Ontology type: schema:ScholarlyArticle Open Access: True
2010-09-16
AUTHORSScott M Gifford, Shalabh Sharma, Johanna M Rinta-Kanto, Mary Ann Moran
ABSTRACTThe potential of metatranscriptomic sequencing to provide insights into the environmental factors that regulate microbial activities depends on how fully the sequence libraries capture community expression (that is, sample-sequencing depth and coverage depth), and the sensitivity with which expression differences between communities can be detected (that is, statistical power for hypothesis testing). In this study, we use an internal standard approach to make absolute (per liter) estimates of transcript numbers, a significant advantage over proportional estimates that can be biased by expression changes in unrelated genes. Coastal waters of the southeastern United States contain 1 × 1012 bacterioplankton mRNA molecules per liter of seawater (∼200 mRNA molecules per bacterial cell). Even for the large bacterioplankton libraries obtained in this study (∼500 000 possible protein-encoding sequences in each of two libraries after discarding rRNAs and small RNAs from >1 million 454 FLX pyrosequencing reads), sample-sequencing depth was only 0.00001%. Expression levels of 82 genes diagnostic for transformations in the marine nitrogen, phosphorus and sulfur cycles ranged from below detection (<1 × 106 transcripts per liter) for 36 genes (for example, phosphonate metabolism gene phnH, dissimilatory nitrate reductase subunit napA) to >2.7 × 109 transcripts per liter (ammonia transporter amt and ammonia monooxygenase subunit amoC). Half of the categories for which expression was detected, however, had too few copy numbers for robust statistical resolution, as would be required for comparative (experimental or time-series) expression studies. By representing whole community gene abundance and expression in absolute units (per volume or mass of environment), ‘omics’ data can be better leveraged to improve understanding of microbially mediated processes in the ocean. More... »
PAGES461-472
http://scigraph.springernature.com/pub.10.1038/ismej.2010.141
DOIhttp://dx.doi.org/10.1038/ismej.2010.141
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1013224697
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/20844569
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Biological Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Genetics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0605",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Microbiology",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Bacteria",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Gene Dosage",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Gene Expression Regulation, Bacterial",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Genetic Variation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Genomic Library",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Proteomics",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "RNA, Messenger",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Seawater",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Sequence Analysis, DNA",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Southeastern United States",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Transcriptome",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Department of Marine Sciences, University of Georgia, Athens, GA, USA",
"id": "http://www.grid.ac/institutes/grid.213876.9",
"name": [
"Department of Marine Sciences, University of Georgia, Athens, GA, USA"
],
"type": "Organization"
},
"familyName": "Gifford",
"givenName": "Scott M",
"id": "sg:person.01230344374.63",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01230344374.63"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Marine Sciences, University of Georgia, Athens, GA, USA",
"id": "http://www.grid.ac/institutes/grid.213876.9",
"name": [
"Department of Marine Sciences, University of Georgia, Athens, GA, USA"
],
"type": "Organization"
},
"familyName": "Sharma",
"givenName": "Shalabh",
"id": "sg:person.01106310447.41",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01106310447.41"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Marine Sciences, University of Georgia, Athens, GA, USA",
"id": "http://www.grid.ac/institutes/grid.213876.9",
"name": [
"Department of Marine Sciences, University of Georgia, Athens, GA, USA"
],
"type": "Organization"
},
"familyName": "Rinta-Kanto",
"givenName": "Johanna M",
"id": "sg:person.0772062047.83",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0772062047.83"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Marine Sciences, University of Georgia, Athens, GA, USA",
"id": "http://www.grid.ac/institutes/grid.213876.9",
"name": [
"Department of Marine Sciences, University of Georgia, Athens, GA, USA"
],
"type": "Organization"
},
"familyName": "Moran",
"givenName": "Mary Ann",
"id": "sg:person.01215610154.59",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01215610154.59"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1186/1471-2105-9-303",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041933952",
"https://doi.org/10.1186/1471-2105-9-303"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ismej.2009.75",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005410968",
"https://doi.org/10.1038/ismej.2009.75"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ismej.2009.8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024002450",
"https://doi.org/10.1038/ismej.2009.8"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nbt0708-741",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018566556",
"https://doi.org/10.1038/nbt0708-741"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature08055",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029921262",
"https://doi.org/10.1038/nature08055"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ismej.2009.72",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023938739",
"https://doi.org/10.1038/ismej.2009.72"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02012642",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033018684",
"https://doi.org/10.1007/bf02012642"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-7-162",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044943589",
"https://doi.org/10.1186/1471-2105-7-162"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ismej.2010.172",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014275780",
"https://doi.org/10.1038/ismej.2010.172"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-4-41",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013163036",
"https://doi.org/10.1186/1471-2105-4-41"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ismej.2010.18",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038520885",
"https://doi.org/10.1038/ismej.2010.18"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2010-11-3-r25",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1050509557",
"https://doi.org/10.1186/gb-2010-11-3-r25"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature03959",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021574562",
"https://doi.org/10.1038/nature03959"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature06810",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047805213",
"https://doi.org/10.1038/nature06810"
],
"type": "CreativeWork"
}
],
"datePublished": "2010-09-16",
"datePublishedReg": "2010-09-16",
"description": "The potential of metatranscriptomic sequencing to provide insights into the environmental factors that regulate microbial activities depends on how fully the sequence libraries capture community expression (that is, sample-sequencing depth and coverage depth), and the sensitivity with which expression differences between communities can be detected (that is, statistical power for hypothesis testing). In this study, we use an internal standard approach to make absolute (per liter) estimates of transcript numbers, a significant advantage over proportional estimates that can be biased by expression changes in unrelated genes. Coastal waters of the southeastern United States contain 1 \u00d7 1012 bacterioplankton mRNA molecules per liter of seawater (\u223c200 mRNA molecules per bacterial cell). Even for the large bacterioplankton libraries obtained in this study (\u223c500\u2009000 possible protein-encoding sequences in each of two libraries after discarding rRNAs and small RNAs from >1 million 454 FLX pyrosequencing reads), sample-sequencing depth was only 0.00001%. Expression levels of 82 genes diagnostic for transformations in the marine nitrogen, phosphorus and sulfur cycles ranged from below detection (<1 \u00d7 106 transcripts per liter) for 36 genes (for example, phosphonate metabolism gene phnH, dissimilatory nitrate reductase subunit napA) to >2.7 \u00d7 109 transcripts per liter (ammonia transporter amt and ammonia monooxygenase subunit amoC). Half of the categories for which expression was detected, however, had too few copy numbers for robust statistical resolution, as would be required for comparative (experimental or time-series) expression studies. By representing whole community gene abundance and expression in absolute units (per volume or mass of environment), \u2018omics\u2019 data can be better leveraged to improve understanding of microbially mediated processes in the ocean.",
"genre": "article",
"id": "sg:pub.10.1038/ismej.2010.141",
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.3076450",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.2994753",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1038436",
"issn": [
"1751-7362",
"1751-7370"
],
"name": "The ISME Journal: Multidisciplinary Journal of Microbial Ecology",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "3",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "5"
}
],
"keywords": [
"comparative expression studies",
"sample sequencing depths",
"microbial metatranscriptomes",
"unrelated genes",
"metatranscriptomic sequencing",
"gene abundance",
"expression differences",
"mRNA molecules",
"expression studies",
"expression changes",
"marine nitrogen",
"transcript numbers",
"copy number",
"microbial activity",
"genes",
"sulfur cycle",
"expression levels",
"southeastern United States",
"coastal waters",
"environmental factors",
"expression",
"metatranscriptomes",
"omics",
"transcripts",
"statistical resolution",
"sequencing",
"abundance",
"absolute estimates",
"community expression",
"sequence",
"library",
"internal standard approach",
"molecules",
"insights",
"proportional estimates",
"phosphorus",
"nitrogen",
"community",
"activity",
"seawater",
"Ocean",
"cycle",
"number",
"understanding",
"quantitative analysis",
"study",
"levels",
"potential",
"factors",
"changes",
"analysis",
"water",
"process",
"half",
"transformation",
"differences",
"estimates",
"sensitivity",
"absolute units",
"resolution",
"data",
"United States",
"approach",
"detection",
"depth",
"units",
"state",
"standard approach",
"significant advantages",
"advantages",
"categories"
],
"name": "Quantitative analysis of a deeply sequenced marine microbial metatranscriptome",
"pagination": "461-472",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1013224697"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/ismej.2010.141"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"20844569"
]
}
],
"sameAs": [
"https://doi.org/10.1038/ismej.2010.141",
"https://app.dimensions.ai/details/publication/pub.1013224697"
],
"sdDataset": "articles",
"sdDatePublished": "2022-08-04T16:58",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_525.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1038/ismej.2010.141"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/ismej.2010.141'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/ismej.2010.141'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/ismej.2010.141'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/ismej.2010.141'
This table displays all metadata directly associated to this object as RDF triples.
261 TRIPLES
21 PREDICATES
122 URIs
99 LITERALS
18 BLANK NODES