The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-01

AUTHORS

Alejandra González-Beltrán, Steffen Neumann, Eamonn Maguire, Susanna-Assunta Sansone, Philippe Rocca-Serra

ABSTRACT

BACKGROUND: The ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing Investigations, Studies and Assays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment. RESULTS: The Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data. CONCLUSIONS: The Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking. SOFTWARE AVAILABILITY: The Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa, where the issue tracker allows users to report bugs or feature requests. More... »

PAGES

s11

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-15-s1-s11

DOI

http://dx.doi.org/10.1186/1471-2105-15-s1-s11

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042251809

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24564732


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/0806", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information Systems", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genomics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mass Spectrometry", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Metabolomics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Oligonucleotide Array Sequence Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Oxford", 
          "id": "https://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Oxford e-Research Centre, University of Oxford, OX1 3QG, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gonz\u00e1lez-Beltr\u00e1n", 
        "givenName": "Alejandra", 
        "id": "sg:person.0624156046.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624156046.37"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Leibniz Institute of Plant Biochemistry", 
          "id": "https://www.grid.ac/institutes/grid.425084.f", 
          "name": [
            "Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, 06120, Halle, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Neumann", 
        "givenName": "Steffen", 
        "id": "sg:person.01050771746.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01050771746.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oxford", 
          "id": "https://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Oxford e-Research Centre, University of Oxford, OX1 3QG, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Maguire", 
        "givenName": "Eamonn", 
        "id": "sg:person.01164135006.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01164135006.11"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oxford", 
          "id": "https://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Oxford e-Research Centre, University of Oxford, OX1 3QG, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sansone", 
        "givenName": "Susanna-Assunta", 
        "id": "sg:person.0635417117.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0635417117.92"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oxford", 
          "id": "https://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Oxford e-Research Centre, University of Oxford, OX1 3QG, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rocca-Serra", 
        "givenName": "Philippe", 
        "id": "sg:person.016542700247.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016542700247.80"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/ng1201-365", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003793347", 
          "https://doi.org/10.1038/ng1201-365"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1201-365", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003793347", 
          "https://doi.org/10.1038/ng1201-365"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btg405", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003878496"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btq415", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004926416"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt.1823", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005906569", 
          "https://doi.org/10.1038/nbt.1823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/mcp.r110.000133", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007429376"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq967", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013987504"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng.1054", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014202300", 
          "https://doi.org/10.1038/ng.1054"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks1262", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015033641"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-489", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016821219", 
          "https://doi.org/10.1186/1471-2105-7-489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2004-5-10-r80", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018457673", 
          "https://doi.org/10.1186/gb-2004-5-10-r80"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkr469", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019388008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac202450g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023979956"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks1174", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026914575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2010-11-10-r106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031289083", 
          "https://doi.org/10.1186/gb-2010-11-10-r106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1329", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034386256", 
          "https://doi.org/10.1038/nbt1329"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks1193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035551539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/2041-1480-1-s1-s7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036820478", 
          "https://doi.org/10.1186/2041-1480-1-s1-s7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gks1004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037489229"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1186624", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042833413"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bts308", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045640729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btl005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046925392"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-012-0401-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049344480", 
          "https://doi.org/10.1007/s11306-012-0401-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkr1051", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050033788"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bts718", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051995921"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/bi0480335", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052731821"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/bi0480335", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052731821"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051437y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053369488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051437y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053369488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.14806/ej.18.b.542", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067372865"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511921247", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098774704"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-01", 
    "datePublishedReg": "2014-01-01", 
    "description": "BACKGROUND: The ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing Investigations, Studies and Assays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment.\nRESULTS: The Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data.\nCONCLUSIONS: The Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking.\nSOFTWARE AVAILABILITY: The Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa, where the issue tracker allows users to report bugs or feature requests.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2105-15-s1-s11", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2771203", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2787307", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2778509", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2779388", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2757103", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "Suppl 1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "15"
      }
    ], 
    "name": "The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again", 
    "pagination": "s11", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c9445a4bb80ed8e262c64be474a7a73e0266c780d9cb65d185088299fe2e450d"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "24564732"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-15-s1-s11"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1042251809"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-15-s1-s11", 
      "https://app.dimensions.ai/details/publication/pub.1042251809"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T00:09", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8695_00000482.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186/1471-2105-15-S1-S11"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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.10.1186/1471-2105-15-s1-s11'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-s1-s11'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-s1-s11'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-15-s1-s11'


 

This table displays all metadata directly associated to this object as RDF triples.

222 TRIPLES      21 PREDICATES      62 URIs      26 LITERALS      14 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-15-s1-s11 schema:about N097f72c797264aa7b02d79e781d909f9
2 N650970f3b5de4c31969bcc1591812251
3 N6ddb179cfc7a4257b605f12f3e36ba52
4 Nb2c59afdd63c49bbaf1cb54e63be8c34
5 Nb609dacf932547c3b102f076f4e70fdf
6 anzsrc-for:08
7 anzsrc-for:0806
8 schema:author N5fee29f2b3e4437597c89cfdd140a61a
9 schema:citation sg:pub.10.1007/s11306-012-0401-0
10 sg:pub.10.1038/nbt.1823
11 sg:pub.10.1038/nbt1329
12 sg:pub.10.1038/ng.1054
13 sg:pub.10.1038/ng1201-365
14 sg:pub.10.1186/1471-2105-7-489
15 sg:pub.10.1186/2041-1480-1-s1-s7
16 sg:pub.10.1186/gb-2004-5-10-r80
17 sg:pub.10.1186/gb-2010-11-10-r106
18 https://doi.org/10.1017/cbo9780511921247
19 https://doi.org/10.1021/ac051437y
20 https://doi.org/10.1021/ac202450g
21 https://doi.org/10.1021/bi0480335
22 https://doi.org/10.1074/mcp.r110.000133
23 https://doi.org/10.1093/bioinformatics/btg405
24 https://doi.org/10.1093/bioinformatics/btl005
25 https://doi.org/10.1093/bioinformatics/btq415
26 https://doi.org/10.1093/bioinformatics/bts308
27 https://doi.org/10.1093/bioinformatics/bts718
28 https://doi.org/10.1093/nar/gkq967
29 https://doi.org/10.1093/nar/gkr1051
30 https://doi.org/10.1093/nar/gkr469
31 https://doi.org/10.1093/nar/gks1004
32 https://doi.org/10.1093/nar/gks1174
33 https://doi.org/10.1093/nar/gks1193
34 https://doi.org/10.1093/nar/gks1262
35 https://doi.org/10.1126/science.1186624
36 https://doi.org/10.14806/ej.18.b.542
37 schema:datePublished 2014-01
38 schema:datePublishedReg 2014-01-01
39 schema:description BACKGROUND: The ISA-Tab format and software suite have been developed to break the silo effect induced by technology-specific formats for a variety of data types and to better support experimental metadata tracking. Experimentalists seldom use a single technique to monitor biological signals. Providing a multi-purpose, pragmatic and accessible format that abstracts away common constructs for describing Investigations, Studies and Assays, ISA is increasingly popular. To attract further interest towards the format and extend support to ensure reproducible research and reusable data, we present the Risa package, which delivers a central component to support the ISA format by enabling effortless integration with R, the popular, open source data crunching environment. RESULTS: The Risa package bridges the gap between the metadata collection and curation in an ISA-compliant way and the data analysis using the widely used statistical computing environment R. The package offers functionality for: i) parsing ISA-Tab datasets into R objects, ii) augmenting annotation with extra metadata not explicitly stated in the ISA syntax; iii) interfacing with domain specific R packages iv) suggesting potentially useful R packages available in Bioconductor for subsequent processing of the experimental data described in the ISA format; and finally v) saving back to ISA-Tab files augmented with analysis specific metadata from R. We demonstrate these features by presenting use cases for mass spectrometry data and DNA microarray data. CONCLUSIONS: The Risa package is open source (with LGPL license) and freely available through Bioconductor. By making Risa available, we aim to facilitate the task of processing experimental data, encouraging a uniform representation of experimental information and results while delivering tools for ensuring traceability and provenance tracking. SOFTWARE AVAILABILITY: The Risa package is available since Bioconductor 2.11 (version 1.0.0) and version 1.2.1 appeared in Bioconductor 2.12, both along with documentation and examples. The latest version of the code is at the development branch in Bioconductor and can also be accessed from GitHub https://github.com/ISA-tools/Risa, where the issue tracker allows users to report bugs or feature requests.
40 schema:genre research_article
41 schema:inLanguage en
42 schema:isAccessibleForFree true
43 schema:isPartOf N60be41e2b8ea4965b3def125fca6ed6e
44 Nd34deb1ab226442f95f77b67333d9dfa
45 sg:journal.1023786
46 schema:name The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again
47 schema:pagination s11
48 schema:productId N002f12a8cd3f4010bf938526911e3777
49 N3635dc7e31be485bbf92ed99cc841234
50 N4996e6823d3b407ba20152bcba8bfeff
51 N7abc91c993dc4e7eacc49d8da1e4aa97
52 N7e8d4aa5fc714263a8cf4edaaf41ade2
53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042251809
54 https://doi.org/10.1186/1471-2105-15-s1-s11
55 schema:sdDatePublished 2019-04-11T00:09
56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
57 schema:sdPublisher Ne11a23787cec43d884e4aba654d79237
58 schema:url http://link.springer.com/10.1186/1471-2105-15-S1-S11
59 sgo:license sg:explorer/license/
60 sgo:sdDataset articles
61 rdf:type schema:ScholarlyArticle
62 N002f12a8cd3f4010bf938526911e3777 schema:name dimensions_id
63 schema:value pub.1042251809
64 rdf:type schema:PropertyValue
65 N097f72c797264aa7b02d79e781d909f9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
66 schema:name Software
67 rdf:type schema:DefinedTerm
68 N33dbbe41f690434fbcb95b6731e93e65 rdf:first sg:person.01050771746.28
69 rdf:rest N4579689875e440dda60ca5aa2f5c0198
70 N3635dc7e31be485bbf92ed99cc841234 schema:name doi
71 schema:value 10.1186/1471-2105-15-s1-s11
72 rdf:type schema:PropertyValue
73 N4579689875e440dda60ca5aa2f5c0198 rdf:first sg:person.01164135006.11
74 rdf:rest Nbe0bf26b3c5e40119032a80a710dfb2e
75 N4996e6823d3b407ba20152bcba8bfeff schema:name nlm_unique_id
76 schema:value 100965194
77 rdf:type schema:PropertyValue
78 N5fee29f2b3e4437597c89cfdd140a61a rdf:first sg:person.0624156046.37
79 rdf:rest N33dbbe41f690434fbcb95b6731e93e65
80 N60be41e2b8ea4965b3def125fca6ed6e schema:volumeNumber 15
81 rdf:type schema:PublicationVolume
82 N650970f3b5de4c31969bcc1591812251 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
83 schema:name Genomics
84 rdf:type schema:DefinedTerm
85 N6ddb179cfc7a4257b605f12f3e36ba52 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
86 schema:name Metabolomics
87 rdf:type schema:DefinedTerm
88 N7abc91c993dc4e7eacc49d8da1e4aa97 schema:name readcube_id
89 schema:value c9445a4bb80ed8e262c64be474a7a73e0266c780d9cb65d185088299fe2e450d
90 rdf:type schema:PropertyValue
91 N7e8d4aa5fc714263a8cf4edaaf41ade2 schema:name pubmed_id
92 schema:value 24564732
93 rdf:type schema:PropertyValue
94 Nac8452412fe54ce6badcd89b5dc141c5 rdf:first sg:person.016542700247.80
95 rdf:rest rdf:nil
96 Nb2c59afdd63c49bbaf1cb54e63be8c34 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
97 schema:name Oligonucleotide Array Sequence Analysis
98 rdf:type schema:DefinedTerm
99 Nb609dacf932547c3b102f076f4e70fdf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
100 schema:name Mass Spectrometry
101 rdf:type schema:DefinedTerm
102 Nbe0bf26b3c5e40119032a80a710dfb2e rdf:first sg:person.0635417117.92
103 rdf:rest Nac8452412fe54ce6badcd89b5dc141c5
104 Nd34deb1ab226442f95f77b67333d9dfa schema:issueNumber Suppl 1
105 rdf:type schema:PublicationIssue
106 Ne11a23787cec43d884e4aba654d79237 schema:name Springer Nature - SN SciGraph project
107 rdf:type schema:Organization
108 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
109 schema:name Information and Computing Sciences
110 rdf:type schema:DefinedTerm
111 anzsrc-for:0806 schema:inDefinedTermSet anzsrc-for:
112 schema:name Information Systems
113 rdf:type schema:DefinedTerm
114 sg:grant.2757103 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-15-s1-s11
115 rdf:type schema:MonetaryGrant
116 sg:grant.2771203 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-15-s1-s11
117 rdf:type schema:MonetaryGrant
118 sg:grant.2778509 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-15-s1-s11
119 rdf:type schema:MonetaryGrant
120 sg:grant.2779388 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-15-s1-s11
121 rdf:type schema:MonetaryGrant
122 sg:grant.2787307 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-15-s1-s11
123 rdf:type schema:MonetaryGrant
124 sg:journal.1023786 schema:issn 1471-2105
125 schema:name BMC Bioinformatics
126 rdf:type schema:Periodical
127 sg:person.01050771746.28 schema:affiliation https://www.grid.ac/institutes/grid.425084.f
128 schema:familyName Neumann
129 schema:givenName Steffen
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01050771746.28
131 rdf:type schema:Person
132 sg:person.01164135006.11 schema:affiliation https://www.grid.ac/institutes/grid.4991.5
133 schema:familyName Maguire
134 schema:givenName Eamonn
135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01164135006.11
136 rdf:type schema:Person
137 sg:person.016542700247.80 schema:affiliation https://www.grid.ac/institutes/grid.4991.5
138 schema:familyName Rocca-Serra
139 schema:givenName Philippe
140 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016542700247.80
141 rdf:type schema:Person
142 sg:person.0624156046.37 schema:affiliation https://www.grid.ac/institutes/grid.4991.5
143 schema:familyName González-Beltrán
144 schema:givenName Alejandra
145 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0624156046.37
146 rdf:type schema:Person
147 sg:person.0635417117.92 schema:affiliation https://www.grid.ac/institutes/grid.4991.5
148 schema:familyName Sansone
149 schema:givenName Susanna-Assunta
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0635417117.92
151 rdf:type schema:Person
152 sg:pub.10.1007/s11306-012-0401-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049344480
153 https://doi.org/10.1007/s11306-012-0401-0
154 rdf:type schema:CreativeWork
155 sg:pub.10.1038/nbt.1823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005906569
156 https://doi.org/10.1038/nbt.1823
157 rdf:type schema:CreativeWork
158 sg:pub.10.1038/nbt1329 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034386256
159 https://doi.org/10.1038/nbt1329
160 rdf:type schema:CreativeWork
161 sg:pub.10.1038/ng.1054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014202300
162 https://doi.org/10.1038/ng.1054
163 rdf:type schema:CreativeWork
164 sg:pub.10.1038/ng1201-365 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003793347
165 https://doi.org/10.1038/ng1201-365
166 rdf:type schema:CreativeWork
167 sg:pub.10.1186/1471-2105-7-489 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016821219
168 https://doi.org/10.1186/1471-2105-7-489
169 rdf:type schema:CreativeWork
170 sg:pub.10.1186/2041-1480-1-s1-s7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036820478
171 https://doi.org/10.1186/2041-1480-1-s1-s7
172 rdf:type schema:CreativeWork
173 sg:pub.10.1186/gb-2004-5-10-r80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018457673
174 https://doi.org/10.1186/gb-2004-5-10-r80
175 rdf:type schema:CreativeWork
176 sg:pub.10.1186/gb-2010-11-10-r106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031289083
177 https://doi.org/10.1186/gb-2010-11-10-r106
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1017/cbo9780511921247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098774704
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1021/ac051437y schema:sameAs https://app.dimensions.ai/details/publication/pub.1053369488
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1021/ac202450g schema:sameAs https://app.dimensions.ai/details/publication/pub.1023979956
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1021/bi0480335 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052731821
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1074/mcp.r110.000133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007429376
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1093/bioinformatics/btg405 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003878496
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1093/bioinformatics/btl005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046925392
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1093/bioinformatics/btq415 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004926416
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1093/bioinformatics/bts308 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045640729
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1093/bioinformatics/bts718 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051995921
198 rdf:type schema:CreativeWork
199 https://doi.org/10.1093/nar/gkq967 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013987504
200 rdf:type schema:CreativeWork
201 https://doi.org/10.1093/nar/gkr1051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050033788
202 rdf:type schema:CreativeWork
203 https://doi.org/10.1093/nar/gkr469 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019388008
204 rdf:type schema:CreativeWork
205 https://doi.org/10.1093/nar/gks1004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037489229
206 rdf:type schema:CreativeWork
207 https://doi.org/10.1093/nar/gks1174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026914575
208 rdf:type schema:CreativeWork
209 https://doi.org/10.1093/nar/gks1193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035551539
210 rdf:type schema:CreativeWork
211 https://doi.org/10.1093/nar/gks1262 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015033641
212 rdf:type schema:CreativeWork
213 https://doi.org/10.1126/science.1186624 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042833413
214 rdf:type schema:CreativeWork
215 https://doi.org/10.14806/ej.18.b.542 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067372865
216 rdf:type schema:CreativeWork
217 https://www.grid.ac/institutes/grid.425084.f schema:alternateName Leibniz Institute of Plant Biochemistry
218 schema:name Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, 06120, Halle, Germany
219 rdf:type schema:Organization
220 https://www.grid.ac/institutes/grid.4991.5 schema:alternateName University of Oxford
221 schema:name Oxford e-Research Centre, University of Oxford, OX1 3QG, Oxford, UK
222 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...