Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
  • Problem Description
• Data Files
• Supplemental Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

Additional funding description:
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

See the "Related Datasets" section for descriptions of related holdings at NCBI (umbrella BioProject PRJNA1093221), JGI (GOLD study Gs0160720), Zenodo (raw data), PanoramaWeb, PRIDE/ProteomeXchange (PXD052873), etc.

DOE = U.S. Department of Energy
JGI = Joint Genome Institute (DOE)
IMG/M = Integrated Microbial Genomes  & Microbiomes (DOE JGI)
GOLD = Genomes Online Database (DOE JGI)
NCBI = National Center for Biotechnology Information
SRA = Sequence Read Archive (NCBI)
NSF = U.S. National Science Foundation
OCE = Division of Ocean Sciences (NSF)
IOS - Division of Integrative Organismal Systems (NSF) 
NIH = National Institutes of Health
NIEHS = National Institute of Environmental Health Sciences (NIH)
PRIDE = PRoteomics IDEntifications Database (EMBL-EBI)

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 <0.1 L min⁻¹ 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. 

Estimated Gene copies (Supplemental File: 984169_v1_estimated_genome_copies.csv):

Reads were then quality controlled for length ( > 50 bp), quality ( > Q30), and any adapter contamination was removed along with mateless pairs. Filtered reads were error-corrected using bbcms v.38.90  (Bushnell, 2014; https://bbmap.org). 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 (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  (Bushnell, 2014; https://bbmap.org). 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; Nunn et al. (2024) 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, Dataset 5).

Methodology is from the results paper Nunn et al. (2024, doi: 10.1038/s41597-024-04013-5).

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.

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.

Data processing is from the results paper Nunn et al. (2024, doi: 10.1038/s41597-024-04013-5).

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.
* https://github.com/mriffle/nf-filter-fasta
* https://www.yeastrc.org/metagomics

All sheets in submitted excel files were exported to csv in preparation for import into BCO-DMO.
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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).
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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]

NSF Award Abstract:
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.

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.