ArticleBMC genomics2026
ERMA: a harmonizing epicPCR data analysis tool.
Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundEpicPCR links functional genes to taxonomic markers at the single-cell level and has become an important method in microbial ecology and antimicrobial resistance research. However, the lack of missing standardized data analysis workflows limits reproducibility and comparability across studies. Existing analyses are often based on custom, unpublished scripts, complicating cross-study benchmarking and reuse.
resultsWe present ERMA, an open-source, Snakemake-based pipeline that enables an automated, standardized, reproducible, and scalable epicPCR data analysis with minimal user input. ERMA supports both Illumina and ONT sequencing data and integrates quality control, database preparation, dual similarity searches against taxonomic and functional reference databases, results integration, filtering, and visualization. The pipeline is adaptable to a broad range of research questions beyond AMR due to modular database preparation and integration of specific UniRef queries. We evaluated ERMA using five publicly available epicPCR datasets covering diverse sample sources and sequencing depths. Across these datasets, ERMA achieved mean concordance rates of approximately 70-83% with the taxonomic results reported in the respective publications. In addition, validation using a defined five-member mock community spiked into clinical wastewater demonstrated full recovery of mock genera, with 85% of ERMA-detected genera also detected by independent 16S rDNA gene analysis of the same wastewater sample. Attrition analysis showed inconsistent filtering patterns across studies, reflecting dataset-specific quality differences.
conclusionWhile large-scale reference datasets for epicPCR remain limited, validation using both publicly available studies and a defined mock community shows strong, interpretable performance across heterogeneous datasets. By providing a transparent, modular, and reproducible workflow, ERMA contributes toward harmonized epicPCR data analysis and establishes a foundation for future methodological standardization and benchmarking efforts as reference datasets improve.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.