SIPdb: A stable isotope probing database and analytical dashboard for linking amplicon sequences to microbial activity using a reverse ecology approach

Abstract

Stable isotope probing (SIP) provides a powerful means to connect microbial sequence data with diverse metabolic activities, but the lack of a framework for SIP-derived data has limited its integration into broader strategies for ecological inference. Here, we introduce the SIPdb, an extensible SQLite database of curated nucleic acid SIP experiments (also in phyloseq format) paired with an interactive RShiny dashboard for analysis and visualization. The initial release compiles 22 studies covering 21 isotopolog substrates across diverse environments, with data standardized using the MISIP metadata standard. In creating the SIPdb, we have provided a standardized pipeline that accommodates the three most common SIP gradient fractionation strategies (binary, multi-fraction, and density-resolved), two isotope incorporator designation strategies (fixed- and sliding-window), and four complementary differential abundance methods (DESeq2, edgeR, limma-voom, and ALDEx2). Using our pipeline, we identified more than 42,000 unique amplicon sequence variants as isotope incorporators across 62 phyla. Benchmarking with synthetic datasets demonstrated consistent performance across incorporator designation strategies, with ALDEx2 providing the highest specificity. Validation against original publications showed that, on average, SIPdb recovered 70.1% of author-reported incorporator taxa, with discrepancies arising from differences in phylotyping or classification approaches. Finally, our reanalysis of a non-SIP study of 1,4-dioxane degradation showed how SIPdb can both validate known degraders and uncover additional candidate taxa involved in community metabolism. The SIPdb establishes a scalable platform for reverse ecology, enabling hypothesis generation, cross-study meta-analysis, and linking taxa to metabolic processes, while serving as an open, extensible resource to accelerate ecological interpretation in microbiome research.