MOTIVATION: Many pathogen identification and microbiome analysis tools have been developed in recent years, with Kraken 2 being one of the most popular. While tools downstream of Kraken 2 can assist in the interpretation of its outputs, a statistical framework to assess the likelihood that a taxon/organism is present in a single sample alongside an automated end-to-end analysis pipeline has not yet been fully implemented. RESULTS: Here, we introduce SPARKI, an R package that performs statistical analysis of Kraken 2 outputs and aids in the identification of pathogens present in next-generation sequencing samples. SPARKI adds to the field by bringing a probabilistic view to Kraken 2 data, serving as a discovery tool and complementing other methods such as KrakenTools, Bracken, and Pavian. AVAILABILITY AND IMPLEMENTATION: SPARKI code is available on GitHub at https://github.com/team113sanger/sparki. SPARKI is also part of an end-to-end pathogen identification pipeline, sparki-nf, which is available at https://github.com/team113sanger/sparki-nf. An additional pipeline for further exploration and validation of SPARKI results is also available at https://github.com/team113sanger/map-to-genome.

Motiviation: Spatial Transcriptomics (ST) is a method which combines high resolution tissue imaging with high troughput transcriptome sequencing data. This data must be aligned with the images for correct visualization, a process that involves several manual steps. Results: Here we present ST Spot Detector, a web tool that automates and facilitates this alignment through a user friendly interface. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Summary: A significant proportion of eukaryote genomes consist of transposable element (TE)-derived sequence. These elements are known to have the capacity to modulate gene function and genome evolution. We have developed RetroSeq for detecting non-reference TE insertions from Illumina paired-end whole-genome sequencing data. We evaluate RetroSeq on a human trio from the 1000 Genomes Project, showing that it produces highly accurate TE calls. Availabilty: RetroSeq is open-source and available from https://github.com/tk2/RetroSeq. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.