Systematic determination of genetic network architecture View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1999-07

AUTHORS

Saeed Tavazoie, Jason D. Hughes, Michael J. Campbell, Raymond J. Cho, George M. Church

ABSTRACT

Technologies to measure whole-genome mRNA abundances1,2,3 and methods to organize and display such data4,5,6,7,8,9,10 are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes5. We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo6. Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions7. The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements8. Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast—without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known. More... »

PAGES

281-285

Journal

TITLE

Nature Genetics

ISSUE

3

VOLUME

22

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/10343

DOI

http://dx.doi.org/10.1038/10343

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009819816

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/10391217


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/2008/06", 
        "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": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Cell Cycle", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "DNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genetic Techniques", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mice", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Multigene Family", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Open Reading Frames", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "RNA, Messenger", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Rhombencephalon", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Saccharomyces cerevisiae", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Genetics, Harvard Medical School, 200 Longwood Ave, 02115, Boston, Massachusetts, USA", 
          "id": "http://www.grid.ac/institutes/grid.38142.3c", 
          "name": [
            "Department of Genetics, Harvard Medical School, 200 Longwood Ave, 02115, Boston, Massachusetts, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tavazoie", 
        "givenName": "Saeed", 
        "id": "sg:person.01105416763.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01105416763.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Harvard University, 02115, Boston, Massachusetts, USA", 
          "id": "http://www.grid.ac/institutes/grid.38142.3c", 
          "name": [
            "Department of Genetics, Harvard Medical School, 200 Longwood Ave, 02115, Boston, Massachusetts, USA", 
            "Graduate Program in Biophysics, 200 Longwood Ave", 
            "Harvard University, 02115, Boston, Massachusetts, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hughes", 
        "givenName": "Jason D.", 
        "id": "sg:person.0652320600.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0652320600.24"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Molecular Applications Group, 607 Hansen Way, Building One, 94303-1110, Palo Alto, California, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Molecular Applications Group, 607 Hansen Way, Building One, 94303-1110, Palo Alto, California, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Campbell", 
        "givenName": "Michael J.", 
        "id": "sg:person.0727430036.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0727430036.43"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Stanford Medical Center, 94304, Palo Alto, CaliforniaUSA", 
          "id": "http://www.grid.ac/institutes/grid.240952.8", 
          "name": [
            "Department of Genetics, B400 Beckman Center, 279 Campus Drive", 
            "Stanford Medical Center, 94304, Palo Alto, CaliforniaUSA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cho", 
        "givenName": "Raymond J.", 
        "id": "sg:person.0601045543.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601045543.61"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Genetics, Harvard Medical School, 200 Longwood Ave, 02115, Boston, Massachusetts, USA", 
          "id": "http://www.grid.ac/institutes/grid.38142.3c", 
          "name": [
            "Department of Genetics, Harvard Medical School, 200 Longwood Ave, 02115, Boston, Massachusetts, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Church", 
        "givenName": "George M.", 
        "id": "sg:person.01115626315.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01115626315.03"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nbt1098-939", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049107556", 
          "https://doi.org/10.1038/nbt1098-939"
        ], 
        "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.10.1038/42755", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017484221", 
          "https://doi.org/10.1038/42755"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1297-1359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052281386", 
          "https://doi.org/10.1038/nbt1297-1359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt0698-566", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014095188", 
          "https://doi.org/10.1038/nbt0698-566"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1999-07", 
    "datePublishedReg": "1999-07-01", 
    "description": "Technologies to measure whole-genome mRNA abundances1,2,3 and methods to organize and display such data4,5,6,7,8,9,10 are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes5. We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo6. Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions7. The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected  cis-regulatory elements8. Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast\u2014without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/10343", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1103138", 
        "issn": [
          "1061-4036", 
          "1546-1718"
        ], 
        "name": "Nature Genetics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "22"
      }
    ], 
    "keywords": [
      "genetic network architecture", 
      "cis-regulatory elements", 
      "statistical characterization", 
      "statistical algorithms", 
      "partitional clustering", 
      "putative cis-regulatory elements", 
      "hindbrain of mice", 
      "transcriptional regulatory networks", 
      "Fourier analysis", 
      "mRNA data", 
      "whole genome mRNA", 
      "mRNA expression data", 
      "new regulons", 
      "regulatory networks", 
      "periodic genes", 
      "hierarchical clustering", 
      "same protein", 
      "regulon", 
      "expression data", 
      "genes", 
      "biological significance", 
      "rapid elucidation", 
      "network architecture", 
      "systematic determination", 
      "clustering", 
      "motif discovery", 
      "systematic set", 
      "motif", 
      "algorithm", 
      "dynamics", 
      "assumption", 
      "approach", 
      "yeast", 
      "set", 
      "valuable tool", 
      "vivo6", 
      "organisms", 
      "biology", 
      "protein", 
      "dendogram", 
      "hindbrain", 
      "system-level exploration", 
      "mRNA", 
      "network", 
      "point", 
      "applications", 
      "expression", 
      "elucidation", 
      "instances", 
      "elements", 
      "data", 
      "discovery", 
      "structure", 
      "members", 
      "tool", 
      "criteria", 
      "mice", 
      "characterization", 
      "time points", 
      "patterns", 
      "analysis", 
      "basis", 
      "architecture", 
      "determination", 
      "significance", 
      "exploration", 
      "knowledge", 
      "promise", 
      "technology", 
      "method"
    ], 
    "name": "Systematic determination of genetic network architecture", 
    "pagination": "281-285", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1009819816"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/10343"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "10391217"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/10343", 
      "https://app.dimensions.ai/details/publication/pub.1009819816"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-10-01T06:30", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221001/entities/gbq_results/article/article_314.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/10343"
  }
]
 

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.10.1038/10343'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/10343'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/10343'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/10343'


 

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

233 TRIPLES      21 PREDICATES      112 URIs      99 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/10343 schema:about N198021537a464967b8cd731005136746
2 N2cad0c19e99342ce876d40cf40e3b283
3 N3cabcd09f15441bb979ab7480012a0ef
4 N5f839bb365414020b45ec4e4a0a0324a
5 N78d7c1cfb3e3488c9b888e72fb52e2b2
6 N8a71afc35a1b41a28dbf0fd5bcb669c9
7 N8f9d5bc388734f9a923bd405d11e204d
8 N98f89c5ef8e443ddb5bfab049a66f04f
9 Nb42280de090c4ade9c27c01d76221a7d
10 Ndbe20e7329b3463b82d69afa7d855880
11 Ne3772c58bbf449d9aa1a8eed6f1246b0
12 anzsrc-for:06
13 anzsrc-for:0604
14 schema:author N75989a45c2234bc6a12bd6dd2c69bf2a
15 schema:citation sg:pub.10.1038/42755
16 sg:pub.10.1038/nbt0698-566
17 sg:pub.10.1038/nbt1098-939
18 sg:pub.10.1038/nbt1296-1675
19 sg:pub.10.1038/nbt1297-1359
20 schema:datePublished 1999-07
21 schema:datePublishedReg 1999-07-01
22 schema:description Technologies to measure whole-genome mRNA abundances1,2,3 and methods to organize and display such data4,5,6,7,8,9,10 are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes5. We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo6. Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions7. The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements8. Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast—without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.
23 schema:genre article
24 schema:isAccessibleForFree false
25 schema:isPartOf N6608e2c460ba4ba3a43d484c9a0ab052
26 N851d8c70cd4a4638a43ff73717d8595d
27 sg:journal.1103138
28 schema:keywords Fourier analysis
29 algorithm
30 analysis
31 applications
32 approach
33 architecture
34 assumption
35 basis
36 biological significance
37 biology
38 characterization
39 cis-regulatory elements
40 clustering
41 criteria
42 data
43 dendogram
44 determination
45 discovery
46 dynamics
47 elements
48 elucidation
49 exploration
50 expression
51 expression data
52 genes
53 genetic network architecture
54 hierarchical clustering
55 hindbrain
56 hindbrain of mice
57 instances
58 knowledge
59 mRNA
60 mRNA data
61 mRNA expression data
62 members
63 method
64 mice
65 motif
66 motif discovery
67 network
68 network architecture
69 new regulons
70 organisms
71 partitional clustering
72 patterns
73 periodic genes
74 point
75 promise
76 protein
77 putative cis-regulatory elements
78 rapid elucidation
79 regulatory networks
80 regulon
81 same protein
82 set
83 significance
84 statistical algorithms
85 statistical characterization
86 structure
87 system-level exploration
88 systematic determination
89 systematic set
90 technology
91 time points
92 tool
93 transcriptional regulatory networks
94 valuable tool
95 vivo6
96 whole genome mRNA
97 yeast
98 schema:name Systematic determination of genetic network architecture
99 schema:pagination 281-285
100 schema:productId N09a53ec85c3a48ebba72414792a0cfff
101 N9929b4e8098c4a0ebc4500aacbcfdaff
102 Nd792ddacb27745d1a0deac148f7bfe5d
103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009819816
104 https://doi.org/10.1038/10343
105 schema:sdDatePublished 2022-10-01T06:30
106 schema:sdLicense https://scigraph.springernature.com/explorer/license/
107 schema:sdPublisher Nb18f1b619ea243b9a43d3c70b2655c9f
108 schema:url https://doi.org/10.1038/10343
109 sgo:license sg:explorer/license/
110 sgo:sdDataset articles
111 rdf:type schema:ScholarlyArticle
112 N09a53ec85c3a48ebba72414792a0cfff schema:name doi
113 schema:value 10.1038/10343
114 rdf:type schema:PropertyValue
115 N198021537a464967b8cd731005136746 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name DNA
117 rdf:type schema:DefinedTerm
118 N1f7f8f48e26f40fd8fa68111ff163562 rdf:first sg:person.0727430036.43
119 rdf:rest Nd56861d17d604c65b72780a13430cc54
120 N2cad0c19e99342ce876d40cf40e3b283 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
121 schema:name Mice
122 rdf:type schema:DefinedTerm
123 N3cabcd09f15441bb979ab7480012a0ef schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name Cell Cycle
125 rdf:type schema:DefinedTerm
126 N50a9de98da254d20ab62ec66beb67128 rdf:first sg:person.01115626315.03
127 rdf:rest rdf:nil
128 N5f839bb365414020b45ec4e4a0a0324a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
129 schema:name Genetic Techniques
130 rdf:type schema:DefinedTerm
131 N6608e2c460ba4ba3a43d484c9a0ab052 schema:issueNumber 3
132 rdf:type schema:PublicationIssue
133 N75989a45c2234bc6a12bd6dd2c69bf2a rdf:first sg:person.01105416763.09
134 rdf:rest N75a85d151d154d51a585cdc2cc1a8149
135 N75a85d151d154d51a585cdc2cc1a8149 rdf:first sg:person.0652320600.24
136 rdf:rest N1f7f8f48e26f40fd8fa68111ff163562
137 N78d7c1cfb3e3488c9b888e72fb52e2b2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
138 schema:name Open Reading Frames
139 rdf:type schema:DefinedTerm
140 N851d8c70cd4a4638a43ff73717d8595d schema:volumeNumber 22
141 rdf:type schema:PublicationVolume
142 N8a71afc35a1b41a28dbf0fd5bcb669c9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Animals
144 rdf:type schema:DefinedTerm
145 N8f9d5bc388734f9a923bd405d11e204d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Gene Expression
147 rdf:type schema:DefinedTerm
148 N98f89c5ef8e443ddb5bfab049a66f04f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
149 schema:name Rhombencephalon
150 rdf:type schema:DefinedTerm
151 N9929b4e8098c4a0ebc4500aacbcfdaff schema:name dimensions_id
152 schema:value pub.1009819816
153 rdf:type schema:PropertyValue
154 Nb18f1b619ea243b9a43d3c70b2655c9f schema:name Springer Nature - SN SciGraph project
155 rdf:type schema:Organization
156 Nb42280de090c4ade9c27c01d76221a7d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Multigene Family
158 rdf:type schema:DefinedTerm
159 Nd56861d17d604c65b72780a13430cc54 rdf:first sg:person.0601045543.61
160 rdf:rest N50a9de98da254d20ab62ec66beb67128
161 Nd792ddacb27745d1a0deac148f7bfe5d schema:name pubmed_id
162 schema:value 10391217
163 rdf:type schema:PropertyValue
164 Ndbe20e7329b3463b82d69afa7d855880 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
165 schema:name RNA, Messenger
166 rdf:type schema:DefinedTerm
167 Ne3772c58bbf449d9aa1a8eed6f1246b0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
168 schema:name Saccharomyces cerevisiae
169 rdf:type schema:DefinedTerm
170 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
171 schema:name Biological Sciences
172 rdf:type schema:DefinedTerm
173 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
174 schema:name Genetics
175 rdf:type schema:DefinedTerm
176 sg:journal.1103138 schema:issn 1061-4036
177 1546-1718
178 schema:name Nature Genetics
179 schema:publisher Springer Nature
180 rdf:type schema:Periodical
181 sg:person.01105416763.09 schema:affiliation grid-institutes:grid.38142.3c
182 schema:familyName Tavazoie
183 schema:givenName Saeed
184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01105416763.09
185 rdf:type schema:Person
186 sg:person.01115626315.03 schema:affiliation grid-institutes:grid.38142.3c
187 schema:familyName Church
188 schema:givenName George M.
189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01115626315.03
190 rdf:type schema:Person
191 sg:person.0601045543.61 schema:affiliation grid-institutes:grid.240952.8
192 schema:familyName Cho
193 schema:givenName Raymond J.
194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601045543.61
195 rdf:type schema:Person
196 sg:person.0652320600.24 schema:affiliation grid-institutes:grid.38142.3c
197 schema:familyName Hughes
198 schema:givenName Jason D.
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0652320600.24
200 rdf:type schema:Person
201 sg:person.0727430036.43 schema:affiliation grid-institutes:None
202 schema:familyName Campbell
203 schema:givenName Michael J.
204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0727430036.43
205 rdf:type schema:Person
206 sg:pub.10.1038/42755 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017484221
207 https://doi.org/10.1038/42755
208 rdf:type schema:CreativeWork
209 sg:pub.10.1038/nbt0698-566 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014095188
210 https://doi.org/10.1038/nbt0698-566
211 rdf:type schema:CreativeWork
212 sg:pub.10.1038/nbt1098-939 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049107556
213 https://doi.org/10.1038/nbt1098-939
214 rdf:type schema:CreativeWork
215 sg:pub.10.1038/nbt1296-1675 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005458398
216 https://doi.org/10.1038/nbt1296-1675
217 rdf:type schema:CreativeWork
218 sg:pub.10.1038/nbt1297-1359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052281386
219 https://doi.org/10.1038/nbt1297-1359
220 rdf:type schema:CreativeWork
221 grid-institutes:None schema:alternateName Molecular Applications Group, 607 Hansen Way, Building One, 94303-1110, Palo Alto, California, USA
222 schema:name Molecular Applications Group, 607 Hansen Way, Building One, 94303-1110, Palo Alto, California, USA
223 rdf:type schema:Organization
224 grid-institutes:grid.240952.8 schema:alternateName Stanford Medical Center, 94304, Palo Alto, CaliforniaUSA
225 schema:name Department of Genetics, B400 Beckman Center, 279 Campus Drive
226 Stanford Medical Center, 94304, Palo Alto, CaliforniaUSA
227 rdf:type schema:Organization
228 grid-institutes:grid.38142.3c schema:alternateName Department of Genetics, Harvard Medical School, 200 Longwood Ave, 02115, Boston, Massachusetts, USA
229 Harvard University, 02115, Boston, Massachusetts, USA
230 schema:name Department of Genetics, Harvard Medical School, 200 Longwood Ave, 02115, Boston, Massachusetts, USA
231 Graduate Program in Biophysics, 200 Longwood Ave
232 Harvard University, 02115, Boston, Massachusetts, USA
233 rdf:type schema:Organization
 




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


...