Functional associations of proteins in entire genomes by means of exhaustive detection of gene fusions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2001-09

AUTHORS

Anton J Enright, Christos A Ouzounis

ABSTRACT

BACKGROUND: It has recently been shown that the detection of gene fusion events across genomes can be used for predicting functional associations of proteins, including physical interaction or complex formation. To obtain such predictions we have made an exhaustive search for gene fusion events within 24 available completely sequenced genomes. RESULTS: Each genome was used as a query against the remaining 23 complete genomes to detect gene fusion events. Using an improved, fully automatic protocol, a total of 7,224 single-domain proteins that are components of gene fusions in other genomes were detected, many of which were identified for the first time. The total number of predicted pairwise functional associations is 39,730 for all genomes. Component pairs were identified by virtue of their similarity to 2,365 multidomain composite proteins. We also show for the first time that gene fusion is a complex evolutionary process with a number of contributory factors, including paralogy, genome size and phylogenetic distance. On average, 9% of genes in a given genome appear to code for single-domain, component proteins predicted to be functionally associated. These proteins are detected by an additional 4% of genes that code for fused, composite proteins. CONCLUSIONS: These results provide an exhaustive set of functionally associated genes and also delineate the power of fusion analysis for the prediction of protein interactions. More... »

PAGES

research0034.1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/gb-2001-2-9-research0034

DOI

http://dx.doi.org/10.1186/gb-2001-2-9-research0034

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050565431

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/11820254


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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Animals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Artificial Gene Fusion", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Bacterial Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Caenorhabditis elegans Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Drosophila Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Evolution, Molecular", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Fungal Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Gene Expression Profiling", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genome", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Multigene Family", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Phylogeny", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Protein Binding", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Recombinant Fusion Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Recombinant Proteins", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Recombination, Genetic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Reproducibility of Results", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Two-Hybrid System Techniques", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "European Bioinformatics Institute", 
          "id": "https://www.grid.ac/institutes/grid.225360.0", 
          "name": [
            "Computational Genomics Group, European Bioinformatics Institute, EMBL Cambridge Outstation, CB10 1SD, Cambridge, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Enright", 
        "givenName": "Anton J", 
        "id": "sg:person.0765550040.56", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0765550040.56"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "European Bioinformatics Institute", 
          "id": "https://www.grid.ac/institutes/grid.225360.0", 
          "name": [
            "Computational Genomics Group, European Bioinformatics Institute, EMBL Cambridge Outstation, CB10 1SD, Cambridge, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ouzounis", 
        "givenName": "Christos A", 
        "id": "sg:person.0630313060.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0630313060.74"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/(sici)1097-0061(199605)12:6<523::aid-yea962>3.0.co;2-c", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002734187"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1097-0061(199605)12:6<523::aid-yea962>3.0.co;2-c", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002734187"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.97.3.1143", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009581960"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/15.9.773", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010319397"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.285.5428.751", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013411081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.94.18.9585", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013747621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/46915", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014553454", 
          "https://doi.org/10.1038/46915"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/46915", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014553454", 
          "https://doi.org/10.1038/46915"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/16.10.915", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023326450"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.96.6.2896", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023469671"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.271.50.31839", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026721901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1097-2765(00)80114-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027451892"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/47048", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029164285", 
          "https://doi.org/10.1038/47048"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/47048", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029164285", 
          "https://doi.org/10.1038/47048"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/47056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029720868", 
          "https://doi.org/10.1038/47056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/47056", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029720868", 
          "https://doi.org/10.1038/47056"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/12597", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030657684", 
          "https://doi.org/10.1038/12597"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/12597", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030657684", 
          "https://doi.org/10.1038/12597"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/mcb.17.7.3640", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030682102"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/16.5.451", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033349291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.271.19.11400", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034071903"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35001009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035773549", 
          "https://doi.org/10.1038/35001009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35001009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035773549", 
          "https://doi.org/10.1038/35001009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/5052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038696050", 
          "https://doi.org/10.1038/5052"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/5052", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038696050", 
          "https://doi.org/10.1038/5052"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.274.53.38147", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042246128"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/25.17.3389", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047265454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.282.5389.699", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047652778"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.278.5338.680", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062558446"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1083215645", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2001-09", 
    "datePublishedReg": "2001-09-01", 
    "description": "BACKGROUND: It has recently been shown that the detection of gene fusion events across genomes can be used for predicting functional associations of proteins, including physical interaction or complex formation. To obtain such predictions we have made an exhaustive search for gene fusion events within 24 available completely sequenced genomes.\nRESULTS: Each genome was used as a query against the remaining 23 complete genomes to detect gene fusion events. Using an improved, fully automatic protocol, a total of 7,224 single-domain proteins that are components of gene fusions in other genomes were detected, many of which were identified for the first time. The total number of predicted pairwise functional associations is 39,730 for all genomes. Component pairs were identified by virtue of their similarity to 2,365 multidomain composite proteins. We also show for the first time that gene fusion is a complex evolutionary process with a number of contributory factors, including paralogy, genome size and phylogenetic distance. On average, 9% of genes in a given genome appear to code for single-domain, component proteins predicted to be functionally associated. These proteins are detected by an additional 4% of genes that code for fused, composite proteins.\nCONCLUSIONS: These results provide an exhaustive set of functionally associated genes and also delineate the power of fusion analysis for the prediction of protein interactions.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/gb-2001-2-9-research0034", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023439", 
        "issn": [
          "1474-760X", 
          "1465-6906"
        ], 
        "name": "Genome Biology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "9", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "2"
      }
    ], 
    "name": "Functional associations of proteins in entire genomes by means of exhaustive detection of gene fusions", 
    "pagination": "research0034.1", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f0e0c36b435042f2546bd6bab35cabbbf2e42f69fae10f2bf81bba1f78b1b79d"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "11820254"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "100960660"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/gb-2001-2-9-research0034"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1050565431"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/gb-2001-2-9-research0034", 
      "https://app.dimensions.ai/details/publication/pub.1050565431"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:02", 
    "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_8663_00000516.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1186%2Fgb-2001-2-9-research0034"
  }
]
 

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-2001-2-9-research0034'

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-2001-2-9-research0034'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/gb-2001-2-9-research0034'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/gb-2001-2-9-research0034'


 

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

230 TRIPLES      21 PREDICATES      72 URIs      41 LITERALS      29 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/gb-2001-2-9-research0034 schema:about N0f159ea0a3624e7392440d20e098ca7c
2 N28008b82801a49dcbfadd5cfb2b56acc
3 N2a17b23a67f9499e9aff7f8f2ee64754
4 N369640ade6bb4397942c09c0b06e5cd9
5 N3802b5e313ff4b83b71142018561d5af
6 N4ba4c1eea4924db29edcd16b4204f2c6
7 N5dc35d47b5434436b2c0fc726f287eb3
8 N67e18aebbf8b42b58f0a2d101a99bf8f
9 N7880a3875fce45d3ad71bffb3468fc2a
10 N81aa348380534cd8bb37e09a71ebb300
11 N8e6003c8f2924dbab00532c62491d232
12 N9d8b180107d14e1cb251dba0a1e7580d
13 Nb792ef62c6484b2db15208e9ae8f27a5
14 Nce83a8ec244045a0bdd5998f258bc527
15 Ne1003fb60e94476cad33eebb45fb0bc4
16 Ne928a639c6974e399155b671036437cc
17 Nf0c03b7ffae044c786711822636c0627
18 Nf454ad80a43a42268f9cf6e0f4079f51
19 Nf7e817418cb14842a2dd8db262db68bf
20 Nf7e817ac89754320bee9629abfd31a31
21 anzsrc-for:06
22 anzsrc-for:0604
23 schema:author N17abd2a83b424114980d55334220d1fc
24 schema:citation sg:pub.10.1038/12597
25 sg:pub.10.1038/35001009
26 sg:pub.10.1038/46915
27 sg:pub.10.1038/47048
28 sg:pub.10.1038/47056
29 sg:pub.10.1038/5052
30 https://app.dimensions.ai/details/publication/pub.1083215645
31 https://doi.org/10.1002/(sici)1097-0061(199605)12:6<523::aid-yea962>3.0.co;2-c
32 https://doi.org/10.1016/s1097-2765(00)80114-8
33 https://doi.org/10.1073/pnas.94.18.9585
34 https://doi.org/10.1073/pnas.96.6.2896
35 https://doi.org/10.1073/pnas.97.3.1143
36 https://doi.org/10.1074/jbc.271.19.11400
37 https://doi.org/10.1074/jbc.271.50.31839
38 https://doi.org/10.1074/jbc.274.53.38147
39 https://doi.org/10.1093/bioinformatics/15.9.773
40 https://doi.org/10.1093/bioinformatics/16.10.915
41 https://doi.org/10.1093/bioinformatics/16.5.451
42 https://doi.org/10.1093/nar/25.17.3389
43 https://doi.org/10.1126/science.278.5338.680
44 https://doi.org/10.1126/science.282.5389.699
45 https://doi.org/10.1126/science.285.5428.751
46 https://doi.org/10.1128/mcb.17.7.3640
47 schema:datePublished 2001-09
48 schema:datePublishedReg 2001-09-01
49 schema:description BACKGROUND: It has recently been shown that the detection of gene fusion events across genomes can be used for predicting functional associations of proteins, including physical interaction or complex formation. To obtain such predictions we have made an exhaustive search for gene fusion events within 24 available completely sequenced genomes. RESULTS: Each genome was used as a query against the remaining 23 complete genomes to detect gene fusion events. Using an improved, fully automatic protocol, a total of 7,224 single-domain proteins that are components of gene fusions in other genomes were detected, many of which were identified for the first time. The total number of predicted pairwise functional associations is 39,730 for all genomes. Component pairs were identified by virtue of their similarity to 2,365 multidomain composite proteins. We also show for the first time that gene fusion is a complex evolutionary process with a number of contributory factors, including paralogy, genome size and phylogenetic distance. On average, 9% of genes in a given genome appear to code for single-domain, component proteins predicted to be functionally associated. These proteins are detected by an additional 4% of genes that code for fused, composite proteins. CONCLUSIONS: These results provide an exhaustive set of functionally associated genes and also delineate the power of fusion analysis for the prediction of protein interactions.
50 schema:genre research_article
51 schema:inLanguage en
52 schema:isAccessibleForFree true
53 schema:isPartOf N25747372e90a4abba1a8da842316267c
54 Ndc8b2f9bf2c5494f887314ddfe526bbc
55 sg:journal.1023439
56 schema:name Functional associations of proteins in entire genomes by means of exhaustive detection of gene fusions
57 schema:pagination research0034.1
58 schema:productId N02148c9ce04c486eb73a1335fb5c30ad
59 N3795865c594741f193901c84af4339c6
60 N3bd425a5e3b6401583927131db067af0
61 N962ee33b6abe4cfcbb8a2808dd89a508
62 Nf6e3339a280b4a149aff36f2014b455f
63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050565431
64 https://doi.org/10.1186/gb-2001-2-9-research0034
65 schema:sdDatePublished 2019-04-10T15:02
66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
67 schema:sdPublisher Ne04153f3af7d43d79bea62502ec8abbb
68 schema:url http://link.springer.com/10.1186%2Fgb-2001-2-9-research0034
69 sgo:license sg:explorer/license/
70 sgo:sdDataset articles
71 rdf:type schema:ScholarlyArticle
72 N02148c9ce04c486eb73a1335fb5c30ad schema:name pubmed_id
73 schema:value 11820254
74 rdf:type schema:PropertyValue
75 N0f159ea0a3624e7392440d20e098ca7c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
76 schema:name Animals
77 rdf:type schema:DefinedTerm
78 N17abd2a83b424114980d55334220d1fc rdf:first sg:person.0765550040.56
79 rdf:rest N47304842f6f14e6c9cb1ff4a64a8610e
80 N25747372e90a4abba1a8da842316267c schema:volumeNumber 2
81 rdf:type schema:PublicationVolume
82 N28008b82801a49dcbfadd5cfb2b56acc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
83 schema:name Bacterial Proteins
84 rdf:type schema:DefinedTerm
85 N2a17b23a67f9499e9aff7f8f2ee64754 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
86 schema:name Gene Expression Profiling
87 rdf:type schema:DefinedTerm
88 N369640ade6bb4397942c09c0b06e5cd9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
89 schema:name Recombination, Genetic
90 rdf:type schema:DefinedTerm
91 N3795865c594741f193901c84af4339c6 schema:name dimensions_id
92 schema:value pub.1050565431
93 rdf:type schema:PropertyValue
94 N3802b5e313ff4b83b71142018561d5af schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
95 schema:name Multigene Family
96 rdf:type schema:DefinedTerm
97 N3bd425a5e3b6401583927131db067af0 schema:name doi
98 schema:value 10.1186/gb-2001-2-9-research0034
99 rdf:type schema:PropertyValue
100 N47304842f6f14e6c9cb1ff4a64a8610e rdf:first sg:person.0630313060.74
101 rdf:rest rdf:nil
102 N4ba4c1eea4924db29edcd16b4204f2c6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Fungal Proteins
104 rdf:type schema:DefinedTerm
105 N5dc35d47b5434436b2c0fc726f287eb3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
106 schema:name Genome
107 rdf:type schema:DefinedTerm
108 N67e18aebbf8b42b58f0a2d101a99bf8f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
109 schema:name Protein Binding
110 rdf:type schema:DefinedTerm
111 N7880a3875fce45d3ad71bffb3468fc2a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Two-Hybrid System Techniques
113 rdf:type schema:DefinedTerm
114 N81aa348380534cd8bb37e09a71ebb300 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
115 schema:name Computational Biology
116 rdf:type schema:DefinedTerm
117 N8e6003c8f2924dbab00532c62491d232 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
118 schema:name Evolution, Molecular
119 rdf:type schema:DefinedTerm
120 N962ee33b6abe4cfcbb8a2808dd89a508 schema:name nlm_unique_id
121 schema:value 100960660
122 rdf:type schema:PropertyValue
123 N9d8b180107d14e1cb251dba0a1e7580d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name Drosophila Proteins
125 rdf:type schema:DefinedTerm
126 Nb792ef62c6484b2db15208e9ae8f27a5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name Recombinant Fusion Proteins
128 rdf:type schema:DefinedTerm
129 Nce83a8ec244045a0bdd5998f258bc527 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Reproducibility of Results
131 rdf:type schema:DefinedTerm
132 Ndc8b2f9bf2c5494f887314ddfe526bbc schema:issueNumber 9
133 rdf:type schema:PublicationIssue
134 Ne04153f3af7d43d79bea62502ec8abbb schema:name Springer Nature - SN SciGraph project
135 rdf:type schema:Organization
136 Ne1003fb60e94476cad33eebb45fb0bc4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Phylogeny
138 rdf:type schema:DefinedTerm
139 Ne928a639c6974e399155b671036437cc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Artificial Gene Fusion
141 rdf:type schema:DefinedTerm
142 Nf0c03b7ffae044c786711822636c0627 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Recombinant Proteins
144 rdf:type schema:DefinedTerm
145 Nf454ad80a43a42268f9cf6e0f4079f51 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Proteins
147 rdf:type schema:DefinedTerm
148 Nf6e3339a280b4a149aff36f2014b455f schema:name readcube_id
149 schema:value f0e0c36b435042f2546bd6bab35cabbbf2e42f69fae10f2bf81bba1f78b1b79d
150 rdf:type schema:PropertyValue
151 Nf7e817418cb14842a2dd8db262db68bf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Caenorhabditis elegans Proteins
153 rdf:type schema:DefinedTerm
154 Nf7e817ac89754320bee9629abfd31a31 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Algorithms
156 rdf:type schema:DefinedTerm
157 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
158 schema:name Biological Sciences
159 rdf:type schema:DefinedTerm
160 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
161 schema:name Genetics
162 rdf:type schema:DefinedTerm
163 sg:journal.1023439 schema:issn 1465-6906
164 1474-760X
165 schema:name Genome Biology
166 rdf:type schema:Periodical
167 sg:person.0630313060.74 schema:affiliation https://www.grid.ac/institutes/grid.225360.0
168 schema:familyName Ouzounis
169 schema:givenName Christos A
170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0630313060.74
171 rdf:type schema:Person
172 sg:person.0765550040.56 schema:affiliation https://www.grid.ac/institutes/grid.225360.0
173 schema:familyName Enright
174 schema:givenName Anton J
175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0765550040.56
176 rdf:type schema:Person
177 sg:pub.10.1038/12597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030657684
178 https://doi.org/10.1038/12597
179 rdf:type schema:CreativeWork
180 sg:pub.10.1038/35001009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035773549
181 https://doi.org/10.1038/35001009
182 rdf:type schema:CreativeWork
183 sg:pub.10.1038/46915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014553454
184 https://doi.org/10.1038/46915
185 rdf:type schema:CreativeWork
186 sg:pub.10.1038/47048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029164285
187 https://doi.org/10.1038/47048
188 rdf:type schema:CreativeWork
189 sg:pub.10.1038/47056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029720868
190 https://doi.org/10.1038/47056
191 rdf:type schema:CreativeWork
192 sg:pub.10.1038/5052 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038696050
193 https://doi.org/10.1038/5052
194 rdf:type schema:CreativeWork
195 https://app.dimensions.ai/details/publication/pub.1083215645 schema:CreativeWork
196 https://doi.org/10.1002/(sici)1097-0061(199605)12:6<523::aid-yea962>3.0.co;2-c schema:sameAs https://app.dimensions.ai/details/publication/pub.1002734187
197 rdf:type schema:CreativeWork
198 https://doi.org/10.1016/s1097-2765(00)80114-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027451892
199 rdf:type schema:CreativeWork
200 https://doi.org/10.1073/pnas.94.18.9585 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013747621
201 rdf:type schema:CreativeWork
202 https://doi.org/10.1073/pnas.96.6.2896 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023469671
203 rdf:type schema:CreativeWork
204 https://doi.org/10.1073/pnas.97.3.1143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009581960
205 rdf:type schema:CreativeWork
206 https://doi.org/10.1074/jbc.271.19.11400 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034071903
207 rdf:type schema:CreativeWork
208 https://doi.org/10.1074/jbc.271.50.31839 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026721901
209 rdf:type schema:CreativeWork
210 https://doi.org/10.1074/jbc.274.53.38147 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042246128
211 rdf:type schema:CreativeWork
212 https://doi.org/10.1093/bioinformatics/15.9.773 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010319397
213 rdf:type schema:CreativeWork
214 https://doi.org/10.1093/bioinformatics/16.10.915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023326450
215 rdf:type schema:CreativeWork
216 https://doi.org/10.1093/bioinformatics/16.5.451 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033349291
217 rdf:type schema:CreativeWork
218 https://doi.org/10.1093/nar/25.17.3389 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047265454
219 rdf:type schema:CreativeWork
220 https://doi.org/10.1126/science.278.5338.680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062558446
221 rdf:type schema:CreativeWork
222 https://doi.org/10.1126/science.282.5389.699 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047652778
223 rdf:type schema:CreativeWork
224 https://doi.org/10.1126/science.285.5428.751 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013411081
225 rdf:type schema:CreativeWork
226 https://doi.org/10.1128/mcb.17.7.3640 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030682102
227 rdf:type schema:CreativeWork
228 https://www.grid.ac/institutes/grid.225360.0 schema:alternateName European Bioinformatics Institute
229 schema:name Computational Genomics Group, European Bioinformatics Institute, EMBL Cambridge Outstation, CB10 1SD, Cambridge, UK
230 rdf:type schema:Organization
 




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


...