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# System prepended metadata

title: Instructions for Running the Notebook in Google Colab

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# Alingnment using Google Colab

# Instructions for Running the Notebook in Google Colab

## Introduction to Google Colab

Google Colab, or "Colab" for short, is a cloud-based service from Google that allows you to write and execute Python code through your browser. It is especially useful for data analysis, machine learning, and collaborating on code-based projects.

With Colab, you can run complex Python notebooks on powerful GPUs, which makes it particularly well-suited for bioinformatics workflows, like processing and analyzing sequencing data. You can upload your notebooks and connect to cloud-hosted computing resources directly from your Google account.

### Getting Started with Google Colab

1. **Open Google Colab**: Navigate to [Google Colab](https://colab.research.google.com/).
2. **Upload the Notebook**: Click on "File" > "Upload Notebook" and upload the `.ipynb` file provided here.
3. **Connect to Runtime**: Click on "Connect" to access the Colab runtime environment, which will allow you to execute the cells of the notebook.

## Overview of Sequencing Data

### What is Sequencing?

Sequencing is the process of determining the precise order of nucleotides in DNA or RNA molecules. The sequences provide valuable information for understanding genetics, identifying variants, and studying gene expression.

### How Illumina Sequencing Works
Illumina sequencing is one of the most widely used next-generation sequencing (NGS) technologies. It relies on a process called **sequencing by synthesis (SBS)**. Here is an overview of how Illumina sequencing works:

1. **Library Preparation**: DNA or RNA is fragmented, and adapters are ligated to both ends of each fragment. These adapters are used for binding to the sequencing flow cell and for amplification.

2. **Cluster Generation**: The prepared library is loaded onto a flow cell, where fragments bind to the surface. Each fragment is amplified through **bridge amplification**, creating clusters of identical DNA molecules.

3. **Sequencing by Synthesis**: During sequencing, fluorescently labeled nucleotides are added to the flow cell. Each nucleotide is incorporated one at a time into the growing DNA strand by a polymerase. Each incorporation releases a fluorescent signal, which is captured by a camera. The specific color of the fluorescence identifies which nucleotide (A, T, G, or C) was added.

4. **Data Collection**: The process continues for multiple cycles, allowing the sequence of each fragment to be determined base by base. The camera captures the fluorescent signal after each cycle, and software processes these signals to determine the sequence of nucleotides.

5. **Data Output**: The output is a set of reads that represent the nucleotide sequences of the fragments. These reads are stored in **FastQ files** and include both the sequence data and quality scores.

Illumina sequencing is highly accurate and capable of producing millions to billions of reads in a single run, making it ideal for applications such as whole genome sequencing, RNA sequencing, and targeted resequencing.

Modern sequencing platforms generate massive amounts of raw data, which must be processed to extract meaningful information. Typically, the workflow involves converting the raw data generated by sequencing machines into more standardized formats, which can then be analyzed further using various bioinformatics tools.

### Introduction to BCL and FastQ Files

- **BCL Files**: When a sequencing run is completed on an Illumina platform, the instrument outputs a collection of files in the format called **Binary Base Call (BCL)** files. BCL files represent raw sequencing data, containing information about base calls and their corresponding quality scores.
- **FastQ Files**: To analyze sequencing data, BCL files are converted into **FastQ** files, which are more convenient and widely used. FastQ files contain both nucleotide sequences and quality scores and are often the starting point for most downstream bioinformatics analyses.

Here is an example of the structure of a FastQ file:

```
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAA
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65
```

- **@SEQ\_ID**: The identifier for the sequence, starting with '@'.
- **Nucleotide Sequence**: A string representing the nucleotide sequence (e.g., A, T, G, C).
- **+**: A separator line, which usually repeats the sequence identifier.
- **Quality Scores**: A string of characters representing the quality scores for each base, where each character corresponds to a Phred quality score.

The conversion process from BCL to FastQ is performed using tools like **bcl2fastq**, which transforms raw data into a human-readable and analysable form.

## Introduction to Cell Ranger

**Cell Ranger** is a software pipeline from 10x Genomics designed to process and analyze single-cell RNA sequencing (scRNA-seq) data. It takes FastQ files generated from sequencing experiments and performs several key operations, including:

- **Alignment**: Mapping reads to a reference genome.
- **Barcode Processing**: Identifying and processing unique barcodes to associate reads with individual cells.
- **Gene Counting**: Counting the number of transcripts per gene in each cell.

For single-cell transcriptomics data generated using 10x Genomics technology, Cell Ranger simplifies the downstream analysis, providing useful outputs like the `feature-barcode matrix`, which can be used for further downstream analyses.

To download Cell Ranger, visit the official download page: [Cell Ranger Download](https://www.10xgenomics.com/support/software/cell-ranger/downloads) to get the latest version of Cell Ranger.

### Running Cell Ranger in the Notebook

The attached notebook contains steps for running alignment and feature extraction with Cell Ranger on single-cell data. Below is a guide to execute it:

## Steps to Run the Notebook in Google Colab

1. **Upload Required Files**: To run the notebook, ensure you have the necessary sequencing data, such as FastQ files, available in your Google Drive. You can mount your Google Drive in Colab by running the following command:

   ```python
   from google.colab import drive
   drive.mount('/content/drive')
   ```

   This will allow you to access files stored in Google Drive directly from the Colab environment.

4. **Check File Integrity with Checksum**: Before processing, it's a good practice to verify the integrity of your files using a checksum. A checksum is a unique string derived from the content of a file, which helps ensure that the file hasn't been altered or corrupted during transfer. You can generate and check the checksum using commands like:
   ```python
   !md5sum /content/drive/MyDrive/fastq_files/your_file.fastq
   ```
   Compare the output with the expected checksum value to confirm the integrity of your data.

2. **Install Dependencies**: The notebook requires specific packages such as `pandas`, `numpy`, and other tools for alignment and data processing. Below are the key steps included in the notebook:

   - **Link Google Drive**: The first step is to link Google Drive to access your data files.

     ```python
     from google.colab import drive
     drive.mount('/content/drive', force_remount=True)
     ```

   - **Install Anaconda and Required Tools**: The notebook installs Anaconda to manage packages and installs tools like `bcl2fastq` for BCL to FastQ conversion.

     ```python
     !wget -c https://repo.anaconda.com/archive/Anaconda3-2022.10-Linux-x86_64.sh
     !chmod +x Anaconda3-2022.10-Linux-x86_64.sh
     !bash ./Anaconda3-2022.10-Linux-x86_64.sh -b -f -p /usr/local
     !conda install -c freenome bcl2fastq -y
     ```

3. **Set Paths to Data**: Update the file paths in the notebook to point to the correct location of your FastQ files in your Google Drive or the Colab runtime. Adjust paths for inputs and outputs to avoid issues during execution.

4. **Download Reference Indexes and Metadata**: The notebook contains steps for downloading reference genome indexes and metadata, which are crucial for proper alignment.

   - **Download Genome Index**: The reference genome index must be downloaded for mapping reads. Use the following command:
     ```python
     !wget -O refdata.tar.gz [URL_TO_REFERENCE_INDEX]
     !tar -xvzf refdata.tar.gz
     ```
   - **Download Metadata**: Metadata files, such as GTF or annotation files, are used during alignment for gene identification.
     ```python
     !wget -O metadata.gtf [URL_TO_METADATA]
     ```

5. **Run Alignment**: The notebook includes sections for running snPATHOseq, which involves:

   - **Installation of Required Packages**: Installation steps for dependencies like `bcl2fastq`.
   - **Linking Data Files**: Making sure the necessary sequencing files are available in the runtime.
   - **Running Alignment Scripts**: Using provided scripts and commands to perform alignment and feature extraction.
     ```python
     !cellranger count --id=sample_output \
       --transcriptome=/path/to/reference \
       --fastqs=/content/drive/MyDrive/fastq_files \
       --sample=sample_name \
       --localcores=8
     ```

6. **Execute Cells**: Run the cells in the notebook sequentially by pressing Shift + Enter, or by clicking the "Run" button on each cell. Start from the top to ensure all dependencies and variables are initialized.

7. **Monitor and Troubleshoot**: Colab offers an interactive experience, allowing you to monitor memory and CPU usage while running the notebook. If any errors occur, carefully read the error message and adjust accordingly—often it is a path or dependency issue.

## Summary

This notebook will guide you through the key steps of processing raw sequencing data using Cell Ranger and snPATHOseq, from FastQ files to feature-barcode matrices. Whether you are experienced or just starting, Colab provides a versatile platform to explore sequencing data analysis in an accessible way.

### Next Steps

- Ensure all required sequencing files are available in Google Drive or a local directory accessible from Colab.
- Follow the steps in the notebook for data pre-processing and downstream analysis.
- If you're new to Colab, it might be useful to go through Google Colab's introductory documentation for a better understanding of how the cloud environment works: [Google Colab Documentation](https://colab.research.google.com/notebooks/intro.ipynb).




