The data is in the form of genomic sequences deposited in a public database, growth curves, and bioinformatic analysis of sequences. This dataset is associated with the following publication: Henson, M., J. Santodomingo , P. Kourtev, R. Jensen, and D. Learman. Metabolic and genomic analysis elucidates strain-level variation in Microbacterium spp. isolated from chromate contaminated sediment. PeerJ. PeerJ Inc., Corte Madera, CA, USA, e1395, (2015).

Sediment samples which were originally collected as part of ASAC 868 (ASAC_868) are now being investigated using molecular microbial techniques as part of ASAC 1228 (ASAC_1228). Samples were collected in a nested survey design in two hydrocarbon impacted areas and two unimpacted areas. Denaturing gradient gel electrophoresis (DGGE) of a region of the 16S RNA gene was used to investigate the microbial community structure. Banding patterns obtained from the DGGE were transformed into a presence / absence matrix and analysed with a multivariate statistical approach. The download file contains an excel spreadsheet, a csv version of the data, plus a readme file.

,This is a Microsoft Excel spreadsheet containing raw absorbance data collected from PM01 and PM02A plates in an Omnilog system for Lactiplantibacillus pentosus LA0445 and MU045. Independent duplicates were run for each bacterial culture.,Resources in this dataset:,

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.

Sequence reads (16S rDNA- and 16S rRNA-based) were processed and analyzed using Mothur software. The results presented in the attached Excel file. Also, the other MS word file includes taxonomic summary tables for bacterial communities on biofilms from the MAIFAS reactor as well as the detailed description of Materials & Methods. This dataset is associated with the following publication: Church, J., H. Ryu, A. Sadmani, A. Randall, J. Santodomingo, and W.H. Lee. Multiscale investigation of a symbiotic microalgal-integrated fixed film activated sludge (MAIFAS) process for nutrient removal and photo-oxygenation. Bioresource Technology. Elsevier Online, New York, NY, USA, 268: 128-138, (2018).

Sequence reads (16S rDNA- and 16S rRNA-based) were processed and analyzed using Mothur software. The results presented in the attached Excel file. Also, the other MS word file includes taxonomic summary tables for bacterial communities on biofilms from the MAIFAS reactor as well as the detailed description of Materials & Methods. This dataset is associated with the following publication: Church, J., H. Ryu, A. Sadmani, A. Randall, J. Santodomingo, and W.H. Lee. Multiscale investigation of a symbiotic microalgal-integrated fixed film activated sludge (MAIFAS) process for nutrient removal and photo-oxygenation. Bioresource Technology. Elsevier Online, New York, NY, USA, 268: 128-138, (2018).

Two 16S rDNA clone libraries, one from a Brown Bay sample and one from an O'Brien Bay sample were generated. These samples were originally collected as part of ASAC project 868 and the microbiology of the samples is now being investigated as part of ASAC 1228. Two data files are included in the download. Both are in "fasta" format, a text-based format for representing either nucleotide sequences or peptide sequences, in which base pairs or amino acids are represented using single-letter codes. Further information about the dataset can also be found in the referenced paper.

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.