Finding sRNA generative locales from high-throughput sequencing data with NiBLS View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-12

AUTHORS

Daniel MacLean, Vincent Moulton, David J Studholme

ABSTRACT

BACKGROUND: Next-generation sequencing technologies allow researchers to obtain millions of sequence reads in a single experiment. One important use of the technology is the sequencing of small non-coding regulatory RNAs and the identification of the genomic locales from which they originate. Currently, there is a paucity of methods for finding small RNA generative locales. RESULTS: We describe and implement an algorithm that can determine small RNA generative locales from high-throughput sequencing data. The algorithm creates a network, or graph, of the small RNAs by creating links between them depending on their proximity on the target genome. For each of the sub-networks in the resulting graph the clustering coefficient, a measure of the interconnectedness of the subnetwork, is used to identify the generative locales. We test the algorithm over a wide range of parameters using RFAM sequences as positive controls and demonstrate that the algorithm has good sensitivity and specificity in a range of Arabidopsis and mouse small RNA sequence sets and that the locales it generates are robust to differences in the choice of parameters. CONCLUSIONS: NiBLS is a fast, reliable and sensitive method for determining small RNA locales in high-throughput sequence data that is generally applicable to all classes of small RNA. More... »

PAGES

93

References to SciGraph publications

  • 2007-06-28. miRNAs control gene expression in the single-cell alga Chlamydomonas reinhardtii in NATURE
  • 2004-09. The role of RNA interference in heterochromatic silencing in NATURE
  • 2004-09. RNA silencing in plants in NATURE
  • 2008-04. Discovering microRNAs from deep sequencing data using miRDeep in NATURE BIOTECHNOLOGY
  • 1998-06. It's a small world in NATURE
  • 2007-09. Graphs in molecular biology in BMC BIOINFORMATICS
  • 2009-02. Application of 'next-generation' sequencing technologies to microbial genetics in NATURE REVIEWS MICROBIOLOGY
  • 2009-04. In the News in NATURE REVIEWS MICROBIOLOGY
  • 1998-06. Collective dynamics of ‘small-world’ networks in NATURE
  • 2008. Rfam in ENCYCLOPEDIA OF GENETICS, GENOMICS, PROTEOMICS AND INFORMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2105-11-93

    DOI

    http://dx.doi.org/10.1186/1471-2105-11-93

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045233097

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/20167070


    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": "Base Sequence", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computational Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "MicroRNAs", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sequence Alignment", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Sequence Analysis, RNA", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Software", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "John Innes Centre", 
              "id": "https://www.grid.ac/institutes/grid.14830.3e", 
              "name": [
                "The Sainsbury Laboratory, John Innes Centre, Colney Lane, NR4 7UH, Norwich, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "MacLean", 
            "givenName": "Daniel", 
            "id": "sg:person.013574744257.00", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013574744257.00"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of East Anglia", 
              "id": "https://www.grid.ac/institutes/grid.8273.e", 
              "name": [
                "University of East Anglia, NR4 7TJ, Norwich, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Moulton", 
            "givenName": "Vincent", 
            "id": "sg:person.01113662355.37", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01113662355.37"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "John Innes Centre", 
              "id": "https://www.grid.ac/institutes/grid.14830.3e", 
              "name": [
                "The Sainsbury Laboratory, John Innes Centre, Colney Lane, NR4 7UH, Norwich, UK"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Studholme", 
            "givenName": "David J", 
            "id": "sg:person.01047645057.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01047645057.86"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1186/1471-2105-8-s6-s8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002114257", 
              "https://doi.org/10.1186/1471-2105-8-s6-s8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4020-6754-9_14571", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002857147", 
              "https://doi.org/10.1007/978-1-4020-6754-9_14571"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/30835", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004648581", 
              "https://doi.org/10.1038/30835"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/30835", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004648581", 
              "https://doi.org/10.1038/30835"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0709632105", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004852973"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature05903", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006452107", 
              "https://doi.org/10.1038/nature05903"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.194201", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008144266"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt1394", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008181500", 
              "https://doi.org/10.1038/nbt1394"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btn025", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012266713"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0611119104", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015465980"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrmicro2122", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017693818", 
              "https://doi.org/10.1038/nrmicro2122"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btn428", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019983784"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gad.1476406", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020934875"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.tig.2006.03.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025450285"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature02874", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025532636", 
              "https://doi.org/10.1038/nature02874"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature02874", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025532636", 
              "https://doi.org/10.1038/nature02874"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gad.1705308", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031278673"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature02875", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039222604", 
              "https://doi.org/10.1038/nature02875"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature02875", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039222604", 
              "https://doi.org/10.1038/nature02875"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrmicro2088", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040141146", 
              "https://doi.org/10.1038/nrmicro2088"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/30918", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041985305", 
              "https://doi.org/10.1038/30918"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/30918", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041985305", 
              "https://doi.org/10.1038/30918"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1101/gr.078212.108", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047542880"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cell.2006.10.040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052700611"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2144/mar03dudoit", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1075260228"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2010-12", 
        "datePublishedReg": "2010-12-01", 
        "description": "BACKGROUND: Next-generation sequencing technologies allow researchers to obtain millions of sequence reads in a single experiment. One important use of the technology is the sequencing of small non-coding regulatory RNAs and the identification of the genomic locales from which they originate. Currently, there is a paucity of methods for finding small RNA generative locales.\nRESULTS: We describe and implement an algorithm that can determine small RNA generative locales from high-throughput sequencing data. The algorithm creates a network, or graph, of the small RNAs by creating links between them depending on their proximity on the target genome. For each of the sub-networks in the resulting graph the clustering coefficient, a measure of the interconnectedness of the subnetwork, is used to identify the generative locales. We test the algorithm over a wide range of parameters using RFAM sequences as positive controls and demonstrate that the algorithm has good sensitivity and specificity in a range of Arabidopsis and mouse small RNA sequence sets and that the locales it generates are robust to differences in the choice of parameters.\nCONCLUSIONS: NiBLS is a fast, reliable and sensitive method for determining small RNA locales in high-throughput sequence data that is generally applicable to all classes of small RNA.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/1471-2105-11-93", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1023786", 
            "issn": [
              "1471-2105"
            ], 
            "name": "BMC Bioinformatics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "11"
          }
        ], 
        "name": "Finding sRNA generative locales from high-throughput sequencing data with NiBLS", 
        "pagination": "93", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "d7762eeef0dac227505444d4f20535c1c18ec6a9884baa38b0bbfffb10505e5b"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "20167070"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "100965194"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/1471-2105-11-93"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1045233097"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/1471-2105-11-93", 
          "https://app.dimensions.ai/details/publication/pub.1045233097"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T10:29", 
        "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/v90/0000000349_0000000349/records_113641_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1186%2F1471-2105-11-93"
      }
    ]
     

    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.10.1186/1471-2105-11-93'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-11-93'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-11-93'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-11-93'


     

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

    186 TRIPLES      21 PREDICATES      57 URIs      28 LITERALS      16 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/1471-2105-11-93 schema:about N218c4ea4fca74602a7b3e702e3fc8bc7
    2 N457cf9a88ddb4763afbc8980b9681b72
    3 N6d1fb30bb0b24569bcca65ffcb18c380
    4 N6ffcec1f867a4e8a93a5c2a777d5d078
    5 N74f2295934994d94b7578073904a2766
    6 Naf9f2bfaec8046b3820ea0899bb6a01f
    7 Ne9bceeb2490a4b0691920dda248ea304
    8 anzsrc-for:06
    9 anzsrc-for:0604
    10 schema:author N495a07ec179f4af1a4e7007f6611ad74
    11 schema:citation sg:pub.10.1007/978-1-4020-6754-9_14571
    12 sg:pub.10.1038/30835
    13 sg:pub.10.1038/30918
    14 sg:pub.10.1038/nature02874
    15 sg:pub.10.1038/nature02875
    16 sg:pub.10.1038/nature05903
    17 sg:pub.10.1038/nbt1394
    18 sg:pub.10.1038/nrmicro2088
    19 sg:pub.10.1038/nrmicro2122
    20 sg:pub.10.1186/1471-2105-8-s6-s8
    21 https://doi.org/10.1016/j.cell.2006.10.040
    22 https://doi.org/10.1016/j.tig.2006.03.003
    23 https://doi.org/10.1073/pnas.0611119104
    24 https://doi.org/10.1073/pnas.0709632105
    25 https://doi.org/10.1093/bioinformatics/btn025
    26 https://doi.org/10.1093/bioinformatics/btn428
    27 https://doi.org/10.1101/gad.1476406
    28 https://doi.org/10.1101/gad.1705308
    29 https://doi.org/10.1101/gr.078212.108
    30 https://doi.org/10.1101/gr.194201
    31 https://doi.org/10.2144/mar03dudoit
    32 schema:datePublished 2010-12
    33 schema:datePublishedReg 2010-12-01
    34 schema:description BACKGROUND: Next-generation sequencing technologies allow researchers to obtain millions of sequence reads in a single experiment. One important use of the technology is the sequencing of small non-coding regulatory RNAs and the identification of the genomic locales from which they originate. Currently, there is a paucity of methods for finding small RNA generative locales. RESULTS: We describe and implement an algorithm that can determine small RNA generative locales from high-throughput sequencing data. The algorithm creates a network, or graph, of the small RNAs by creating links between them depending on their proximity on the target genome. For each of the sub-networks in the resulting graph the clustering coefficient, a measure of the interconnectedness of the subnetwork, is used to identify the generative locales. We test the algorithm over a wide range of parameters using RFAM sequences as positive controls and demonstrate that the algorithm has good sensitivity and specificity in a range of Arabidopsis and mouse small RNA sequence sets and that the locales it generates are robust to differences in the choice of parameters. CONCLUSIONS: NiBLS is a fast, reliable and sensitive method for determining small RNA locales in high-throughput sequence data that is generally applicable to all classes of small RNA.
    35 schema:genre research_article
    36 schema:inLanguage en
    37 schema:isAccessibleForFree true
    38 schema:isPartOf N786b4fe6cd0d4cc68527ce9b2a82cb7c
    39 N9b9f955c7c5644ae90ca87ccf04d26d7
    40 sg:journal.1023786
    41 schema:name Finding sRNA generative locales from high-throughput sequencing data with NiBLS
    42 schema:pagination 93
    43 schema:productId N3e71e6bf6edf44f0a4aebe6d66ee3268
    44 N7b3899fc76ff47319d771418428fe264
    45 Nbc578fba5d8e4670b4e198644e6b0786
    46 Nbf1f6a14a57e4b1caabc586ff90f07c9
    47 Nc2611f4260c449b199d4e6cb76dc9884
    48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045233097
    49 https://doi.org/10.1186/1471-2105-11-93
    50 schema:sdDatePublished 2019-04-11T10:29
    51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    52 schema:sdPublisher Ne8d9815157fd4b20b79f4ac2a473d58b
    53 schema:url https://link.springer.com/10.1186%2F1471-2105-11-93
    54 sgo:license sg:explorer/license/
    55 sgo:sdDataset articles
    56 rdf:type schema:ScholarlyArticle
    57 N218c4ea4fca74602a7b3e702e3fc8bc7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    58 schema:name Base Sequence
    59 rdf:type schema:DefinedTerm
    60 N3e71e6bf6edf44f0a4aebe6d66ee3268 schema:name dimensions_id
    61 schema:value pub.1045233097
    62 rdf:type schema:PropertyValue
    63 N457cf9a88ddb4763afbc8980b9681b72 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    64 schema:name MicroRNAs
    65 rdf:type schema:DefinedTerm
    66 N495a07ec179f4af1a4e7007f6611ad74 rdf:first sg:person.013574744257.00
    67 rdf:rest N74fbdb70c9094f338657df97dc081d5d
    68 N6d1fb30bb0b24569bcca65ffcb18c380 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    69 schema:name Sequence Alignment
    70 rdf:type schema:DefinedTerm
    71 N6ffcec1f867a4e8a93a5c2a777d5d078 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    72 schema:name Sequence Analysis, RNA
    73 rdf:type schema:DefinedTerm
    74 N74f2295934994d94b7578073904a2766 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    75 schema:name Computational Biology
    76 rdf:type schema:DefinedTerm
    77 N74fbdb70c9094f338657df97dc081d5d rdf:first sg:person.01113662355.37
    78 rdf:rest Nebf97158d9624d199501025804499f41
    79 N786b4fe6cd0d4cc68527ce9b2a82cb7c schema:volumeNumber 11
    80 rdf:type schema:PublicationVolume
    81 N7b3899fc76ff47319d771418428fe264 schema:name readcube_id
    82 schema:value d7762eeef0dac227505444d4f20535c1c18ec6a9884baa38b0bbfffb10505e5b
    83 rdf:type schema:PropertyValue
    84 N9b9f955c7c5644ae90ca87ccf04d26d7 schema:issueNumber 1
    85 rdf:type schema:PublicationIssue
    86 Naf9f2bfaec8046b3820ea0899bb6a01f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    87 schema:name Software
    88 rdf:type schema:DefinedTerm
    89 Nbc578fba5d8e4670b4e198644e6b0786 schema:name pubmed_id
    90 schema:value 20167070
    91 rdf:type schema:PropertyValue
    92 Nbf1f6a14a57e4b1caabc586ff90f07c9 schema:name nlm_unique_id
    93 schema:value 100965194
    94 rdf:type schema:PropertyValue
    95 Nc2611f4260c449b199d4e6cb76dc9884 schema:name doi
    96 schema:value 10.1186/1471-2105-11-93
    97 rdf:type schema:PropertyValue
    98 Ne8d9815157fd4b20b79f4ac2a473d58b schema:name Springer Nature - SN SciGraph project
    99 rdf:type schema:Organization
    100 Ne9bceeb2490a4b0691920dda248ea304 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    101 schema:name Algorithms
    102 rdf:type schema:DefinedTerm
    103 Nebf97158d9624d199501025804499f41 rdf:first sg:person.01047645057.86
    104 rdf:rest rdf:nil
    105 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    106 schema:name Biological Sciences
    107 rdf:type schema:DefinedTerm
    108 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    109 schema:name Genetics
    110 rdf:type schema:DefinedTerm
    111 sg:journal.1023786 schema:issn 1471-2105
    112 schema:name BMC Bioinformatics
    113 rdf:type schema:Periodical
    114 sg:person.01047645057.86 schema:affiliation https://www.grid.ac/institutes/grid.14830.3e
    115 schema:familyName Studholme
    116 schema:givenName David J
    117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01047645057.86
    118 rdf:type schema:Person
    119 sg:person.01113662355.37 schema:affiliation https://www.grid.ac/institutes/grid.8273.e
    120 schema:familyName Moulton
    121 schema:givenName Vincent
    122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01113662355.37
    123 rdf:type schema:Person
    124 sg:person.013574744257.00 schema:affiliation https://www.grid.ac/institutes/grid.14830.3e
    125 schema:familyName MacLean
    126 schema:givenName Daniel
    127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013574744257.00
    128 rdf:type schema:Person
    129 sg:pub.10.1007/978-1-4020-6754-9_14571 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002857147
    130 https://doi.org/10.1007/978-1-4020-6754-9_14571
    131 rdf:type schema:CreativeWork
    132 sg:pub.10.1038/30835 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004648581
    133 https://doi.org/10.1038/30835
    134 rdf:type schema:CreativeWork
    135 sg:pub.10.1038/30918 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041985305
    136 https://doi.org/10.1038/30918
    137 rdf:type schema:CreativeWork
    138 sg:pub.10.1038/nature02874 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025532636
    139 https://doi.org/10.1038/nature02874
    140 rdf:type schema:CreativeWork
    141 sg:pub.10.1038/nature02875 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039222604
    142 https://doi.org/10.1038/nature02875
    143 rdf:type schema:CreativeWork
    144 sg:pub.10.1038/nature05903 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006452107
    145 https://doi.org/10.1038/nature05903
    146 rdf:type schema:CreativeWork
    147 sg:pub.10.1038/nbt1394 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008181500
    148 https://doi.org/10.1038/nbt1394
    149 rdf:type schema:CreativeWork
    150 sg:pub.10.1038/nrmicro2088 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040141146
    151 https://doi.org/10.1038/nrmicro2088
    152 rdf:type schema:CreativeWork
    153 sg:pub.10.1038/nrmicro2122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017693818
    154 https://doi.org/10.1038/nrmicro2122
    155 rdf:type schema:CreativeWork
    156 sg:pub.10.1186/1471-2105-8-s6-s8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002114257
    157 https://doi.org/10.1186/1471-2105-8-s6-s8
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1016/j.cell.2006.10.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052700611
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1016/j.tig.2006.03.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025450285
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1073/pnas.0611119104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015465980
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1073/pnas.0709632105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004852973
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1093/bioinformatics/btn025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012266713
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1093/bioinformatics/btn428 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019983784
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1101/gad.1476406 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020934875
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1101/gad.1705308 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031278673
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1101/gr.078212.108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047542880
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1101/gr.194201 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008144266
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.2144/mar03dudoit schema:sameAs https://app.dimensions.ai/details/publication/pub.1075260228
    180 rdf:type schema:CreativeWork
    181 https://www.grid.ac/institutes/grid.14830.3e schema:alternateName John Innes Centre
    182 schema:name The Sainsbury Laboratory, John Innes Centre, Colney Lane, NR4 7UH, Norwich, UK
    183 rdf:type schema:Organization
    184 https://www.grid.ac/institutes/grid.8273.e schema:alternateName University of East Anglia
    185 schema:name University of East Anglia, NR4 7TJ, Norwich, UK
    186 rdf:type schema:Organization
     




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


    ...