	—testDatasetPath=testDataset.csv	Enter a UNIX-like path for a .csv file; Example: /home/User/dataset.csv

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