ArticleScientific data2026
Canine Fecal Microbiome Dataset: Ultra-deep Multi-platform Sequencing Across Extraction and Library Protocols.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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9 authors.
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Abstract
The canine gut microbiome is an important model for microbiome research, yet methodological variation in DNA isolation, library preparation, and sequencing complicates cross-study comparisons. Here we present a three-component dataset to evaluate methodological effects. First, an ultra-deep sequencing dataset was generated from a single dog fecal sample using both short- (Illumina NovaSeq) and long-read (Oxford Nanopore MinION) platforms. Second, fecal samples from eight co-housed dogs were collected over one year to compare two DNA extraction workflows across 40 samples. Third, three full-length 16S rRNA primer sets were evaluated using synthetic microbial community standards and human and canine fecal samples, all sequenced on the MinION platform. The dataset comprises 75.2 GB of raw sequencing data and quality control and taxonomic classification outputs. The single-sample multi-platform dataset contributes 9.19 GB, the longitudinal cohort 43.45 GB, and the primer comparison dataset 22.61 GB across two accessions. Together, these data provide a multi-platform resource for evaluating extraction, sequencing, and primer-associated methodological effects in canine fecal microbiome profiling.
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