http://lod.bco-dmo.org/id/dataset/935469
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
2024-08-12
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
18s DNA sequences of planktonic animals that are potential prey of siphonophores in water off California USA from 2016-2020
2024-12-05
publication
2024-12-05
revision
BCO-DMO Linked Data URI
2024-12-05
creation
http://lod.bco-dmo.org/id/dataset/935469
Casey W. Dunn
Yale University
principalInvestigator
Alejandro Damian-Serrano
Yale University
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: Damian-Serrano, A., Dunn, C. W. (2024) 18s DNA sequences of planktonic animals that are potential prey of siphonophores in water off California USA from 2016-2020. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2024-12-05 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/935469 [access date]
Methods and Sampling: <p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">The methods below are adapted from&nbsp;Damian-Serrano, Hetherington et al. (2022).</span></span></p>
<p>&nbsp;</p>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">We built an 18S gene barcoding database of potential siphonophore prey items to expand on the available reference sequences in public databases. To do this, we collected 60 specimens of 30 species of zooplankton and micronekton from the California Current using a Tucker trawl. We targeted plausible prey species from motile open-ocean taxa that cohabitate with siphonophores and are underrepresented in SILVA databases (high quality ribosomal RNA databases, arb-silva.de), including fishes, crustaceans, jellyfishes, urochordates, chaetognaths, polychaetes, and mollusks. Specimens were photographed alive, then tissue was sampled and frozen, and finally the rest of the animal was fixed in formalin as a voucher to be identified and preserved at the Yale Peabody Museum of Natural History.&nbsp;</span></span></p>
<p>&nbsp;</p>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">DNA extraction, quality control, PCR, and amplicon cleanup was carried out in a similar fashion as the metabarcoding protocol described above (and detailed in Damian Serrano 2022, doi:&nbsp;</span><span style="color:rgb(0, 0, 0); font-family:aptos,sans-serif">10.17504/protocols.io.5qpvo57o7l4o/v2</span><span style="font-family:arial,helvetica,sans-serif">), using the PCR program with an annealing temperature of 54°C, and a single pair of primers (166F and 134R), spanning the full extent of the sequence containing all barcode regions used in the gut content metabarcoding (from V3 to V9). Purified amplicons were sent in plates with the forward and reverse primer separately for Sanger sequencing from both ends at the Yale DNA Analysis Facility. A total of 89 newly-submitted sequences were then assembled and trimmed at a 95% quality cutoff in Geneious and concatenated with the latest SILVA database (SILVA_138_SSURef_NR99 downloaded on February 23, 2021) pruned to remove non-eukaryotic sequences.&nbsp;</span></span></p>
<p>&nbsp;</p>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Taxonomic identities of the reads in siphonophore gut contents were assigned using the assignment software METAXA2 (<span style="color:rgb(32, 32, 32)">Bengtsson‐Palme 2015)</span>&nbsp;with a 70% reliability cutoff, comparing the sequences against our custom-built library built using SILVA138 that includes the new prey sequences.</span></span></p>
<p>&nbsp;</p>
<h3><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Siphonophore collection</span></span></h3>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">In order to sample a representative set of taxa across the siphonophore phylogeny, we targeted a set of 41 species (aiming for 10 specimens per species) including cystonects, apolemiids, pyrostephids, euphysonects, and calycophorans from shallow and deep waters. Most species were sampled from the Offshore California Current Ecosystem (OCCE) except for the Portuguese man-o-war&nbsp;</span><em><span style="font-family:helvetica">Physalia</span></em><span style="font-family:arial,helvetica,sans-serif">&nbsp;<em>physalis</em>, which was collected off Bermuda in the Sargasso Sea;&nbsp;<em>Sulculeolaria chuni</em>&nbsp;and some&nbsp;<em>Nanomia</em>&nbsp;spp. (labeled as “Atlantic”) which were collected off Rhode Island in the Block Island sound;&nbsp;<em>Forskalia</em>&nbsp;sp. M123-SS8 and shallow&nbsp;<em>Nanomia</em>&nbsp;sp. KiloMoana2018-BW7-4 which were collected off the coast of Hawaii. While all the&nbsp;<em>Nanomia</em>&nbsp;populations sampled in this study have been referred to as&nbsp;<em>Nanomia bijuga</em>, we suspect that there may be undescribed cryptic&nbsp;<em>Nanomia</em>&nbsp;species among the specimens sampled based on the disparate tentillum morphologies we observed. Therefore, we decided to have them labeled at the genus level. One&nbsp;<em>Nanomia</em>&nbsp;specimen (KiloMoana2018−BW7−4) was collected off the coast of Kona, Hawaii. The pleustonic (surface floating)&nbsp;<em>P.&nbsp;physalis</em>&nbsp;samples were collected manually using a bucket from a small boat. Species found between the 0-20m deep were collected using blue water diving techniques following the guidelines in Haddock &amp; Heine (2005). Species from 200-4000m were collected using ROVs. All animals were collected live and brought back to the ship (or field station in Bermuda for&nbsp;<em>P</em>.&nbsp;<em>Physalis</em>) for dissection. Live colonies were photographed (sometimes recorded on video), and zooids of diagnostic value (nectophores, bracts, tentacles) were dissected, when possible, fixed in 4% formalin, and stored as vouchers at the Yale Peabody Museum of Natural History (voucher catalog numbers provided in specimen metadata&nbsp;S15 Table&nbsp;of Damian-Serrano et al., 2022, doi: </span><span style="color:rgb(0, 0, 0); font-family:aptos,sans-serif">10.1371/journal.pone.0267761</span><span style="font-family:arial,helvetica,sans-serif">).</span></span></p>
<h3>&nbsp;</h3>
<h3><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Gut content metabarcoding</span></span></h3>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Shortly after collection of the live specimens, we dissected and pooled several gastrozooids from each colony, making sure that those with visible gut contents are included in addition to several other without conspicuous prey, and also including visible egested food pellets at the bottom of the sampling container.&nbsp;</span></span></p>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">To extract DNA, we digested the samples with proteinase K at 56°C for 1-2h, and used the DNeasy Blood &amp; Tissue kit (Qiagen, Hilden, Germany) eluting twice at 56°C for 10min into a final volume of 100μl. For barcode amplification, we used a set of six primer pairs that amplify six barcode regions within the 18S gene (‘V3’, ‘V5-V7S’, ‘V5-V7L’, ‘V7’, ‘V7p+V8’, and ‘V9’). The primers were designed using Geneious 11.1.5 (<span style="color:rgb(0, 0, 0)">Kearse 2012)</span>, constraining the search to short (&gt;300 bp) amplicon products with a high chance of remaining uncleaved after digestion in the gastrozooid, flanked by priming sites conserved (to a maximum mismatch of 3bp) across metazoans. The search for conserved priming sites was conducted on an alignment of 18S genes from 975 species across all metazoan phyla downloaded from GenBank (available in&nbsp;github.com/dunnlab/siphweb_metabarcoding/Primer_design). The primer search was optimized to only retrieve non-degenerate primer pairs with compatible annealing temperatures and without problematic dimerization and hairpin temperatures. Primer sequences are shown in&nbsp;Table 1 (<span style="color:rgb(0, 0, 0)">Damian Serrano 2022</span>, doi:&nbsp;</span><span style="color:rgb(0, 0, 0); font-family:aptos,sans-serif">10.1371/journal.pone.0267761</span><span style="font-family:arial,helvetica,sans-serif">), and their properties can be found in Table T1 in the protocol (<span style="color:rgb(0, 0, 0)">Damian Serrano 2022</span></span><span style="font-family:arial,helvetica,sans-serif">).</span></span></p>
<p>&nbsp;</p>
<h3><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Prey reference database</span></span></h3>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">In order to enhance the accuracy of the taxonomic assignments of reads, we also built an 18S gene barcoding database of potential prey items to expand on the available reference sequences in public databases. To do this, we collected 60 specimens of 30 species of zooplankton and micronekton from the OCCE using a Tucker trawl. We targeted plausible prey species from motile open-ocean taxa that cohabitate with siphonophores and are underrepresented in SILVA databases, including fishes, crustaceans, jellyfishes, urochordates, chaetognaths, polychaetes, and mollusks. Specimens were photographed alive, then tissue was sampled and frozen, and finally the rest of the animal was fixed in formalin as a voucher to be identified and preserved at the Yale Peabody Museum of Natural History. DNA extraction, quality control, PCR, and amplicon cleanup was carried out in a similar fashion as the metabarcoding protocol described above (and detailed in&nbsp;<span style="color:rgb(0, 0, 0)">Damian Serrano 2022</span>, doi:&nbsp;</span><span style="color:rgb(0, 0, 0); font-family:aptos,sans-serif">10.17504/protocols.io.5qpvo57o7l4o/v2</span><span style="font-family:arial,helvetica,sans-serif">), using the PCR program with an annealing temperature of 54°C, and a single pair of primers (166F and 134R), spanning the full extent of the sequence containing all barcode regions used in the gut content metabarcoding (from V3 to V9). Purified amplicons were sent in plates with the forward and reverse primer separately for Sanger sequencing from both ends at the Yale DNA Analysis Facility. A total of 89 newly-submitted sequences were then assembled and trimmed at a 95% quality cutoff in Geneious and concatenated with the latest SILVA database (SILVA_138_SSURef_NR99 downloaded on February 23, 2021) pruned to remove non-eukaryotic sequences.</span></span></p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1829835 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1829835
onGoing
Casey W. Dunn
Yale University
203-432-3109
165 Prospect St.
New Haven
CT
06511
USA
casey.dunn@yale.edu
pointOfContact
Alejandro Damian-Serrano
Yale University
1479 Onyx Bridge Institute of Ecology and Evolution
Eugene
OR
97401
USA
adamians@uoregon.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
BioProject
SRA Study
Experiment
Run
BioSample
geo_loc_name
lat
lon
Collection_Date
BioSampleModel
Organism
TaxID
isolation_source
Host
Sample Name
source_material_id
spots
Bases
spots_with_mates
AvgSpotLen
size_MB
Extraction
Index
samp_collect_device
Platform
Instrument
Assay_Type
Library_Name
LibrarySource
LibraryLayout
LibrarySelection
Consent
create_date
ReleaseDate
version
Center_Name
DATASTORE_filetype
DATASTORE_region
DATASTORE_provider
Applied Biosystems SimpliAmp thermal cycler
Qubit 2.0 fluorometer
ROV Doc Ricketts
NanoDrop 3300
theme
None, User defined
NCBI BioProject accession
NCBI SRA study accession (SRP)
NCBI SRA experiment accession (SRX)
NCBI SRA run accession (SRR)
NCBI BioSample accession
region
latitude
longitude
date
No BCO-DMO term
species scientific name (binomial)
taxon_code
sample identification
file_size
instrument
ISO_DateTime_UTC
featureType
BCO-DMO Standard Parameters
qPCR Thermal Cycler
Qubit fluorometer
ROV Doc Ricketts
Thermo Scientific NanoDrop spectrophotometer
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: The effects of predator traits on the structure of oceanic food webs
https://osprey.bco-dmo.org/project/738543
Collaborative research: The effects of predator traits on the structure of oceanic food webs
<p>Food webs describe who eats whom, tracing the flow of energy from plants up to large animals. While many connections in food webs on land are quite familiar (lions eat antelope and antelope eat grass, for example), there are large gaps in our understanding of ocean food webs. Closing these gaps is critical to understanding how nutrients and energy move through ocean ecosystems, how organisms interact in the ocean, and how best to manage ocean resources. This project will study ocean food web structure with a focus on siphonophores, an abundant group of predators in the open ocean that range in length from less than an inch to more than one hundred feet. Siphonophores are closely related to corals and many jellyfish. They are known to be important predators within ocean food webs, but they are difficult to study because they live across great ocean depths and are gelatinous and fragile. The details of what they eat, as well as many other features of their biology, remain poorly known. This project will combine direct observations of feeding, genetic analysis of siphonophore gut contents, and stable isotope analyses to identify what different species of siphonophores eat. The team will also examine why they eat what they do. This will provide a new understanding of how the structure of food webs arise, aiding in our ability to predict future changes to food webs as the global climate shifts. Siphonophores feed in a very unique manner--they have highly specialized tentacles that are used solely for capturing prey--thus, the prey captured is determined largely by the anatomy and function of these tentacles. The project will describe these tentacles, reconstruct their evolutionary history, and investigate how evolutionary shifts in tentacle structure have led to changes in diet. This project will train one PhD student, one Master's student, a postdoc, and undergraduate students, including individuals of underrepresented groups. This project will support the production of scientifically rigorous yet engaging videos, foster the expansion of a citizen-science program, and create K-12 teaching modules. </p>
<p>This project will advance three scientific aims: First, it will identify the diet of a diverse range of siphonophores using DNA metabarcoding of gut contents and prey field, remotely operated vehicle (ROV) video of prey encounters, and stable isotope analysis. These approaches are highly complementary and allow for extensive cross validation. Second, the project will characterize the selectivity of siphonophore diets by comparing them to the relative prey abundances in the habitats of each of these species. Third, the project will characterize the structure of the siphonophore prey capture apparatus across species through detailed morphological analysis of their tentacles and nematocysts. These data will be integrated in an ecological and evolutionary framework to identify predator features associated with prey specialization. In a larger context, addressing these questions will advance our understanding of oceanic predation by revealing how evolutionary changes in predator selectivity correspond to evolutionary changes in habitat and feeding apparatus and how these changes shape current food web structure in the open ocean. We will test and refine an integrated approach to describing the structure and origin of food web topology, and evaluate the potential for phylogenetic relationships to explain prey selectivity. </p>
<p>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.</p>
SiphWeb
largerWorkCitation
project
eng; USA
oceans
-160.37
-64.58
19.58
41.06
2016-06-11
2020-10-04
North Pacific
0
BCO-DMO catalogue of parameters from 18s DNA sequences of planktonic animals that are potential prey of siphonophores in water off California USA from 2016-2020
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/935581.rdf
Name: BioProject
Units: unitless
Description: <p>The NCBI identifier for the BioProject associated with the data</p>
http://lod.bco-dmo.org/id/dataset-parameter/935582.rdf
Name: SRA Study
Units: unitless
Description: <p>The NCBI identifier for the SRA study associated with the data</p>
http://lod.bco-dmo.org/id/dataset-parameter/935583.rdf
Name: Experiment
Units: unitless
Description: <p>The NCBI identifier for the sequencing experiment</p>
http://lod.bco-dmo.org/id/dataset-parameter/935584.rdf
Name: Run
Units: unitless
Description: <p>The unique NCBI identifier for each sequencing run</p>
http://lod.bco-dmo.org/id/dataset-parameter/935585.rdf
Name: BioSample
Units: unitless
Description: <p>The NCBI identifier for the BioSample associated with the data</p>
http://lod.bco-dmo.org/id/dataset-parameter/935586.rdf
Name: geo_loc_name
Units: unitless
Description: <p>The specific geographical location where the sample was collected</p>
http://lod.bco-dmo.org/id/dataset-parameter/935587.rdf
Name: lat
Units: decimal degrees
Description: <p>latitude, South is negative</p>
http://lod.bco-dmo.org/id/dataset-parameter/935588.rdf
Name: lon
Units: decimal degrees
Description: <p>longitude, West is negative</p>
http://lod.bco-dmo.org/id/dataset-parameter/935589.rdf
Name: Collection_Date
Units: unitless
Description: <p>The date when the sample was collected</p>
http://lod.bco-dmo.org/id/dataset-parameter/935590.rdf
Name: BioSampleModel
Units: unitless
Description: <p>The model describing the BioSample (e.g., human, environmental)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935591.rdf
Name: Organism
Units: unitless
Description: <p>The scientific name of the organism from which the sample was taken</p>
http://lod.bco-dmo.org/id/dataset-parameter/935592.rdf
Name: TaxID
Units: unitless
Description: <p>NCBI taxon identifier</p>
http://lod.bco-dmo.org/id/dataset-parameter/935593.rdf
Name: isolation_source
Units: unitless
Description: <p>The source from which the sample was isolated (e.g., soil, blood)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935594.rdf
Name: Host
Units: unitless
Description: <p>The host organism from which the sample was obtained</p>
http://lod.bco-dmo.org/id/dataset-parameter/935595.rdf
Name: Sample Name
Units: unitless
Description: <p>The name of the sample</p>
http://lod.bco-dmo.org/id/dataset-parameter/935596.rdf
Name: source_material_id
Units: unitless
Description: <p>The NCBI identifier for the source material</p>
http://lod.bco-dmo.org/id/dataset-parameter/935597.rdf
Name: spots
Units: unitless
Description: <p>The spot model is Illumina GA centric. The flowcells have the locations where the adapters have stuck them to the glass of the lane. There are X and Y coordinates that identify these 'spots'. As the camera reads the fluorescent flashes during sequencing, the coordinates indicate which spot the new base is added to. All of the bases for a single location constitute the spot.</p>
http://lod.bco-dmo.org/id/dataset-parameter/935598.rdf
Name: Bases
Units: unitless
Description: <p>The total number of bases sequenced</p>
http://lod.bco-dmo.org/id/dataset-parameter/935599.rdf
Name: spots_with_mates
Units: unitless
Description: <p>spots with mates</p>
http://lod.bco-dmo.org/id/dataset-parameter/935600.rdf
Name: AvgSpotLen
Units: Base pairs
Description: <p>The average length of the spots (reads) in the run</p>
http://lod.bco-dmo.org/id/dataset-parameter/935601.rdf
Name: size_MB
Units: MB
Description: <p>The total size of the sequencing data files</p>
http://lod.bco-dmo.org/id/dataset-parameter/935602.rdf
Name: Extraction
Units: unitless
Description: <p>The identifier for the extracted DNA</p>
http://lod.bco-dmo.org/id/dataset-parameter/935603.rdf
Name: Index
Units: unitless
Description: <p>Index sequence used in the library preparation</p>
http://lod.bco-dmo.org/id/dataset-parameter/935604.rdf
Name: samp_collect_device
Units: unitless
Description: <p>The device used to collect the sample</p>
http://lod.bco-dmo.org/id/dataset-parameter/935605.rdf
Name: Platform
Units: unitless
Description: <p>The sequencing platform used (e.g., Illumina, PacBio)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935606.rdf
Name: Instrument
Units: unitless
Description: <p>The sequencing instrument used (e.g., Illumina MiSeq)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935607.rdf
Name: Assay_Type
Units: unitless
Description: <p>The type of sequencing assay performed (e.g., RNA-Seq, WGS)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935608.rdf
Name: Library_Name
Units: unitless
Description: <p>The name of the sequencing library</p>
http://lod.bco-dmo.org/id/dataset-parameter/935609.rdf
Name: LibrarySource
Units: unitless
Description: <p>The source material for the library (e.g., genomic, transcriptomic)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935610.rdf
Name: LibraryLayout
Units: unitless
Description: <p>The layout of the sequencing library (e.g., paired-end, single-end)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935611.rdf
Name: LibrarySelection
Units: unitless
Description: <p>The method used to select the nucleic acid library (e.g., PCR, random)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935612.rdf
Name: Consent
Units: unitless
Description: <p>Information on the consent for data usage</p>
http://lod.bco-dmo.org/id/dataset-parameter/935613.rdf
Name: create_date
Units: unitless
Description: <p>The date when the submission was created</p>
http://lod.bco-dmo.org/id/dataset-parameter/935614.rdf
Name: ReleaseDate
Units: unitless
Description: <p>The date when the data was released to the public</p>
http://lod.bco-dmo.org/id/dataset-parameter/935615.rdf
Name: version
Units: unitless
Description: <p>The version of the NCBI submission</p>
http://lod.bco-dmo.org/id/dataset-parameter/935616.rdf
Name: Center_Name
Units: unitless
Description: <p>The name of the sequencing center</p>
http://lod.bco-dmo.org/id/dataset-parameter/935617.rdf
Name: DATASTORE_filetype
Units: unitless
Description: <p>The file type stored in the NCBI DataStore (e.g., FASTQ, BAM)</p>
http://lod.bco-dmo.org/id/dataset-parameter/935618.rdf
Name: DATASTORE_region
Units: unitless
Description: <p>The geographical region of the DataStore</p>
http://lod.bco-dmo.org/id/dataset-parameter/935619.rdf
Name: DATASTORE_provider
Units: unitless
Description: <p>The provider of the DataStore where files are kept</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
https://osprey.bco-dmo.org/dataset/935469/data/download
download
onLine
dataset
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">The methods below are adapted from&nbsp;Damian-Serrano, Hetherington et al. (2022).</span></span></p>
<p>&nbsp;</p>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">We built an 18S gene barcoding database of potential siphonophore prey items to expand on the available reference sequences in public databases. To do this, we collected 60 specimens of 30 species of zooplankton and micronekton from the California Current using a Tucker trawl. We targeted plausible prey species from motile open-ocean taxa that cohabitate with siphonophores and are underrepresented in SILVA databases (high quality ribosomal RNA databases, arb-silva.de), including fishes, crustaceans, jellyfishes, urochordates, chaetognaths, polychaetes, and mollusks. Specimens were photographed alive, then tissue was sampled and frozen, and finally the rest of the animal was fixed in formalin as a voucher to be identified and preserved at the Yale Peabody Museum of Natural History.&nbsp;</span></span></p>
<p>&nbsp;</p>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">DNA extraction, quality control, PCR, and amplicon cleanup was carried out in a similar fashion as the metabarcoding protocol described above (and detailed in Damian Serrano 2022, doi:&nbsp;</span><span style="color:rgb(0, 0, 0); font-family:aptos,sans-serif">10.17504/protocols.io.5qpvo57o7l4o/v2</span><span style="font-family:arial,helvetica,sans-serif">), using the PCR program with an annealing temperature of 54°C, and a single pair of primers (166F and 134R), spanning the full extent of the sequence containing all barcode regions used in the gut content metabarcoding (from V3 to V9). Purified amplicons were sent in plates with the forward and reverse primer separately for Sanger sequencing from both ends at the Yale DNA Analysis Facility. A total of 89 newly-submitted sequences were then assembled and trimmed at a 95% quality cutoff in Geneious and concatenated with the latest SILVA database (SILVA_138_SSURef_NR99 downloaded on February 23, 2021) pruned to remove non-eukaryotic sequences.&nbsp;</span></span></p>
<p>&nbsp;</p>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Taxonomic identities of the reads in siphonophore gut contents were assigned using the assignment software METAXA2 (<span style="color:rgb(32, 32, 32)">Bengtsson‐Palme 2015)</span>&nbsp;with a 70% reliability cutoff, comparing the sequences against our custom-built library built using SILVA138 that includes the new prey sequences.</span></span></p>
<p>&nbsp;</p>
<h3><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Siphonophore collection</span></span></h3>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">In order to sample a representative set of taxa across the siphonophore phylogeny, we targeted a set of 41 species (aiming for 10 specimens per species) including cystonects, apolemiids, pyrostephids, euphysonects, and calycophorans from shallow and deep waters. Most species were sampled from the Offshore California Current Ecosystem (OCCE) except for the Portuguese man-o-war&nbsp;</span><em><span style="font-family:helvetica">Physalia</span></em><span style="font-family:arial,helvetica,sans-serif">&nbsp;<em>physalis</em>, which was collected off Bermuda in the Sargasso Sea;&nbsp;<em>Sulculeolaria chuni</em>&nbsp;and some&nbsp;<em>Nanomia</em>&nbsp;spp. (labeled as “Atlantic”) which were collected off Rhode Island in the Block Island sound;&nbsp;<em>Forskalia</em>&nbsp;sp. M123-SS8 and shallow&nbsp;<em>Nanomia</em>&nbsp;sp. KiloMoana2018-BW7-4 which were collected off the coast of Hawaii. While all the&nbsp;<em>Nanomia</em>&nbsp;populations sampled in this study have been referred to as&nbsp;<em>Nanomia bijuga</em>, we suspect that there may be undescribed cryptic&nbsp;<em>Nanomia</em>&nbsp;species among the specimens sampled based on the disparate tentillum morphologies we observed. Therefore, we decided to have them labeled at the genus level. One&nbsp;<em>Nanomia</em>&nbsp;specimen (KiloMoana2018−BW7−4) was collected off the coast of Kona, Hawaii. The pleustonic (surface floating)&nbsp;<em>P.&nbsp;physalis</em>&nbsp;samples were collected manually using a bucket from a small boat. Species found between the 0-20m deep were collected using blue water diving techniques following the guidelines in Haddock &amp; Heine (2005). Species from 200-4000m were collected using ROVs. All animals were collected live and brought back to the ship (or field station in Bermuda for&nbsp;<em>P</em>.&nbsp;<em>Physalis</em>) for dissection. Live colonies were photographed (sometimes recorded on video), and zooids of diagnostic value (nectophores, bracts, tentacles) were dissected, when possible, fixed in 4% formalin, and stored as vouchers at the Yale Peabody Museum of Natural History (voucher catalog numbers provided in specimen metadata&nbsp;S15 Table&nbsp;of Damian-Serrano et al., 2022, doi: </span><span style="color:rgb(0, 0, 0); font-family:aptos,sans-serif">10.1371/journal.pone.0267761</span><span style="font-family:arial,helvetica,sans-serif">).</span></span></p>
<h3>&nbsp;</h3>
<h3><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Gut content metabarcoding</span></span></h3>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Shortly after collection of the live specimens, we dissected and pooled several gastrozooids from each colony, making sure that those with visible gut contents are included in addition to several other without conspicuous prey, and also including visible egested food pellets at the bottom of the sampling container.&nbsp;</span></span></p>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">To extract DNA, we digested the samples with proteinase K at 56°C for 1-2h, and used the DNeasy Blood &amp; Tissue kit (Qiagen, Hilden, Germany) eluting twice at 56°C for 10min into a final volume of 100μl. For barcode amplification, we used a set of six primer pairs that amplify six barcode regions within the 18S gene (‘V3’, ‘V5-V7S’, ‘V5-V7L’, ‘V7’, ‘V7p+V8’, and ‘V9’). The primers were designed using Geneious 11.1.5 (<span style="color:rgb(0, 0, 0)">Kearse 2012)</span>, constraining the search to short (&gt;300 bp) amplicon products with a high chance of remaining uncleaved after digestion in the gastrozooid, flanked by priming sites conserved (to a maximum mismatch of 3bp) across metazoans. The search for conserved priming sites was conducted on an alignment of 18S genes from 975 species across all metazoan phyla downloaded from GenBank (available in&nbsp;github.com/dunnlab/siphweb_metabarcoding/Primer_design). The primer search was optimized to only retrieve non-degenerate primer pairs with compatible annealing temperatures and without problematic dimerization and hairpin temperatures. Primer sequences are shown in&nbsp;Table 1 (<span style="color:rgb(0, 0, 0)">Damian Serrano 2022</span>, doi:&nbsp;</span><span style="color:rgb(0, 0, 0); font-family:aptos,sans-serif">10.1371/journal.pone.0267761</span><span style="font-family:arial,helvetica,sans-serif">), and their properties can be found in Table T1 in the protocol (<span style="color:rgb(0, 0, 0)">Damian Serrano 2022</span></span><span style="font-family:arial,helvetica,sans-serif">).</span></span></p>
<p>&nbsp;</p>
<h3><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">Prey reference database</span></span></h3>
<p><span style="font-size:14px"><span style="font-family:arial,helvetica,sans-serif">In order to enhance the accuracy of the taxonomic assignments of reads, we also built an 18S gene barcoding database of potential prey items to expand on the available reference sequences in public databases. To do this, we collected 60 specimens of 30 species of zooplankton and micronekton from the OCCE using a Tucker trawl. We targeted plausible prey species from motile open-ocean taxa that cohabitate with siphonophores and are underrepresented in SILVA databases, including fishes, crustaceans, jellyfishes, urochordates, chaetognaths, polychaetes, and mollusks. Specimens were photographed alive, then tissue was sampled and frozen, and finally the rest of the animal was fixed in formalin as a voucher to be identified and preserved at the Yale Peabody Museum of Natural History. DNA extraction, quality control, PCR, and amplicon cleanup was carried out in a similar fashion as the metabarcoding protocol described above (and detailed in&nbsp;<span style="color:rgb(0, 0, 0)">Damian Serrano 2022</span>, doi:&nbsp;</span><span style="color:rgb(0, 0, 0); font-family:aptos,sans-serif">10.17504/protocols.io.5qpvo57o7l4o/v2</span><span style="font-family:arial,helvetica,sans-serif">), using the PCR program with an annealing temperature of 54°C, and a single pair of primers (166F and 134R), spanning the full extent of the sequence containing all barcode regions used in the gut content metabarcoding (from V3 to V9). Purified amplicons were sent in plates with the forward and reverse primer separately for Sanger sequencing from both ends at the Yale DNA Analysis Facility. A total of 89 newly-submitted sequences were then assembled and trimmed at a 95% quality cutoff in Geneious and concatenated with the latest SILVA database (SILVA_138_SSURef_NR99 downloaded on February 23, 2021) pruned to remove non-eukaryotic sequences.</span></span></p>
Specified by the Principal Investigator(s)
<h2>Bioinformatic pipeline</h2>
<p>Amplicon libraries were demultiplexed by primer sequence using custom bash code. Primer sequences were removed using&nbsp;<em>cutadapt</em>&nbsp;(Martin 2011). The forward and reverse reads were matched and repaired using&nbsp;<em>bbtools</em>&nbsp;(Bushnell 2017), then denoised and de-replicated using the DADA2 (Callahan 2016)&nbsp;plugin in QIIME2 (Bolyen&nbsp;2019)&nbsp;with a truncation quality threshold of 28. We&nbsp;<em>de novo</em>&nbsp;clustered the unique features into operational taxonomic units (OTUs) using the VSEARCH (Rognes 2016)&nbsp;plugin in QIIME2 with a similarity threshold of 95%. To reduce computational load, only the top 100 most abundant features among the clustered OTUs were selected for taxonomic assignment. Taxonomic identities were assigned using the assignment software METAXA2 (Bengtsson‐Palme 2015)&nbsp;with a 70% reliability cutoff, comparing the sequences against the SILVA123.1 reference library (Quast 2012), and against our custom-built library built using SILVA138 as a foundation. The SILVA123.1 database contains 61383 eukaryotic reference sequences, while our custom database (built off SILVA138.1) contains 79044. Animals in the SILVA123.1 taxonomy are annotated to the ranks of superphylum, phylum, subphylum, class, subclass, order, family, genus, and species. However, the SILVA138.1 animal taxonomy was annotated at the levels of clade (e.g. Bilateria, Protostomia, Deuterostomia, Ecdysozoa, Lophotrochozoa), phylum, class, subclass, order, suborder, and species. All bioinformatics analyses were carried out in the Yale High Performance Computing Cluster. The taxonomic assignments and read count data were merged, then parsed to match the sample of origin and the DNA sequence they derived from. Sequence post-processing scripts can be found in the GitHub repository https://github.com/dunnlab/siphweb_metabarcoding/Scripts (Damian-Serrano,&nbsp;2024).</p>
Specified by the Principal Investigator(s)
Processed submitted files using the BCO-DMO tool named Laminar to create the primary data file named 935469_v1_gut_dna_seq_potential_prey_of_siphonophores.csv and the supplemental data file named 935469_supl_rrna_partial_seq_potential_prey_of_siphonophores.
- Created a SRA Run Info table which was downloaded from NCBI on page listing experiments https://www.ncbi.nlm.nih.gov/sra/?term=SRP321688
Added to this table to include information about Spots.
Additional metadata: spotsbasesspots_with_matesavgLengthsize_MB TaxID
The SRA Run Info table was named SraRunInfo.csv.
- Imported the submitted file named Example_Dunn_NCBI_SRA_RunTable.xlsx and the SRA Run Info file SraRunInfo.csv into Laminar.
- Renamed parameters in the file Example_Dunn_NCBI_SRA_RunTable.xlsx to follow BCO-DMO naming convention by replacing spaces with underscores.
- Removed two fields, geo_loc_name_country and geo_loc_name_country_continent, which contained the value of 'uncalculated' as it doesn't add any information to the dataset. The values for these two parameters was not entered in the NCBI submission, and that's why the value is 'uncalculated'. The location information of 'Water off California' is included on the dataset page.
- Joined the two submitted files into one table by joining on the parameter Run.
- Removed the parameter Bytes since the parameter size_MB is the same information with the size in MB which is easier to read.
- Converted the Collection_Date parameter format from %m-%d-%y to %Y-%m-%d to be in the ISO 8601 format.
- Renamed any parameters in the joined table that have spaces in their names with an underscore to follow BCO-DMO naming conventions.
- Split the column lat_lon which contained both latitude and longitude values into separate latitude and longitude columns. Then converted latitude values to positive or negative values following the convention of South is negative. And converted longitude values to positive or negative values following the convention of West is negative. Finally, deleted the lat_lon column since the information is now in separate columns.
- Renamed this table to 935469_supl_rrna_partial_seq_potential_prey_of_siphonophores.
- Imported the submitted file named Dunn_NCBI_MZ_data.xlsx into Laminar to process it. These are the steps performed for this file.
- Removed parameters with no values except for the Cruise_ID parameter which is filled in with information from the submitter.
- Removed the column Date because the submitter requested it in an email dated 10/15/2024. This is the comment from that email "Sorry about the double date columns. That is the result of a table join I did to integrate two different tables. Please disregard the Date column."
- Renamed parameters by replacing spaces with underscores according to BCO-DMO naming convention.
- Added a prefix of NCBI_ to the Accession parameter to make it clear where the Accession number comes from.
- Replaced Clade value for prey organism Calanus pacificus from Ctenophora to Copepoda. This change was performed from referencing the NCBI accession value and the taxonomy listed for the organism: Eukaryota; Metazoa; Ecdysozoa; Arthropoda; Crustacea; Multicrustacea; Hexanauplia; Copepoda; Calanoida; Calanidae; Calanus.
- Replaced Clade value for prey organism Parasagitta elegans from Ctenophora to Chaetognatha. This change was performed from referencing the NCBI accession value and the taxonomy listed for the organism: Eukaryota; Metazoa; Spiralia; Gnathifera; Chaetognatha; Sagittoidea; Aphragmophora; Ctenodontina; Sagittidae; Parasagitta.
- Replaced Clade value for prey organism Pleurobrachia bachei from Ostracoda to Ctenophora. This change was performed from referencing the NCBI accession value and the taxonomy listed for the organism: Eukaryota; Metazoa; Ctenophora; Tentaculata; Cydippida; Pleurobrachiidae; Pleurobrachia.
- Renamed this processed file to 935469_supl_rrna_partial_seq_potential_prey_of_siphonophores.
Created a taxonomy file named species_taxonomy_of_hosts_found_in_primary_file.csv that contains taxonomy information from the World Register of Marine Species (WoRMS) at https://www.marinespecies.org/index.php for the hosts listed in the primary dataset file 935469_v1_gut_dna_seq_potential_prey_of_siphonophores.csv.
Created a taxonomy file named species_taxonomy_of_prey_found_in_supplemental_file.csv that contains taxonomy information from the World Register of Marine Species (WoRMS) at https://www.marinespecies.org/index.php for the prey listed in the supplemental dataset file 935469_supl_rrna_partial_seq_potential_prey_of_siphonophores.csv.
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For questions regarding this resource, please contact BCO-DMO via the email address provided.
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Applied Biosystems SimpliAmp thermal cycler
Applied Biosystems SimpliAmp thermal cycler
PI Supplied Instrument Name: Applied Biosystems SimpliAmp thermal cycler Instrument Name: qPCR Thermal Cycler Instrument Short Name:qPCR Instrument Description: An instrument for quantitative polymerase chain reaction (qPCR), also known as real-time polymerase chain reaction (Real-Time PCR).
Qubit 2.0 fluorometer
Qubit 2.0 fluorometer
PI Supplied Instrument Name: Qubit 2.0 fluorometer Instrument Name: Qubit fluorometer Instrument Short Name: Instrument Description: Benchtop fluorometer. The Invitrogen Qubit Fluorometer accurately and quickly measures the concentration of DNA, RNA, or protein in a single sample. It can also be used to assess RNA integrity and quality.
Manufactured by Invitrogen, Carlsbad, CA, USA (Invitrogen is one of several brands under the Thermo Fisher Scientific corporation.)
ROV Doc Ricketts
ROV Doc Ricketts
PI Supplied Instrument Name: ROV Doc Ricketts Instrument Name: ROV Doc Ricketts Instrument Short Name:Doc Ricketts Instrument Description: The remotely operated vehicle (ROV) Doc Ricketts is operated by the Monterey Bay Aquarium Research Institute (MBARI). ROV Doc Ricketts is capable of diving to 4000 meters (about 2.5 miles). The R/V Western Flyer is the support vessel for Doc Ricketts and was designed with a center well whose floor can be opened to allow Doc Ricketts to be launched from within the ship into the water below. For a complete description, see: https://www.mbari.org/at-sea/vehicles/remotely-operated-vehicles/rov-doc-ricketts/
NanoDrop 3300
NanoDrop 3300
PI Supplied Instrument Name: NanoDrop 3300 Instrument Name: Thermo Scientific NanoDrop spectrophotometer Instrument Short Name:NanoDrop spectrophotometer 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.