Dataset: Switch RNAseq

Name: Transcriptional profile of marine bacterium Ruegeria pomeroyi in a three-member co-culture study
Published: 2017-11-29
Keywords: biota, oceans

Cite This Dataset

DOI:10.1575/1912/bco-dmo.719970.1

Project:

Bacterial Taxa that Control Sulfur Flux from the Ocean to the Atmosphere (OceanSulfurFluxBact)

Program:

Dimensions of Biodiversity (Dimensions of Biodiversity)

Principal Investigator:

Mary Ann Moran (University of Georgia, UGA)

BCO-DMO Data Manager:

Shannon Rauch (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2017-11-29

Restricted:

Validated:

Yes

Current State:

Final no updates expected

Transcriptional profile of marine bacterium Ruegeria pomeroyi in a three-member co-culture study

Abstract:

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.

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Archival Copy

Version Date	Archive	Persistent Identifier	Date Assigned
2017-11-29	Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)	10.1575/1912/bco-dmo.719970.1	2020-01-14

Description

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.

These data are published in table 2 of Landa et al., 2017, ISME Journal, in press.

Methods & Sampling

Published methodology in Landa et al., 2017, ISME Journal, in press.

One 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).

Reference cited:
Stewart FJ, Ottesen EA, DeLong EF. (2010). Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics. ISME J 4: 896–907. doi:10.1038/ismej.2010.18

Data Processing Description

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 greater 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. Genes 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.

References cited:
Langmead B, Salzberg SL. (2012). Fast gapped-read alignment with Bowtie 2. Nat Methods 9: 357–359. doi:10.1038/nmeth.1923

Anders S, Pyl PT, Huber W. (2015). HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31: 166–169. doi:10.1093/bioinformatics/btu638

Love 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

BCO-DMO Processing:
- modified parameter names to conform with BCO-DMO naming conventions;
- replaced spaces with underscores in Description column.

Data Files

File
Switch_RNAseq.csv (Comma Separated Values (.csv), 641.69 KB) MD5:710349f0860d73cd73c7318474aa4db7 Primary data file for dataset ID 719970

More Information about this dataset

Funding Sources

Funding Source	Award Number
NSF Division of Ocean Sciences	OCE-1342694

Deployments

None available yet

Instruments

Automated DNA Sequencer

Supplied Name: HiSeq Illumina 2500

Supplied Description:

Transcripts were sequenced on a HiSeq Illumina 2500 at the Hudson Alpha Institute for Biotechnology (AL, USA).

Instrument Type

Generic Name: Automated DNA Sequencer

Acronym: Automated Sequencer

Generic Description:

General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.

Parameters

Date and time formats are described in python strftime() syntax.

Supplied Name	Supplied description	Supplied Units	Standard Name
Locus_Tag	gene identifier	unitless	sample
Description	gene function, if known	unitless	brief_desc
Day7_R1	Transcript count by day and replicate: day 7, replicate 1	TPM (transcripts per million)	count
Day7_R2	Transcript count by day and replicate: day 7, replicate 2	TPM (transcripts per million)	count
Day9_R1	Transcript count by day and replicate: day 9, replicate 1	TPM (transcripts per million)	count
Day9_R2	Transcript count by day and replicate: day 9, replicate 2	TPM (transcripts per million)	count
Day12_R1	Transcript count by day and replicate: day 12, replicate 1	TPM (transcripts per million)	count
Day12_R2	Transcript count by day and replicate: day 12, replicate 2	TPM (transcripts per million)	count
Day15_R1	Transcript count by day and replicate: day 15, replicate 1	TPM (transcripts per million)	count
Day15_R2	Transcript count by day and replicate: day 15, replicate 2	TPM (transcripts per million)	count
Day18_R1	Transcript count by day and replicate: day 18, replicate 1	TPM (transcripts per million)	count
Day18_R2	Transcript count by day and replicate: day 18, replicate 2	TPM (transcripts per million)	count
Day23_R1	Transcript count by day and replicate: day 23, replicate 1	TPM (transcripts per million)	count
Day23_R2	Transcript count by day and replicate: day 23, replicate 2	TPM (transcripts per million)	count
Day30_R1	Transcript count by day and replicate: day 30, replicate 1	TPM (transcripts per million)	count
Day30_R2	Transcript count by day and replicate: day 30, replicate 2	TPM (transcripts per million)	count
Day37_R1	Transcript count by day and replicate: day 37, replicate 1	TPM (transcripts per million)	count
Day37_R2	Transcript count by day and replicate: day 37, replicate 2	TPM (transcripts per million)	count

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