Dataset: SIMCO 1 and 2: Transcripts per liter

Name: Transcript counts per liter from samples collected during the SIMCO 1 & 2 (July & Oct 2014) incubation experiments on the linkage between DOM changes and microbial transcription patterns at Sapelo Island, GA.
Published: 2016-10-26
Keywords: biota, oceans

Cite This Dataset

Project:

High Resolution Linkages Between DOC Turnover and Bacterioplankton in a Coastal Ocean (SIMCO)

Principal Investigator:

Mary Ann Moran (University of Georgia, UGA)

BCO-DMO Data Manager:

Nancy Copley (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version Date:

2016-10-26

Restricted:

Validated:

Yes

Current State:

Final no updates expected

Transcript counts per liter from samples collected during the SIMCO 1 & 2 (July & Oct 2014) incubation experiments on the linkage between DOM changes and microbial transcription patterns at Sapelo Island, GA.

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Description

Incubation experiments examining concurrent changes in DOC composition and microbial transcription patterns were carried out in July 2014 (SIMCO1) and October 2014 (SIMCO2) at Sapelo Island, GA. This dataset contains the number of transcripts per liter of seawater for each taxon. Cells were collected by filtration at the time of initial surface water collection (Time 0) and after a 24 hr incubation (Time 24) for both high and low tide water collections on each of the two sample dates. Filters were processed for RNA extraction and mRNA-enriched material was sequenced on an Illumina HiSeq2500 run to produce single-end 250 bp reads.

Methods & Sampling

Water samples were collected from Marsh Landing Dock on Sapelo Island GA, prefiltered through a 3 micron membrane filter to remove most eukaryotic microbes, and incubated in 10 L carboys in situ in the dark for 24 h. At the beginning and end of the incubation, three carboys were sacrificed for nucleic acid extraction on 0.2 micron membrane filters, and microbial transcripts were analyzed from two of the three replicates according to methods in Satinsky et al. 2013 and 2014 and protocols.io (https://www.protocols.io/g/moran-lab).

Data Processing Description

Reads were put through a quality control pipeline that removed rRNAs and internal standards added for quantification (Satinsky et al. 2013). Transcripts per liter of seawater at the beginning and end of the incubations were calculated based on internal standard recovery as described in Satinsky et al. 2013.

References:
Brandon M. Satinsky, Scott M. Gifford, Byron C. Crump, Mary Ann Moran, 2013. Chapter 12: "Use of Internal Standards for Quantitative Metatranscriptome and Metagenome Analysis" in Methods in Enzymology, Vol. 531. ISSN 0076-6879, http://dx.doi.org/10.1016/B978-0-12-407863-5.00012-5.

Brandon M. Satinskya, Byron C. Crumpb, Christa B. Smithc, Shalabh Sharmac, Brian L. Zielinskid, Mary Dohertye, Jun Mengc, Shulei Sunf, Patricia M. Medeirosc, John H. Pauld, Victoria J. Colese, Patricia L. Yagerc, and Mary Ann Moranc. 2014. Microspatial gene expression patterns in the Amazon River Plume. PNAS 111(30):11085-11090. www.pnas.org/cgi/doi/10.1073/pnas.1402782111

BCO-DMO Processing:
- added conventional header with dataset name, PI name, version date
- column names reformatted to comply with BCO-DMO standards
- replaced spaces with underscores

Data Files

File
transcripts_per_L.csv (Comma Separated Values (.csv), 309 bytes) MD5:ebc622eba89e94b5a2ff595c2a5ea3fe Primary data file for dataset ID 661866

More Information about this dataset

Funding Sources

Funding Source	Award Number
NSF Division of Ocean Sciences	OCE-1356010

Deployments

Deployment	Synonyms	Start Date	Platform	Investigator
Moran_Sapelo_2012-14		2012-09-01	Univ_Georgia	Mary Ann Moran (Principal Investigator)	data info

Instruments

Automated DNA Sequencer

Supplied Name: Illumina HiSeq2500

Supplied Description:

mRNA-enriched material was sequenced

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
sample	sample identification: S1 = July 2014, S2 = October 2014, L versus H = low tide or high tide; 0 or 24 =time zero or time 24 h (at the time of sample collection or after 24 h incubation in the dark), and -1 or -2 = replicate designations.	unitless	sample
transcripts_per_L	number of transcripts per liter	transcripts/liter	count

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Database

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Dataset: SIMCO 1 and 2: Transcripts per liter