Analysis of Metagenomics Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Elizabeth M. Glass , Folker Meyer

ABSTRACT

Improved sampling of diverse environments and advances in the development and application of next-generation sequencing technologies are accelerating the rate at which new metagenomes are produced. Over the past few years, the major challenge associated with metagenomics has shifted from generating to analyzing sequences. Metagenomic analysis includes the identification, and functional and evolutionary analysis of the genomic sequences of a community of organisms. There are many challenges involved in the analysis of these data sets including sparse metadata, a high volume of sequence data, genomic heterogeneity, and incomplete sequences. Because of the nature of metagenomic data, analysis is very complex and requires new approaches and significant compute resources. Recently, several computational systems and tools have been developed and applied to analyze their functional and phylogenetic composition. The metagenomics RAST server (MG-RAST) is a high-throughput system that has been built to provide high-performance computing to researchers interested in analyzing metagenomic data. It has removed one of the primary bottlenecks in metagenome sequence analysis, the availability of high-performance computing for annotating data. More... »

PAGES

219-229

Book

TITLE

Bioinformatics for High Throughput Sequencing

ISBN

978-1-4614-0781-2
978-1-4614-0782-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4614-0782-9_13

DOI

http://dx.doi.org/10.1007/978-1-4614-0782-9_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041096276


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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Argonne National Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.187073.a", 
          "name": [
            "Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL\u00a060439, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Glass", 
        "givenName": "Elizabeth M.", 
        "id": "sg:person.014362205622.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014362205622.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Argonne National Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.187073.a", 
          "name": [
            "Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL\u00a060439, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Meyer", 
        "givenName": "Folker", 
        "id": "sg:person.0623154651.88", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0623154651.88"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nbt.1411", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002107134", 
          "https://doi.org/10.1038/nbt.1411"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq747", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003548827"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-9-386", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006083026", 
          "https://doi.org/10.1186/1471-2105-9-386"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1101/gr.229202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006260064"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth976", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007149601", 
          "https://doi.org/10.1038/nmeth976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nmeth976", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007149601", 
          "https://doi.org/10.1038/nmeth976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkn803", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008205393"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gki866", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009961531"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkm864", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010797283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkm869", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011248126"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/aem.00358-07", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013422473"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq1150", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017361452"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-7-57", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020041008", 
          "https://doi.org/10.1186/1471-2164-7-57"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1180598", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020795396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1180598", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020795396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03959", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021574562", 
          "https://doi.org/10.1038/nature03959"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03959", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021574562", 
          "https://doi.org/10.1038/nature03959"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/abio.1996.0432", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027675298"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1347", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027918254", 
          "https://doi.org/10.1038/nbt1347"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt1346", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028001312", 
          "https://doi.org/10.1038/nbt1346"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkl889", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029901284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkq1102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032022164"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btl247", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033954668"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1128/aem.03006-05", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034568952"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nbt0507-540", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035830869", 
          "https://doi.org/10.1038/nbt0507-540"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkm796", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037628101"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/omi.2008.0019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042297337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/25.17.3389", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047265454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature06810", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047805213", 
          "https://doi.org/10.1038/nature06810"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature06513", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052335368", 
          "https://doi.org/10.1038/nature06513"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/omi.2008.0a10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053138169"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012", 
    "datePublishedReg": "2012-01-01", 
    "description": "Improved sampling of diverse environments and advances in the development and application of next-generation sequencing technologies are accelerating the rate at which new metagenomes are produced. Over the past few years, the major challenge associated with metagenomics has shifted from generating to analyzing sequences. Metagenomic analysis includes the identification, and functional and evolutionary analysis of the genomic sequences of a community of organisms. There are many challenges involved in the analysis of these data sets including sparse metadata, a high volume of sequence data, genomic heterogeneity, and incomplete sequences. Because of the nature of metagenomic data, analysis is very complex and requires new approaches and significant compute resources. Recently, several computational systems and tools have been developed and applied to analyze their functional and phylogenetic composition. The metagenomics RAST server (MG-RAST) is a high-throughput system that has been built to provide high-performance computing to researchers interested in analyzing metagenomic data. It has removed one of the primary bottlenecks in metagenome sequence analysis, the availability of high-performance computing for annotating data.", 
    "editor": [
      {
        "familyName": "Rodr\u00edguez-Ezpeleta", 
        "givenName": "Naiara", 
        "type": "Person"
      }, 
      {
        "familyName": "Hackenberg", 
        "givenName": "Michael", 
        "type": "Person"
      }, 
      {
        "familyName": "Aransay", 
        "givenName": "Ana M.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-1-4614-0782-9_13", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-1-4614-0781-2", 
        "978-1-4614-0782-9"
      ], 
      "name": "Bioinformatics for High Throughput Sequencing", 
      "type": "Book"
    }, 
    "name": "Analysis of Metagenomics Data", 
    "pagination": "219-229", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-1-4614-0782-9_13"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c3c4ad7c09911613b12a946024783a07942e4233a372545402e88c43df836b8e"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1041096276"
        ]
      }
    ], 
    "publisher": {
      "location": "New York, NY", 
      "name": "Springer New York", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-1-4614-0782-9_13", 
      "https://app.dimensions.ai/details/publication/pub.1041096276"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T22:57", 
    "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_8695_00000268.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-1-4614-0782-9_13"
  }
]
 

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.10.1007/978-1-4614-0782-9_13'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-1-4614-0782-9_13'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-1-4614-0782-9_13'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-1-4614-0782-9_13'


 

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

176 TRIPLES      23 PREDICATES      55 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-1-4614-0782-9_13 schema:about anzsrc-for:06
2 anzsrc-for:0604
3 schema:author Ncf65ec57027a469aa7140c576d2090b4
4 schema:citation sg:pub.10.1038/nature03959
5 sg:pub.10.1038/nature06513
6 sg:pub.10.1038/nature06810
7 sg:pub.10.1038/nbt.1411
8 sg:pub.10.1038/nbt0507-540
9 sg:pub.10.1038/nbt1346
10 sg:pub.10.1038/nbt1347
11 sg:pub.10.1038/nmeth976
12 sg:pub.10.1186/1471-2105-9-386
13 sg:pub.10.1186/1471-2164-7-57
14 https://doi.org/10.1006/abio.1996.0432
15 https://doi.org/10.1089/omi.2008.0019
16 https://doi.org/10.1089/omi.2008.0a10
17 https://doi.org/10.1093/bioinformatics/btl247
18 https://doi.org/10.1093/nar/25.17.3389
19 https://doi.org/10.1093/nar/gki866
20 https://doi.org/10.1093/nar/gkl889
21 https://doi.org/10.1093/nar/gkm796
22 https://doi.org/10.1093/nar/gkm864
23 https://doi.org/10.1093/nar/gkm869
24 https://doi.org/10.1093/nar/gkn803
25 https://doi.org/10.1093/nar/gkq1102
26 https://doi.org/10.1093/nar/gkq1150
27 https://doi.org/10.1093/nar/gkq747
28 https://doi.org/10.1101/gr.229202
29 https://doi.org/10.1126/science.1180598
30 https://doi.org/10.1128/aem.00358-07
31 https://doi.org/10.1128/aem.03006-05
32 schema:datePublished 2012
33 schema:datePublishedReg 2012-01-01
34 schema:description Improved sampling of diverse environments and advances in the development and application of next-generation sequencing technologies are accelerating the rate at which new metagenomes are produced. Over the past few years, the major challenge associated with metagenomics has shifted from generating to analyzing sequences. Metagenomic analysis includes the identification, and functional and evolutionary analysis of the genomic sequences of a community of organisms. There are many challenges involved in the analysis of these data sets including sparse metadata, a high volume of sequence data, genomic heterogeneity, and incomplete sequences. Because of the nature of metagenomic data, analysis is very complex and requires new approaches and significant compute resources. Recently, several computational systems and tools have been developed and applied to analyze their functional and phylogenetic composition. The metagenomics RAST server (MG-RAST) is a high-throughput system that has been built to provide high-performance computing to researchers interested in analyzing metagenomic data. It has removed one of the primary bottlenecks in metagenome sequence analysis, the availability of high-performance computing for annotating data.
35 schema:editor N4b8ac50a737b4ce7a1ef2636bd9011d2
36 schema:genre chapter
37 schema:inLanguage en
38 schema:isAccessibleForFree false
39 schema:isPartOf N1b0c6053d3d24d1eae29741015b188d8
40 schema:name Analysis of Metagenomics Data
41 schema:pagination 219-229
42 schema:productId N476a7abda6224f929720d26f5fda2828
43 Nb35ef76c57b44453a249edb6d5b8234b
44 Ne7e386c902024c88a047b5e89fdf0262
45 schema:publisher Nf9df3c20d434476ca45f00b8f022810b
46 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041096276
47 https://doi.org/10.1007/978-1-4614-0782-9_13
48 schema:sdDatePublished 2019-04-15T22:57
49 schema:sdLicense https://scigraph.springernature.com/explorer/license/
50 schema:sdPublisher N8e47750ef5d94552a356653060a3b54f
51 schema:url http://link.springer.com/10.1007/978-1-4614-0782-9_13
52 sgo:license sg:explorer/license/
53 sgo:sdDataset chapters
54 rdf:type schema:Chapter
55 N09081125e00048a6b84ecb1caaeec175 schema:familyName Rodríguez-Ezpeleta
56 schema:givenName Naiara
57 rdf:type schema:Person
58 N16cd2496753e49efb212dedeb0f41492 rdf:first Nc9c397474dc74efebb6d2ba3e4d9f712
59 rdf:rest Naf185ffeca1d468789f4b900048810b1
60 N1b0c6053d3d24d1eae29741015b188d8 schema:isbn 978-1-4614-0781-2
61 978-1-4614-0782-9
62 schema:name Bioinformatics for High Throughput Sequencing
63 rdf:type schema:Book
64 N476a7abda6224f929720d26f5fda2828 schema:name doi
65 schema:value 10.1007/978-1-4614-0782-9_13
66 rdf:type schema:PropertyValue
67 N4b8ac50a737b4ce7a1ef2636bd9011d2 rdf:first N09081125e00048a6b84ecb1caaeec175
68 rdf:rest N16cd2496753e49efb212dedeb0f41492
69 N8c030216099a492bba023f17b949a4f3 schema:familyName Aransay
70 schema:givenName Ana M.
71 rdf:type schema:Person
72 N8e47750ef5d94552a356653060a3b54f schema:name Springer Nature - SN SciGraph project
73 rdf:type schema:Organization
74 Na4d29fd2efaf4c2ab1c79f0b0488225c rdf:first sg:person.0623154651.88
75 rdf:rest rdf:nil
76 Naf185ffeca1d468789f4b900048810b1 rdf:first N8c030216099a492bba023f17b949a4f3
77 rdf:rest rdf:nil
78 Nb35ef76c57b44453a249edb6d5b8234b schema:name dimensions_id
79 schema:value pub.1041096276
80 rdf:type schema:PropertyValue
81 Nc9c397474dc74efebb6d2ba3e4d9f712 schema:familyName Hackenberg
82 schema:givenName Michael
83 rdf:type schema:Person
84 Ncf65ec57027a469aa7140c576d2090b4 rdf:first sg:person.014362205622.34
85 rdf:rest Na4d29fd2efaf4c2ab1c79f0b0488225c
86 Ne7e386c902024c88a047b5e89fdf0262 schema:name readcube_id
87 schema:value c3c4ad7c09911613b12a946024783a07942e4233a372545402e88c43df836b8e
88 rdf:type schema:PropertyValue
89 Nf9df3c20d434476ca45f00b8f022810b schema:location New York, NY
90 schema:name Springer New York
91 rdf:type schema:Organisation
92 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
93 schema:name Biological Sciences
94 rdf:type schema:DefinedTerm
95 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
96 schema:name Genetics
97 rdf:type schema:DefinedTerm
98 sg:person.014362205622.34 schema:affiliation https://www.grid.ac/institutes/grid.187073.a
99 schema:familyName Glass
100 schema:givenName Elizabeth M.
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014362205622.34
102 rdf:type schema:Person
103 sg:person.0623154651.88 schema:affiliation https://www.grid.ac/institutes/grid.187073.a
104 schema:familyName Meyer
105 schema:givenName Folker
106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0623154651.88
107 rdf:type schema:Person
108 sg:pub.10.1038/nature03959 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021574562
109 https://doi.org/10.1038/nature03959
110 rdf:type schema:CreativeWork
111 sg:pub.10.1038/nature06513 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052335368
112 https://doi.org/10.1038/nature06513
113 rdf:type schema:CreativeWork
114 sg:pub.10.1038/nature06810 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047805213
115 https://doi.org/10.1038/nature06810
116 rdf:type schema:CreativeWork
117 sg:pub.10.1038/nbt.1411 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002107134
118 https://doi.org/10.1038/nbt.1411
119 rdf:type schema:CreativeWork
120 sg:pub.10.1038/nbt0507-540 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035830869
121 https://doi.org/10.1038/nbt0507-540
122 rdf:type schema:CreativeWork
123 sg:pub.10.1038/nbt1346 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028001312
124 https://doi.org/10.1038/nbt1346
125 rdf:type schema:CreativeWork
126 sg:pub.10.1038/nbt1347 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027918254
127 https://doi.org/10.1038/nbt1347
128 rdf:type schema:CreativeWork
129 sg:pub.10.1038/nmeth976 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007149601
130 https://doi.org/10.1038/nmeth976
131 rdf:type schema:CreativeWork
132 sg:pub.10.1186/1471-2105-9-386 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006083026
133 https://doi.org/10.1186/1471-2105-9-386
134 rdf:type schema:CreativeWork
135 sg:pub.10.1186/1471-2164-7-57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020041008
136 https://doi.org/10.1186/1471-2164-7-57
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1006/abio.1996.0432 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027675298
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1089/omi.2008.0019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042297337
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1089/omi.2008.0a10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053138169
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1093/bioinformatics/btl247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033954668
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1093/nar/25.17.3389 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047265454
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1093/nar/gki866 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009961531
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1093/nar/gkl889 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029901284
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1093/nar/gkm796 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037628101
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1093/nar/gkm864 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010797283
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1093/nar/gkm869 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011248126
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1093/nar/gkn803 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008205393
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1093/nar/gkq1102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032022164
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1093/nar/gkq1150 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017361452
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1093/nar/gkq747 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003548827
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1101/gr.229202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006260064
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1126/science.1180598 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020795396
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1128/aem.00358-07 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013422473
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1128/aem.03006-05 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034568952
173 rdf:type schema:CreativeWork
174 https://www.grid.ac/institutes/grid.187073.a schema:alternateName Argonne National Laboratory
175 schema:name Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
176 rdf:type schema:Organization
 




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


...