Differential expression analysis for sequence count data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-10

AUTHORS

Simon Anders, Wolfgang Huber

ABSTRACT

High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package. More... »

PAGES

r106

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/gb-2010-11-10-r106

DOI

http://dx.doi.org/10.1186/gb-2010-11-10-r106

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031289083

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20979621


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "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": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Binomial Distribution", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Chromatin Immunoprecipitation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Drosophila", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "High-Throughput Nucleotide Sequencing", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Linear Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Genetic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Saccharomyces cerevisiae", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Analysis, RNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Stem Cells", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tissue Culture Techniques", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "European Molecular Biology Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.4709.a", 
          "name": [
            "European Molecular Biology Laboratory, Mayerhofstra\u00dfe 1, 69117, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Anders", 
        "givenName": "Simon", 
        "id": "sg:person.0626036202.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0626036202.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "European Molecular Biology Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.4709.a", 
          "name": [
            "European Molecular Biology Laboratory, Mayerhofstra\u00dfe 1, 69117, Heidelberg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Huber", 
        "givenName": "Wolfgang", 
        "id": "sg:person.0750614167.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750614167.42"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1101/gr.094482.109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003846316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-10-221", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010098420", 
          "https://doi.org/10.1186/1471-2164-10-221"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature07488", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010813801", 
          "https://doi.org/10.1038/nature07488"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.093955.109", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015714971"
        ], 
        "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/biostatistics/kxm030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019122906"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.0006-341x.2005.030833.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021242670"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp616", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023247882"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/0-387-29362-0_23", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025432622", 
          "https://doi.org/10.1007/0-387-29362-0_23"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth1068", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036304799", 
          "https://doi.org/10.1038/nmeth1068"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btm453", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036891129"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3314912", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038060437"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btp612", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043232906"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth.1226", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045381177", 
          "https://doi.org/10.1038/nmeth.1226"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.079558.108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045837493"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2009-10-3-r25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049583368", 
          "https://doi.org/10.1186/gb-2009-10-3-r25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/gb-2010-11-3-r25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050509557", 
          "https://doi.org/10.1186/gb-2010-11-3-r25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-11-94", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053091615", 
          "https://doi.org/10.1186/1471-2105-11-94"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/10.1.36", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059415252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1158441", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062457766"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1183621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062461349"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2202/1544-6115.1027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069289261"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2532055", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069977485"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3001850", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070164065"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2010-10", 
    "datePublishedReg": "2010-10-01", 
    "description": "High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/gb-2010-11-10-r106", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023439", 
        "issn": [
          "1474-760X", 
          "1465-6906"
        ], 
        "name": "Genome Biology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "10", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "11"
      }
    ], 
    "name": "Differential expression analysis for sequence count data", 
    "pagination": "r106", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "950e603db9a07bf1caa35fd5bff80573d4141a28ee94fdf46c15976a52fea158"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "20979621"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100960660"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/gb-2010-11-10-r106"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1031289083"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/gb-2010-11-10-r106", 
      "https://app.dimensions.ai/details/publication/pub.1031289083"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T16:42", 
    "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_8669_00000513.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2Fgb-2010-11-10-r106"
  }
]
 

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/gb-2010-11-10-r106'

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/gb-2010-11-10-r106'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/gb-2010-11-10-r106'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/gb-2010-11-10-r106'


 

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

209 TRIPLES      21 PREDICATES      66 URIs      34 LITERALS      22 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/gb-2010-11-10-r106 schema:about N07a2ff1dd69f43df8fff6e3ef4b181f5
2 N1f8a8a57771a4e7284d561075d952826
3 N2832e7dc9efa4369afeee7e7a821a608
4 N55456f6bbbdd476b954ed41207885cb0
5 N57097e0f60294bb38b8c5acc9eb179d8
6 N5a7cdf5aabfe43109c0d364746dfaa43
7 N5fd49a808be34956ace17711d3e9e880
8 N6ff02cd8e36e4389a32dcd5cc740da51
9 N7323e1e9d26f417087f8795c4dc56468
10 N7d9020f4d0104230bb978a46bdad2e5e
11 N87a282bf70374ae08c776f2179e15009
12 Nb5714004872a4db68457a4b96f21eea9
13 Nc6e63b1bccbb4d909d0ee84711347bed
14 anzsrc-for:01
15 anzsrc-for:0104
16 schema:author Nb3481b80c3a146b6b31d7f5732c55e09
17 schema:citation sg:pub.10.1007/0-387-29362-0_23
18 sg:pub.10.1038/nature07488
19 sg:pub.10.1038/nmeth.1226
20 sg:pub.10.1038/nmeth1068
21 sg:pub.10.1186/1471-2105-11-94
22 sg:pub.10.1186/1471-2164-10-221
23 sg:pub.10.1186/gb-2004-5-10-r80
24 sg:pub.10.1186/gb-2009-10-3-r25
25 sg:pub.10.1186/gb-2010-11-3-r25
26 https://doi.org/10.1093/bioinformatics/btm453
27 https://doi.org/10.1093/bioinformatics/btp612
28 https://doi.org/10.1093/bioinformatics/btp616
29 https://doi.org/10.1093/biomet/10.1.36
30 https://doi.org/10.1093/biostatistics/kxm030
31 https://doi.org/10.1101/gr.079558.108
32 https://doi.org/10.1101/gr.093955.109
33 https://doi.org/10.1101/gr.094482.109
34 https://doi.org/10.1111/j.0006-341x.2005.030833.x
35 https://doi.org/10.1126/science.1158441
36 https://doi.org/10.1126/science.1183621
37 https://doi.org/10.2202/1544-6115.1027
38 https://doi.org/10.2307/2532055
39 https://doi.org/10.2307/3001850
40 https://doi.org/10.2307/3314912
41 schema:datePublished 2010-10
42 schema:datePublishedReg 2010-10-01
43 schema:description High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
44 schema:genre research_article
45 schema:inLanguage en
46 schema:isAccessibleForFree true
47 schema:isPartOf N92d6eb77ac0841c49e384bda96c03190
48 Nfe9265e4ebc8467b9c53a0dad0685d1d
49 sg:journal.1023439
50 schema:name Differential expression analysis for sequence count data
51 schema:pagination r106
52 schema:productId N46f43f44d4eb45b19ea435b7934903a6
53 N81619f7fa70b40bbbdb69bb947f0aac0
54 N9d00ba90713c4f4fbd4f79a36c834009
55 Nd27abb8ed6f74fbe996a67f158f27670
56 Ne5c83179f4bf40ff85dfe86a779becdd
57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031289083
58 https://doi.org/10.1186/gb-2010-11-10-r106
59 schema:sdDatePublished 2019-04-10T16:42
60 schema:sdLicense https://scigraph.springernature.com/explorer/license/
61 schema:sdPublisher Nb511c77e667e488b910d49e3f27a6eea
62 schema:url http://link.springer.com/10.1186%2Fgb-2010-11-10-r106
63 sgo:license sg:explorer/license/
64 sgo:sdDataset articles
65 rdf:type schema:ScholarlyArticle
66 N07a2ff1dd69f43df8fff6e3ef4b181f5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
67 schema:name Chromatin Immunoprecipitation
68 rdf:type schema:DefinedTerm
69 N1af54351f7514901914d0b049f266437 rdf:first sg:person.0750614167.42
70 rdf:rest rdf:nil
71 N1f8a8a57771a4e7284d561075d952826 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
72 schema:name High-Throughput Nucleotide Sequencing
73 rdf:type schema:DefinedTerm
74 N2832e7dc9efa4369afeee7e7a821a608 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
75 schema:name Sequence Analysis, RNA
76 rdf:type schema:DefinedTerm
77 N46f43f44d4eb45b19ea435b7934903a6 schema:name pubmed_id
78 schema:value 20979621
79 rdf:type schema:PropertyValue
80 N55456f6bbbdd476b954ed41207885cb0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
81 schema:name Linear Models
82 rdf:type schema:DefinedTerm
83 N57097e0f60294bb38b8c5acc9eb179d8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Stem Cells
85 rdf:type schema:DefinedTerm
86 N5a7cdf5aabfe43109c0d364746dfaa43 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
87 schema:name Tissue Culture Techniques
88 rdf:type schema:DefinedTerm
89 N5fd49a808be34956ace17711d3e9e880 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
90 schema:name Models, Genetic
91 rdf:type schema:DefinedTerm
92 N6ff02cd8e36e4389a32dcd5cc740da51 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Computational Biology
94 rdf:type schema:DefinedTerm
95 N7323e1e9d26f417087f8795c4dc56468 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
96 schema:name Saccharomyces cerevisiae
97 rdf:type schema:DefinedTerm
98 N7d9020f4d0104230bb978a46bdad2e5e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
99 schema:name Binomial Distribution
100 rdf:type schema:DefinedTerm
101 N81619f7fa70b40bbbdb69bb947f0aac0 schema:name doi
102 schema:value 10.1186/gb-2010-11-10-r106
103 rdf:type schema:PropertyValue
104 N87a282bf70374ae08c776f2179e15009 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Animals
106 rdf:type schema:DefinedTerm
107 N92d6eb77ac0841c49e384bda96c03190 schema:issueNumber 10
108 rdf:type schema:PublicationIssue
109 N9d00ba90713c4f4fbd4f79a36c834009 schema:name dimensions_id
110 schema:value pub.1031289083
111 rdf:type schema:PropertyValue
112 Nb3481b80c3a146b6b31d7f5732c55e09 rdf:first sg:person.0626036202.10
113 rdf:rest N1af54351f7514901914d0b049f266437
114 Nb511c77e667e488b910d49e3f27a6eea schema:name Springer Nature - SN SciGraph project
115 rdf:type schema:Organization
116 Nb5714004872a4db68457a4b96f21eea9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Gene Expression Profiling
118 rdf:type schema:DefinedTerm
119 Nc6e63b1bccbb4d909d0ee84711347bed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Drosophila
121 rdf:type schema:DefinedTerm
122 Nd27abb8ed6f74fbe996a67f158f27670 schema:name nlm_unique_id
123 schema:value 100960660
124 rdf:type schema:PropertyValue
125 Ne5c83179f4bf40ff85dfe86a779becdd schema:name readcube_id
126 schema:value 950e603db9a07bf1caa35fd5bff80573d4141a28ee94fdf46c15976a52fea158
127 rdf:type schema:PropertyValue
128 Nfe9265e4ebc8467b9c53a0dad0685d1d schema:volumeNumber 11
129 rdf:type schema:PublicationVolume
130 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
131 schema:name Mathematical Sciences
132 rdf:type schema:DefinedTerm
133 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
134 schema:name Statistics
135 rdf:type schema:DefinedTerm
136 sg:journal.1023439 schema:issn 1465-6906
137 1474-760X
138 schema:name Genome Biology
139 rdf:type schema:Periodical
140 sg:person.0626036202.10 schema:affiliation https://www.grid.ac/institutes/grid.4709.a
141 schema:familyName Anders
142 schema:givenName Simon
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0626036202.10
144 rdf:type schema:Person
145 sg:person.0750614167.42 schema:affiliation https://www.grid.ac/institutes/grid.4709.a
146 schema:familyName Huber
147 schema:givenName Wolfgang
148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0750614167.42
149 rdf:type schema:Person
150 sg:pub.10.1007/0-387-29362-0_23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025432622
151 https://doi.org/10.1007/0-387-29362-0_23
152 rdf:type schema:CreativeWork
153 sg:pub.10.1038/nature07488 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010813801
154 https://doi.org/10.1038/nature07488
155 rdf:type schema:CreativeWork
156 sg:pub.10.1038/nmeth.1226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045381177
157 https://doi.org/10.1038/nmeth.1226
158 rdf:type schema:CreativeWork
159 sg:pub.10.1038/nmeth1068 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036304799
160 https://doi.org/10.1038/nmeth1068
161 rdf:type schema:CreativeWork
162 sg:pub.10.1186/1471-2105-11-94 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053091615
163 https://doi.org/10.1186/1471-2105-11-94
164 rdf:type schema:CreativeWork
165 sg:pub.10.1186/1471-2164-10-221 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010098420
166 https://doi.org/10.1186/1471-2164-10-221
167 rdf:type schema:CreativeWork
168 sg:pub.10.1186/gb-2004-5-10-r80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018457673
169 https://doi.org/10.1186/gb-2004-5-10-r80
170 rdf:type schema:CreativeWork
171 sg:pub.10.1186/gb-2009-10-3-r25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049583368
172 https://doi.org/10.1186/gb-2009-10-3-r25
173 rdf:type schema:CreativeWork
174 sg:pub.10.1186/gb-2010-11-3-r25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050509557
175 https://doi.org/10.1186/gb-2010-11-3-r25
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1093/bioinformatics/btm453 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036891129
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1093/bioinformatics/btp612 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043232906
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1093/bioinformatics/btp616 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023247882
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1093/biomet/10.1.36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059415252
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1093/biostatistics/kxm030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019122906
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1101/gr.079558.108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045837493
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1101/gr.093955.109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015714971
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1101/gr.094482.109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003846316
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1111/j.0006-341x.2005.030833.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1021242670
194 rdf:type schema:CreativeWork
195 https://doi.org/10.1126/science.1158441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062457766
196 rdf:type schema:CreativeWork
197 https://doi.org/10.1126/science.1183621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062461349
198 rdf:type schema:CreativeWork
199 https://doi.org/10.2202/1544-6115.1027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069289261
200 rdf:type schema:CreativeWork
201 https://doi.org/10.2307/2532055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069977485
202 rdf:type schema:CreativeWork
203 https://doi.org/10.2307/3001850 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070164065
204 rdf:type schema:CreativeWork
205 https://doi.org/10.2307/3314912 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038060437
206 rdf:type schema:CreativeWork
207 https://www.grid.ac/institutes/grid.4709.a schema:alternateName European Molecular Biology Laboratory
208 schema:name European Molecular Biology Laboratory, Mayerhofstraße 1, 69117, Heidelberg, Germany
209 rdf:type schema:Organization
 




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


...