Published methodology in Landa et al., 2017, ISME Journal, in press.
\nOne liter samples were filtered through 2-um polycarbonate (PC) membranes to collect bacterial cells and flash-frozen in liquid nitrogen and stored at -80C. Filters were extracted for RNA by the acid phenol:chloroform:isoamylalcohol method. Potential traces of DNA were removed using the Turbo DNA-free kit (Invitrogen, Waltham, MA, USA). Samples were tested for residual DNA by a 40-cycle PCR targeting the 16S rRNA gene of R. pomeroyi. Samples were depleted of rRNA using custom probes for small and large subunit rRNA genes from all three microbes (Stewart et al., 2010). Libraries were prepared for two replicate cubitainers at 8 time points (16 samples) using the KAPA Stranded mRNA-Seq Kit (Kapa Biosystems, Wilmington, MA, USA) at the Georgia Genomics Facility (University of Georgia) and sequenced on a HiSeq Illumina 2500 at the Hudson Alpha Institute for Biotechnology (AL, USA).
\nReference cited:
\nStewart FJ, Ottesen EA, DeLong EF. (2010). Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics. ISME J 4: 896\u2013907. doi:10.1038/ismej.2010.18
Transcriptional profile of marine bacterium Ruegeria pomeroyi in a three-member co-culture study. This dataset contains the processed, QC'ed, normalized sequence data. The full raw data file is deposited in the NCBI BioProject database under accession PRJNA381627.
\nThese data are published in table 2 of Landa et al., 2017, ISME Journal, in press.
Sequence quality control was performed using the FASTX toolkit, imposing a minimum quality score of 20 over 80% of read length. Reads aligning to an in-house rRNA database were removed (blastn, score cutoff\u00a0greater than or equal to 50). Remaining reads were mapped to the R. pomeroyi genome (Bowtie 2; Langmead and Salzberg, 2012) and counted (HTSeq; Anders et al., 2015). Counts were converted to transcripts per million (TPM, Table S2) and data were also deposited in the NCBI BioProject database under accession PRJNA381627.\u00a0Genes with differential expression between selected time points were determined with DESeq2 (Love et al., 2014), and linear relationships of gene expression with phytoplankton abundance with Pearson correlations.
\nReferences cited:
\nLangmead B, Salzberg SL. (2012). Fast gapped-read alignment with Bowtie 2. Nat Methods 9: 357\u2013359. doi:10.1038/nmeth.1923
Anders S, Pyl PT, Huber W. (2015). HTSeq\u2014a Python framework to work with high-throughput sequencing data. Bioinformatics 31: 166\u2013169. doi:10.1093/bioinformatics/btu638
\nLove MI, Huber W, Anders S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550. doi:10.1186/s13059-014-0550-8
\nBCO-DMO Processing:
\n- modified parameter names to conform with BCO-DMO naming conventions;
\n- replaced spaces with underscores in Description column.