Ontology type: schema:ScholarlyArticle Open Access: True
2014-04-15
AUTHORSLi Shen, Ningyi Shao, Xiaochuan Liu, Eric Nestler
ABSTRACTBackgroundUnderstanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge.ResultsWe have developed ngs.plot – a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready.ConclusionsWe conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data. More... »
PAGES284
http://scigraph.springernature.com/pub.10.1186/1471-2164-15-284
DOIhttp://dx.doi.org/10.1186/1471-2164-15-284
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1006261518
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/24735413
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"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Algorithms",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Animals",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Computational Biology",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Data Mining",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Databases, Genetic",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Embryonic Stem Cells",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Epigenomics",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Genomics",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "High-Throughput Nucleotide Sequencing",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Humans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Promoter Regions, Genetic",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Reproducibility of Results",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Sequence Analysis, DNA",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Sequence Analysis, RNA",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Software",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Web Browser",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Workflow",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 10029, New York, New York, USA",
"id": "http://www.grid.ac/institutes/grid.59734.3c",
"name": [
"Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 10029, New York, New York, USA"
],
"type": "Organization"
},
"familyName": "Shen",
"givenName": "Li",
"id": "sg:person.014303005140.71",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014303005140.71"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 10029, New York, New York, USA",
"id": "http://www.grid.ac/institutes/grid.59734.3c",
"name": [
"Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 10029, New York, New York, USA"
],
"type": "Organization"
},
"familyName": "Shao",
"givenName": "Ningyi",
"id": "sg:person.015423674477.49",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015423674477.49"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 10029, New York, New York, USA",
"id": "http://www.grid.ac/institutes/grid.59734.3c",
"name": [
"Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 10029, New York, New York, USA"
],
"type": "Organization"
},
"familyName": "Liu",
"givenName": "Xiaochuan",
"id": "sg:person.01243014363.93",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01243014363.93"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 10029, New York, New York, USA",
"id": "http://www.grid.ac/institutes/grid.59734.3c",
"name": [
"Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 10029, New York, New York, USA"
],
"type": "Organization"
},
"familyName": "Nestler",
"givenName": "Eric",
"id": "sg:person.01142573400.96",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01142573400.96"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1038/nature11232",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003040774",
"https://doi.org/10.1038/nature11232"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1479-7364-2-4-266",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020592660",
"https://doi.org/10.1186/1479-7364-2-4-266"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2009-10-3-r25",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049583368",
"https://doi.org/10.1186/gb-2009-10-3-r25"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2011-12-8-r83",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025406648",
"https://doi.org/10.1186/gb-2011-12-8-r83"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrg3305",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040199034",
"https://doi.org/10.1038/nrg3305"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature11247",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029065430",
"https://doi.org/10.1038/nature11247"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nprot.2012.016",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030124536",
"https://doi.org/10.1038/nprot.2012.016"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature10102",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000816758",
"https://doi.org/10.1038/nature10102"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2012-13-8-r77",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053472112",
"https://doi.org/10.1186/gb-2012-13-8-r77"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature09906",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043304663",
"https://doi.org/10.1038/nature09906"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmeth.1985",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037530750",
"https://doi.org/10.1038/nmeth.1985"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nbt.1754",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019307928",
"https://doi.org/10.1038/nbt.1754"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2011-12-6-r54",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047606497",
"https://doi.org/10.1186/gb-2011-12-6-r54"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature11243",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053162355",
"https://doi.org/10.1038/nature11243"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrg3080",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026954006",
"https://doi.org/10.1038/nrg3080"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-12-155",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021180129",
"https://doi.org/10.1186/1471-2105-12-155"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrg2795",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025284665",
"https://doi.org/10.1038/nrg2795"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nbt1010-1045",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048301228",
"https://doi.org/10.1038/nbt1010-1045"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ng.322",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051265360",
"https://doi.org/10.1038/ng.322"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature10066",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045516695",
"https://doi.org/10.1038/nature10066"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrg2626",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023911485",
"https://doi.org/10.1038/nrg2626"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2008-9-9-r137",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027608848",
"https://doi.org/10.1186/gb-2008-9-9-r137"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrm3589",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028986515",
"https://doi.org/10.1038/nrm3589"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature09934",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041070614",
"https://doi.org/10.1038/nature09934"
],
"type": "CreativeWork"
}
],
"datePublished": "2014-04-15",
"datePublishedReg": "2014-04-15",
"description": "BackgroundUnderstanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge.ResultsWe have developed ngs.plot \u2013 a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready.ConclusionsWe conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data.",
"genre": "article",
"id": "sg:pub.10.1186/1471-2164-15-284",
"inLanguage": "en",
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.2440993",
"type": "MonetaryGrant"
},
{
"id": "sg:grant.2435910",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1023790",
"issn": [
"1471-2164"
],
"name": "BMC Genomics",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "15"
}
],
"keywords": [
"next-generation sequencing data",
"sequencing data",
"DNA-interacting proteins",
"functional DNA elements",
"genome-wide scale",
"protein-DNA interactions",
"next-generation sequencing technologies",
"big sequencing data",
"mammalian genomes",
"DNA elements",
"protein regulators",
"genomic information",
"genomic databases",
"sequencing technologies",
"gene expression",
"diverse phenotypes",
"DNA sequencing",
"enrichment patterns",
"quick mining",
"important regions",
"genome",
"standalone program",
"regulator",
"sequencing",
"protein",
"phenotype",
"expression",
"plots",
"useful tool",
"interaction",
"region",
"patterns",
"understanding",
"considerable challenge",
"data",
"millions",
"NG",
"elements",
"ResultsWe",
"amount",
"tool",
"relationship",
"dataset",
"database",
"ConclusionsWe",
"information",
"conjunction",
"mining",
"visualization",
"massive datasets",
"example",
"amount of data",
"scale",
"program",
"challenges",
"technology",
"era",
"interpretation",
"gap",
"publications",
"cost",
"figures"
],
"name": "ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases",
"pagination": "284",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1006261518"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1186/1471-2164-15-284"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"24735413"
]
}
],
"sameAs": [
"https://doi.org/10.1186/1471-2164-15-284",
"https://app.dimensions.ai/details/publication/pub.1006261518"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-10T10:10",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_646.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1186/1471-2164-15-284"
}
]
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.10.1186/1471-2164-15-284'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-284'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-284'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2164-15-284'
This table displays all metadata directly associated to this object as RDF triples.
312 TRIPLES
22 PREDICATES
129 URIs
97 LITERALS
24 BLANK NODES