You can try this here, or try it later on your own data.

Get data

We will use the same Illumina data as we used above:

• illumina_R1.fastq.gz: the Illumina forward reads
• illumina_R2.fastq.gz: the Illumina reverse reads

Assemble

Run Spades:

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina

• -1 is input file of forward reads
• -2 is input file of reverse reads
• --careful minimizes mismatches and short indels
• --cov-cutoff auto computes the coverage threshold (rather than the default setting, “off”)
• -o is the output directory

Results

Move into the output directory and look at the contigs:

infoseq contigs.fasta

MitoFinder: Mitofinder is a pipeline to assemble mitochondrial genomes and annotate mitochondrial genes from trimmed read sequencing data.

MitoHiFi: a python pipeline for mitochondrial genome assembly from PacBio high fidelity reads

MITObim: MITObim is a tool specifically developed for the iterative assembly of mitochondrial genomes. It starts with a reference mitogenome and iteratively refines the assembly using the read data.

MITOS: MITOS is a web-based platform that provides a pipeline for annotating mitochondrial genomes. It integrates multiple software tools for assembly, annotation, and visualization of mitogenomes.

MIRA: MIRA (Mimicking Intelligent Read Assembly) is a versatile genome assembly tool that can be used for mitochondrial genome assembly. It supports various sequencing technologies and allows for reference-based or de novo assembly.

NOVOPlasty: NOVOPlasty is a user-friendly tool designed for de novo assembly of organelle genomes, including mitochondria. It utilizes a seed-and-extend algorithm and is suitable for both short-read and long-read data.

MITOS2: MITOS2 is an updated version of the MITOS pipeline, which automates the annotation of mitochondrial genomes. It provides improved accuracy and additional features for mitochondrial genome analysis.

GetOrganelle: While primarily designed for chloroplast genome assembly, GetOrganelle can also be used for mitochondrial genome assembly. It is particularly useful for dealing with high-throughput sequencing data.

SPAdes: SPAdes (St. Petersburg genome assembler) is a versatile genome assembly tool that can be employed for mitochondrial genome assembly, especially when dealing with complex datasets that may contain nuclear mitochondrial DNA sequences (numts).

IDBA-UD: IDBA-UD (Iterative De Bruijn Graph De Novo Assembler) is another de novo assembly tool that can be used for mitochondrial genome assembly, especially in cases with relatively low coverage.

Velvet: Velvet is a de novo assembly tool that can be applied to mitochondrial genome assembly, especially when working with short-read data.

When selecting a mitochondrial genome assembly tool, it's important to consider the specific characteristics of your sequencing data, such as read length and coverage, as well as the complexity of the mitochondrial genome. Additionally, some tools are better suited for specific organisms or research objectives, so choosing the right tool will depend on your particular project requirements.

nf-core/mag v5 (https://github.com/nf-core/mag) addresses this challenge by extending its reproducible Nextflow-based workflow for modern genome-resolved metagenomics.

A key addition is support for long-read-only metagenomic assembly and bin refinement, enabling long-read data to be processed through an integrated workflow. The release also introduces five additional binning tools, providing complementary strategies for recovering genomes from complex microbial communities.

The workflow has also expanded beyond conventional bacterial and archaeal MAGs, with improved classification of viruses and eukaryotes, together with enhanced genome-quality assessment.

Conceptually, the workflow brings together:

Reads → QC → Assembly → Binning → Bin refinement → Taxonomic classification → MAG quality assessment

The real strength of nf-core/mag is not any single algorithm, but the integration and reproducibility of multiple tools within a standardized workflow. This is particularly important for large-scale metagenomic studies where software versions, parameters, databases and computational environments can strongly influence results.

After seven years of development involving multiple curator teams and the wider nf-core community, v5 demonstrates how community-driven workflow development can keep metagenomic analysis aligned with rapidly evolving sequencing technologies.

The future of genome-resolved metagenomics is not simply longer reads—it is better integration of assembly, binning, refinement and quality control within reproducible computational workflows.

Read the full paper in Bioinformatics https://academic.oup.com/bioinformatics/article/42/9/btag628/8770536?

Address of the bookmark: https://github.com/alastair-droop/fqtools

The software requires as input an assembly in FASTA format and paired reads mapped to the assembly in a BAM file. Mapping information such as the fragment coverage and insert size distribution is analysed to locate mis-assemblies. REAPR works best using mapped read pairs from a large insert library (at least 1000bp). Additionally, if a short insert Illumina library is also available, REAPR can combine this with the large insert library in order to score each base of the assembly.

http://www.sanger.ac.uk/science/tools/reapr

Address of the bookmark: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-5-r47

Progressive Cactus is a whole-genome alignment package.

Requirements

• git
• gcc 4.2 or newer
• python 2.7
• wget
• 64bit processor and build environment
• 150GB+ of memory on at least one machine when aligning mammal-sized genomes; less memory is needed for smaller genomes.
• Parasol or SGE for cluster support.
• 750M disk space

Instructions

IMPORTANT NOTE: Progressive Cactus does not presently support installation into paths that contain spaces. Until this is resolved, you can use a softlink as a workaround: ln -s "path with spaces" "installation path without spaces"

In the parent directory of where you want Progressive Cactus installed:

git clone git://github.com/glennhickey/progressiveCactus.git
cd progressiveCactus
git pull
git submodule update --init
make

It is also convenient to add the location of progressiveCactus/bin to your PATH environment variable. In order to run the included tools (ex hal2maf) in the submodules/ directory structure, first source progressiveCactus/environment to load the installed environment.

If any errors occur during the build process, you are unlikely to be able to use the tool. Please submit a GitHub issue so we can help out: not only will you help yourself, but others who wish to use the tool as well.

Note that all dependencies are also built and included in the submodules/ directory. This increases the size and build time but greatly simplifies installation and version management. The installation does not create or modify any files outside the progressiveCactus/ directory.

Address of the bookmark: https://github.com/glennhickey/progressiveCactus

http://www.iam.u-tokyo.ac.jp/chromosomeinformatics/rnakato/cgaln/

Address of the bookmark: http://www.iam.u-tokyo.ac.jp/chromosomeinformatics/rnakato/cgaln/

Samtools

Reading/writing/editing/indexing/viewing SAM/BAM/CRAM format

BCFtools

Reading/writing BCF2/VCF/gVCF files and calling/filtering/summarising SNP and short indel sequence variants

HTSlib

A C library for reading/writing high-throughput sequencing data

Samtools and BCFtools both use HTSlib internally, but these source packages contain their own copies of htslib so they can be built independently.

Address of the bookmark: http://www.htslib.org/

Tool at https://github.com/kbseah/genome-bin-tools

Address of the bookmark: http://journal.frontiersin.org/article/10.3389/fmicb.2015.01451/full

The most used mappers of DNA-seq data are BWA and Bowtie for DNA-Seq data and Tophat, STAR or HISAT for RNA-Seq data. Mappers differ in which options they can take in, how fast and how accurate they are. Bowtie is faster than BWA, but looses some sensitivity (does not map an equal amount of reads to the correct position in the genome).

Address of the bookmark: http://wiki.bits.vib.be/index.php/Mapping_of_NGS_data