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-# What is this?
-
-The github.dev web-based editor is a lightweight editing experience that runs entirely in your browser. You can navigate files and source code repositories from GitHub, and make and commit code changes.
-
-There are two ways to go directly to a VS Code environment in your browser and start coding:
-
-* Press the . key on any repository or pull request.
-* Swap `.com` with `.dev` in the URL. For example, this repo https://github.com/github/dev becomes http://github.dev/github/dev
-
-Preview the gif below to get a quick demo of github.dev in action.
-
-![github dev](https://user-images.githubusercontent.com/856858/130119109-4769f2d7-9027-4bc4-a38c-10f297499e8f.gif)
-
-# Why?
-It’s a quick way to edit and navigate code. It's especially useful if you want to edit multiple files at a time or take advantage of all the powerful code editing features of Visual Studio Code when making a quick change. For more information, see our [documentation](https://github.co/codespaces-editor-help).
diff --git a/operon_identification/README.md b/operon_identification/README.md
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+# Identification of Operon Structure in Clostridioides difficile
+
+## Project Description
+
+This project aims to identify and characterize the operon structures of Clostridioides difficile, a spore-forming, anaerobic pathogen responsible for severe gastrointestinal infections. 
+- Operons are clusters of co-transcribed genes regulated as a single unit, playing a crucial role in bacterial gene expression, adaptation, and survival. 
+- Understanding operon structures in C. difficile is essential for deciphering bacterial regulatory mechanisms, metabolic pathways, and potential drug resistance mechanisms.
+
+- To achieve this, computational and RNA-Seq-based approaches will be employed to predict operons using transcriptomic data from C. difficile under various conditions. 
+- The study will integrate genomic annotations, expression profiles, and computational tools such as and COSMO to infer operon boundaries. 
+- The findings of this research could provide novel insights into bacterial gene regulation, antibiotic resistance, and potential therapeutic targets for controlling C. difficile infections.
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+### **Methods Overview:**  
+
+1. **Data Collection:**  
+   - Obtain *Clostridioides difficile* genome sequences and annotations (FASTA, GFF) from public databases.  
+   - Acquire RNA-Seq datasets under different growth conditions.  
+
+2. **RNA-Seq Preprocessing:**  
+   - Perform quality control (FastQC).  
+   - Trim low-quality reads and adapters (Trimmomatic).  
+   - Align reads to the reference genome (Bowtie2/Hisat2).  
+
+3. **Operon Prediction:**  
+   - Use Rockhopper for transcription unit identification.  
+   - Run COSMO to integrate gene co-expression patterns and predict operon structures.  
+
+4. **Validation & Analysis:**  
+   - Compare predicted operons with known databases.  
+   - Analyze differential gene expression to infer operon regulation.  
+
+5. **Interpretation & Reporting:**  
+   - Identify operons linked to antibiotic resistance and metabolic pathways.  
+   - Summarize findings and visualize operon structures.