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Discipline 01 of 18 24 Tools Stage 1: Raw Read Quality & Decontamination

🔬 1. Quality Control, Trimming & Preprocessing

Raw high-throughput sequencing data requires stringent quality profiling, adapter clipping, low-quality tail trimming, and artifact filtering prior to downstream alignment.

Essential Software: FastQC MultiQC fastp Cutadapt Trimmomatic mosdepth
Comparative Analysis Production Benchmarks

Algorithm Architecture & Decision Matrices

Detailed comparison cards highlighting algorithmic trade-offs, strengths, weaknesses, and recommended production setups.

Read QC & Sequencing Reports
FastQC MultiQC Qualimap RSeQC mosdepth
Universal Phred scoring, k-mer metrics, BAM mapping rates, genomic depth, and aggregated multi-tool dashboards.
Adapter & Quality Trimming
fastp Cutadapt Trimmomatic BBDuk (BBTools) SOAPnuke Trim Galore
Automated adapter clipping, sliding-window trimming, polyG/X clipping, BGI/MGI and Illumina filtering.
Long-Read QC & Filtering (PacBio/ONT)
NanoPlot LongQC MinIONQC Filtlong Porechop NanoFilt / pycoQC
Read length distribution, Q-score thresholds, pore performance metrics, Oxford Nanopore chimera clipping.
Contamination Screening & Read Wrangling
FastQ Screen Kraken2 BBMap SeqKit seqtk FASTX-Toolkit pysam
Host genome decontamination, microbial filtering, FASTA/FASTQ subsampling, formatting and programmatic extraction.
Hardware & Execution Profiles

Primary Software & Algorithms in Quality Control & Trimming

Direct command-line recipes, Conda environment specifications, memory footprints, and license metadata.

QC & Trimming
💻 < 8GB RAM C++ • Multi-threaded

fastp

Docs ↗ By Chen Lab (HaploX)
(License: GPL-3.0)

Ultra-fast all-in-one FASTQ preprocessor. Automatic adapter detection, quality trimming, polyG tail removal (NextSeq/NovaSeq), and HTML/JSON report generation.

Formats: Input: FASTQ (.gz) • Output: Filtered FASTQ, HTML, JSON
💻 4-8 CPU Cores • 4 GB RAM • Fast NVMe I/O
conda install -c bioconda fastp
Quality Assessment
💻 < 8GB RAM Java • Cross-Platform

FastQC

Docs ↗ By Babraham Bioinformatics
(License: GPL-3.0)

Universal quality control tool for high-throughput sequencing data. Evaluates per-base Phred scores, GC bias, k-mer overrepresentation, and adapter contamination.

Formats: Input: FASTQ, BAM, SAM • Output: HTML QC Report, ZIP
💻 2-4 CPU Cores • 2-4 GB RAM
conda install -c bioconda fastqc
Report Aggregation
💻 < 8GB RAM Python 3 • Modular

MultiQC

Docs ↗ By Phil Ewels & Seqera Labs
(License: GPL-3.0)

Modular reporting tool that searches directories for analysis logs from 140+ bioinformatics tools and compiles them into a unified interactive HTML dashboard.

Formats: Input: Log files (FastQC, STAR, GATK, etc.) • Output: Interactive HTML, JSON
💻 1-2 CPU Cores • 2 GB RAM
pip install multiqc
Adapter & Quality Trimming
💻 < 8GB RAM Java • Multithreaded

Trimmomatic

Docs ↗ By Bolger & Usadel (RWTH Aachen)
(License: GPL-3.0)

Flexible read trimming tool designed for Illumina paired-end and single-end data. Features sliding-window Phred quality filtering and palindrome adapter clipping.

Formats: Input: FASTQ (.gz) • Output: Paired/Unpaired FASTQ
💻 4-8 CPU Cores • 4-8 GB RAM
conda install -c bioconda trimmomatic
Adapter Removal
💻 < 8GB RAM Python • C Extensions

Cutadapt

Docs ↗ By Marcel Martin (TU Dortmund)
(License: MIT)

Finds and removes adapter sequences, primers, poly-A tails, and error-prone sequencing ends. Crucial for small RNA-seq, ChIP-seq, and amplicon protocols.

Formats: Input: FASTQ, FASTA • Output: Clipped FASTQ
💻 2-4 CPU Cores • 2 GB RAM
conda install -c bioconda cutadapt
Long-Read QC
💻 < 8GB RAM Python • Seaborn / Bokeh

NanoPlot

Docs ↗ By Wouter De Coster Lab
(License: GPL-3.0)

Plotting tool for Oxford Nanopore Technologies (ONT) and PacBio sequencing data. Generates read length vs average quality heatmaps and dynamic HTML summaries.

Formats: Input: FASTQ, BAM, summary.txt • Output: Interactive HTML, PDF
💻 4 CPU Cores • 4-8 GB RAM
conda install -c bioconda nanoplot
Contamination Screen
💻 16-32GB RAM Perl • Bowtie2 / BWA

FastQ Screen

Docs ↗ By Babraham Bioinformatics
(License: GPL-3.0)

Screens FASTQ sequence libraries against a multi-genome panel (Human, Mouse, E. coli, Yeast, Adapter, Vector, rRNA) to identify contamination and cross-hybridization.

Formats: Input: FASTQ (.gz) • Output: HTML, PNG, Text Matrix
💻 8 CPU Cores • 16 GB RAM • Multi-genome indices
conda install -c bioconda fastq-screen
Recommended Workflow Progression

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