MOTIVATION: Large sequencing datasets are being produced and deposited into public archives at unprecedented rates. The availability of tools that can reliably and efficiently generate and store sequencing read summary statistics has become critical. RESULTS: As part of the effort by the Vertebrate Genomes Project (VGP) to generate high-quality reference genomes at scale, we sought to address the community's need for efficient sequence data evaluation by developing rdeval, a standalone tool to quickly compute and interactively display sequencing read metrics. Rdeval can either run on the fly or store key sequence data metrics in tiny read 'snapshot' files. Statistics can then be efficiently recalled from snapshots for additional processing. Rdeval can convert fa*[.gz] files to and from other popular formats including BAM and CRAM for better compression. Overall, while CRAM achieves the best compression, the gain compared to BAM is marginal, and BAM achieves the best compromise between data compression and access speed. Rdeval also generates a detailed visual report with multiple data analytics that can be exported in various formats. We showcase rdeval's functionalities using long-read data from different sequencing platforms and species, including human. For PacBio long-read sequencing, our analysis shows dramatic improvements in both read length and quality over time, as well as the benefit of increased coverage for genome assembly, though the magnitude varies by taxa. AVAILABILITY AND IMPLEMENTATION: Rdeval is implemented in C++ for data processing and in R for data visualization. Precompiled releases (Linux, MacOS, Windows) and commented source code for rdeval are available under MIT license at https://github.com/vgl-hub/rdeval. Documentation is available on ReadTheDocs (https://rdeval-documentation.readthedocs.io). Rdeval is also available in Bioconda and in Galaxy (https://usegalaxy.org). An automated test workflow ensures the consistency of software updates.

BACKGROUND: PacBio high fidelity (HiFi) sequencing reads are both long (15-20 kb) and highly accurate (> Q20). Because of these properties, they have revolutionised genome assembly leading to more accurate and contiguous genomes. In eukaryotes the mitochondrial genome is sequenced alongside the nuclear genome often at very high coverage. A dedicated tool for mitochondrial genome assembly using HiFi reads is still missing. RESULTS: MitoHiFi was developed within the Darwin Tree of Life Project to assemble mitochondrial genomes from the HiFi reads generated for target species. The input for MitoHiFi is either the raw reads or the assembled contigs, and the tool outputs a mitochondrial genome sequence fasta file along with annotation of protein and RNA genes. Variants arising from heteroplasmy are assembled independently, and nuclear insertions of mitochondrial sequences are identified and not used in organellar genome assembly. MitoHiFi has been used to assemble 374 mitochondrial genomes (368 Metazoa and 6 Fungi species) for the Darwin Tree of Life Project, the Vertebrate Genomes Project and the Aquatic Symbiosis Genome Project. Inspection of 60 mitochondrial genomes assembled with MitoHiFi for species that already have reference sequences in public databases showed the widespread presence of previously unreported repeats. CONCLUSIONS: MitoHiFi is able to assemble mitochondrial genomes from a wide phylogenetic range of taxa from Pacbio HiFi data. MitoHiFi is written in python and is freely available on GitHub ( https://github.com/marcelauliano/MitoHiFi ). MitoHiFi is available with its dependencies as a Docker container on GitHub (ghcr.io/marcelauliano/mitohifi:master).

MOTIVATION: With the current pace at which reference genomes are being produced, the availability of tools that can reliably and efficiently generate genome assembly summary statistics has become critical. Additionally, with the emergence of new algorithms and data types, tools that can improve the quality of existing assemblies through automated and manual curation are required. RESULTS: We sought to address both these needs by developing gfastats, as part of the Vertebrate Genomes Project (VGP) effort to generate high-quality reference genomes at scale. Gfastats is a standalone tool to compute assembly summary statistics and manipulate assembly sequences in FASTA, FASTQ or GFA [.gz] format. Gfastats stores assembly sequences internally in a GFA-like format. This feature allows gfastats to seamlessly convert FAST* to and from GFA [.gz] files. Gfastats can also build an assembly graph that can in turn be used to manipulate the underlying sequences following instructions provided by the user, while simultaneously generating key metrics for the new sequences. AVAILABILITY AND IMPLEMENTATION: Gfastats is implemented in C++. Precompiled releases (Linux, MacOS, Windows) and commented source code for gfastats are available under MIT licence at https://github.com/vgl-hub/gfastats. Examples of how to run gfastats are provided in the GitHub. Gfastats is also available in Bioconda, in Galaxy (https://assembly.usegalaxy.eu) and as a MultiQC module (https://github.com/ewels/MultiQC). An automated test workflow is available to ensure consistency of software updates. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.