Model based analysis of real-time PCR data from DNA binding dye protocols View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-12

AUTHORS

Mariano J Alvarez, Guillermo J Vila-Ortiz, Mariano C Salibe, Osvaldo L Podhajcer, Fernando J Pitossi

ABSTRACT

BACKGROUND: Reverse transcription followed by real-time PCR is widely used for quantification of specific mRNA, and with the use of double-stranded DNA binding dyes it is becoming a standard for microarray data validation. Despite the kinetic information generated by real-time PCR, most popular analysis methods assume constant amplification efficiency among samples, introducing strong biases when amplification efficiencies are not the same. RESULTS: We present here a new mathematical model based on the classic exponential description of the PCR, but modeling amplification efficiency as a sigmoidal function of the product yield. The model was validated with experimental results and used for the development of a new method for real-time PCR data analysis. This model based method for real-time PCR data analysis showed the best accuracy and precision compared with previous methods when used for quantification of in-silico generated and experimental real-time PCR results. Moreover, the method is suitable for the analyses of samples with similar or dissimilar amplification efficiency. CONCLUSION: The presented method showed the best accuracy and precision. Moreover, it does not depend on calibration curves, making it ideal for fully automated high-throughput applications. More... »

PAGES

85

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-8-85

DOI

http://dx.doi.org/10.1186/1471-2105-8-85

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010432928

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17349040


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/2008/0102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Applied Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Binding Sites", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "DNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fluorescent Dyes", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Kinetics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Chemical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Genetic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reverse Transcriptase Polymerase Chain Reaction", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sensitivity and Specificity", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Columbia University", 
          "id": "https://www.grid.ac/institutes/grid.21729.3f", 
          "name": [
            "Gentron Research Unit, Arenales 1457 \u2013 2\u00b0 Piso, C1061AAO, Buenos Aires, Argentina", 
            "Gene Therapy Laboratory, Leloir Institute, CONICET, University of Buenos Aires, Patricias Argentinas 435, C1405BWE, Buenos Aires, Argentina", 
            "Joint Centers for Systems Biology, Columbia University, 1130 St Nicholas Avenue, 10032, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Alvarez", 
        "givenName": "Mariano J", 
        "id": "sg:person.01243321304.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01243321304.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Gentron Research Unit, Arenales 1457 \u2013 2\u00b0 Piso, C1061AAO, Buenos Aires, Argentina"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Vila-Ortiz", 
        "givenName": "Guillermo J", 
        "id": "sg:person.0761415523.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761415523.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Gentron Research Unit, Arenales 1457 \u2013 2\u00b0 Piso, C1061AAO, Buenos Aires, Argentina"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Salibe", 
        "givenName": "Mariano C", 
        "id": "sg:person.01220324225.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01220324225.57"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Gene Therapy Laboratory, Leloir Institute, CONICET, University of Buenos Aires, Patricias Argentinas 435, C1405BWE, Buenos Aires, Argentina"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Podhajcer", 
        "givenName": "Osvaldo L", 
        "id": "sg:person.01276732557.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01276732557.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Neuroimmunomodulation and Gene Therapy Laboratory, Leloir Institute, CONICET, University of Buenos Aires, Patricias Argentinas 435, C1405BWE, Buenos Aires, Argentina"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pitossi", 
        "givenName": "Fernando J", 
        "id": "sg:person.01226055113.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01226055113.93"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/bit.20555", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001286770"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gnh177", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002768967"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004559888", 
          "https://doi.org/10.1038/ng1034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1034", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004559888", 
          "https://doi.org/10.1038/ng1034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0006-291x(90)90698-m", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004929204"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gng093", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007876747"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/abio.2000.4823", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008731126"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-3940(02)01423-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009175625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-3940(02)01423-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009175625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/abio.2001.5530", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018776800"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.4.6.357", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022411407"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1373/49.1.51", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025280249"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.86.24.9717", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027412567"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/meth.2001.1262", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027621591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/meth.2001.1262", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027621591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/29.9.e45", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030834024"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gng122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031852387"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.mcp.2003.09.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038028481"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/meth.2001.1266", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038223906"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/meth.2001.1266", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038223906"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gng106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041075236"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0006-291x(02)00478-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042988538"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gng073", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044637981"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt0993-1026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045863719", 
          "https://doi.org/10.1038/nbt0993-1026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gnh101", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049354396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jcb.240390102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050250085"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/17.22.9437", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051729928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.87.7.2725", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053413299"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077603669", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2144/97221bi01", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083030934"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083278915", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2144/99261rv01", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083377024"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007-12", 
    "datePublishedReg": "2007-12-01", 
    "description": "BACKGROUND: Reverse transcription followed by real-time PCR is widely used for quantification of specific mRNA, and with the use of double-stranded DNA binding dyes it is becoming a standard for microarray data validation. Despite the kinetic information generated by real-time PCR, most popular analysis methods assume constant amplification efficiency among samples, introducing strong biases when amplification efficiencies are not the same.\nRESULTS: We present here a new mathematical model based on the classic exponential description of the PCR, but modeling amplification efficiency as a sigmoidal function of the product yield. The model was validated with experimental results and used for the development of a new method for real-time PCR data analysis. This model based method for real-time PCR data analysis showed the best accuracy and precision compared with previous methods when used for quantification of in-silico generated and experimental real-time PCR results. Moreover, the method is suitable for the analyses of samples with similar or dissimilar amplification efficiency.\nCONCLUSION: The presented method showed the best accuracy and precision. Moreover, it does not depend on calibration curves, making it ideal for fully automated high-throughput applications.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/1471-2105-8-85", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "8"
      }
    ], 
    "name": "Model based analysis of real-time PCR data from DNA binding dye protocols", 
    "pagination": "85", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "31b3e7fd1b298ae65e17532f8e0101eac85c1bfadf7108040185ecd67d4e0e84"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "17349040"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100965194"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-8-85"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1010432928"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-8-85", 
      "https://app.dimensions.ai/details/publication/pub.1010432928"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:33", 
    "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/v90/0000000346_0000000346/records_99812_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2F1471-2105-8-85"
  }
]
 

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.10.1186/1471-2105-8-85'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-85'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-85'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-85'


 

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

230 TRIPLES      21 PREDICATES      67 URIs      31 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-8-85 schema:about N054b94e776204459b0e6638bda082332
2 N3b63916147524aed85f627a5bb35aac6
3 N4820e5f042094da9bcb84a3540400b9b
4 N4a566c39f50d472fbc466327ce5f9441
5 N95c7be00d80e45ce91ee4ee3d211c6bb
6 Ndaf49f73df1e452cbf1e1a58230340b9
7 Ne58b40d67f664d61ae4b7c4115ed91b0
8 Ne69512f920994936be089354c89d1bbf
9 Neff47b73c13247f091c37edd7b82127f
10 Nfab2f3c0067448109b9f31a19c81ca83
11 anzsrc-for:01
12 anzsrc-for:0102
13 schema:author N074182da28f34dc0b125fcdcbae1da49
14 schema:citation sg:pub.10.1038/nbt0993-1026
15 sg:pub.10.1038/ng1034
16 https://app.dimensions.ai/details/publication/pub.1077603669
17 https://app.dimensions.ai/details/publication/pub.1083278915
18 https://doi.org/10.1002/bit.20555
19 https://doi.org/10.1002/jcb.240390102
20 https://doi.org/10.1006/abio.2000.4823
21 https://doi.org/10.1006/abio.2001.5530
22 https://doi.org/10.1006/meth.2001.1262
23 https://doi.org/10.1006/meth.2001.1266
24 https://doi.org/10.1016/0006-291x(90)90698-m
25 https://doi.org/10.1016/j.mcp.2003.09.001
26 https://doi.org/10.1016/s0006-291x(02)00478-3
27 https://doi.org/10.1016/s0304-3940(02)01423-4
28 https://doi.org/10.1073/pnas.86.24.9717
29 https://doi.org/10.1073/pnas.87.7.2725
30 https://doi.org/10.1093/nar/17.22.9437
31 https://doi.org/10.1093/nar/29.9.e45
32 https://doi.org/10.1093/nar/gng073
33 https://doi.org/10.1093/nar/gng093
34 https://doi.org/10.1093/nar/gng106
35 https://doi.org/10.1093/nar/gng122
36 https://doi.org/10.1093/nar/gnh101
37 https://doi.org/10.1093/nar/gnh177
38 https://doi.org/10.1101/gr.4.6.357
39 https://doi.org/10.1373/49.1.51
40 https://doi.org/10.2144/97221bi01
41 https://doi.org/10.2144/99261rv01
42 schema:datePublished 2007-12
43 schema:datePublishedReg 2007-12-01
44 schema:description BACKGROUND: Reverse transcription followed by real-time PCR is widely used for quantification of specific mRNA, and with the use of double-stranded DNA binding dyes it is becoming a standard for microarray data validation. Despite the kinetic information generated by real-time PCR, most popular analysis methods assume constant amplification efficiency among samples, introducing strong biases when amplification efficiencies are not the same. RESULTS: We present here a new mathematical model based on the classic exponential description of the PCR, but modeling amplification efficiency as a sigmoidal function of the product yield. The model was validated with experimental results and used for the development of a new method for real-time PCR data analysis. This model based method for real-time PCR data analysis showed the best accuracy and precision compared with previous methods when used for quantification of in-silico generated and experimental real-time PCR results. Moreover, the method is suitable for the analyses of samples with similar or dissimilar amplification efficiency. CONCLUSION: The presented method showed the best accuracy and precision. Moreover, it does not depend on calibration curves, making it ideal for fully automated high-throughput applications.
45 schema:genre research_article
46 schema:inLanguage en
47 schema:isAccessibleForFree true
48 schema:isPartOf N7dd4b6d5414147bcbddec0ddef8f8e6e
49 Nd0426b02f37c4f4e87ceb541e1a38d95
50 sg:journal.1023786
51 schema:name Model based analysis of real-time PCR data from DNA binding dye protocols
52 schema:pagination 85
53 schema:productId N99a8c501eff24172a6b140456b72e5ef
54 N9f090eff96e54592970bea78a8dae108
55 Nad4ef3f35f184ae888a49529217d3b10
56 Ndf68c8ad773547908be16a5a6c30ad67
57 Ne5308f7e23fd434097ae22d11558fc72
58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010432928
59 https://doi.org/10.1186/1471-2105-8-85
60 schema:sdDatePublished 2019-04-11T09:33
61 schema:sdLicense https://scigraph.springernature.com/explorer/license/
62 schema:sdPublisher Nee357e109f824aae9bdd9c31df756a65
63 schema:url https://link.springer.com/10.1186%2F1471-2105-8-85
64 sgo:license sg:explorer/license/
65 sgo:sdDataset articles
66 rdf:type schema:ScholarlyArticle
67 N014138198944439d80c85b804a2a5ac3 schema:name Gentron Research Unit, Arenales 1457 – 2° Piso, C1061AAO, Buenos Aires, Argentina
68 rdf:type schema:Organization
69 N054b94e776204459b0e6638bda082332 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
70 schema:name Reproducibility of Results
71 rdf:type schema:DefinedTerm
72 N074182da28f34dc0b125fcdcbae1da49 rdf:first sg:person.01243321304.76
73 rdf:rest Nce4674a7173a4cc19a3cd437f0319482
74 N19fc838f3f2a42f7b416dd3538df367a rdf:first sg:person.01226055113.93
75 rdf:rest rdf:nil
76 N3170152c5ca748bdacacdcf272e2a101 schema:name Gentron Research Unit, Arenales 1457 – 2° Piso, C1061AAO, Buenos Aires, Argentina
77 rdf:type schema:Organization
78 N3b63916147524aed85f627a5bb35aac6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
79 schema:name Models, Chemical
80 rdf:type schema:DefinedTerm
81 N440d227786204b7885ebc2a743f8d103 rdf:first sg:person.01276732557.49
82 rdf:rest N19fc838f3f2a42f7b416dd3538df367a
83 N4820e5f042094da9bcb84a3540400b9b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Models, Genetic
85 rdf:type schema:DefinedTerm
86 N4a566c39f50d472fbc466327ce5f9441 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Binding Sites
88 rdf:type schema:DefinedTerm
89 N7dd4b6d5414147bcbddec0ddef8f8e6e schema:volumeNumber 8
90 rdf:type schema:PublicationVolume
91 N95c7be00d80e45ce91ee4ee3d211c6bb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
92 schema:name Kinetics
93 rdf:type schema:DefinedTerm
94 N99a8c501eff24172a6b140456b72e5ef schema:name doi
95 schema:value 10.1186/1471-2105-8-85
96 rdf:type schema:PropertyValue
97 N9f090eff96e54592970bea78a8dae108 schema:name dimensions_id
98 schema:value pub.1010432928
99 rdf:type schema:PropertyValue
100 Nad4ef3f35f184ae888a49529217d3b10 schema:name readcube_id
101 schema:value 31b3e7fd1b298ae65e17532f8e0101eac85c1bfadf7108040185ecd67d4e0e84
102 rdf:type schema:PropertyValue
103 Nce4674a7173a4cc19a3cd437f0319482 rdf:first sg:person.0761415523.39
104 rdf:rest Ne7e5e017302a4beaaf512727fbae3b95
105 Nd02b3f42a7394d9384cb4eaef7fefe5d schema:name Neuroimmunomodulation and Gene Therapy Laboratory, Leloir Institute, CONICET, University of Buenos Aires, Patricias Argentinas 435, C1405BWE, Buenos Aires, Argentina
106 rdf:type schema:Organization
107 Nd0426b02f37c4f4e87ceb541e1a38d95 schema:issueNumber 1
108 rdf:type schema:PublicationIssue
109 Ndaf49f73df1e452cbf1e1a58230340b9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
110 schema:name Computer Simulation
111 rdf:type schema:DefinedTerm
112 Ndf68c8ad773547908be16a5a6c30ad67 schema:name nlm_unique_id
113 schema:value 100965194
114 rdf:type schema:PropertyValue
115 Ne5308f7e23fd434097ae22d11558fc72 schema:name pubmed_id
116 schema:value 17349040
117 rdf:type schema:PropertyValue
118 Ne58b40d67f664d61ae4b7c4115ed91b0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name DNA
120 rdf:type schema:DefinedTerm
121 Ne69512f920994936be089354c89d1bbf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
122 schema:name Sensitivity and Specificity
123 rdf:type schema:DefinedTerm
124 Ne7e5e017302a4beaaf512727fbae3b95 rdf:first sg:person.01220324225.57
125 rdf:rest N440d227786204b7885ebc2a743f8d103
126 Nee357e109f824aae9bdd9c31df756a65 schema:name Springer Nature - SN SciGraph project
127 rdf:type schema:Organization
128 Nef5f5000c25443008c7dae373e26da4d schema:name Gene Therapy Laboratory, Leloir Institute, CONICET, University of Buenos Aires, Patricias Argentinas 435, C1405BWE, Buenos Aires, Argentina
129 rdf:type schema:Organization
130 Neff47b73c13247f091c37edd7b82127f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Fluorescent Dyes
132 rdf:type schema:DefinedTerm
133 Nfab2f3c0067448109b9f31a19c81ca83 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Reverse Transcriptase Polymerase Chain Reaction
135 rdf:type schema:DefinedTerm
136 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
137 schema:name Mathematical Sciences
138 rdf:type schema:DefinedTerm
139 anzsrc-for:0102 schema:inDefinedTermSet anzsrc-for:
140 schema:name Applied Mathematics
141 rdf:type schema:DefinedTerm
142 sg:journal.1023786 schema:issn 1471-2105
143 schema:name BMC Bioinformatics
144 rdf:type schema:Periodical
145 sg:person.01220324225.57 schema:affiliation N014138198944439d80c85b804a2a5ac3
146 schema:familyName Salibe
147 schema:givenName Mariano C
148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01220324225.57
149 rdf:type schema:Person
150 sg:person.01226055113.93 schema:affiliation Nd02b3f42a7394d9384cb4eaef7fefe5d
151 schema:familyName Pitossi
152 schema:givenName Fernando J
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01226055113.93
154 rdf:type schema:Person
155 sg:person.01243321304.76 schema:affiliation https://www.grid.ac/institutes/grid.21729.3f
156 schema:familyName Alvarez
157 schema:givenName Mariano J
158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01243321304.76
159 rdf:type schema:Person
160 sg:person.01276732557.49 schema:affiliation Nef5f5000c25443008c7dae373e26da4d
161 schema:familyName Podhajcer
162 schema:givenName Osvaldo L
163 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01276732557.49
164 rdf:type schema:Person
165 sg:person.0761415523.39 schema:affiliation N3170152c5ca748bdacacdcf272e2a101
166 schema:familyName Vila-Ortiz
167 schema:givenName Guillermo J
168 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0761415523.39
169 rdf:type schema:Person
170 sg:pub.10.1038/nbt0993-1026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045863719
171 https://doi.org/10.1038/nbt0993-1026
172 rdf:type schema:CreativeWork
173 sg:pub.10.1038/ng1034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004559888
174 https://doi.org/10.1038/ng1034
175 rdf:type schema:CreativeWork
176 https://app.dimensions.ai/details/publication/pub.1077603669 schema:CreativeWork
177 https://app.dimensions.ai/details/publication/pub.1083278915 schema:CreativeWork
178 https://doi.org/10.1002/bit.20555 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001286770
179 rdf:type schema:CreativeWork
180 https://doi.org/10.1002/jcb.240390102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050250085
181 rdf:type schema:CreativeWork
182 https://doi.org/10.1006/abio.2000.4823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008731126
183 rdf:type schema:CreativeWork
184 https://doi.org/10.1006/abio.2001.5530 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018776800
185 rdf:type schema:CreativeWork
186 https://doi.org/10.1006/meth.2001.1262 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027621591
187 rdf:type schema:CreativeWork
188 https://doi.org/10.1006/meth.2001.1266 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038223906
189 rdf:type schema:CreativeWork
190 https://doi.org/10.1016/0006-291x(90)90698-m schema:sameAs https://app.dimensions.ai/details/publication/pub.1004929204
191 rdf:type schema:CreativeWork
192 https://doi.org/10.1016/j.mcp.2003.09.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038028481
193 rdf:type schema:CreativeWork
194 https://doi.org/10.1016/s0006-291x(02)00478-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042988538
195 rdf:type schema:CreativeWork
196 https://doi.org/10.1016/s0304-3940(02)01423-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009175625
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1073/pnas.86.24.9717 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027412567
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1073/pnas.87.7.2725 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053413299
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1093/nar/17.22.9437 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051729928
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1093/nar/29.9.e45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030834024
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1093/nar/gng073 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044637981
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1093/nar/gng093 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007876747
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1093/nar/gng106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041075236
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1093/nar/gng122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031852387
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1093/nar/gnh101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049354396
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1093/nar/gnh177 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002768967
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1101/gr.4.6.357 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022411407
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1373/49.1.51 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025280249
221 rdf:type schema:CreativeWork
222 https://doi.org/10.2144/97221bi01 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083030934
223 rdf:type schema:CreativeWork
224 https://doi.org/10.2144/99261rv01 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083377024
225 rdf:type schema:CreativeWork
226 https://www.grid.ac/institutes/grid.21729.3f schema:alternateName Columbia University
227 schema:name Gene Therapy Laboratory, Leloir Institute, CONICET, University of Buenos Aires, Patricias Argentinas 435, C1405BWE, Buenos Aires, Argentina
228 Gentron Research Unit, Arenales 1457 – 2° Piso, C1061AAO, Buenos Aires, Argentina
229 Joint Centers for Systems Biology, Columbia University, 1130 St Nicholas Avenue, 10032, New York, NY, USA
230 rdf:type schema:Organization
 




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


...