Gene Expression Profiling Using 3′ Tag Digital Approach View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Yan W. Asmann , E. Aubrey Thompson , Jean-Pierre A. Kocher

ABSTRACT

Massive parallel sequencing will become the method of choice for transcriptome profiling. Two protocols have been developed to quantify level of expressions: full-length RNA sequencing (RNA-SEQ) and 3′ tag digital gene expression (DGE). We have studied the performance of 3′ tag DGE profiling and used this protocol to compare the expression profiles of brain RNA to universal human reference RNA. This comparison highlighted that DGE is highly quantitative with excellent correlation of differential expression with quantitative real-time PCR. Our analysis also showed that when compared to microarray, one lane of 3′ DGE sequencing had wider dynamic range for transcriptome profiling and was able to detect expressed genes that are below the detection threshold of microarray. We conclude that 3′ tag DGE profiling is highly sensitive and reproducible for transcriptome profiling. It outperforms microarray platforms in detecting lower abundant transcripts. More... »

PAGES

77-87

Book

TITLE

Expression Profiling in Neuroscience

ISBN

978-1-61779-447-6
978-1-61779-448-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-61779-448-3_5

DOI

http://dx.doi.org/10.1007/978-1-61779-448-3_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050203690


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/2008/0604", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Genetics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic Comprehensive Cancer Center, Rochester, MN, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Asmann", 
        "givenName": "Yan W.", 
        "id": "sg:person.01046622724.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01046622724.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Rochester, MN, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Thompson", 
        "givenName": "E. Aubrey", 
        "id": "sg:person.012362237257.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012362237257.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic Comprehensive Cancer Center, Rochester, MN, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kocher", 
        "givenName": "Jean-Pierre A.", 
        "id": "sg:person.01370515234.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01370515234.41"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1471-2164-10-531", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005207270", 
          "https://doi.org/10.1186/1471-2164-10-531"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1296-1675", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005458398", 
          "https://doi.org/10.1038/nbt1296-1675"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nm1491", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006868953", 
          "https://doi.org/10.1038/nm1491"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nm1491", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006868953", 
          "https://doi.org/10.1038/nm1491"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature07638", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009442631", 
          "https://doi.org/10.1038/nature07638"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1180", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014377811", 
          "https://doi.org/10.1038/ng1180"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ng1180", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014377811", 
          "https://doi.org/10.1038/ng1180"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1146/annurev.genom.9.081307.164359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015853776"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.tig.2007.12.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027335183"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg2484", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030687647", 
          "https://doi.org/10.1038/nrg2484"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1239", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037875102", 
          "https://doi.org/10.1038/nbt1239"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1239", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037875102", 
          "https://doi.org/10.1038/nbt1239"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1160342", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042163407"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.286.5439.531", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042995627"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth1157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048416659", 
          "https://doi.org/10.1038/nmeth1157"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.1223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048586936", 
          "https://doi.org/10.1038/nmeth.1223"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.270.5235.467", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062551475"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.270.5235.484", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062551479"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.8091218", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062652223"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012", 
    "datePublishedReg": "2012-01-01", 
    "description": "Massive parallel sequencing will become the method of choice for transcriptome profiling. Two protocols have been developed to quantify level of expressions: full-length RNA sequencing (RNA-SEQ) and 3\u2032 tag digital gene expression (DGE). We have studied the performance of 3\u2032 tag DGE profiling and used this protocol to compare the expression profiles of brain RNA to universal human reference RNA. This comparison highlighted that DGE is highly quantitative with excellent correlation of differential expression with quantitative real-time PCR. Our analysis also showed that when compared to microarray, one lane of 3\u2032 DGE sequencing had wider dynamic range for transcriptome profiling and was able to detect expressed genes that are below the detection threshold of microarray. We conclude that 3\u2032 tag DGE profiling is highly sensitive and reproducible for transcriptome profiling. It outperforms microarray platforms in detecting lower abundant transcripts.", 
    "editor": [
      {
        "familyName": "Karamanos", 
        "givenName": "Yannis", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-61779-448-3_5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.5246518", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2452552", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2455266", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2705122", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": {
      "isbn": [
        "978-1-61779-447-6", 
        "978-1-61779-448-3"
      ], 
      "name": "Expression Profiling in Neuroscience", 
      "type": "Book"
    }, 
    "name": "Gene Expression Profiling Using 3\u2032 Tag Digital Approach", 
    "pagination": "77-87", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-61779-448-3_5"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "ef27b9c4a2746170efa062967022440a92af198cc79a9d496e0a7949c302200c"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1050203690"
        ]
      }
    ], 
    "publisher": {
      "location": "Totowa, NJ", 
      "name": "Humana Press", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-61779-448-3_5", 
      "https://app.dimensions.ai/details/publication/pub.1050203690"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T15:24", 
    "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_8672_00000274.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-1-61779-448-3_5"
  }
]
 

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.10.1007/978-1-61779-448-3_5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-61779-448-3_5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-61779-448-3_5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-61779-448-3_5'


 

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

147 TRIPLES      23 PREDICATES      43 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-61779-448-3_5 schema:about anzsrc-for:06
2 anzsrc-for:0604
3 schema:author Ndcf61931e55241ea8160d782600af67e
4 schema:citation sg:pub.10.1038/nature07638
5 sg:pub.10.1038/nbt1239
6 sg:pub.10.1038/nbt1296-1675
7 sg:pub.10.1038/ng1180
8 sg:pub.10.1038/nm1491
9 sg:pub.10.1038/nmeth.1223
10 sg:pub.10.1038/nmeth1157
11 sg:pub.10.1038/nrg2484
12 sg:pub.10.1186/1471-2164-10-531
13 https://doi.org/10.1016/j.tig.2007.12.007
14 https://doi.org/10.1126/science.1160342
15 https://doi.org/10.1126/science.270.5235.467
16 https://doi.org/10.1126/science.270.5235.484
17 https://doi.org/10.1126/science.286.5439.531
18 https://doi.org/10.1126/science.8091218
19 https://doi.org/10.1146/annurev.genom.9.081307.164359
20 schema:datePublished 2012
21 schema:datePublishedReg 2012-01-01
22 schema:description Massive parallel sequencing will become the method of choice for transcriptome profiling. Two protocols have been developed to quantify level of expressions: full-length RNA sequencing (RNA-SEQ) and 3′ tag digital gene expression (DGE). We have studied the performance of 3′ tag DGE profiling and used this protocol to compare the expression profiles of brain RNA to universal human reference RNA. This comparison highlighted that DGE is highly quantitative with excellent correlation of differential expression with quantitative real-time PCR. Our analysis also showed that when compared to microarray, one lane of 3′ DGE sequencing had wider dynamic range for transcriptome profiling and was able to detect expressed genes that are below the detection threshold of microarray. We conclude that 3′ tag DGE profiling is highly sensitive and reproducible for transcriptome profiling. It outperforms microarray platforms in detecting lower abundant transcripts.
23 schema:editor N41d39b95e6554052bc11908370bae78a
24 schema:genre chapter
25 schema:inLanguage en
26 schema:isAccessibleForFree false
27 schema:isPartOf N39f44aff843c4e0090d249c88fa0e36b
28 schema:name Gene Expression Profiling Using 3′ Tag Digital Approach
29 schema:pagination 77-87
30 schema:productId N06408d8fbbfe40be977c859ee3c0ae86
31 N15d41e24f2fe4e2fbb7492353f2ece14
32 N3d015c7c4ed44231a80bb1da41627403
33 schema:publisher N4118515188e942838c9b5d1cc254ffad
34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050203690
35 https://doi.org/10.1007/978-1-61779-448-3_5
36 schema:sdDatePublished 2019-04-15T15:24
37 schema:sdLicense https://scigraph.springernature.com/explorer/license/
38 schema:sdPublisher N30768c3135b84c3fbbb587119b0c0203
39 schema:url http://link.springer.com/10.1007/978-1-61779-448-3_5
40 sgo:license sg:explorer/license/
41 sgo:sdDataset chapters
42 rdf:type schema:Chapter
43 N06408d8fbbfe40be977c859ee3c0ae86 schema:name readcube_id
44 schema:value ef27b9c4a2746170efa062967022440a92af198cc79a9d496e0a7949c302200c
45 rdf:type schema:PropertyValue
46 N15d41e24f2fe4e2fbb7492353f2ece14 schema:name doi
47 schema:value 10.1007/978-1-61779-448-3_5
48 rdf:type schema:PropertyValue
49 N30768c3135b84c3fbbb587119b0c0203 schema:name Springer Nature - SN SciGraph project
50 rdf:type schema:Organization
51 N39f44aff843c4e0090d249c88fa0e36b schema:isbn 978-1-61779-447-6
52 978-1-61779-448-3
53 schema:name Expression Profiling in Neuroscience
54 rdf:type schema:Book
55 N3d015c7c4ed44231a80bb1da41627403 schema:name dimensions_id
56 schema:value pub.1050203690
57 rdf:type schema:PropertyValue
58 N4118515188e942838c9b5d1cc254ffad schema:location Totowa, NJ
59 schema:name Humana Press
60 rdf:type schema:Organisation
61 N41d39b95e6554052bc11908370bae78a rdf:first N606a6fa2e96a4706b9b90ace769c8517
62 rdf:rest rdf:nil
63 N4e8a3c471da24481865f254e9e4ac92c rdf:first sg:person.012362237257.28
64 rdf:rest Ncfe8cf9fcc804a30b13be752eb93d92a
65 N606a6fa2e96a4706b9b90ace769c8517 schema:familyName Karamanos
66 schema:givenName Yannis
67 rdf:type schema:Person
68 N6beb7ceb65004ba2abf2940c18579a2a schema:name Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Rochester, MN, USA
69 rdf:type schema:Organization
70 N7f0d144c260b4f9194a5fbd2c6c7cbbe schema:name Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic Comprehensive Cancer Center, Rochester, MN, USA
71 rdf:type schema:Organization
72 Nc1410285308d44c381827cbd8c14cbb5 schema:name Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic Comprehensive Cancer Center, Rochester, MN, USA
73 rdf:type schema:Organization
74 Ncfe8cf9fcc804a30b13be752eb93d92a rdf:first sg:person.01370515234.41
75 rdf:rest rdf:nil
76 Ndcf61931e55241ea8160d782600af67e rdf:first sg:person.01046622724.44
77 rdf:rest N4e8a3c471da24481865f254e9e4ac92c
78 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
79 schema:name Biological Sciences
80 rdf:type schema:DefinedTerm
81 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
82 schema:name Genetics
83 rdf:type schema:DefinedTerm
84 sg:grant.2452552 http://pending.schema.org/fundedItem sg:pub.10.1007/978-1-61779-448-3_5
85 rdf:type schema:MonetaryGrant
86 sg:grant.2455266 http://pending.schema.org/fundedItem sg:pub.10.1007/978-1-61779-448-3_5
87 rdf:type schema:MonetaryGrant
88 sg:grant.2705122 http://pending.schema.org/fundedItem sg:pub.10.1007/978-1-61779-448-3_5
89 rdf:type schema:MonetaryGrant
90 sg:grant.5246518 http://pending.schema.org/fundedItem sg:pub.10.1007/978-1-61779-448-3_5
91 rdf:type schema:MonetaryGrant
92 sg:person.01046622724.44 schema:affiliation N7f0d144c260b4f9194a5fbd2c6c7cbbe
93 schema:familyName Asmann
94 schema:givenName Yan W.
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01046622724.44
96 rdf:type schema:Person
97 sg:person.012362237257.28 schema:affiliation N6beb7ceb65004ba2abf2940c18579a2a
98 schema:familyName Thompson
99 schema:givenName E. Aubrey
100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012362237257.28
101 rdf:type schema:Person
102 sg:person.01370515234.41 schema:affiliation Nc1410285308d44c381827cbd8c14cbb5
103 schema:familyName Kocher
104 schema:givenName Jean-Pierre A.
105 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01370515234.41
106 rdf:type schema:Person
107 sg:pub.10.1038/nature07638 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009442631
108 https://doi.org/10.1038/nature07638
109 rdf:type schema:CreativeWork
110 sg:pub.10.1038/nbt1239 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037875102
111 https://doi.org/10.1038/nbt1239
112 rdf:type schema:CreativeWork
113 sg:pub.10.1038/nbt1296-1675 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005458398
114 https://doi.org/10.1038/nbt1296-1675
115 rdf:type schema:CreativeWork
116 sg:pub.10.1038/ng1180 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014377811
117 https://doi.org/10.1038/ng1180
118 rdf:type schema:CreativeWork
119 sg:pub.10.1038/nm1491 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006868953
120 https://doi.org/10.1038/nm1491
121 rdf:type schema:CreativeWork
122 sg:pub.10.1038/nmeth.1223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048586936
123 https://doi.org/10.1038/nmeth.1223
124 rdf:type schema:CreativeWork
125 sg:pub.10.1038/nmeth1157 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048416659
126 https://doi.org/10.1038/nmeth1157
127 rdf:type schema:CreativeWork
128 sg:pub.10.1038/nrg2484 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030687647
129 https://doi.org/10.1038/nrg2484
130 rdf:type schema:CreativeWork
131 sg:pub.10.1186/1471-2164-10-531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005207270
132 https://doi.org/10.1186/1471-2164-10-531
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/j.tig.2007.12.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027335183
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1126/science.1160342 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042163407
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1126/science.270.5235.467 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062551475
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1126/science.270.5235.484 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062551479
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1126/science.286.5439.531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042995627
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1126/science.8091218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062652223
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1146/annurev.genom.9.081307.164359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015853776
147 rdf:type schema:CreativeWork
 




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


...