https://github.com/mrzresearcharena/ipro70
Identification of Sigma70 Promoter
https://github.com/mrzresearcharena/ipro70
bioinformatics computational-biology genomics machine-learning sigma70-promoter
Last synced: 2 months ago
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Identification of Sigma70 Promoter
- Host: GitHub
- URL: https://github.com/mrzresearcharena/ipro70
- Owner: mrzResearchArena
- Created: 2018-10-12T01:10:48.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-10-12T01:13:12.000Z (over 6 years ago)
- Last Synced: 2025-01-16T12:55:15.729Z (4 months ago)
- Topics: bioinformatics, computational-biology, genomics, machine-learning, sigma70-promoter
- Language: Python
- Homepage: http://rafsanjani.pythonanywhere.com/
- Size: 143 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# iPro70: Identification of Sigma70 Promoter
## 1. Download Package
### 1.1. Direct Download
We can directly [download](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/mrzResearchArena/iPro70) by clicking the link.
**Note:** The package will download in zip format `(.zip)` named `iPro70-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/iPro70.git `
- Clone over SSH: `user@machine:~$ git clone [email protected]:mrzResearchArena/iPro70.git `
**Note:** If the clone was successful, a new sub-directory appears on our local drive. This directory has the same name (iPro70) 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 ``
```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 --fullDataset=1 --optimumDataset=1 --fasta=/home/user/iPro70/DNADatasets/FASTA.txt --label=/home/user/iPro70/DNADatasets/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,`***
```console
user@machine:~$ python main.py -full=1 -optimum=1 -fa=/home/user/iPro70/DNADatasets/FASTA.txt -la=/home/user/iPro70/DNADatasets/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
```console
user@machine:~$ python main.py --testDataset=1 --fasta=/home/user/iPro70/DNADatasets/demoFASTA.txt --label=/home/user/iPro70/DNADatasets/demoLabel.txt --kTuple=3 --kGap=5 --pseudoKNC=1 --monoMono=1 --monoDi=1 --monoTri=1 --diMono=1 --diDi=1 --diTri=1 --triMono=1 --triDi=1
```
***`or,`***
```console
user@machine:~$ python main.py -test=1 -fa=/home/user/iPro70/DNADatasets/demoFASTA.txt -la=/home/user/DNADatasets/demoLabel.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 84 features are extracted for the DNA sequence|
|monoMonoKGap| X_X |when --kGap=1, then 16 features are extracted for the DNA sequence|
|monoDiKGap| X_XX |when --kGap=1, then 64 features are extracted for the DNA sequence|
|monoTriKGap| X_XXX |when --kGap=1, then 256 features are extracted for the DNA sequence|
|diMonoKGap| XX_X |when --kGap=1, then 64 features are extracted for the DNA sequence|
|diDiKGap| XX_XX |when --kGap=1, then 256 features are extracted for the DNA sequence|
|diTriKGap| XX_XXX |when --kGap=1, then 1,024 features are extracted for the DNA sequence|
|triMonoKGap| XXX_X |when --kGap=1, then 256 features are extracted for the DNA sequence|
|triDiKGap| XXX_XX |when --kGap=1, then 1,024 features are extracted for the DNA sequence|
**Note:** When sequence becomes DNA then X = { A, C, G, T }.
### 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
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**[2]** Dong-Sheng Cao, Qing-Song Xu, and Yi-Zeng Liang. propy: a tool to generate various modes of
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**[3]** Bin Liu. Bioseq-analysis: a platform for dna, rna and protein sequence analysis based on machine
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