https://github.com/mrzresearcharena/pyfeat
A Python-based Effective Feature Generation Tool from DNA, RNA, and Protein Sequences
https://github.com/mrzresearcharena/pyfeat
bioinformatics computational-biology genomics proteomics
Last synced: 6 months ago
JSON representation
A Python-based Effective Feature Generation Tool from DNA, RNA, and Protein Sequences
- Host: GitHub
- URL: https://github.com/mrzresearcharena/pyfeat
- Owner: mrzResearchArena
- License: gpl-3.0
- Created: 2018-05-02T08:44:28.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-08-14T09:20:26.000Z (about 3 years ago)
- Last Synced: 2025-04-14T06:12:15.539Z (6 months ago)
- Topics: bioinformatics, computational-biology, genomics, proteomics
- Language: Python
- Homepage: http://rafsanjani.pythonanywhere.com/
- Size: 5.5 MB
- Stars: 95
- Watchers: 6
- Forks: 30
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/mrzResearchArena/PyFeat) 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 ``
```console
user@machine:~$ pip install scikit-learn
```
**`or,`**
`Using anaconda environment:` conda install ``
```console
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
```console
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,`***
```console
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
```console
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,`***
```console
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)
```console
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)
```console
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)
```console
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
**[1]** Bin Liu, Fule Liu, Longyun Fang, Xiaolong Wang, and Kuo-Chen Chou. repdna: a
python package to generate various modes of feature vectors for dna sequences by in-
corporating user-defined physicochemical properties and sequence-order effects. Bioin-
formatics, 31(8):1307–1309, 2014.
**[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]** 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.
**[4]** Md Rafsan Jani, Md Toha Khan Mozlish, Sajid Ahmed, Dewan Md Farid, and Swakkhar
Shatabda. irecspot-ef: Effective sequence based features for recombination hotspot
prediction. Computers in biology and medicine, 2018.
**[5]** Bin Liu, Fule Liu, Longyun Fang, Xiaolong Wang, and Kuo-Chen Chou. reprna: a web
server for generating various feature vectors of rna sequences. Molecular Genetics and
Genomics, 291(1):473–481, 2016.
**[6]** Nan Xiao, Dong-Sheng Cao, Min-Feng Zhu, and Qing-Song Xu. protr/protrweb: R
package and web server for generating various numerical representation schemes of pro-
tein sequences. Bioinformatics, 31(11):1857–1859, 2015.
**[7]** Bin Liu. Bioseq-analysis: a platform for dna, rna and protein sequence analysis based
on machine learning approaches. Briefings in bioinformatics, 2017.
**[8]** 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.
**[9]** 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.
**[10]** Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier
Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine
learning in python. Journal of machine learning research, 12(Oct):2825–2830, 2011.
**[11]** Eamonn Keogh and Abdullah Mueen. Curse of dimensionality. In Encyclopedia of
Machine Learning and Data Mining, pages 314–315. Springer, 2017.
**[12]** Ruihu Wang. Adaboost for feature selection, classification and its relation with svm, a
review. Physics Procedia, 25:800–807, 2012.
**[13]** Hao Lin, En-Ze Deng, Hui Ding, Wei Chen, and Kuo-Chen Chou. ipro54-pseknc: a
sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo
k-tuple nucleotide composition. Nucleic acids research, 42(21):12961–12972, 2014.
**[14]** Wei Chen, Pengmian Feng, Hui Ding, and Hao Lin. Pai: Predicting adenosine to inosine
editing sites by using pseudo nucleotide compositions. Scientific reports, 6:35123, 2016.
**[15]** Hao Lin, Zhi-Yong Liang, Hua Tang, and Wei Chen. Identifying sigma70 promoters
with novel pseudo nucleotide composition. IEEE/ACM transactions on computational
biology and bioinformatics, 2017.
**[16]** Mahmoud Ghandi, Dongwon Lee, Morteza Mohammad-Noori, and Michael A Beer.
Enhanced regulatory sequence prediction using gapped k-mer features. PLoS computa-
tional biology, 10(7):e1003711, 2014.
**[17]** Shahana Yasmin Chowdhury, Swakkhar Shatabda, and Abdollah Dehzangi. Idnaprot-
es: Identification of dna-binding proteins using evolutionary and structural features.
Scientific Reports, 7(1):14938, 2017.