Mitochondrial genomes are essential resources for evolutionary biology, population genetics, and biodiversity research. Their compact size, maternal inheritance, and relatively conserved structure make them a popular choice for studies ranging from phylogenetics to species identification. With the advent of next-generation sequencing (NGS), assembling complete mitochondrial genomes has become more accessible, particularly through user-friendly platforms like Galaxy. In this post, I walk through a Galaxy-based workflow for mitochondrial genome assembly, using NOVOplasty as the core assembler.

Assembling a complete mitochondrial genome from raw Illumina reads is straightforward in Galaxy because every step, from quality checks to circularised contigs, runs in a point-and-click interface and is automatically recorded in a shareable workflow. Below is an annotated guide you can adapt for your own project, followed by notes on how it compares with the recent empirical study in Ecology & Evolution (Homchan et al. 2024).

Why Use Galaxy?

Galaxy provides a graphical interface for bioinformatics pipelines, lowering the barrier to entry for researchers who may not have access to high-performance computing clusters or strong command-line skills. For mitochondrial genome assembly, Galaxy integrates tools for:

• Quality control (FastQC)
• Read trimming (Trimmomatic)
• De novo assembly (NOVOplasty)
• Post-assembly validation and refinement

The workflow described below is modular and can be adapted to diverse taxa and sequencing platforms.

Why this combination works

1. Quality Assurance loops Running FastQC before and after Trimmomatic proves that adapter and quality trimming actually improves per-base scores and eliminates over-represented sequences, preventing garbage-in, garbage-out assemblies.
2. Seed-and-extend strategy NOVOPlasty requires a short homologous “seed” (typically COI) to start extension. Providing a conspecific or congeneric seed sharply reduces the risk of nuclear mitochondrial segments (NUMTs) invading the assembly.
3. Automatic circularisation NOVOPlasty closes the circular molecule, outputs a single FASTA (out_c) plus auxiliary logs (out_ct, out_mc), and flags ambiguous regions. You can import the FASTA directly into downstream annotation tools such as MITOS2 or MitoZ.

Tips and pitfalls

• Seed choice matters: A 600-bp COI from the same genus is usually enough; divergent seeds can mis-prime and pull in nuclear copies.
• Read depth: Mitochondrial copies in whole-genome libraries are plentiful (often >100×). Nonetheless, check NOVOPlasty’s coverage report (out_mc) to confirm uniform depth; spikes may indicate repeats.
• Heteroplasmy: If multiple contigs appear, inspect out_ar1/out_ar2 (alternative reconstructions). Genuine heteroplasmy shows consistent SNP blocks rather than random low-coverage errors.
• Parameter tweaking: For shorter read lengths (<125 bp) reduce k-mer below 33.

Next steps after assembly

1. Annotation: Upload the circular FASTA to MITOS2 or MitoZ within Galaxy or externally to generate GenBank-ready annotations.
2. Validation: Map the trimmed reads back to the assembly with BWA-MEM and visualise in IGV to spot residual indels or misjoins.
3. Phylogenetic placement: Extract the 13 protein-coding genes with MitoFish scripts, align in MAFFT and build a tree in IQ-TREE; all these tools have Galaxy wrappers.
4. Data release: Submit the annotated FASTA and raw reads to NCBI GenBank and SRA, citing the Galaxy workflow DOI for reproducibility credit.

You can transform raw Illumina reads into a fully circularised mitochondrial genome, all within a browser and with every action logged. Following the structure validated by Homchan et al. (2024) ensures high-quality assemblies and reproducible science , exactly the point of Galaxy’s workflow-centric philosophy.

Mitochondrial genome assembly in Galaxy was originally published in Bioinformatics in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Join and Contribute to Bioinformatics in Plain English was originally published in Bioinformatics in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.