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Merge pull request #12 from TheReconPilot/master
Corrected typo in README
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DeleMa_Detect/README.md

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- [DeleMa Detect Documentation](#delema-detect-documentation)
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- [Overview](#overview)
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- [``` app.py ```](#-apppy-)
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- [```app.py```](#apppy)
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- [```DataVisualization```](#datavisualization)
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- [```Models_notebooks```](#models_notebooks)
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- [```Procfile```](#procfile)
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# DeleMa Detect Documentation
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<img src="https://2020.igem.org/wiki/images/e/e9/T--IISER-Pune-India--delema-demo-desktop-small.gif" alt="DeleMa Detect Demo" width="70%" style="display:block;margin:auto;">
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<img src="https://2020.igem.org/wiki/images/e/e9/T--IISER-Pune-India--delema-demo-desktop-small.gif" alt="DeleMa Detect Demo" width="90%" style="display:block;margin:auto;">
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<br>
2020

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## Overview
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<figure>
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<img src=./Deployed-model-screenshots/delema_detect_summary.png alt="Trulli" width=600>
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<img src=./Deployed-model-screenshots/delema_detect_summary.png width=600>
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<figcaption>Fig.1 - Overview of how DeLeMa Detect was built</figcaption>
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</figure>
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### ``` app.py ```
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### ```app.py```
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The main python script uses the Flask micro web framework to create a local host where the model is deployed. It contains auxiliary functions ```model_predict()```, ```index()``` and ```upload()```
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1. ```model_predict``` calls the `model.h5` file stored in ```./model/```. We created many models and tested each one's accuracy, size and processing power. Since the size of a few models were greater than 50MB (a soft limit setup by Github) we have uploaded them on [Google Drive](https://drive.google.com/drive/folders/11ULc4FWlB3VScfZIR4y3o8KJgljHZPFe?usp=sharing). Based on the model one wants, it can be downloaded placed in ```./model/```
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1. ```model_predict``` calls the `model.h5` file stored in ```./model/```. We created many models and tested each one's accuracy, size and processing power. Since the size of a few models were greater than 50MB (a soft limit setup by Github) we have uploaded them on [Google Drive](https://drive.google.com/drive/folders/11ULc4FWlB3VScfZIR4y3o8KJgljHZPFe?usp=sharing). Based on the model one wants, it can be downloaded and placed in ```./model/```
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Although the dimensions of the uploaded image can be anything, each model takes a particular input image size which is controlled and preprocessed in ```app.py```, namely :
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| Sr no | Model | ```target_size``` |
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<br><br>
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The result for an Image that has been Identified as Uninfected.
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<br><br>
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<figure>
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<img src=./Deployed-model-screenshots/desktop-page-result-infected.png alt="Infected" width="60%" style="display:block;margin:auto;">
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<figcaption>Results for a Parasitized ir infected Blood Smear Image</figcaption>
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<figcaption>Results for a Parasitized/Infected Blood Smear Image</figcaption>
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</figure>
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<br><br>
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The result for an Image that has been Identified as Uninfected
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<br><br>
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### On Mobile
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The [Web App](https://delema-detect-igem-iiserpune.herokuapp.com/) works on mobile too. The procedure is the same. Upload the image and tap on Predict.
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<br><br>
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# Sample BLood Smear Images
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## Sample Blood Smear Images
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We have added upto 20 blood smear images for testing purposes at ```./uploads/```. There are two directories called ```Parasitized``` and ```Uninfected```, each containing upto 10 images. These can be downloaded and tested by running the application locally or on our Heroku platform.
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Peptides_against_Malaria/README.md

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- [Overview](#overview)
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- [Analysis of Molecular Dynamics Simulations](#analysis-of-molecular-dynamics-simulations)
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- [How does the distance between the centroid of the Protein and peptide evolve over time?](#how-does-the-distance-between-the-centroid-of-the-protein-and-peptide-evolve-over-time)
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- [How does the distance between the centroid of the protein and peptide evolve over time?](#how-does-the-distance-between-the-centroid-of-the-protein-and-peptide-evolve-over-time)
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- [```PDB_centroid_analyser```](#pdb_centroid_analyser)
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- [Hydrogen Bond Analysis](#hydrogen-bond-analysis)
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- [Computing the Hydrogen Bond Profile](#computing-the-hydrogen-bond-profile)
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</figure>
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Here :
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- Atom no --> group (1)
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- Chain name --> group (3)
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- X co-ordinates --> group (5) + group (6)
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- Y co-ordinates --> group (8) + group (9)
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- Z co-ordinates --> group (11) + group (12)
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- Atom --> group(14)
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- Atom no --> group (1)
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- Chain name --> group (3)
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- X co-ordinates --> group (5) + group (6)
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- Y co-ordinates --> group (8) + group (9)
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- Z co-ordinates --> group (11) + group (12)
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- Atom --> group(14)
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After reading these lines, the function ```get_centroid()``` obtains the centroid (x, y, z) of a given chain. We perform calculations in numpy arrays using the standard centroid formula:
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<img src="https://render.githubusercontent.com/render/math?math=\text{Centroid}\ (\rm G) = \dfrac{\sum_i (w_i \times r_i)}{ \sum_i w_i}">
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A wrapper function, ```main()``` calls other multiple functions to determine the distance between the centroid of two chains using `euclidean_distance()`. This is done for all the .pdb files submitted in the text file. *Special care* has to be taken to ensure that all .pdb files are in the official prescribed format, otherwise, the regex syntax will not be able to identify the chain, atom or element. Finally, the script writes a .csv file in the same directory which includes the following fields:
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A wrapper function, ```main()``` calls other multiple functions to determine the distance between the centroid of two chains using `euclidean_distance()`. This is done for all the .pdb files submitted in the text file. *Special care* has to be taken to ensure that all .pdb files are in the official prescribed format; otherwise, the regex syntax will not be able to identify the chain, atom or element. Finally, the script writes a .csv file in the same directory which includes the following fields:
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1. Filename
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2. Centroid of Chain1 "{chain-name given}"
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--> This .csv file serves as a good checkpoint for performing further analysis.
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The script can be run on the command line/terminal and also imported into other scripts if particular functions seem interesting. The command-line version takes two arguments:
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The script can be run on the command line/terminal and also imported into other scripts if some particular function seems interesting. The command-line version takes two arguments:
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1. A text file containing all .pdb names
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2. The output .csv filename.
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Sample code to be run on CLI :
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``` PDB_centroid_analyser.py pdbnames.txt output_centroid_data.csv```
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```sh
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PDB_centroid_analyser.py pdbnames.txt output_centroid_data.csv
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```
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The other functions included in PDB_centroid_analyser are :
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1. `get_centorid()` --> returns the centroid of a particular chain from a .pdb file
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To understand effective binding, we analyze the number of intermolecular Hydrogen bonds between the protein and the peptide. This entails the analysis of hbonds over each snapshot of the MD simulation to determine which residues and specifically which atom in these residues of the peptide inhibitor form hydrogen bonds with the protein. We also aim to determine the relative abundance of Hydrogen bonds formed over the entire simulation period.
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To perform these calculations, I wrote a script that functions on build over [UCSF Chimera](https://www.cgl.ucsf.edu/chimera/).
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To perform these calculations, we wrote a script that functions and builds over [UCSF Chimera](https://www.cgl.ucsf.edu/chimera/).
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### Computing the Hydrogen Bond Profile
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**Note** : It is assumed that the chimera executable file is stored at ```~/chimera/bin/chimera```. If not the path to the executable has to be specified on Line 11.
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**Note**: It is assumed that the chimera executable file is stored at ```~/chimera/bin/chimera```. If not the path to the executable has to be specified on Line 11.
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The commands to be run in Chimera is submitted in a .cmd file, with each line containing the command based on chimera documentation.
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##### Working
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The script loops over each line of the text line (basically a file name), reads that particular file and processes it based on the instructions given in the .cmd file. After processing, the last 8 lines of the output (required information is stored here) is written to an external file. We then use the Linux stream editor ('sed') to replace the filename to be read next in the .cmd file, and a similar process continues until all files in the .txt file are processed.
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The script loops over each line of the text file (basically a file name), reads that particular file and processes it based on the instructions given in the .cmd file. After processing, the last 8 lines of the output (required information is stored here) is written to an external file. We then use the Linux stream editor ('sed') to replace the filename to be read next in the .cmd file, and a similar process continues until all files in the .txt file are processed.
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#### ```PDB_Hbond_analyser.py ```
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##### Working
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The python script contains a host of preprocessing functions that read data from the output files of ```Hbond-analyzer.sh, ```i.e. files ending with ```hbond_info.txt```. The script processes each line in the .txt file using grep expressions and returns a dataframe containing information on the Hydrogen bonds (Donor atom, Acceptor atom, Donor-Hydrogen, Distance D-A, Distance D-H-A, No of Hydrogen bonds). This is used for further Data Analysis and Interpretation.
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The python script contains a host of preprocessing functions that read data from the output files of ```Hbond-analyzer.sh```, i.e. files ending with ```hbond_info.txt```. The script processes each line in the .txt file using grep expressions and returns a dataframe containing information on the Hydrogen bonds (Donor atom, Acceptor atom, Donor-Hydrogen, Distance D-A, Distance D-H-A, No of Hydrogen bonds). This is used for further Data Analysis and Interpretation.
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<br>
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