MOTIVATION: Iso-Seq RNA long-read sequencing enables the identification of full-length transcripts and isoforms, removing the need for complex analysis such as transcriptome assembly. However, the raw sequencing data need to be processed in a series of steps before annotation is complete. Here, we present nf-core/isoseq, a pipeline for automatic read processing and genome annotation. Following nf-core guidelines, the pipeline has few dependencies and can be run on any of platforms. AVAILABILITY AND IMPLEMENTATION: The pipeline is freely available online on the nf-core website (https://nf-co.re/isoseq) and on GitHub (https://github.com/nf-core/isoseq) under MIT License (DOI: 10.5281/zenodo.7116979).

BACKGROUND: Several tools are available for visualizing genomic data. Some, such as Gbrowse and Jbrowse, are very efficient for small genomic regions, but they are not suitable for entire genomes. Others, like Phenogram and CViT, can be used to visualise whole genomes, but are not designed to display very dense genomic features (eg: interspersed repeats). We have therefore developed DensityMap, a lightweight Perl program that can display the densities of several features (genes, ncRNA, cpg, etc.) along chromosomes on the scale of the whole genome. A critical advantage of DensityMap is that it uses GFF annotation files directly to compute the densities of features without needing additional information from the user. The resulting picture is readily configurable, and the colour scales used can be customized for a best fit to the data plotted. RESULTS: DensityMap runs on Linux architecture with few requirements so that users can easily and quickly visualize the distributions and densities of genomic features for an entire genome. The input is GFF3-formated data representing chromosomes (linkage groups or pseudomolecules) and sets of features which are used to calculate representations in density maps. In practise, DensityMap uses a tilling window to compute the density of one or more features and the number of bases covered by these features along chromosomes. The densities are represented by colour scales that can be customized to highlight critical points. DensityMap can compare the distributions of features; it calculates several chromosomal density maps in a single image, each of which describes a different genomic feature. It can also use the genome nucleotide sequence to compute and plot a density map of the GC content along chromosomes. CONCLUSIONS: DensityMap is a compact, easily-used tool for displaying the distribution and density of all types of genomic features within a genome. It is flexible enough to visualize the densities of several types of features in a single representation. The images produced are readily configurable and their SVG format ensures that they can be edited.