http://lod.bco-dmo.org/id/dataset/997761
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
2026-04-30
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Acropora cervicornis 16s amplicon rDNA sequencing data with associated data on tank exposure to white band disease and survival outcomes.
2026-04-30
publication
2026-04-30
revision
BCO-DMO Linked Data URI
2026-04-30
creation
http://lod.bco-dmo.org/id/dataset/997761
Steven V. Vollmer
Northeastern University
principalInvestigator
Jason D. Selwyn
Texas A&M, Corpus Christi
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: Vollmer, S. V., Trytten, E., Selwyn, J. D., Douglas, B. (2026) Acropora cervicornis 16s amplicon rDNA sequencing data with associated data on tank exposure to white band disease and survival outcomes. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2026-04-30 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/997761 [access date]
Acropora cervicornis 16s amplicon rDNA sequencing Dataset Description: Methods and Sampling: <p>Coral fragments (n=550) from 50 Acropora cervicornis genotypes were collected from Coral Restoration Foundation's in-situ nursery in Tavernier, Florida Keys in June 2021 (permit CRF-2021-001). One fragment from 18 genotypes was immediately sampled (day 0); three polyps per fragment were preserved in DNA/RNA Shield (Zymo Research) at -20°C. Remaining fragments (10 per genotype) were distributed across 10 recirculating 18-liter tanks at Florida Keys Marine Laboratory (50 fragments/tank), lesioned with a WaterPik, and exposed to either healthy or diseased tissue homogenate doses (50 mL, 5 tanks each). Homogenates were prepared from five healthy and five diseased nursery-collected fragments and normalized by optical density. Fragments were monitored twice daily for white band disease signs. Three polyps were sampled from each fragment on day 3 and day 7 (or when diseased), preserved in DNA/RNA Shield at -20°C for &lt;2 months. Diseased fragments were removed after sampling. Total samples sequenced: 234 (18 day 0, 206 tank samples across 46 genotypes, 10 homogenate doses).</p>
<p>Genomic DNA was extracted using GenElute Bacterial Genomic DNA kit (Sigma-Aldrich). The V3-V4 region of bacterial 16S rRNA gene was amplified using primers S-D-Bact-0341b-S-17 (5'-CCTACGGGNGGCWGCAG-3') and S-D-Bact-0785-a-A-21 (5'-GACTACHVGGGTATCTAATCC-3') (Herlemann et al. 2011, Klindworth et al. 2013). First PCR (23 μL): 1 μL DNA, 1.25 μL each primer (10 μM), 12.5 μL Phusion Mix (2X, Thermo Fisher), 7 μL water; cycling: 98°C 1 min, 28 cycles (98°C 30s, 63°C 30s, 72°C 30s), 72°C 5 min. Products cleaned with ZR-96 DNA Clean-Up kit (Zymo Research). Second PCR added Illumina Nextera XT indexes (25 μL): 5 μL purified product, 2.5 μL each index, 12.5 μL Phusion Mix, 2.5 μL water; cycling: 98°C 1 min, 12 cycles (98°C 30s, 55°C 30s, 72°C 30s), 72°C 5 min. Indexed products were normalized (SequalPrep, Thermo Fisher), pooled, and concentrated (DNA Clean &amp; Concentrator, Zymo Research). Libraries sequenced on two Illumina MiSeq runs (2×300 bp, v3 chemistry).</p>
<p>Raw reads processed using DADA2 v1.28.0 (Callahan et al. 2016) in R v4.4.1: filterAndTrim (trimLeft=25, truncLen=c(250,230), maxEE=c(2,2)); dereplication; error rate estimation on first 40 samples (err=NULL, selfConsist=TRUE); sample inference (pool=TRUE); paired read merging; ASV table generation; chimera removal (removeBimeraDenovo, consensus method). Taxonomic classification used BLCA v3.0 (Gao et al. 2017) with NCBI 16S database (80% confidence, accessed 2/5/2024); ASVs unclassified below class reclassified using SILVA SSU r138 (Quast et al. 2013, 50% threshold). ASV sequences aligned with DECIPHER v2.26.0 (Wright 2016); neighbor-joining tree constructed with phangorn v2.11.1 (Schliep 2011). Data filtered to remove non-bacteria, chloroplast, mitochondria, cyanobacteria, and samples with &lt;1000 reads. For differential abundance: removed ASVs in &lt;20% samples and ASVs absent from day 0 and homogenate doses. Final filtered dataset: 254 ASVs across 211 samples; unfiltered (5,716 ASVs) used for visualization and alpha diversity. Normalized using TMM with effective library size (ELib-TMM, Robinson &amp; Oshlack 2010) in edgeR v3.42.4 (Robinson et al. 2010); transformed to log₂ CPM + 0.5. Data managed in phyloseq v1.44.0 (McMurdie &amp; Holmes 2013).</p>
<p>Alpha diversity (Shannon, Simpson) calculated on rarefied data (1029 reads, seed 68748) using microbiome v1.22.0 (Lahti &amp; Shetty 2019); tested with linear mixed-effects models (fixed: time, exposure, outcome; random: genotype, tank). Beta diversity: Bray-Curtis dissimilarity (Bray &amp; Curtis 1957) visualized by NMDS; PERMANOVA tested effects of time, exposure, outcome, genotype, tank (10,000 permutations, seed 68748). Disease resistance correlations: Kendall's tau between day 0 abundances (ASV, genus, family) and resistance scores (Vollmer et al. 2023); FDR-corrected. Differential abundance: weighted linear mixed-effects models using variancePartition v1.30.2 with dream (Hoffman &amp; Roussos 2021, Hoffman &amp; Schadt 2016); weights from voomWithDreamWeights (Law et al. 2014); fixed effects: time, exposure, outcome; random: genotype, tank. Post-hoc contrasts using emmeans v1.8.9: (1) transplant effect: day 0 vs. mean(day 3+7 healthy); (2) exposure: healthy- vs. disease-exposed; (3) outcome: diseased vs. healthy; (4) temporal response on diseased corals: day 0 vs. 3 (early), day 3 vs. 7 (late); (5) outcome within disease-exposed: diseased vs. healthy at peak timepoint (day 3 for early, day 7 for late responders); (6) doses: diseased vs. healthy (linear models). All P-values FDR-corrected (Benjamini &amp; Hochberg 1995, α=0.05). Putative pathogens required: positive disease outcome association, late response (day 3-7 increase), higher abundance on diseased vs. healthy in disease-exposed tanks (day 7), and enrichment in diseased dose. Community composition visualized using Fantaxtic v0.2.0 (Teunisse 2022).<br />
&nbsp;</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1924145 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1924145
onGoing
Steven V. Vollmer
Northeastern University
781-581-7370 ext. 312
Marine Science Center 430 Nahant Road
Nahant
MA
01908
USA
steve.vollmer@gmail.com
pointOfContact
Jason D. Selwyn
Texas A&M, Corpus Christi
j.selwyn@northeastern.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
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.
Multi-omic bases of coral disease resistance
https://osprey.bco-dmo.org/project/823361
Multi-omic bases of coral disease resistance
<p>NSF Award Abstract:</p>
<p>Coral disease outbreaks have radically altered the structure and function of tropical coral reefs worldwide. As progress has been made towards understanding the basic cause of many coral diseases, significant gaps remain in our knowledge of how corals respond to and resist disease infection, even as calls are being made for science to assist in coral evolution by selecting thermal or disease tolerant coral species or genotypes - often called "super corals". This project uses the endangered Caribbean staghorn coral Acropora cervicornis and White Band Disease (WBD) as a host-pathogen system to study the genetics of coral disease resistance. WBD epidemics decimated this key shallow-water Caribbean coral and led to its endangered listing. While the recovery of staghorn corals has been slow, data indicate that up to 15% or more of staghorn corals are highly disease resistant. This project uses modern genomic tools to identify genetic markers for staghorn coral disease resistance. The identification of genetic markers for disease resistance (i) provides needed information on the efficacy of "assisted evolution" for coral resiliency (ii) helps predict how well staghorn coral can resist future disease outbreaks, (iii) assists conservation efforts aimed at identifying and selecting corals with high disease resistance, and (iv) spurs the development of molecular assays for coral disease resistance. This research provides graduate and undergraduate training in the STEM fields of microbiology, genetics, and computational biology. The project is as a platform to develop outreach curricula to teach students about coral diseases and reef health, which are disseminated via Northeastern University's K-12 outreach program and the Smithsonian Tropical Research Institute's outreach program in Panama. This project is co-funded by the Biological Oceanography Program in the Division of Ocean Sciences and the Symbiosis, Defense, and Self-recognition Program in the Division of Integrative Organismal Systems.</p>
<p>It is increasingly becoming clear that the future of coral reefs depends on the resilience of reef-building corals to adapt or acclimate to their changing environment, which in turn requires that key traits like thermal tolerance and disease resistance are genetically heritable, identifiable, and quantifiable. Using staghorn corals and WBD as a model host-pathogen system, this project identifies the genetic underpinnings of disease resistance in Caribbean staghorn corals using state-of-the-art, multi-omic approaches linking patterns of variation across the staghorn coral genome, transcriptome and proteome. For Aim 1, genome-wide SNP variation from 200 staghorn coral genotypes from two populations [100 Florida; 100 Panama] is used to identify genomic regions associated with disease resistance using genome-wide association (GWA) analyses. For Aim 2, tank-based transmission experiments are used to profile key differences in the transcriptomic (mRNA and miRNA) and proteomic response of resistant versus susceptible staghorn corals during disease exposure. Mulit-omic data are analyzed using: (1) eQTL to link SNPs to mRNA expression, (2) miRNA-mRNA interactions and correlation networks to test for post-transcriptional gene regulation, and (3) network-based approaches. For Aim 3, 16s rDNA amplicon sequencing are used to identify changes in the staghorn coral microbiome due to disease resistance and exposure using microbial DNA from the resistant and susceptible corals used in the tank-based experiment (Aim 2). In addition to identify genetic markers associated with coral disease resistance, this study produces (1) the most complete multi-omic analysis of coral immunity and disease resistance to date, and (2) the first functional analyses of miRNA post-translational gene regulation in a cnidarian host-pathogen system. Data on the genetics of coral disease resistance provide valuable information on the efficacy of "assisted evolution" for coral resiliency. By using nursery raised staghorn corals from Florida, this project directly identifies highly disease resistant corals that can be used in the large-scale out planting efforts.</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>
coral disease resistance
largerWorkCitation
project
eng; USA
oceans
-81.04
-81.04
24.73
24.73
2026-04-30
Bocas del Toro, Panama and Coral Restoration Nursery, Marathon, Florida, US
0
BCO-DMO catalogue of parameters from Acropora cervicornis 16s amplicon rDNA sequencing data with associated data on tank exposure to white band disease and survival outcomes.
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
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/997761/data/download
download
onLine
dataset
<p>Coral fragments (n=550) from 50 Acropora cervicornis genotypes were collected from Coral Restoration Foundation's in-situ nursery in Tavernier, Florida Keys in June 2021 (permit CRF-2021-001). One fragment from 18 genotypes was immediately sampled (day 0); three polyps per fragment were preserved in DNA/RNA Shield (Zymo Research) at -20°C. Remaining fragments (10 per genotype) were distributed across 10 recirculating 18-liter tanks at Florida Keys Marine Laboratory (50 fragments/tank), lesioned with a WaterPik, and exposed to either healthy or diseased tissue homogenate doses (50 mL, 5 tanks each). Homogenates were prepared from five healthy and five diseased nursery-collected fragments and normalized by optical density. Fragments were monitored twice daily for white band disease signs. Three polyps were sampled from each fragment on day 3 and day 7 (or when diseased), preserved in DNA/RNA Shield at -20°C for &lt;2 months. Diseased fragments were removed after sampling. Total samples sequenced: 234 (18 day 0, 206 tank samples across 46 genotypes, 10 homogenate doses).</p>
<p>Genomic DNA was extracted using GenElute Bacterial Genomic DNA kit (Sigma-Aldrich). The V3-V4 region of bacterial 16S rRNA gene was amplified using primers S-D-Bact-0341b-S-17 (5'-CCTACGGGNGGCWGCAG-3') and S-D-Bact-0785-a-A-21 (5'-GACTACHVGGGTATCTAATCC-3') (Herlemann et al. 2011, Klindworth et al. 2013). First PCR (23 μL): 1 μL DNA, 1.25 μL each primer (10 μM), 12.5 μL Phusion Mix (2X, Thermo Fisher), 7 μL water; cycling: 98°C 1 min, 28 cycles (98°C 30s, 63°C 30s, 72°C 30s), 72°C 5 min. Products cleaned with ZR-96 DNA Clean-Up kit (Zymo Research). Second PCR added Illumina Nextera XT indexes (25 μL): 5 μL purified product, 2.5 μL each index, 12.5 μL Phusion Mix, 2.5 μL water; cycling: 98°C 1 min, 12 cycles (98°C 30s, 55°C 30s, 72°C 30s), 72°C 5 min. Indexed products were normalized (SequalPrep, Thermo Fisher), pooled, and concentrated (DNA Clean &amp; Concentrator, Zymo Research). Libraries sequenced on two Illumina MiSeq runs (2×300 bp, v3 chemistry).</p>
<p>Raw reads processed using DADA2 v1.28.0 (Callahan et al. 2016) in R v4.4.1: filterAndTrim (trimLeft=25, truncLen=c(250,230), maxEE=c(2,2)); dereplication; error rate estimation on first 40 samples (err=NULL, selfConsist=TRUE); sample inference (pool=TRUE); paired read merging; ASV table generation; chimera removal (removeBimeraDenovo, consensus method). Taxonomic classification used BLCA v3.0 (Gao et al. 2017) with NCBI 16S database (80% confidence, accessed 2/5/2024); ASVs unclassified below class reclassified using SILVA SSU r138 (Quast et al. 2013, 50% threshold). ASV sequences aligned with DECIPHER v2.26.0 (Wright 2016); neighbor-joining tree constructed with phangorn v2.11.1 (Schliep 2011). Data filtered to remove non-bacteria, chloroplast, mitochondria, cyanobacteria, and samples with &lt;1000 reads. For differential abundance: removed ASVs in &lt;20% samples and ASVs absent from day 0 and homogenate doses. Final filtered dataset: 254 ASVs across 211 samples; unfiltered (5,716 ASVs) used for visualization and alpha diversity. Normalized using TMM with effective library size (ELib-TMM, Robinson &amp; Oshlack 2010) in edgeR v3.42.4 (Robinson et al. 2010); transformed to log₂ CPM + 0.5. Data managed in phyloseq v1.44.0 (McMurdie &amp; Holmes 2013).</p>
<p>Alpha diversity (Shannon, Simpson) calculated on rarefied data (1029 reads, seed 68748) using microbiome v1.22.0 (Lahti &amp; Shetty 2019); tested with linear mixed-effects models (fixed: time, exposure, outcome; random: genotype, tank). Beta diversity: Bray-Curtis dissimilarity (Bray &amp; Curtis 1957) visualized by NMDS; PERMANOVA tested effects of time, exposure, outcome, genotype, tank (10,000 permutations, seed 68748). Disease resistance correlations: Kendall's tau between day 0 abundances (ASV, genus, family) and resistance scores (Vollmer et al. 2023); FDR-corrected. Differential abundance: weighted linear mixed-effects models using variancePartition v1.30.2 with dream (Hoffman &amp; Roussos 2021, Hoffman &amp; Schadt 2016); weights from voomWithDreamWeights (Law et al. 2014); fixed effects: time, exposure, outcome; random: genotype, tank. Post-hoc contrasts using emmeans v1.8.9: (1) transplant effect: day 0 vs. mean(day 3+7 healthy); (2) exposure: healthy- vs. disease-exposed; (3) outcome: diseased vs. healthy; (4) temporal response on diseased corals: day 0 vs. 3 (early), day 3 vs. 7 (late); (5) outcome within disease-exposed: diseased vs. healthy at peak timepoint (day 3 for early, day 7 for late responders); (6) doses: diseased vs. healthy (linear models). All P-values FDR-corrected (Benjamini &amp; Hochberg 1995, α=0.05). Putative pathogens required: positive disease outcome association, late response (day 3-7 increase), higher abundance on diseased vs. healthy in disease-exposed tanks (day 7), and enrichment in diseased dose. Community composition visualized using Fantaxtic v0.2.0 (Teunisse 2022).<br />
&nbsp;</p>
Specified by the Principal Investigator(s)
<p>Raw reads processed using DADA2 v1.28.0 (Callahan et al. 2016) in R v4.4.1: filterAndTrim (trimLeft=25, truncLen=c(250,230), maxEE=c(2,2)); dereplication; error rate estimation on first 40 samples (err=NULL, selfConsist=TRUE); sample inference (pool=TRUE); paired read merging; ASV table generation; chimera removal (removeBimeraDenovo, consensus method). Taxonomic classification used BLCA v3.0 (Gao et al. 2017) with NCBI 16S database (80% confidence, accessed 2/5/2024); ASVs unclassified below class reclassified using SILVA SSU r138 (Quast et al. 2013, 50% threshold). ASV sequences aligned with DECIPHER v2.26.0 (Wright 2016); neighbor-joining tree constructed with phangorn v2.11.1 (Schliep 2011). Data filtered to remove non-bacteria, chloroplast, mitochondria, cyanobacteria, and samples with &lt;1000 reads. For differential abundance: removed ASVs in &lt;20% samples and ASVs absent from day 0 and homogenate doses. Final filtered dataset: 254 ASVs across 211 samples; unfiltered (5,716 ASVs) used for visualization and alpha diversity. Normalized using TMM with effective library size (ELib-TMM, Robinson &amp; Oshlack 2010) in edgeR v3.42.4 (Robinson et al. 2010); transformed to log₂ CPM + 0.5. Data managed in phyloseq v1.44.0 (McMurdie &amp; Holmes 2013).</p>
<p>See related datasets and publications for the NCBI BioProject accession and Analysis scripts</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact