Jamie Billington | Search PubMed

PubMed2025

SPARKI: a tool for the statistical analysis of pathogen identification results.

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.

Software↗

PubMed2025

Ancestry and somatic profile predict acral melanoma origin and prognosis.

Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

Journal Article↗