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Bishop, H. V.

Publications and source records attributed to Bishop, H. V..

2 recordsLinked to original sources
bioRxiv2026

The Celiac Microbiome Repository (CMR): A Curated Collection of Celiac Disease Gut Microbiome Sequencing Data

Celiac disease is an autoimmune condition where the gut microbiome is increasingly recognised as a key environmental factor. While high-throughput sequencing has led to a surge in celiac-related gut microbiome profiling data, these datasets remain fragmented, heterogeneous, and often lack the metadata required for large-scale integration into pooled, cross-cohort datasets. To address this, we developed the Celiac Microbiome Repository (CMR), a curated, open-access collection of celiac-related 16S rRNA gene and shotgun metagenomic sequencing datasets. We employed a systematic curation workflow to identify datasets across the NCBI Sequence Read Archive (SRA) and Scopus, followed by manual metadata extraction and direct author engagement. All 16S data was reprocessed through DADA2 and shotgun data through MetaPhlAn4 to facilitate comparison across studies. The CMR version 1.0 comprises 28 datasets containing 3,245 samples from 13 countries and 5 body sites. Our analysis reveals that while publicly available celiac microbiome samples have accumulated at a rate of approximately 140 per year, significant barriers to accessibility exist. Just 20 of 58 eligible datasets were found to have both raw data and essential metadata readily available within public archives. The repository features a dual-interface design, consisting of a GitHub backend for programmatic access and an R Shiny frontend for interactive data exploration. By providing this curated and harmonised resource, the CMR enables the research community to leverage public data for global meta-analyses and machine learning applications. Ultimately, this work provides the foundation needed to move beyond isolated, small-scale studies toward high-powered discoveries in celiac disease research. Database URLs: https://github.com/CeliacMicrobiomeRepo/celiac-repository | https://celiac.shinyapps.io/celiac-webapp

bioinformatics↗
bioRxiv2026

Micro16S: Universal Phylogenetic 16S rRNA Gene Representations for Deep Learning of the Microbiome

1Existing self-supervised microbiome models represent taxa as discrete, independent units restricted to fixed vocabularies, disregarding their evolutionary context. Here we present Micro16S, a deep learning approach that embeds 16S ribosomal RNA gene sequences into a continuous vector space according to phylogenetic relationships derived from the Genome Taxonomy Database. Using a combination of triplet and pair loss objectives, the model learns representations where spatial proximity reflects phylogenetic relatedness, while remaining largely invariant to the specific 16S rRNA region. Evaluations demonstrate taxonomically coherent clustering across most ranks and substantially improved region invariance compared to k-mer frequency baselines. A transformer pretrained on 50,418 unlabelled gut microbiome samples using these embeddings captured biologically meaningful community structure, though classical machine learning baselines outperformed Micro16S across six benchmark classification tasks, highlighting the limitations of the current system. These results establish the feasibility of phylogenetic embeddings for microbiome deep learning and identify mining algorithm design and class imbalance as primary targets for future improvement.

bioinformatics↗