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Dataset Title:  NCBI accession metadata for 18S rRNA gene tag sequences from DNA and RNA from
samples collected in coastal California in 2013 and 2014
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_745527)
Range: longitude = -118.48 to -118.26°E, latitude = 33.45 to 33.72°N
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Data Access Form | Files
 
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  bioproject_accession {
    String bcodmo_name "unknown";
    String description "NCBI BioProject identifier";
    String long_name "Bioproject Accession";
    String units "unitless";
  }
  sample_name {
    String bcodmo_name "unknown";
    String description "Sample name";
    String long_name "Sample Name";
    String units "unitless";
  }
  SRA_run_ID {
    String bcodmo_name "unknown";
    String description "SRA Run identifier";
    String long_name "SRA Run ID";
    String units "unitless";
  }
  SRA_run_link {
    String bcodmo_name "unknown";
    String description "URL for SRA Run Page at NCBI";
    String long_name "SRA Run Link";
    String units "unitless";
  }
  library_ID {
    String bcodmo_name "unknown";
    String description "SRA title";
    String long_name "Library ID";
    String units "unitless";
  }
  SRA_study_ID {
    String bcodmo_name "unknown";
    String description "SRA study identifier";
    String long_name "SRA Study ID";
    String units "unitless";
  }
  SRA_title {
    String bcodmo_name "unknown";
    String description "Descriptive title of SRA accession";
    String long_name "SRA Title";
    String units "unitless";
  }
  library_strategy {
    String bcodmo_name "unknown";
    String description "Library strategy (\"AMPLICON\")";
    String long_name "Library Strategy";
    String units "unitless";
  }
  library_source {
    String bcodmo_name "unknown";
    String description "Library source (\"TRANSCRIPTOMIC\" or \"GENOMIC\")";
    String long_name "Library Source";
    String units "unitless";
  }
  library_selection {
    String bcodmo_name "unknown";
    String description "Library selection (\"PCR\")";
    String long_name "Library Selection";
    String units "unitless";
  }
  library_layout {
    String bcodmo_name "unknown";
    String description "Library layout (\"paired\")";
    String long_name "Library Layout";
    String units "unitless";
  }
  platform {
    String bcodmo_name "platform";
    String description "Sequencing platform (\"Illumina\")";
    String long_name "Platform";
    String units "unitless";
  }
  instrument_model {
    String bcodmo_name "unknown";
    String description "Sequencing instrument model (\"Illumina MiSeq\")";
    String long_name "Instrument Model";
    String units "unitless";
  }
  design_description {
    String bcodmo_name "unknown";
    String description "Sequencing design description";
    String long_name "Design Description";
    String units "unitless";
  }
  filetype {
    String bcodmo_name "unknown";
    String description "Type of file 1";
    String long_name "Filetype";
    String units "unitless";
  }
  filename {
    String bcodmo_name "unknown";
    String description "Name of file 1";
    String long_name "Filename";
    String units "unitless";
  }
  filetpe2 {
    String bcodmo_name "unknown";
    String description "Type of file 2";
    String long_name "Filetpe2";
    String units "unitless";
  }
  filename2 {
    String bcodmo_name "unknown";
    String description "Name of file 2";
    String long_name "Filename2";
    String units "unitless";
  }
  depth2 {
    String bcodmo_name "unknown";
    String description "Nominal depth (\"Surface\" or number of meters) of sample collection";
    String long_name "Depth";
    String standard_name "depth";
    String units "various";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 33.45, 33.72;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude of sample collection";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -118.48, -118.26;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude of sample collection";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Samples were collected seasonally at the San Pedro Ocean Time-series (SPOT)
station at four depths (surface, subsurface chlorophyll maximum, 150 and 890
m).\\u00a0The SPOT station was sampled from 5 m, the subsurface chlorophyll
maximum (SCM), 150 and 890 m using 10 L Niskin bottles mounted on a CTD
rosette, during regularly scheduled cruises
([https://dornsife.usc.edu/spot/](\\\\\"https://dornsife.usc.edu/spot/\\\\\")).
 
Seawater from all samples was sequentially pre-filtered through 200 \\u03bcm
and 80 \\u03bcm Nitex mesh to reduce abundances of multicellular eukaryotes
(metazoa). Near-surface and SCM seawater (2 L) and 150 and 890 m seawater (4
L) was filtered onto GF/F filters (nominal pore size 0.7 \\u03bcm; Whatman,
International Ltd, Florham Park, NJ, USA) and immediately flash frozen in
liquid nitrogen for later DNA and RNA extraction.
 
Total DNA and RNA were extracted simultaneously from each sample using the All
Prep DNA/RNA Mini kit (Qiagen, Valencia, CA, USA, #80204). Genomic DNA was
removed during the RNA extraction with RNase-Free DNase reagents (Qiagen,
#79254). Total extracted RNA was checked for residual genomic DNA by
performing a polymerase chain reaction (PCR) using DNA specific primers to
ensure that no amplified products appeared when run on an agarose gel. RNA was
reverse transcribed into cDNA using iScript Reverse Transcription Supermix
with random hexamers (Bio-Rad Laboratories, Hercules, CA, USA, #170-8840).
 
The resulting cDNA and DNA from each sample were PCR amplified using V4
forward (5\\u2032\\u00a0-CCAGCA[GC]C[CT]GCGGTA ATTCC-3\\u2032\\u00a0) and reverse
(5\\u2032\\u00a0-ACTTTCGTTCTTGAT[CT][AG]A-3\\u2032\\u00a0) primers (Stoeck et al.
2010). Duplicate PCR reactions were performed in 50 \\u03bcL volumes of: 1X
Phusion High-Fidelity DNA buffer, 1 unit of Phusion DNA polymerase (New
England Biolabs, Ipswich, MA, USA, #M0530S), 200 \\u03bcM of dNTPs, 0.5 \\u03bcM
of each V4 forward and reverse primer, 3% DMSO, 50 mM of MgCl and 5 ng of
either DNA or cDNA template per reaction. The PCR thermal cycler program
consisted of a 98\\u25e6C denaturation step for 30 s, followed by 10 cycles of
10 s at 98\\u25e6C, 30 s at 53\\u25e6C and 30 s at 72\\u25e6C, and then 15 cycles
of 10 s at 98\\u25e6C, 30 s at 48\\u25e6C and 30 s at 72\\u25e6C, and a final
elongation step at 72\\u25e6C for 10 min, as described in Rodr\\u0131
\\u0301guez-Mart\\u0131 \\u0301nez et al. (2012). PCR products were purified
(Qiagen, #28104) and duplicate samples were pooled. The \\u223c400 bp cDNA and
DNA PCR products were quality checked on an Agilent Bioanalyzer 2100 (Agilent
Technologies, Santa Clara, CA).
 
Sampling Locations:  
 SPOT (33\\u25e6 33\\u2032 N, 118\\u25e6 24\\u2032 W) - surface, DCM, 150 m, and
890 m  
 Port of LA (33\\u25e6 42.75\\u2032 N, 118\\u25e6 15.55\\u2032 W) - surface  
 Catalina (33\\u25e6 27.17\\u2032 N, 118\\u25e6 28.51\\u2032 W)-surface
 
For protocols see:  
[https://www.protocols.io/view/sample-collection-from-the-field-for-
downstream-mo-hisb4ee](\\\\\"https://www.protocols.io/view/sample-collection-
from-the-field-for-downstream-mo-hisb4ee\\\\\")  
[https://www.protocols.io/view/rna-and-optional-dna-extraction-from-
environmental-hk3b4yn](\\\\\"https://www.protocols.io/view/rna-and-optional-dna-
extraction-from-environmental-hk3b4yn\\\\\")  
[https://www.protocols.io/view/18s-v4-tag-sequencing-pcr-amplification-and-
librar-hdmb246](\\\\\"https://www.protocols.io/view/18s-v4-tag-sequencing-pcr-
amplification-and-librar-hdmb246\\\\\")";
    String awards_0_award_nid "743048";
    String awards_0_award_number "OCE-1737409";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1737409";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"18S rRNA gene tag sequences 
  PI: David Caron 
  Data version 1: 2018-09-05";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2018-09-04T20:29:24Z";
    String date_modified "2020-03-06T16:52:33Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.745527.1";
    Float64 Easternmost_Easting -118.26;
    Float64 geospatial_lat_max 33.72;
    Float64 geospatial_lat_min 33.45;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -118.26;
    Float64 geospatial_lon_min -118.48;
    String geospatial_lon_units "degrees_east";
    String history 
"2022-08-20T02:14:59Z (local files)
2022-08-20T02:14:59Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_745527.das";
    String infoUrl "https://www.bco-dmo.org/dataset/745527";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_nid "745591";
    String instruments_0_description "A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends.  The bottles can be attached individually on a hydrowire or deployed in 12, 24 or 36 bottle Rosette systems mounted on a frame and combined with a CTD.  Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/";
    String instruments_0_instrument_name "Niskin bottle";
    String instruments_0_instrument_nid "413";
    String instruments_1_acronym "CTD SBE 911plus";
    String instruments_1_dataset_instrument_nid "745592";
    String instruments_1_description "The Sea-Bird SBE 911plus is a type of CTD instrument package for continuous measurement of conductivity, temperature and pressure.  The SBE 911plus includes the SBE 9plus Underwater Unit and the SBE 11plus Deck Unit (for real-time readout using conductive wire) for deployment from a vessel. The combination of the SBE 9plus and SBE 11plus is called a SBE 911plus.  The SBE 9plus uses Sea-Bird's standard modular temperature and conductivity sensors (SBE 3plus and SBE 4). The SBE 9plus CTD can be configured with up to eight auxiliary sensors to measure other parameters including dissolved oxygen, pH, turbidity, fluorescence, light (PAR), light transmission, etc.). more information from Sea-Bird Electronics";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0058/";
    String instruments_1_instrument_name "CTD Sea-Bird SBE 911plus";
    String instruments_1_instrument_nid "591";
    String instruments_2_acronym "Automated Sequencer";
    String instruments_2_dataset_instrument_nid "745568";
    String instruments_2_description "General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.";
    String instruments_2_instrument_name "Automated DNA Sequencer";
    String instruments_2_instrument_nid "649";
    String instruments_2_supplied_name "Illumina MiSeq";
    String keywords "accession, bco, bco-dmo, biological, bioproject, bioproject_accession, chemical, data, dataset, depth, depth2, description, design, design_description, dmo, erddap, filename, filename2, filetpe2, filetype, instrument, instrument_model, latitude, layout, library, library_ID, library_layout, library_selection, library_source, library_strategy, link, longitude, management, model, name, oceanography, office, platform, preliminary, run, sample, sample_name, selection, source, sra, SRA_run_ID, SRA_run_link, SRA_study_ID, SRA_title, strategy, study, title";
    String license "https://www.bco-dmo.org/dataset/745527/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/745527";
    Float64 Northernmost_Northing 33.72;
    String param_mapping "{'745527': {'lat': 'master - latitude', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/745527/parameters";
    String people_0_affiliation "University of Southern California";
    String people_0_affiliation_acronym "USC";
    String people_0_person_name "David Caron";
    String people_0_person_nid "50524";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of Southern California";
    String people_1_affiliation_acronym "USC";
    String people_1_person_name "Sarah K Hu";
    String people_1_person_nid "745520";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "University of Southern California";
    String people_2_affiliation_acronym "USC";
    String people_2_person_name "Sarah K Hu";
    String people_2_person_nid "745520";
    String people_2_role "Contact";
    String people_2_role_type "related";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Amber York";
    String people_3_person_nid "643627";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "SPOT";
    String projects_0_acronym "SPOT";
    String projects_0_description 
"Planktonic marine microbial communities consist of a diverse collection of bacteria, archaea, viruses, protists (phytoplankton and protozoa) and small animals (metazoan). Collectively, these species are responsible for virtually all marine pelagic primary production where they form the basis of food webs and carry out a large fraction of respiratory processes. Microbial interactions include the traditional role of predation, but recent research recognizes the importance of parasitism, symbiosis and viral infection. Characterizing the response of pelagic microbial communities and processes to environmental influences is fundamental to understanding and modeling carbon flow and energy utilization in the ocean, but very few studies have attempted to study all of these assemblages in the same study. This project is comprised of long-term (monthly) and short-term (daily) sampling at the San Pedro Ocean Time-series (SPOT) site. Analysis of the resulting datasets investigates co-occurrence patterns of microbial taxa (e.g. protist-virus and protist-prokaryote interactions, both positive and negative) indicating which species consistently co-occur and potentially interact, followed by examination gene expression to help define the underlying mechanisms. This study augments 20 years of baseline studies of microbial abundance, diversity, rates at the site, and will enable detection of low-frequency changes in composition and potential ecological interactions among microbes, and their responses to changing environmental forcing factors. These responses have important consequences for higher trophic levels and ocean-atmosphere feedbacks. The broader impacts of this project include training graduate and undergraduate students, providing local high school student with summer lab experiences, and PI presentations at local K-12 schools, museums, aquaria and informal learning centers in the region. Additionally, the PIs advise at the local, county and state level regarding coastal marine water quality.
This research project is unique in that it is a holistic study (including all microbes from viruses to small metazoa) of microbial species diversity and ecological activities, carried out at the SPOT site off the coast of southern California. In studying all microbes simultaneously, this work aims to identify important ecological interactions among microbial species, and identify the basis(es) for those interactions. This research involves (1) extensive analyses of prokaryote (archaean and bacterial) and eukaryote (protistan and micro-metazoan) diversity via the sequencing of marker genes, (2) studies of whole-community gene expression by eukaryotes and prokaryotes in order to identify key functional characteristics of microorganismal groups and the detection of active viral infections, and (3) metagenomic analysis of viruses and bacteria to aid interpretation of transcriptomic analyses using genome-encoded information. The project includes exploratory metatranscriptomic analysis of poorly-understood aphotic and hypoxic-zone protists, to examine their stratification, functions and hypothesized prokaryotic symbioses.";
    String projects_0_end_date "2021-07";
    String projects_0_geolocation "San Pedro Channel off the coast of Los Angeles";
    String projects_0_name "Protistan, prokaryotic, and viral processes at the San Pedro Ocean Time-series";
    String projects_0_project_nid "743049";
    String projects_0_start_date "2017-08";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 33.45;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "bioproject_accession,SRA_study_ID,SRA_title,library_strategy,library_selection,library_layout,platform,instrument_model,filetype,filetpe2";
    String summary "Raw DNA and RNA V4 tag sequences include spatially and temporally distinct samples from coastal California.  Samples were collected in Niskin bottles with a CTD rosette at the San Pedro Ocean Time-series (SPOT) between April of 2013 and January of 2014.  This dataset contains sequence data accession numbers and metadata for the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (SRA Study ID: SRP070577, BioProject: PRJNA311248).";
    String title "NCBI accession metadata for 18S rRNA gene tag sequences from DNA and RNA from samples collected in coastal California in 2013 and 2014";
    String version "1";
    Float64 Westernmost_Easting -118.48;
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its selection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.


 
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