Contributors | Affiliation | Role |
---|---|---|
Eren, A. Murat | University of Chicago | Principal Investigator |
Freel, Kelle C. | University of Hawaiʻi at Mānoa | Scientist |
Fuessel, Jessika | University of Chicago | Scientist |
Rappé, Michael S. | University of Hawaiʻi at Mānoa | Scientist |
Tucker, Sarah J. | University of Hawaiʻi at Mānoa | Scientist |
York, Amber D. | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Related Data:
NCBI Project ID PRJNA1201851 offers access to all raw data for short-read and long-read metagenomes, as well as metatranscriptomes and transfer RNA (tRNA) transcripts.
doi:10.6084/m9.figshare.28784717 serves anvi’o contigs-db files for the individual co-assemblies of short-read (SR) as well as long-read (LR) sequencing of metagenomes. Please note that an anvi’o contigs-db includes gene calls, functional annotations, HMM hits, and other information about each contig, and you can always use the program anvi-export-contigs to get a FASTA file for sequences.
doi:10.6084/m9.figshare.28784762 serves FASTA files for metagenome-assembled genomes (MAGs) we have reconstructed from short-read and long-read sequencing of the metagenomes. They are the outputs of quite a preliminary effort, thus secondary attempts to recover genomes from the co-assemblies are most welcome (and very much encouraged). Please see the Supplementary Table for taxonomic annotation and completion/redundancy estimates of the MAGs.
doi:10.6084/m9.figshare.28784765. The EcoPhylo output that describes the phylogeography of ribosomal protein L14 (https://anvio.org/help/main/workflows/ecophylo/).
See more about related data and code at https://merenlab.org/data/hads/
The methods characterized below are part of the following publication, currently in prep: Tucker SJ, Fuessel J, Freel KC, Kiefl E, Freel EB, Ramfelt O, Sullivan CE, Gajigan AP, Mochimaru H, Souza MR, Quinn M, Ratum C, Tran LL, Miller SE, Trigodet F, Lolans K, Morrison HG, Fallon B, Huettel B, Pan T, Rappé MS, Eren AM. A high-resolution diel survey of surface ocean metagenomes, metatranscriptomes, and transfer RNA (tRNA) transcripts.
Hawaiʻi Diel Sampling (HaDS) collection and processing:
We sampled surface seawater from August 18, 2021 9:00 AM to August 20, 2021 9:00 AM Hawaiʻi Standard Time (HST), from a depth of ~0.25 m at station HP1 within coastal Kāneʻohe Bay, Oʻahu, Hawaiʻi, and station STO1 in the adjacent offshore. Sampling took place every 1.5 hours for 48 hours and yielded 33 sampling events at each site. We collected seawater samples for flow cytometry, fluorometric chlorophyll a concentrations, and nucleic acids, and recorded in situ measurements of seawater temperature, pH, and salinity with a YSI 6600 or YSI EcoSense EC300 (YSI Incorporated, Yellow Springs, OH, USA) and and in situ measurements of Photosynthetic Photon Flux Fluence Rate (PPFFR) with a LI-193 Underwater Spherical Quantum Sensor and LI-250A Light Meter (LI-COR Environmental, Lincoln, NE, USA).
Roughly 10-20L of seawater was first pre-filtered through an 85-μm Nitex mesh and then ~4 (HP1) or ~9 (STO1) L of seawater was filtered through 0.2-μm pore-sized polyethersulfone (PES) Sterivex cartridge filters (MilliporeSigma, Burlington, MA, USA) to collect microbial cells for nucleic acids isolation. RNAlater (Invitrogen, Waltham, MA, United States) was immediately added to the Sterivex filters and stored the samples at −80 °C until further processing.
Seawater subsamples (125 mL) were collected on 25-mm diameter GF/F glass microfiber filters (Whatman, GE Healthcare Life Sciences, Chicago, IL, USA) and the filters were stored in aluminum foil at −80 °C until analysis of fluorometric chlorophyll a concentrations. Chlorophyll a was extracted in 100% acetone and the concentration determined with a Turner 10-AU fluorometer (Turner Designs, Sunnyvale, CA, USA), according to standard techniques (Welschmeyer 1994). Seawater aliquots (2 mL) in a final concentration of 0.95% (v:v) paraformaldehyde (Electron Microscopy Services, Hatfield, PA, USA) were collected and stored at −80 °C for cellular enumeration. Abundances of heterotrophic bacteria, photosynthetic picoeukaryotes, Synechococcus, and Prochlorococcus were enumerated by the SOEST Flow Cytometry Facility on a Beckman Coulter CytoFLEX S, following the method of Monger and Landry (1993). Dissolved inorganic nutrients were measured by the SOEST Laboratory for Analytical Biogeochemistry (S-LAB) on a Seal Analytical AA3 HR Nutrient Autoanalyzer, while concentrations of dissolved organic carbon and total nitrogen were measured by the S-LAB on a Shimadzu High-Temperature TOC-L Combustion Analyzer.
DNA extraction, preparation, and sequencing for short-read metagenomics:
Co-extraction of RNA and DNA was adapted from previously published protocols (Donaldson et al. 2020) and was used to extract DNA from Sterivex cartridge filters for short-read metagenomic sequencing. Cells suspended in the RNAlater within the Sterivex filter cartridges were collected by attaching a 10 mL air-filled clean syringe to the Sterivex cartridge and pushing the liquid into a nuclease-free 1 mL centrifuge vial. After centrifugation for 15 min at 4 °C at 13,000 rpm, we discarded the supernatant and kept the cell pellet on ice for further processing. The cartridge was opened holding the filter with a sterilized PVC pipe cutter and the cylinder with the PES membrane removed. Using a sterilized scalpel, the filter membrane was removed from the barrel. We cut the filter into ~6-8 small pieces in a sterile petri dish and transferred the pieces to a Lysing Matrix E (MP Biomedicals, Irvine, CA, USA) vial and added 450 µL Buffer A, 210 µL 20% SDS and 500 µL phenol:chloroform:IAA 125:24:1 pH 8. (50 mL Buffer A: 2 mL 5 M NaCl, 2 mL 0.5 M EDTA, 46 mL dH2O, all solutions were molecular grade and nuclease-free). Using in 50 µl Buffer A, we resuspended the cell pellet obtained from RNAlater and vortexed vigorously for 30 sec followed by a quick centrifugation step in a microcentrifuge and transferred the solution to the Lysing Matrix E vial. The cells were lysed with a Bead Ruptor Elite (OMNI International, Kennesaw, GA, USA) on setting 6 for 30 sec followed by two min on ice followed by another lysing cycle with the same settings. The vials were centrifuged for 3 min at 4 °C at 13,000 rpm and the upper aqueous phase removed to a fresh, nuclease-free 1.5 mL centrifuge vial. Following the addition of 500 µL phenol:chloroform:IAA, we repeated the centrifugation step. After transferring the aqueous phase to a new centrifuge vial, we added 3 M sodium acetate at 1/10th of the transferred volume and mixed the solution by inversion. Following the addition of 500 µL ice-cold pure ethanol, we placed the samples in the freezer at -20 °C overnight.
The next day, samples were thawed and centrifuged at 13,000 rpm at 4 °C for 20 min, and the supernatant was carefully decanted to retain the nucleic acid pellet. We washed the pellet with 500 µL 70% ethanol at 4 °C and vortexed shortly followed by centrifugation at 13,000 rpm at 4 °C for 10 min. We decanted the liquid carefully and air-dried the pellet for 15 min. We added 100 µL of nuclease-free dH2O to the pellet and vortexed the pellet for 20 min with a Vortex-Genie 2 (Scientific Industries, Bohemia, NY, USA) and a Vortex adapter 24 (Qiagen, Hilden, Germany). We purified the DNA with the DNA Clean & Concentrator-100 (Zymo Research, Irvine, CA, USA) and eluted in a final volume of 30 µL.
Following shearing of DNA to 400 bp on a Covaris S220 focused-ultrasonicator system, DNA was cleaned and concentrated with AMPure XP beads (Beckman Coulter, Brea, CA, USA) and metagenomic sequencing libraries prepared using the Ovation Ultralow System V2 with indexed sequencing adapters (Tecan Genomics, Männedorf, Switzerland). Final libraries were pooled at equimolar amounts and size-selected the library using a BluePippin 1.5% agarose gel cassette (Sage Science, Beverly, MA, USA) targeting the 468–572 bp region. DNA concentration was evaluated using the Qubit dsDNA high-sensitivity (HS) assays (Thermo Fisher Scientific) and the purity estimated with a NanoDrop Microvolume Spectrophotometer (Thermo Fisher Scientific) before library preparation. We quantified the short-read metagenomic libraries using real-time PCR using KAPA library quantification reagents (Roche 07960204001) prior to sequencing. We paired-end sequenced the final pool on a NextSeq 500/550 High Output v2.5 using the 300 cycles kit (Illumina, San Diego, CA, USA). The Keck Sequencing Facility at the Marine Biological Laboratory in Woods Hole, MA, USA carried out the short-read metagenomic library preparation and sequencing.
Nucleic acid extraction, preparation, and sequencing for long-read metagenomics and metatranscriptomics:
The filters were first removed from the Sterivex cartridge as described above, and cut into small pieces to transfer them to 2 mL ZR BashingBead lysis tubes with 0.1 & 0.5 mm beads (Zymo Research Europe, Germany) for subsequent cell disruption with a Qiagen TissueLyser (3 min. at 25 Hz). Next, DNA and total RNA were isolated from the same starting material using the ZymoBIOMICS DNA/RNA Miniprep Kit (Zymo Research Europe, Germany), with an additional DNase I treatment of the extracted RNA.
DNA directly served as input for a barcoded DNA library preparation without prior fragmentation. DNA was quality-controlled using HS Qubit assays (Thermo, Waltham, USA) and pulse-field capillary electrophoresis (Agilent FEMTOpulse). We prepared HiFi SMRTbell libraries (PacBio, Menlo Park, CA, USA) for long-read DNA sequencing following manufacturer protocols using the Ultra Low DNA Input approach and sequenced the libraries on a PacBio Revio device. For demultiplexing, adapter trimming, and deduplication of the sequencing data we relied on the PacBio SMRTlink software suite.
RNAseq libraries were prepared with the Universal Prokaryotic RNA-Seq Prokaryotic Any Deplete Kit by Tecan Genomics, including the proprietary adapter addition step of the kit and dual 8-mer barcode indexing. RNA was evaluated quality using HS Qubit assays (Thermo, Waltham, USA) and a PicoChip (Agilent Bioanalyzer) for total RNA. The libraries were paired-end sequenced on an Illumina NextSeq2000. The MPI Cologne Genomics Center conducted nucleic acid extraction, library preparation, and sequencing for long-read metagenomics and metatranscriptomics.
RNA extraction, preparation, and transfer RNA sequencing:
We prepared the following solutions: 1) marine microbe lysis solution: 40 mM EDTA, 50 mM TRIS and 0,75 M sucrose final concentration, 2) lysozyme stock solution: 100 mg/mL in TE buffer pH 8, RNAse free, and 3) Lysis solution 670 µL per sample: 600 µL marine microbe lysis solution, 67 µL lysozyme stock solution, 0.2 µL SUPERase (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA, 20 U/μL). For each Sterivex cartridge, we collected and pelleted cells that were suspended in RNAlater following the same steps described above and then resuspended the pellet with 70 µL of lysis solution. We removed the filter from the Sterivex cartridge using the steps described above, cut the filter into small pieces using a clean scalpel, and transferred the filter pieces to a 14 mL culture tube containing 600 µL of lysis solution. We then added the lysis solution with the resuspended cell pellet to the corresponding 14 mL tube with the filter pieces, and vortexed vigorously for 2 min. The samples were incubated in a 37 °C water bath for 45-60 min with gentle agitation of the samples every 10 min. Next, we added 7.4 µL of proteinase K (800 U/mL, New England Biolabs, Ipswich, MA, USA) and 74 µL of 10% SDS for a final concentration of 1%. The sample was placed in a 37 °C water bath for 2 h with gentle agitation every 10 min. Samples were stored at -80 °C until extraction.
The samples were thawed on ice. Next, we transferred the lysate to Lysing Matrix C tubes (MP Biomedicals, Santa Ana, CA, USA), which contain 1 mm silica lysing beads. We added 400 µL of 0.3 M NaOAc/HOAc, 10 mM EDTA, pH4.8 and an equal volume of acetate-saturated phenol/chloroform and vortexed briefly. The samples were placed in a reciprocating bead beater for 2-min at maximum intensity, pausing between 1 min intervals followed by centrifugation at 13,000 x g for 15 min at 4 °C. The aqueous phase was transferred to a fresh Lysing Matrix C tube and the phenol/chloroform extraction steps repeated. To precipitate the RNA, we added 1 v of 100 % isopropanol, vortexed each sample thoroughly, and incubated them overnight at -20 °C. The sample was centrifuged at 4 °C for 15 min at 13,000 x g, the supernatant discarded, and the pellet washed with 1 mL of chilled 75% ethanol. Following centrifugation for 5 min at 13,000 x g at 4 °C and removal of the ethanol, we let the RNA pellet air dry for 10 min at room temperature and then redissolved it in 30 µL of an acid-elution buffer (10 mM NaOAc/1 mM EDTA, pH 4.8). The sample was cleaned with the Zymo Research Oligo Clean & Concentrate, and eluted in RNAse-free water. Finally, we added an equivalent amount of acid-elution buffer to each extract to reach a final concentration of 5 mM NaOac/0.05 mM EDTA, pH 4.8. We evaluated the quality and quantity of the RNA used in the tRNA libraries using a Qubit RNA HS assay (ThermoFisher Scientific, USA) and a Nanodrop Microvolume Spectrophotometer (ThermoFisher Scientific, USA). Previously published protocols for transfer RNA library preparation (MSR-seq; Watkins et al., 2022) were followed with paired-end multiplexed small RNA sequencing conducted on an Illumina NovaSeq 6000. Subsequently, we demultiplexed sequence data with custom scripts.
Kāneʻohe Bay Time-series (KByT) collection and processing:
To contextualize the diel data, we deeply sequenced four metagenomes collected at stations HP1 and STO1 on December 23, 2020 and May 24, 2021. These sampling events were part of the routine Kāneʻohe Bay Time-series sampling and processing of nucleic acids and other associated biogeochemical parameters followed previously published protocols (Tucker et al. 2021, 2025). Briefly, approximately one L of seawater collected from ~2m depth at HP1 and STO1 was prefiltered using an 85 μm Nitex mesh and subsequently collected on a 25-mm diameter 0.1-μm pore-sized polyethersulfone (PES) membrane for nucleic acids (Supor-100, Pall Gelman Inc., Ann Arbor, MI, USA). The filters were submerged in DNA lysis buffer (Suzuki et al., 2001) and stored at −80 °C until extraction. Genomic DNA was extracted using a Qiagen Blood and Tissue Kit (Qiagen Inc., Valencia, CA, USA) with modifications (Becker, Brandon & Rappé, 2007). All other processing of subsamples follow the protocols described above. The short-read metagenomic library preparation and sequencing protocols for these 4 deeply sequenced samples are the same as those used for the diel short-read metagenomes.
Deployment description:
Small boat operations between the Hawaiʻi Institute of Marine Biology, coastal station HP1, and offshore station STO1 were conducted every 1.5 hours for 48 hours between 18 August 2021 and 20 August 2021. Small boat operations conducted through routine Kāneʻohe Bay Time-series (KByT) sampling yielded two additional sampling events at station HP1 and station STO1 on 23 December 2020 and 24 May 2021.
* Table within submitted file "final_submission_may22205_bco_dmo_Tucker.txt" was imported into the BCO-DMO data system for this dataset. Values "NA" imported as missing data values. Table will appear as Data File: 963210_v1_biogeochem-and-sampling-info-hads.csv (along with other download format options).
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]
* Date converted to ISO 8601 format
* ISO DateTime with timezone (UTC) column added in ISO 8601 format. (from local time provided in HST)
* lat_lon combined column *e.g. "21.4829 N 157.7663 W" split into lat and lon in decimal degrees lat "21.4829", lon "-157.7663" for improved geospatial readiness.
* For sample HADS_20210819_H2100_STO1, converted Time_sampled "21.25" to 21:25 to match the format of the column.
File |
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963210_v1_biogeochem-and-sampling-info-hads.csv (Comma Separated Values (.csv), 40.57 KB) MD5:e048d05fbae77cc81ffb241c40fdc6bf Primary data file for dataset ID 963210, version 1 |
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Hawaiʻi Diel Sampling (HaDS) information (Meren Lab Page). filename: Meren_Lab_HaDS_Website_20250604.pdf (Portable Document Format (.pdf), 4.73 MB) MD5:f9a817b4af0cf74aa99e72d43b358a53 PDF containing the contents of Meren Lab website page "Hawaiʻi Diel Sampling (HaDS)" https://merenlab.org/data/hads/ on 2025-06-04 (© 2025 Meren Lab) which includes further explanation of related datasets and code. |
Parameter | Description | Units |
HADS_Universal_Sample_ID | Universal Sample ID. Universal Sample ID is used to connect multiple sample types within the Hawai_i Diel Sampling (HaDS). It is composed of the Project ID "HADS", the date of sampling (yyyymmdd), the sampling hour, and the sampling station. | unitless |
NCBI_BioProject | NCBI BioProject Accession. | unitless |
BioSample_accession | NCBI Biosample Accession. | unitless |
sampleID_MTX | MTX Sample ID. MTX Sample ID has similar components of the univeral sample ID plus the sample type (mtx for metatranscriptomics) and the sample number that is unique only among MTX samples. | unitless |
sampleID_TRNA | TRNA Sample ID. TRNA Sample ID has similar components of the univeral sample ID plus the sample type (TRNA for transfer RNA sequencing) and the sample number that is unique only among TRNA samples. | unitless |
sampleID_MGX | MGX Sample ID. MGX Sample ID has similar components of the univeral sample ID plus the sample type (MGX for short-read metagenomics) and the sample number that is unique only among MGX samples. | unitless |
sampleID_HMW | HMW Sample ID. HMW Sample ID has similar components of the univeral sample ID plus the sample type (HMW for long-read metagenomics that uses high-molecular weight DNA) and the sample number that is unique only among HMW samples. | unitless |
Station | Station ID for collection. | unitless |
Date | Collection date (ISO 8601 format) | unitless |
Time_sampled | Local time (Hawaiii Standard Time) of sampling surface seawater. | hh:mm |
ISO_DateTime_UTC | DateTime with timezone (ISO 8601 format) for sampling surface seawater (UTC time zone) | unitless |
lat | Latitiude of sample collection site. | Decimal Degrees |
lon | Longitude of sample collection site. | Decimal Degrees |
Temp | Temperature of surface seawater in situ. | degrees Celsius |
Salinity | Salinity of surface seawater in situ. | ppt |
Depth | Depth of sample collection. | meters |
pH | pH of surface seawater in situ. | pH scale |
PPFFR | Photosynthetic Photon Flux Fluence Rate in situ. | Micromoles of photons per square meter per second (umol/m2/s) |
PRO_mL | Surface seawater cellular abundances of Prochlorocococcus cells counted on CytoFLEX S flow cytometer. | cells per mL |
SYN_mL | Surface seawater cellular abundances of Synechococcus cells counted on CytoFLEX S flow cytometer. | cells per mL |
PEUK_mL | Surface seawater cellular abundances of photosynthetic picoeukaryotes counted on CytoFLEX S flow cytometer. | cells per mL |
HBACT_mL | Surface seawater cellular abundances of non-cyanobacterial (presumably heterotrophic) bacteria and archaea (referred to as heterotrophic bacteria) counted on CytoFLEX S flow cytometer. | cells per mL |
chla | Extracted chlorophyll a concentrations from surface seawater. | micrograms per Liter |
Phosphate_uM | Inorganic nutrients. Concentrations of phosphate in surface seawater sample (PO43-). | micromolar (uM) |
Silicate_uM | Inorganic nutrients. Concentrations of silicate in surface seawater sample (SiO4 ). | micromolar (uM) |
NitrateNitrite_uM | Inorganic nutrients. Concentrations of nitrate+nitrite in surface seawater sample (NO2- + NO3- ). | micromolar (uM) |
Ammonia_uM | Inorganic nutrients. Concentrations of ammonia in surface seawater sample (NH4). | micromolar (uM) |
DOC_uM | Dissolved organic carbon. Concentrations of dissolved organic carbon in surface seawater sample. | micromolar (uM) |
TN_uM | Total Nitrogen. Concentrations of total nitrogen in surface seawater sample. | micromolar (uM) |
MGX_SRA_accession | Short-read metagenome NCBI Sequence Read Archive Accession. | unitless |
MGX_filename | Short-read metagenome R1 file name. | unitless |
MGX_filename2 | Short-read metagenome R2 file name. | unitless |
MTX_SRA_accession | Metatranscriptome NCBI Sequence Read Archive Accession. | unitless |
MTX_filename | Metatranscriptome R1 file name. | unitless |
MTX_filename2 | Metatranscriptome R2 file name. | unitless |
TRNA_SRA_accession | tRNA-seq NCBI Sequence Read Archive Accession. | unitless |
TRNA_filename | tRNA R1 file name. | unitless |
TRNA_filename2 | tRNA R2 file name. | unitless |
HMW_SRA_accession | Long-read metagenomics NCBI Sequence Read Archive Accession. | unitless |
HMW_filename | Long-read metagenomics file name. | unitless |
Dataset-specific Instrument Name | Agilent FEMTOpulse |
Generic Instrument Name | Agilent Femto Pulse System |
Dataset-specific Description | quality control of DNA libraries |
Generic Instrument Description | Femto Pulse System. Parallel capillary electrophoresis instrument for analysis of nucleic acids. Compatible with the Femto Pulse 12-capillary array.
The Agilent Femto Pulse System offers an excellent solution for analyzing high molecular weight DNA samples with high accuracy. Utilizing pulsed-field electrophoresis technology, this system delivers precise quantification and qualification of DNA samples up to 165 kb, crucial for evaluating the quality of long-read sequencing libraries and predicting the average read length in long-read NGS.
In comparison to other non-denaturing gel-based analytical instruments, the Femto Pulse System stands out by achieving significantly higher sensitivity for nucleic acid smears and fragments. This capability enables the detection of low-abundance samples, making it an ideal instrument for highly sensitive analysis of RNA or cfDNA samples.
(from https://www.agilent.com/en/product/automated-electrophoresis/femto-pulse...) |
Dataset-specific Instrument Name | Qubit (Thermo, Waltham, USA) |
Generic Instrument Name | Automated DNA Sequencer |
Dataset-specific Description | quantify DNA and RNA |
Generic Instrument Description | A DNA sequencer is an instrument that determines the order of deoxynucleotides in deoxyribonucleic acid sequences. |
Dataset-specific Instrument Name | Illumina NextSeq 500/550 High Output v2.5 |
Generic Instrument Name | Automated DNA Sequencer |
Dataset-specific Description | sequencing of omic libraries |
Generic Instrument Description | A DNA sequencer is an instrument that determines the order of deoxynucleotides in deoxyribonucleic acid sequences. |
Dataset-specific Instrument Name | Illumina NextSeq2000 |
Generic Instrument Name | Automated DNA Sequencer |
Dataset-specific Description | sequencing of omic libraries |
Generic Instrument Description | A DNA sequencer is an instrument that determines the order of deoxynucleotides in deoxyribonucleic acid sequences. |
Dataset-specific Instrument Name | Illumina NovaSeq 6000 |
Generic Instrument Name | Automated DNA Sequencer |
Dataset-specific Description | sequencing of omic libraries |
Generic Instrument Description | A DNA sequencer is an instrument that determines the order of deoxynucleotides in deoxyribonucleic acid sequences. |
Dataset-specific Instrument Name | PacBio Revio device |
Generic Instrument Name | Automated DNA Sequencer |
Dataset-specific Description | sequencing of omic libraries |
Generic Instrument Description | A DNA sequencer is an instrument that determines the order of deoxynucleotides in deoxyribonucleic acid sequences. |
Dataset-specific Instrument Name | PicoChip (Agilent Bioanalyzer) |
Generic Instrument Name | Bioanalyzer |
Dataset-specific Description | quality and quantity assessment of RNA |
Generic Instrument Description | A Bioanalyzer is a laboratory instrument that provides the sizing and quantification of DNA, RNA, and proteins. One example is the Agilent Bioanalyzer 2100. |
Dataset-specific Instrument Name | Beckman Coulter CytoFLEX S |
Generic Instrument Name | Flow Cytometer |
Dataset-specific Description | cellular enumeration of Prochlorococcus, Synechococcus, heterotrophic bacteria, and photosynthetic picoeukaryotes |
Generic Instrument Description | Flow cytometers (FC or FCM) are automated instruments that quantitate properties of single cells, one cell at a time. They can measure cell size, cell granularity, the amounts of cell components such as total DNA, newly synthesized DNA, gene expression as the amount messenger RNA for a particular gene, amounts of specific surface receptors, amounts of intracellular proteins, or transient signalling events in living cells.
(from: http://www.bio.umass.edu/micro/immunology/facs542/facswhat.htm) |
Dataset-specific Instrument Name | Bead Ruptor Elite (OMNI International, Kennesaw, GA, USA) |
Generic Instrument Name | Homogenizer |
Dataset-specific Description | Bead Ruptor Elite (OMNI International, Kennesaw, GA, USA)- bead beating for cell lysis |
Generic Instrument Description | A homogenizer is a piece of laboratory equipment used for the homogenization of various types of material, such as tissue, plant, food, soil, and many others. |
Dataset-specific Instrument Name | Qiagen TissueLyser |
Generic Instrument Name | Homogenizer |
Dataset-specific Description | cell lysis |
Generic Instrument Description | A homogenizer is a piece of laboratory equipment used for the homogenization of various types of material, such as tissue, plant, food, soil, and many others. |
Dataset-specific Instrument Name | LI-193 Underwater Spherical Quantum Sensor and LI-250A Light Meter (LI-COR Environmental, Lincoln, NE, USA) |
Generic Instrument Name | Light Meter |
Dataset-specific Description | in situ measurements of Photosynthetic Photon Flux Fluence Rate |
Generic Instrument Description | Light meters are instruments that measure light intensity. Common units of measure for light intensity are umol/m2/s or uE/m2/s (micromoles per meter squared per second or microEinsteins per meter squared per second). (example: LI-COR 250A) |
Dataset-specific Instrument Name | YSI EcoSense EC300 (YSI Incorporated, Yellow Springs, OH, USA) |
Generic Instrument Name | Multi Parameter Portable Meter |
Dataset-specific Description | in situ seawater measurements |
Generic Instrument Description | An analytical instrument that can measure multiple parameters, such as pH, EC, TDS, DO and temperature with one device and is portable or hand-held. |
Dataset-specific Instrument Name | Vortex-Genie 2 (Scientific Industries, Bohemia, NY, USA) |
Generic Instrument Name | no_bcodmo_term |
Dataset-specific Description | vortexing samples |
Generic Instrument Description | No relevant match in BCO-DMO instrument vocabulary. |
Dataset-specific Instrument Name | Vortex adapter 24 (Qiagen, Hilden, Germany) |
Generic Instrument Name | no_bcodmo_term |
Dataset-specific Description | vortexing samples |
Generic Instrument Description | No relevant match in BCO-DMO instrument vocabulary. |
Dataset-specific Instrument Name | Seal Analytical AA3 HR Nutrient Autoanalyzer |
Generic Instrument Name | Nutrient Autoanalyzer |
Dataset-specific Description | measured concentrations of inorganic nutrients |
Generic Instrument Description | Nutrient Autoanalyzer is a generic term used when specific type, make and model were not specified. In general, a Nutrient Autoanalyzer is an automated flow-thru system for doing nutrient analysis (nitrate, ammonium, orthophosphate, and silicate) on seawater samples. |
Dataset-specific Instrument Name | BluePippin 1.5% agarose gel cassette (Sage Science, Beverly, MA, USA) |
Generic Instrument Name | Sage Science BluePippin DNA size selection device |
Dataset-specific Description | size-selection of omics libraries |
Generic Instrument Description | An automated DNA size selection instrument, with pulsed-field electrophoresis for resolving and collecting high molecular weight DNA. The instrument is used to automatically extract DNA fragments of a user selected size for downstream technologies such as miRNA isolation, DNA sequencing, RNA-seq, genotyping, DNA sequencing, ChIP-seq, and Long-read sequencing. The instrument uses electrophoresis along with laser detection or other imaging technology to determine when to start collecting DNA based on size ranges entered by the user. Once the DNA is no longer in the desired size range, collection ceases. The instrument has electrophoresis voltage options: 25V, 100V or 150V constant, or 100V pulsed field. The optical detection wavelength is 470 nm excitation, and 525 nm emission. The instrument can run up to 5 samples/gel cassettes at a time, with no possibility of cross contamination. |
Dataset-specific Instrument Name | Shimadzu High-Temperature TOC-L Combustion Analyzer |
Generic Instrument Name | Shimadzu TOC-L Analyzer |
Dataset-specific Description | Measured the concentration of dissolved organic carbon and total nitrogen |
Generic Instrument Description | A Shimadzu TOC-L Analyzer measures DOC by high temperature combustion method.
Developed by Shimadzu, the 680 degree C combustion catalytic oxidation method is now used worldwide. One of its most important features is the capacity to efficiently oxidize hard-to-decompose organic compounds, including insoluble and macromolecular organic compounds. The 680 degree C combustion catalytic oxidation method has been adopted for the TOC-L series.
http://www.shimadzu.com/an/toc/lab/toc-l2.html |
Dataset-specific Instrument Name | NanoDrop Spectrophotometer (Thermo Fisher Scientific) |
Generic Instrument Name | Thermo Scientific NanoDrop spectrophotometer |
Dataset-specific Description | measure DNA purity |
Generic Instrument Description | Thermo Scientific NanoDrop spectrophotometers provide microvolume quantification and purity assessments of DNA, RNA, and protein samples. NanoDrop spectrophotometers work on the principle of ultraviolet-visible spectrum (UV-Vis) absorbance. The range consists of the NanoDrop One/OneC UV-Vis Spectrophotometers, NanoDrop Eight UV-Vis Spectrophotometer and NanoDrop Lite Plus UV Spectrophotometer. |
Dataset-specific Instrument Name | |
Generic Instrument Name | Turner Designs Fluorometer 10-AU |
Dataset-specific Description | chlorophyll a extractions |
Generic Instrument Description | The Turner Designs 10-AU Field Fluorometer is used to measure Chlorophyll fluorescence. The 10AU Fluorometer can be set up for continuous-flow monitoring or discrete sample analyses. A variety of compounds can be measured using application-specific optical filters available from the manufacturer. (read more from Turner Designs, turnerdesigns.com, Sunnyvale, CA, USA) |
Dataset-specific Instrument Name | Covaris S220 focused-ultrasonicator system |
Generic Instrument Name | ultrasonic cell disrupter (sonicator) |
Dataset-specific Description | mechanical shearing of genomic DNA |
Generic Instrument Description | Instrument that applies sound energy to agitate particles in a sample. |
Dataset-specific Instrument Name | YSI 6600 (YSI Incorporated, Yellow Springs, OH, USA) |
Generic Instrument Name | YSI Sonde 6-Series |
Dataset-specific Description | In situ seawater measurements |
Generic Instrument Description | YSI 6-Series water quality sondes and sensors are instruments for environmental monitoring and long-term deployments. YSI datasondes accept multiple water quality sensors (i.e., they are multiparameter sondes). Sondes can measure temperature, conductivity, dissolved oxygen, depth, turbidity, and other water quality parameters. The 6-Series includes several models. More from YSI. |
Physiological responses of marine microorganisms to environmental conditions are key drivers of both the ecology and evolution of microbial communities and the biogeochemistry of the world's oceans. Marine bacteria respond to fluctuating environmental conditions with both transcriptional and translation regulation. While the study of transcriptional regulation in marine microbial communities has gained traction through the application of metatranscriptomics, still relatively little is understood about how marine microbial populations utilize translational regulation to shape protein synthesis.
Translational regulation through transfer RNAs (tRNAs) allows cells to regulate gene expression beyond transcription and yield proteins that are not encoded by the genome. Due to the central role of tRNAs in protein synthesis, the characterization of tRNA modifications and tRNA abundances offer direct insights into translational processes. However, molecular and computational challenges have limited the capacity to conduct investigations of tRNAs in naturally occurring microbial habitats. Here we apply high-throughput sequencing of tRNAs collected from surface ocean marine microbial communities to observe epi-changes in translational regulation in response to changing ocean conditions. These metaepitranscriptomic data are collected in tandem with metagenomic and metatranscriptomic data as well as measures of the physical, chemical, and biological ocean conditions. Through this integrated dataset, we aim to develop new computational approaches to analyze high-throughput transfer RNA sequencing data and use these approaches to gain new insights into the ecology and environmental responses of major marine populations.
Functions carried out by microscopic inhabitants of the surface ocean affect every aspect of life on our planet, regardless of distance from the coast. Ocean phytoplankton are responsible for half of the photosynthesis on Earth, the first step in a complex system that annually withdraws 50 billion metric tons of carbon from the atmosphere to sustain their growth. Of this, 25 billion metric tons participate in a rapid cycle in which biologically reactive material is released into seawater and converted back into carbon dioxide by marine bacteria within hours to days. The chemical-microbe network at the heart of this fast cycle remains poorly constrained; consequently, its primary currencies and controls remain elusive; its sensitivities to changing ocean conditions are unknown; and its responses to future climate scenarios are not predictable. The Center for Chemical Currencies of a Microbial Planet (C-CoMP) integrates research, education and knowledge transfer activities to develop a mechanistic understanding of surface ocean carbon flux within the context of a changing ocean and through increased participation in ocean sciences. C-CoMP supports science teams that merge biology, chemistry, modeling, and informatics to close long-standing knowledge gaps in the identities and dynamics of organic molecules that serve as the currencies of elemental transfer between the ocean and atmosphere. C-CoMP fosters education, outreach, and knowledge transfer activities that engage students of all ages, broaden participation in the next generation of ocean scientists, and extend novel open-science approaches into complementary academic and industrial communities. The Center framework is critical to this mission, uniquely facilitating an open exchange of experimental and computational science, methodological and conceptual challenges, and collaborations that establish integrated science and education partnerships. With expanded participation in ocean science research and ocean literacy across the US society, the next generation of ocean scientists will better reflect the diverse US population.
Climate-carbon feedbacks on the marine carbon reservoir are major uncertainties for future climate projections, and the trajectory and rate of ocean changes depend directly on microbial responses to temperature increases, ocean acidification, and other perturbations driven by climate change. C-CoMP research closes an urgent knowledge gap in the mechanisms driving carbon flow between ocean and atmosphere, with global implications for predictive climate models. The Center supports interdisciplinary science teams following open and reproducible science practices to address: (1) the chemical currencies of surface ocean carbon flux; (2) the structure and regulation of the chemical-microbe network that mediates this flux; and (3) sensitivity of the network and its feedbacks on climate. C-CoMP leverages emerging tools and technologies to tackle critical challenges in these themes, in synergy with existing ocean programs and consistent with NSF’s Big Ideas. C-CoMP education and outreach activities seek to overcome barriers to ocean literacy and diversify participation in ocean research. The Center is developing (1) initiatives to expand ocean literacy in K-12 and the broader public, (2) ocean sciences undergraduate curricula and research opportunities that provide multiple entry points into research experiences, (3) post-baccalaureate programs to transition undergraduates into graduate education and careers in ocean science, and (4) interdisciplinary graduate student and postdoctoral programs that prepare the next generation of ocean scientists. The C-CoMP team includes education faculty who evaluate the impacts of education and outreach activities and export successful STEM initiatives to the education community. C-CoMP is revolutionizing the technologies for studying chemical transformations in microbial systems to build understanding of the outsized impact of microbes on elemental cycles. Open science, cross-disciplinary collaborations, community engagement, and inclusive practices foster strategic advances in critical science problems and STEM initiatives. C-CoMP science, education, and knowledge-transfer themes are efficiently addressed through a sustained network of scientists addressing critical research challenges while broadening the workforce that will tackle multi-disciplinary problems with academic, industrial and policy partners.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
The Program's Data Management Plan (DMP) is available as a PDF document.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) | |
Simons Foundation (Simons) | |
Simons Foundation (Simons) |