Fastqc Reporter Technical Documentation

By Oluwasusi David

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Table of Contents

1. Introduction
2. Installation
3. Program Design
   • Overview of Functionality
   • Folder Structure
   • Dependencies Used
4. Example Usage
   • Options and Results
5. Error Handling
6. References

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Introduction

Fastqc reporter is a Command Line Interface (CLI) tool built to parse fastqc files into sections and generate reports. It also generates graphical representations and a flag file indicating the QC test result (pass, fail, or warn).

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Installation

To run this program, the following are required:

• Python 3.9 or higher
• Conda or venv (Conda is used in this documentation)

To create a new virtual environment and install dependencies:

conda create -c conda-forge -n name_of_my_env seaborn pandas matplotlib

Activate the virtual environment:

source activate name_of_my_env

Run the program with its parameters (refer to the Example Usage section).

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Program Design

Fastqc reporter follows an object-oriented approach with two main classes:

• FastQCParser
• Section

The FastQCParser class parses fastqc files into sections and manages optional parameters. The Section class writes reports and flag files for each section.

Each section inherits from the base Section class and defines its own implementation of the plot_section() method to generate the necessary plots.

Overview of Functionality

Fastqc reporter uses Python's argparse module to handle command-line arguments. Required parameters:

• Path to the fastqc file
• Output folder for plots, reports, and flag files

Optional parameters are handled via add_argument with store_true, making them optional.

Workflow:

1. Instantiate FastQCParser with required parameters.
2. Parse the fastqc file into a dictionary.
3. Handle optional parameters and call appropriate methods.
4. Generate reports, plots, and flag files.

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Folder Structure

The script is structured as follows:

fastqc_reporter/
│── fastqc_reporter.py  # Entry point, defines parser options
│── constants.py  # Defines section titles
│── model/  # Contains all classes used in the script
│   ├── __init__.py
│   ├── section.py
│   ├── fastqc_parser.py
│── data/  # Contains test fastqc files

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Dependencies Used

• Matplotlib & Seaborn - For plotting graphs
• Pandas - To extract and manage section data using pandas.read_csv()

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Example Usage

To run the program in its default form:

python3 fastqc_reporter.py ./data/fastqc_data1.txt ./solution1/

Output Example:

Basic Statistics pass
#Measure Value
Filename 4_age21_S12_L001_R2_001_concat.fastq.gz
File type Conventional base calls
Encoding Sanger / Illumina 1.9
Total Sequences 37287903
Sequences flagged as poor quality 0
Sequence length 75
%GC 55

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Options and Results

Users can specify sections to run using options:

Option	Description
-t / --per_tile_seq_qual	Per Tile Sequence Quality
-s / --per_seq_qual_scores	Per Sequence Quality Scores
-c / --per_base_seq_content	Per Base Sequence Content
-g / --per_seq_GC_cont	Per Sequence GC Content
-n / --per_base_N_cont	Per Base N Content
-l / --seq_len_dist	Sequence Length Distribution
-d / --seq_dup	Sequence Duplication Levels
-o / --over_seq	Overrepresented Sequences
-p / --adap_cont	Adapter Content
-k / --kmer_count	K-mer Content
-a / --all	Run all sections

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Error Handling

The script implements error handling using Python's try-except block to manage:

• Invalid user input
• Malformed fastqc files
• File permission errors
• Parsing errors

If an error occurs, the program exits with a non-zero exit code and prints an error message.

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References

• Akalin, A. (2020). Computational genomics with R (Chapter 7: Quality check on sequencing reads). Bookdown
• Babraham Institute. (n.d.). FastQC per tile sequence quality analysis. FastQC Help
• Kong, Y. (2011). Btrim: A fast, lightweight adapter and quality trimming program for next-generation sequencing technologies. Genomics
• Illumina. (2018). Ask a scientist - What is GC-Bias? YouTube
• O'Rawe, J. F., Ferson, S., & Lyon, G. (2015). Accounting for uncertainty in DNA sequencing data. Trends in Genetics
• Pandas Documentation. pandas.read_csv
• Seaborn Documentation. Seaborn functions

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