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 N1b14c5e0016140b6bd33063a3ac400e7
2 N2781814f8a9646fb8f5abc1f8d893b2b
3 N3c3445564c264dbbb3111354feefbb36
4 N46ff82321c8e4f2a9df25296e8d38085
5 N593bc736bd03497cac49593e8a9403b3
6 N596d51cf29924e8ea4b9ff54d2de4770
7 N8b5744e6e3f547349024dc7a1c104bae
8 Na8044f0d73884862acae468e02b9f32e
9 Nc8cbe33d0eae48f3ac5dd35f7af2c266
10 Nd3c856537c2b46cba619b9a720f3b67c
11 Nd6be6caf03c34a5c86fdffec90590aa6
12 Ne50a45bf19f04a87ac58ca7ffd97b067
13 Nea7d65b3a9ed44faa925ab709551a287
14 anzsrc-for:01
15 anzsrc-for:0104
16 schema:author N285ddc6df5234e2aade9f4ed6ea37941
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 Na4da7dae4cc844beb6a2b182f7ebddca
48 Na7504bdc2a734d4fba88e9ea40a9f615
49 sg:journal.1023439
50 schema:name Differential expression analysis for sequence count data
51 schema:pagination r106
52 schema:productId N1807a61cf73c4ec58e0f1055b889058f
53 N44ba0a3650f24ccbb6e869863953f3a0
54 N4a8406183901423889df8f74bcb2a9b5
55 Nb2e0d8230d7d4f8e93f1a8bd3283f858
56 Nef6f541c1c574605a26d6be9efb42e41
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 N5918c4b5a9024489ae99112d13155e7c
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 N1807a61cf73c4ec58e0f1055b889058f schema:name nlm_unique_id
67 schema:value 100960660
68 rdf:type schema:PropertyValue
69 N1b14c5e0016140b6bd33063a3ac400e7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
70 schema:name Models, Genetic
71 rdf:type schema:DefinedTerm
72 N2781814f8a9646fb8f5abc1f8d893b2b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
73 schema:name Saccharomyces cerevisiae
74 rdf:type schema:DefinedTerm
75 N285ddc6df5234e2aade9f4ed6ea37941 rdf:first sg:person.0626036202.10
76 rdf:rest Nf2bb4e30c436466d9552500c0416d9d7
77 N3c3445564c264dbbb3111354feefbb36 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
78 schema:name Computational Biology
79 rdf:type schema:DefinedTerm
80 N44ba0a3650f24ccbb6e869863953f3a0 schema:name readcube_id
81 schema:value 950e603db9a07bf1caa35fd5bff80573d4141a28ee94fdf46c15976a52fea158
82 rdf:type schema:PropertyValue
83 N46ff82321c8e4f2a9df25296e8d38085 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
84 schema:name Animals
85 rdf:type schema:DefinedTerm
86 N4a8406183901423889df8f74bcb2a9b5 schema:name dimensions_id
87 schema:value pub.1031289083
88 rdf:type schema:PropertyValue
89 N5918c4b5a9024489ae99112d13155e7c schema:name Springer Nature - SN SciGraph project
90 rdf:type schema:Organization
91 N593bc736bd03497cac49593e8a9403b3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
92 schema:name Tissue Culture Techniques
93 rdf:type schema:DefinedTerm
94 N596d51cf29924e8ea4b9ff54d2de4770 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
95 schema:name Chromatin Immunoprecipitation
96 rdf:type schema:DefinedTerm
97 N8b5744e6e3f547349024dc7a1c104bae schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
98 schema:name Linear Models
99 rdf:type schema:DefinedTerm
100 Na4da7dae4cc844beb6a2b182f7ebddca schema:volumeNumber 11
101 rdf:type schema:PublicationVolume
102 Na7504bdc2a734d4fba88e9ea40a9f615 schema:issueNumber 10
103 rdf:type schema:PublicationIssue
104 Na8044f0d73884862acae468e02b9f32e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Sequence Analysis, RNA
106 rdf:type schema:DefinedTerm
107 Nb2e0d8230d7d4f8e93f1a8bd3283f858 schema:name pubmed_id
108 schema:value 20979621
109 rdf:type schema:PropertyValue
110 Nc8cbe33d0eae48f3ac5dd35f7af2c266 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Gene Expression Profiling
112 rdf:type schema:DefinedTerm
113 Nd3c856537c2b46cba619b9a720f3b67c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Stem Cells
115 rdf:type schema:DefinedTerm
116 Nd6be6caf03c34a5c86fdffec90590aa6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
117 schema:name Binomial Distribution
118 rdf:type schema:DefinedTerm
119 Ne50a45bf19f04a87ac58ca7ffd97b067 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Drosophila
121 rdf:type schema:DefinedTerm
122 Nea7d65b3a9ed44faa925ab709551a287 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
123 schema:name High-Throughput Nucleotide Sequencing
124 rdf:type schema:DefinedTerm
125 Nef6f541c1c574605a26d6be9efb42e41 schema:name doi
126 schema:value 10.1186/gb-2010-11-10-r106
127 rdf:type schema:PropertyValue
128 Nf2bb4e30c436466d9552500c0416d9d7 rdf:first sg:person.0750614167.42
129 rdf:rest rdf:nil
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)


...