Background PCR amplification of bacterial 16S rRNA genes provides the most comprehensive and flexible means of sampling bacterial communities. Sequence analysis of these cloned fragments can provide a qualitative and quantitative insight of the microbial population under scrutiny although this approach is not suited to large-scale screenings. Other methods, such as denaturing gradient gel electrophoresis, heteroduplex or terminal restriction fragment analysis are rapid and therefore amenable to field-scale experiments. A very recent addition to these analytical tools is represented by microarray technology. Results Here we present our results using a Universal DNA Microarray approach as an analytical tool for bacterial discrimination. The proposed procedure is based on the properties of the DNA ligation reaction and requires the design of two probes specific for each target sequence. One oligo carries a fluorescent label and the other a unique sequence (cZipCode or complementary ZipCode) which identifies a ligation product. Ligated fragments, obtained in presence of a proper template (a PCR amplified fragment of the 16s rRNA gene) contain either the fluorescent label or the unique sequence and therefore are addressed to the location on the microarray where the ZipCode sequence has been spotted. Such an array is therefore "Universal" being unrelated to a specific molecular analysis. Here we present the design of probes specific for some groups of bacteria and their application to bacterial diagnostics. Conclusions The combined use of selective probes, ligation reaction and the Universal Array approach yielded an analytical procedure with a good power of discrimination among bacteria.

Data used to generate Figures 2, 3, and 4 as we ll as Table 3. This dataset is associated with the following publication: Banerji, A., M. Bagley, J. Shoemaker, D. Tettenhorst, C. Nietch, J. Allen, and J. Santodomingo. Evaluating putative ecological drivers of microcystin spatiotemporal dynamics using metabarcoding and environmental data. Harmful Algae. Elsevier B.V., Amsterdam, NETHERLANDS, 86: 84-95, (2019).

Data used to generate Figures 2, 3, and 4 as we ll as Table 3. This dataset is associated with the following publication: Banerji, A., M. Bagley, J. Shoemaker, D. Tettenhorst, C. Nietch, J. Allen, and J. Santodomingo. Evaluating putative ecological drivers of microcystin spatiotemporal dynamics using metabarcoding and environmental data. Harmful Algae. Elsevier B.V., Amsterdam, NETHERLANDS, 86: 84-95, (2019).

This data collection consists of microbial count data from seawater microbiomes based on both flow cytometry and 16S rRNA copy numbers from droplet digital PCR (ddPCR) that were used to validate a framework for quantifying absolute abundance of microbial communities for use in downstream ecotoxicology modelling. Seawater was collected from the AIMS jetty (19°16′38.4”S, 147°03′32.1”E) and the relationship between cell counts and absolute 16S rRNA gene copy numbers was determined by preparing varying proportions of natural seawater and artificial seawater suspensions (natural seawater proportions of 100, 80, 60, 40, 20, and 0% of a total 500 mL sample, with ASW making up the remaining proportion). For full methodological details please refer to the full research publication, Thomas et al. 2025 (https://doi.org/10.1186/s40793-025-00741-2) its supplementary materials and the associated GitHub repository (GitHub - MarieCThomas/Quantitative-PMA-16S-rRNA-Gene-Sequencing-Workflow). Additionally, raw sequencing data can be found in the NCBI Sequence Read Archives under BioProject number PRJNA1176196.

This repository provides analysis code and results produced during evaluation of metagenomic sequencing (MGS) data collected through the Mosaic Standards Challenge. The Mosaic Standards Challenge asked participating laboratories analyze the same set of 7 samples using their own favored MGS laboratory methods. Each lab submitted their raw sequencing results and protocol information. The resulting MGS data was analyzed through a common bioinformatic pipeline and then evaluated to determine the effects of methodological choices.

This repository provides analysis code and results produced during evaluation of metagenomic sequencing (MGS) data collected through the Mosaic Standards Challenge. The Mosaic Standards Challenge asked participating laboratories analyze the same set of 7 samples using their own favored MGS laboratory methods. Each lab submitted their raw sequencing results and protocol information. The resulting MGS data was analyzed through a common bioinformatic pipeline and then evaluated to determine the effects of methodological choices.

Amplicon sequence variants from enrichment cultures and non-enriched water samples generated with primer sets targeting the V4 region of the 16SrRNA. “This research dataset has been reviewed in accordance with U.S. Environmental Protection Agency (U.S. EPA), Office of Research and Development, and approved for release. Mention of brand names or vendors does not constitute an endorsement of products or services by the U.S. EPA.”

Continuous monitoring of bacterial community structure in the ISS-Kibo. This data contains numerous sequence reads of 16S rRNA gene fragments.

A table (DP_OTU.xlsx) contains rows as OTUs, columns as samples, and entries representing the abundance of each OTU. This dataset is associated with the following publication: Gomez-Alvarez, V., H. Liu, J. Pressman, and D. Wahman. Metagenomic Profile of Microbial Communities in a Drinking Water Storage Tank Sediment after Sequential Exposure to Monochloramine, Free Chlorine, and Monochloramine. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 1(5): 1283-1294, (2021).

A table (DP_OTU.xlsx) contains rows as OTUs, columns as samples, and entries representing the abundance of each OTU. This dataset is associated with the following publication: Gomez-Alvarez, V., H. Liu, J. Pressman, and D. Wahman. Metagenomic Profile of Microbial Communities in a Drinking Water Storage Tank Sediment after Sequential Exposure to Monochloramine, Free Chlorine, and Monochloramine. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 1(5): 1283-1294, (2021).