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A Python-based Effective Feature Generation Tool from DNA, RNA, and Protein Sequences
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README.md

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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