mrzResearchArena / iDNAbind-Pro

Identification of DNA-binding protein

iDNAbind-Pro: Identification of DNA-binding protein

 

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 iDNAbind-Pro-master.zip.

or,

1.2. Clone a GitHub Repository (Optional)

Cloning a repository syncs it to our local machine. 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/iDNAbind-Pro.git
• Clone over SSH: user@machine:~$ git clone [e-mail]:mrzResearchArena/iDNAbind-Pro.git

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

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 --fullDataset=1 --optimumDataset=1 --fasta=/home/user/iDNAbind-Pro/ProteinDatasets/PDB1075_FASTA.txt --label=/home/user/iDNAbind-Pro/ProteinDatasets/PDB1075_Label.txt --kTuple=3 --kGap=5 --pseudoKNC=1 --monoMono=1 --monoDi=1 --monoTri=1 --diMono=1 --diDi=1 --diTri=1 --triMono=1 --triDi=1

or,

user@machine:~$ python main.py -full=1 -optimum=1 -fa=/home/user/iDNAbind-Pro/ProteinDatasets/PDB1075_FASTA.txt -la=/home/user/iDNAbind-Pro/ProteinDatasets/PDB1075_Label.txt -ktuple=3 -kgap=5 -pseudo=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 --testDataset=1 --fasta=/home/user/iDNAbind-Pro/ProteinDatasets/PDB186_independentFASTA.txt --label=/home/user/iDNAbind-Pro/ProteinDatasets/PDB186_independentLabel.txt --kTuple=3 --kGap=5 --pseudoKNC=1 --monoMono=1 --monoDi=1 --monoTri=1 --diMono=1 --diDi=1 --diTri=1 --triMono=1 --triDi=1

or,

user@machine:~$ python main.py -test=1 -fa=/home/user/iDNAbind-Pro/ProteinDatasets/PDB186_independentFASTA.txt -la=/home/user/iDNAbind-Pro/ProteinDatasets/PDB186_PDB186_independentLabel.txt -ktuple=3 -kgap=5 -pseudo=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
--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	Maximum number of gapped; Example: -kGap=5
--kTuple	-ktuple	integer	--kTuple=3	Maximum number of nucleotides; 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.
--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: Features Description

Feature Name	Feature Structure / Formula	Number of Features
pseudoKNC	X, XX, XXX	when --kTuple=3, then 8,420 features are extracted for the protein sequence
monoMonoKGap	X_X	when --kGap=1, then 400 features are extracted for the protein sequence
monoDiKGap	X_XX	when --kGap=1, then 8,000 features are extracted for the protein sequence
monoTriKGap	X_XXX	when --kGap=1, then 160,000 features are extracted for the protein sequence
diMonoKGap	XX_X	when --kGap=1, then 8,000 features are extracted for the protein sequence
diDiKGap	XX_XX	when --kGap=1, then 160,000 features are extracted for the protein sequence
diTriKGap	XX_XXX	when --kGap=1, then 3,200,000 features are extracted for the protein sequence
triMonoKGap	XXX_X	when --kGap=1, then 160,000 features are extracted for the protein sequence
triDiKGap	XXX_XX	when --kGap=1, then 3,200,000 features are extracted for the protein sequence

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

     

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

     

References

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[2] Dong-Sheng Cao, Qing-Song Xu, and Yi-Zeng Liang. propy: a tool to generate various modes of chous pseaac. Bioinformatics, 29(7):960–962, 2013.

[3] Bin Liu. Bioseq-analysis: a platform for dna, rna and protein sequence analysis based on machine learning approaches. Briefings in bioinformatics, 2017.

[4] 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, et al. ifeature: a python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics, 1:4, 2018.

[5] Bin Liu, Hao Wu, Deyuan Zhang, Xiaolong Wang, and Kuo-Chen Chou. Pse-analysis: a python package for dna/rna and protein/peptide sequence analysis based on pseudo components and kernel methods. Oncotarget, 8(8):13338, 2017.

[6] Bin Liu, Fule Liu, Xiaolong Wang, Junjie Chen, Longyun Fang, and Kuo-Chen Chou. Pse-in-one: a web server for generating various modes of pseudo components of dna, rna, and protein sequences. Nucleic acids research, 43(W1):W65–W71, 2015.

[7] Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Ma- chine learning in python. Journal of machine learning research, 12(Oct):2825–2830, 2011.