MOTIVATION: Metabolism occurs in a cell type-specific manner, but which cells regulate metabolite levels remains unclear. RESULTS: Here, we integrate some of the largest metabolite quantitative trait loci datasets, TOPMed and UK Biobank, with one of the most extensive single-cell RNA sequencing resources, Tabula Sapiens. This integration allows us to identify cell types that regulate metabolites body-wide. We find hepatocytes are the primary regulatory cell type for most metabolites, associating with 385/410 (94%) metabolites for whom an association is found. Additionally, our multi-gene approach reveals more metabolite associations with beta cells compared to those identified using a single-gene approach. For example, we identify novel metabolite-cell type associations, such as the association between phenylpropanoic acid and beta cells, this metabolite that was previously thought to be regulated by the microbiome. AVAILABILITY: Code used in this work is available via Github at https://github.com/haimkru/Metabolite-Cell-Type-Associations.

MOTIVATION: An abundance of new reference genomes is becoming available through large-scale sequencing efforts. While the reference FASTA for each genome is available, there is currently no automated mechanism to query a specific sequence across all new reference genomes. RESULTS: We developed ACES (Analysis of Conservation with an Extensive list of Species) as a computational workflow to query specific sequences of interest (e.g. enhancers, promoters, exons) against reference genomes with an available reference FASTA. This automated workflow generates BLAST hits against each of the reference genomes, a multiple sequence alignment file, a graphical fragment assembly file and a phylogenetic tree file. These data files can then be used by the researcher in several ways to provide key insights into conservation of the query sequence. AVAILABILITY AND IMPLEMENTATION: ACES is available at https://github.com/TNTurnerLab/ACES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.