http://lod.bco-dmo.org/id/dataset/984169
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2025-09-10
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Metagenomic sample information, genetic accession identifiers (NCBI SRA, JGI IMG), and estimated gene copies from Orcas Island coastal waters (2 m depth) from 2021-05-27 to 2021-06-18
2025-09-10
publication
2025-09-10
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2025-09-19
publication
https://doi.org/10.26008/1912/bco-dmo.984169.1
Julia Kubanek
Georgia Institute of Technology
principalInvestigator
Brook L. Nunn
University of Washington
principalInvestigator
Tatiana A. Rynearson
University of Rhode Island
principalInvestigator
Miranda Mudge
University of Washington
principalInvestigator
Emma Timmins-Schiffman
University of Washington
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Nunn, B. L., Kubanek, J., Rynearson, T. A., Timmins-Schiffman, E., Mudge, M., Bartlett, E. (2025) Metagenomic sample information, genetic accession identifiers (NCBI SRA, JGI IMG), and estimated gene copies from Orcas Island coastal waters (2 m depth) from 2021-05-27 to 2021-06-18. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-09-10 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.984169.1 [access date]
Dataset Description: <p>Additional funding description:<br />
This dataset was supported by NSF OCE-2401646, OCE-2401645, OCE-2401644, University of Washington Royalty Research Fund, NIH NIEHS grant R21ES034337-01, NSF IOS-2041497, NIH fellowship F31 ES032733-01A1<br />
<br />
See the "Related Datasets" section for descriptions of related holdings at NCBI (umbrella BioProject&nbsp;PRJNA1093221), JGI (GOLD study Gs0160720), Zenodo (raw data),&nbsp;PanoramaWeb,&nbsp;PRIDE/ProteomeXchange (PXD052873),&nbsp;etc.<br />
<br />
DOE = U.S. Department of Energy<br />
JGI = Joint Genome Institute (DOE)<br />
IMG/M = Integrated Microbial Genomes&nbsp; &amp; Microbiomes (DOE JGI)<br />
GOLD = Genomes Online Database (DOE JGI)<br />
NCBI = National Center for Biotechnology Information<br />
SRA = Sequence Read Archive (NCBI)<br />
NSF = U.S. National Science Foundation<br />
OCE = Division of Ocean Sciences (NSF)<br />
IOS -&nbsp;Division of Integrative Organismal Systems (NSF)&nbsp;<br />
NIH = National Institutes of Health<br />
NIEHS = National Institute of Environmental Health Sciences (NIH)<br />
PRIDE =&nbsp;PRoteomics IDEntifications Database (EMBL-EBI)</p> Methods and Sampling: <p>The 1–2 L of pre-filtered water was collected and then passed through a 47 mm × 0.22 µm polyethersulfone filter (Tisch, part number SF15021) using the Alexis peristaltic field pump (Proactive Environmental) at a flow rate of &lt;0.1 L min<sup>−1</sup> to isolate the bacterial fraction. Filters were immediately placed in plastic resealable baggies (5 × 4 cm), then 200 µl of DNA/RNA Shield (Zymo Research) was added to the whole filter within 1 minute of collection and filter baggies were transferred to liquid nitrogen while in the field. Samples were transferred to the University of Washington and placed in a −80 °C freezer prior to extraction.&nbsp;<br />
<br />
<span style="font-size:13px">Estimated Gene copies (Supplemental File: 984169_v1_estimated_genome_copies.csv):<br />
<br />
Reads were then quality controlled for length ( &gt; 50 bp), quality ( &gt; Q30), and any adapter contamination was removed along with mateless pairs. Filtered reads were error-corrected using bbcms v.38.90&nbsp;</span>&nbsp;(Bushnell, 2014; https://bbmap.org).&nbsp;<span style="font-size:13px">This was run with the following command line options: bbcms.sh -Xmx100g metadatafile = counts.metadata.json mincount = 2 highcountfraction = 0.6 in = bbcms.input.fastq.gz out1 = input.corr.left.fastq.gzout2 = input.corr.right.fastq.gz. The readset was assembled with metaSPAdes v. 3.15.2&nbsp;(Nurk, 2017). This was run using the following command line options: spades.py -m2000–tmp-dir cromwell_root -o spades3–only-assembler -k 33,55,77,99,127–meta -t 16-1 input.corr.left.fastq.gz -2 input.corr.right.fastq.gz. The input read set was mapped to the final assembly and coverage information generated with bbmap v. BBMap:38.86&nbsp;</span>&nbsp;(Bushnell, 2014; https://bbmap.org)<span style="font-size:13px">. This was run using the following command line options: bbmap.sh build = 1 overwrite = true fastareadlen = 500 -Xmx100g threads = 16 nodisk = trueinterleaved = true ambiguous = random rgid = filename in = reads.fastq.gz ref = reference.fastaout = pairedMapped.bam. Following assembly, the metagenomes underwent processing through the DOE JGI Metagenome Annotation Pipeline (MAP version 5.1.13) and were subsequently loaded into the Integrated Microbial Genomes and Microbiomes (IMG/M) platform (Clum, 2021). Complete metagenomic datasets for each time point are listed by individual IMG Genome IDs (under the Study Name “Seawater microbial communities from East Sound, Orcas Island, WA, USA” (https://gold.jgi.doe.gov/study?id=Gs0160720). For each time point, estimated gene copies for all classes in the domain “Bacteria” were downloaded (3.14.2024; </span>Nunn et al. (2024)&nbsp;<span style="font-size:13px">Dataset 5). At each time point, the cumulative count of gene copies was calculated, and subsequently, each taxonomic class was divided by the total count to determine the class distribution (Nunn et al. (2024) Fig. 4,&nbsp;Dataset 5).<br />
<br />
Methodology is from the results paper Nunn et al. (2024, doi: 10.1038/s41597-024-04013-5).</span></p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2401646 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2401646
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2401645 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2401645
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2401644 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2401644
Funding provided by National Institute of Environmental Health Sciences (NIEHS) Award Number: R21ES034337-01 Award URL: https://reporter.nih.gov/search/wmUb13OUa0C-1UrPp0t1kQ/project-details/10497336
Funding provided by NSF Division of Integrative Organismal Systems (NSF IOS) Award Number: IOS-2041497 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2041497
Funding provided by National Institutes of Health (NIH) Award Number: ES032733-01A1 Award URL: https://reporter.nih.gov/search/b7x9vIROQ0-SYmLKyS0NFw/project-details/10312280
completed
Julia Kubanek
Georgia Institute of Technology
404-894-8424
School of Biological Sciences Georgia Institute of Technology
Atlanta
GA
30332-0230
USA
julia.kubanek@biosci.gatech.edu
pointOfContact
Brook L. Nunn
University of Washington
206-616-9023
Department of Genome Sciences, Foege Building S113 3720 15th Ave NE
Seattle
WA
98195
USA
brookh@uw.edu
pointOfContact
Tatiana A. Rynearson
University of Rhode Island
401-874-6022
Graduate School of Oceanography, University of Rhode Island 215 South Ferry Rd.
Narragansett
RI
02882
USA
rynearson@uri.edu
pointOfContact
Miranda Mudge
University of Washington
mudge832@gmail.com
pointOfContact
Emma Timmins-Schiffman
University of Washington
emmats@uw.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
DateID_PT
ISO_DateTime_UTC
Latitude
Longitude
Experiment_Accession
Experiment_Title
Instrument
Submitter
Study_Accession
Study_Title
Sample_Accession
Total_Size_Mb
Total_RUNs
Total_Spots
Total_Bases
Library_Name
Library_Strategy
Library_Source
Library_Selection
IMG_GenomeID
Domain
Sequencing_Status
Study_Name
Genome_Name_Sample_Name
Sequencing_Center
IMG_Submission_ID
GOLD_Analysis_Project_ID
GOLD_Analysis_Project_Type
GOLD_Sequencing_Project_ID
Genome_Size_assembled
Gene_Count_assembled
Genome_MetaBAT_Bin_Count_assembled
Estimated_Number_of_Genomes_assembled
Estimated_Average_Genome_Size_assembled
NovaSeq 6000 with S4 flow cell
Varioskan Lux (fluorescent plate reader)
theme
None, User defined
Date and time
ISO_DateTime_UTC
latitude
longitude
NCBI SRA experiment accession (SRX)
sample description
instrument
person name
NCBI SRA study accession (SRP)
brief description
NCBI SRA Sample accession (SRS)
file_size
seq_descrip
accession number
sample identification
featureType
BCO-DMO Standard Parameters
Automated DNA Sequencer
plate reader
instrument
BCO-DMO Standard Instruments
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Collaborative Research: Rhythm and Blooms: Deciphering metabolic, functional and taxonomic interactions over the life cycle of a phytoplankton bloom
https://osprey.bco-dmo.org/project/931507
Collaborative Research: Rhythm and Blooms: Deciphering metabolic, functional and taxonomic interactions over the life cycle of a phytoplankton bloom
<p><em>NSF Award Abstract:</em><br />
Floating, single-celled algae, or phytoplankton, form the base of marine food webs. When phytoplankton have sufficient nutrients to grow quickly and generate dense populations, known as blooms, they influence productivity of the entire food web, including rich coastal fisheries. The present research explores how the environment (nutrients) as well as physical and chemical interactions between individual cells in a phytoplankton community and their associated bacteria act to control the timing of bloom events in a dynamic coastal ecosystem. The work reveals key biomolecules within the base of the food web that can inform food web functioning (including fisheries) and be used in global computational models that forecast the impacts of phytoplankton activities on global carbon cycling. A unique set of samples and data collected in 2021 and 2022 that captured phytoplankton and bacterial communities before, during, and after phytoplankton blooms, is analyzed using genomic methods and the results are used to interrogate these communities for biomolecules associated with blooms stages. The team mentors undergraduates, graduate students, and postdoctoral researchers in the fields of biochemical oceanography, genome sciences, and time-series multivariate statistics. University of Washington organized hackathons develop publicly accessible portals for the simplified interrogation and visualization of 'omics data by high schoolers and undergraduates and are implemented in investigator-led undergraduate teaching modules and the University of Rhode Island Ocean Classroom. The research team also returns to Orcas Island, WA, where the field sampling takes place, to host a series of annual Science Weekends to foster scientific engagement with the local community.</p>
<p>Phytoplankton blooms, from initiation to decline, play vital roles in biogeochemical cycling by fueling primary production, influencing nutrient availability, impacting carbon sequestration in aquatic ecosystems, and supporting secondary production. In addition to environmental conditions, the physical and chemical interactions between individual phytoplankton can significantly modulate blooms, influencing the growth, maintenance, and senescence of phytoplankton. Recent work in steady-state open ocean ecosystems has shown that important chemicals are transferred amongst plankton on time-dependent metabolic schedules that are related to diel cycles. It is unknown how these metabolic schedules operate in dynamic coastal environments that experience perturbations, such as phytoplankton blooms. Here, the investigators are examining metabolic scheduling using long-term, diel sample sets to reveal how chemical and biological signals associated with the initiation, maintenance, and cessation of phytoplankton blooms are modulated on both short (hrs) and long (days-weeks) time scales. Findings are advancing the ability to predict and manage phytoplankton dynamics, providing crucial insights into ecological stability and future oceanographic sampling strategies. Additionally, outcomes of this study are providing a new foundational understanding of the succession of microbial communities and their chemical interactions across a range of timescales. In the long term, this research has the potential to identify predictors of the timing of phytoplankton blooms, optimize fisheries management, and guide future research on carbon sequestration.</p>
Rhythm and Blooms
largerWorkCitation
project
eng; USA
oceans
-122.883233
-122.883233
48.6765
48.6765
2021-05-27
2021-06-18
Coastal waters East Sound WA
0
BCO-DMO catalogue of parameters from Metagenomic sample information, genetic accession identifiers (NCBI SRA, JGI IMG), and estimated gene copies from Orcas Island coastal waters (2 m depth) from 2021-05-27 to 2021-06-18
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/984278.rdf
Name: DateID_PT
Units: unitless
Description: <p>Character value for the combined date and time of sample collection (time zone: US Pacific (PST/PDT))</p>
http://lod.bco-dmo.org/id/dataset-parameter/984279.rdf
Name: ISO_DateTime_UTC
Units: unitless
Description: <p>Datetime with timezone (ISO format) of sample collection (time zone: UTC)</p>
http://lod.bco-dmo.org/id/dataset-parameter/984280.rdf
Name: Latitude
Units: unitless
Description: <p>Latitudinal coordinate of where experiment occurred</p>
http://lod.bco-dmo.org/id/dataset-parameter/984281.rdf
Name: Longitude
Units: unitless
Description: <p>Longitudinal coordinate of where experiment occurred</p>
http://lod.bco-dmo.org/id/dataset-parameter/984282.rdf
Name: Experiment_Accession
Units: unitless
Description: <p>SRA Experiment Accession number</p>
http://lod.bco-dmo.org/id/dataset-parameter/984283.rdf
Name: Experiment_Title
Units: unitless
Description: <p>SRA Experiment Title (which indicates the location the sample was taken, the date the sample was taken, the day number the sample was taken out of 22 days, and the time the sample was taken in (US Pacific PST/PDT time zone)</p>
http://lod.bco-dmo.org/id/dataset-parameter/984284.rdf
Name: Instrument
Units: unitless
Description: <p>instruments sequencing was completed on</p>
http://lod.bco-dmo.org/id/dataset-parameter/984285.rdf
Name: Submitter
Units: unitless
Description: <p>who submitted the archive entry</p>
http://lod.bco-dmo.org/id/dataset-parameter/984286.rdf
Name: Study_Accession
Units: unitless
Description: <p>study accession number</p>
http://lod.bco-dmo.org/id/dataset-parameter/984287.rdf
Name: Study_Title
Units: unitless
Description: <p>contains the study title</p>
http://lod.bco-dmo.org/id/dataset-parameter/984288.rdf
Name: Sample_Accession
Units: unitless
Description: <p>contains the SRA sample accession number</p>
http://lod.bco-dmo.org/id/dataset-parameter/984289.rdf
Name: Total_Size_Mb
Units: Megabytes (Mb)
Description: <p>the total size of the file</p>
http://lod.bco-dmo.org/id/dataset-parameter/984290.rdf
Name: Total_RUNs
Units: unitless
Description: <p>the total number of runs</p>
http://lod.bco-dmo.org/id/dataset-parameter/984291.rdf
Name: Total_Spots
Units: unitless
Description: <p>total spots</p>
http://lod.bco-dmo.org/id/dataset-parameter/984292.rdf
Name: Total_Bases
Units: unitless
Description: <p>total bases</p>
http://lod.bco-dmo.org/id/dataset-parameter/984293.rdf
Name: Library_Name
Units: unitless
Description: <p>library name</p>
http://lod.bco-dmo.org/id/dataset-parameter/984294.rdf
Name: Library_Strategy
Units: unitless
Description: <p>library strategy</p>
http://lod.bco-dmo.org/id/dataset-parameter/984295.rdf
Name: Library_Source
Units: unitless
Description: <p>library source</p>
http://lod.bco-dmo.org/id/dataset-parameter/984296.rdf
Name: Library_Selection
Units: unitless
Description: <p>library selection</p>
http://lod.bco-dmo.org/id/dataset-parameter/984297.rdf
Name: IMG_GenomeID
Units: unitless
Description: <p>JGI Integrated Microbial Genomes & Microbiomes identification number</p>
http://lod.bco-dmo.org/id/dataset-parameter/984298.rdf
Name: Domain
Units: unitless
Description: <p>type of sample sequenced</p>
http://lod.bco-dmo.org/id/dataset-parameter/984299.rdf
Name: Sequencing_Status
Units: unitless
Description: <p>status of JGI sequencing effort for this ID</p>
http://lod.bco-dmo.org/id/dataset-parameter/984300.rdf
Name: Study_Name
Units: unitless
Description: <p>name of the umbrella project study</p>
http://lod.bco-dmo.org/id/dataset-parameter/984301.rdf
Name: Genome_Name_Sample_Name
Units: unitless
Description: <p>name of the specific sample includes standard annotation location_sample_datecollected_daycollected_timecollected</p>
http://lod.bco-dmo.org/id/dataset-parameter/984302.rdf
Name: Sequencing_Center
Units: unitless
Description: <p>location of sequencing</p>
http://lod.bco-dmo.org/id/dataset-parameter/984303.rdf
Name: IMG_Submission_ID
Units: unitless
Description: <p>Submission ID number</p>
http://lod.bco-dmo.org/id/dataset-parameter/984304.rdf
Name: GOLD_Analysis_Project_ID
Units: unitless
Description: <p>Joint Genome Institute Genome OnLine Database project ID</p>
http://lod.bco-dmo.org/id/dataset-parameter/984305.rdf
Name: GOLD_Analysis_Project_Type
Units: unitless
Description: <p>type of Joint Genome Institute Genome OnLine Database project</p>
http://lod.bco-dmo.org/id/dataset-parameter/984306.rdf
Name: GOLD_Sequencing_Project_ID
Units: unitless
Description: <p>Joint Genome Institute Genome OnLine Database project ID</p>
http://lod.bco-dmo.org/id/dataset-parameter/984307.rdf
Name: Genome_Size_assembled
Units: unitless
Description: <p>size of assembled genome</p>
http://lod.bco-dmo.org/id/dataset-parameter/984308.rdf
Name: Gene_Count_assembled
Units: unitless
Description: <p>number of genes in metagenome</p>
http://lod.bco-dmo.org/id/dataset-parameter/984309.rdf
Name: Genome_MetaBAT_Bin_Count_assembled
Units: unitless
Description: <p>number of metaBAT counts</p>
http://lod.bco-dmo.org/id/dataset-parameter/984310.rdf
Name: Estimated_Number_of_Genomes_assembled
Units: unitless
Description: <p>estimated number of genomes assembled</p>
http://lod.bco-dmo.org/id/dataset-parameter/984311.rdf
Name: Estimated_Average_Genome_Size_assembled
Units: unitless
Description: <p>estimated average genome size</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
82096
https://darchive.mblwhoilibrary.org/server/api/core/bitstreams/0a059d3a-29f5-4b46-8ec2-74f9a74627d0/content
download
https://doi.org/10.26008/1912/bco-dmo.984169.1
download
onLine
dataset
<p>The 1–2 L of pre-filtered water was collected and then passed through a 47 mm × 0.22 µm polyethersulfone filter (Tisch, part number SF15021) using the Alexis peristaltic field pump (Proactive Environmental) at a flow rate of &lt;0.1 L min<sup>−1</sup> to isolate the bacterial fraction. Filters were immediately placed in plastic resealable baggies (5 × 4 cm), then 200 µl of DNA/RNA Shield (Zymo Research) was added to the whole filter within 1 minute of collection and filter baggies were transferred to liquid nitrogen while in the field. Samples were transferred to the University of Washington and placed in a −80 °C freezer prior to extraction.&nbsp;<br />
<br />
<span style="font-size:13px">Estimated Gene copies (Supplemental File: 984169_v1_estimated_genome_copies.csv):<br />
<br />
Reads were then quality controlled for length ( &gt; 50 bp), quality ( &gt; Q30), and any adapter contamination was removed along with mateless pairs. Filtered reads were error-corrected using bbcms v.38.90&nbsp;</span>&nbsp;(Bushnell, 2014; https://bbmap.org).&nbsp;<span style="font-size:13px">This was run with the following command line options: bbcms.sh -Xmx100g metadatafile = counts.metadata.json mincount = 2 highcountfraction = 0.6 in = bbcms.input.fastq.gz out1 = input.corr.left.fastq.gzout2 = input.corr.right.fastq.gz. The readset was assembled with metaSPAdes v. 3.15.2&nbsp;(Nurk, 2017). This was run using the following command line options: spades.py -m2000–tmp-dir cromwell_root -o spades3–only-assembler -k 33,55,77,99,127–meta -t 16-1 input.corr.left.fastq.gz -2 input.corr.right.fastq.gz. The input read set was mapped to the final assembly and coverage information generated with bbmap v. BBMap:38.86&nbsp;</span>&nbsp;(Bushnell, 2014; https://bbmap.org)<span style="font-size:13px">. This was run using the following command line options: bbmap.sh build = 1 overwrite = true fastareadlen = 500 -Xmx100g threads = 16 nodisk = trueinterleaved = true ambiguous = random rgid = filename in = reads.fastq.gz ref = reference.fastaout = pairedMapped.bam. Following assembly, the metagenomes underwent processing through the DOE JGI Metagenome Annotation Pipeline (MAP version 5.1.13) and were subsequently loaded into the Integrated Microbial Genomes and Microbiomes (IMG/M) platform (Clum, 2021). Complete metagenomic datasets for each time point are listed by individual IMG Genome IDs (under the Study Name “Seawater microbial communities from East Sound, Orcas Island, WA, USA” (https://gold.jgi.doe.gov/study?id=Gs0160720). For each time point, estimated gene copies for all classes in the domain “Bacteria” were downloaded (3.14.2024; </span>Nunn et al. (2024)&nbsp;<span style="font-size:13px">Dataset 5). At each time point, the cumulative count of gene copies was calculated, and subsequently, each taxonomic class was divided by the total count to determine the class distribution (Nunn et al. (2024) Fig. 4,&nbsp;Dataset 5).<br />
<br />
Methodology is from the results paper Nunn et al. (2024, doi: 10.1038/s41597-024-04013-5).</span></p>
Specified by the Principal Investigator(s)
<p>DNA extraction was performed using Qiagen’s DNeasy PowerWater kit following the manufacturer’s instructions. Quant-iT DNA Assay Kits (Invitrogen) were used to generate accurate DNA quantitation (at the recommended molecular weight) in conjunction with a fluorescent plate reader (Varioskan Lux, SkanIt Software 7.0.2). On average, 1.3 µg of high-quality DNA was isolated from each time point collected and 300 ng of isolated DNA was aliquoted into DNA-free tubes (Matrix, catalog #3743) and sent to the Department of Energy (DOE) Joint Genome Institute (JGI) for sequencing.</p>
<p>Of the 133 time points collected between May 27 (13:00) to June 18 (13:00) of 2021, a total of 128 DNA samples were successfully sequenced at the Department of Energy (DOE) Joint Genome Institute (JGI) using Illumina technology.</p>
<p>Data processing is from the results paper Nunn et al. (2024, doi: 10.1038/s41597-024-04013-5).</p>
<p>These tools (from M. Riffle, JGI) describe the data processing done by JGI to produce the metagenomics dataset. These are good references for understanding the data workflow for the metagenomics data.<br />
* https://github.com/mriffle/nf-filter-fasta<br />
* https://www.yeastrc.org/metagomics</p>
Specified by the Principal Investigator(s)
All sheets in submitted excel files were exported to csv in preparation for import into BCO-DMO.
---
Data file "984169_v1_metagenome.csv" history:
The following sheets were combined into the resulting table attached to this dataset 984169_v1_metagenome.csv:
* NCBI and JGI Linking.xlsx (Sheet1).
* Nunn_OrcasIsland_Data_JGI_metadata.xlsx (Sheets 1 and 2). These data were joined using the identifiers supplied in lookup table "NCBI and JGI Linking.xlsx"
* ISO DateTime with timezone (UTC) column added in ISO 8601 format, converted from DateTime_PT (time in US/Pacific time zone PST/PDT).
---
Supplemental file history:
The csv export of the lookup table "NCBI and JGI Linking.xlsx" was attached to this dataset as a supplemental file "NCBI_and_JGI_Linking.csv"
* Sheet 3 of Nunn_OrcasIsland_Data_JGI_metadata.xlsx was exported as csv and attached as a supplemental file: "984169_v1_estimated_genome_copies.csv" with an additional column added with DateTime with timezone in UTC. Column names were not edited in this table except spaces were converted to underscores.
* The data submitter indicated the names in the dataset should match the names as published in the corresponding literature (results publications). Taxonomic names with current standing as synonyms or misspellings at LPSN were not corrected to be the names as shown at NCBI (or correct name shown at LPSN as of 2025-09-10). A supplemental file "name_matches_and_ids.csv" was added with more information about the names used in this dataset as well as information in the Problems/Issues section.
The originally submitted file "Nunn_OrcasIsland_Data_JGI_metadata.xlsx" was attached as a supplemental file with no additions or changes.
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Missing Data Identifiers:
* In the BCO-DMO data system missing data identifiers are displayed according to the format of data you access. For example, in csv files it will be blank (null) values. In Matlab .mat files it will be NaN values. When viewing data online at BCO-DMO, the missing value will be shown as blank (null) values.
* Column names adjusted to conform to BCO-DMO naming conventions designed to support broad re-use by a variety of research tools and scripting languages. [Only numbers, letters, and underscores. Can not start with a number]
Specified by BCO-DMO Data Managers
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
NovaSeq 6000 with S4 flow cell
NovaSeq 6000 with S4 flow cell
PI Supplied Instrument Name: NovaSeq 6000 with S4 flow cell PI Supplied Instrument Description:Illumina technology was used for DNA sequencing. NovaSeq 6000 with S4 flow cell. Instrument Name: Automated DNA Sequencer Instrument Short Name:Automated Sequencer Instrument Description: A DNA sequencer is an instrument that determines the order of deoxynucleotides in deoxyribonucleic acid sequences.
Varioskan Lux (fluorescent plate reader)
Varioskan Lux (fluorescent plate reader)
PI Supplied Instrument Name: Varioskan Lux (fluorescent plate reader) PI Supplied Instrument Description:SkanIt Software 7.0.2 was used as the fluorescent plate reader (Varioskan Lux). Instrument Name: plate reader Instrument Short Name: Instrument Description: Plate readers (also known as microplate readers) are laboratory instruments designed to detect biological, chemical or physical events of samples in microtiter plates. They are widely used in research, drug discovery, bioassay validation, quality control and manufacturing processes in the pharmaceutical and biotechnological industry and academic organizations. Sample reactions can be assayed in 6-1536 well format microtiter plates. The most common microplate format used in academic research laboratories or clinical diagnostic laboratories is 96-well (8 by 12 matrix) with a typical reaction volume between 100 and 200 uL per well. Higher density microplates (384- or 1536-well microplates) are typically used for screening applications, when throughput (number of samples per day processed) and assay cost per sample become critical parameters, with a typical assay volume between 5 and 50 µL per well. Common detection modes for microplate assays are absorbance, fluorescence intensity, luminescence, time-resolved fluorescence, and fluorescence polarization. From: http://en.wikipedia.org/wiki/Plate_reader, 2014-09-0-23.