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

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Abstract
Stable isotope probing (SIP) connects microbial sequence data to diverse metabolic activities, but the lack of a unifying 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, standardized using the MISIP metadata standard. SIPdb provides a standardized pipeline accommodating the three most common SIP gradient fractionation strategies (binary, multi-fraction, and density-resolved), two incorporator designation strategies (fixed- and sliding-window), and four complementary differential abundance methods (DESeq2, edgeR, limma-voom, and ALDEx2). Using this pipeline, we identified over 42 000 unique amplicon sequence variants as isotope incorporators across 62 phyla. Benchmarking with SIPSim-generated synthetic datasets showed that position-resolved designs performed best and that differential abundance method contributed comparably to variation in incorporator designation, with DESeq2 suggested as a balanced default. Validation against original publications showed that, on average, SIPdb recovered 70.1% of author-reported incorporators, with discrepancies arising from differences in phylotyping or classification approaches. Finally, 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. 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.

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