AdapterRemoval v2: rapid adapter trimming, identification, and read merging View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12

AUTHORS

Mikkel Schubert, Stinus Lindgreen, Ludovic Orlando

ABSTRACT

BACKGROUND: As high-throughput sequencing platforms produce longer and longer reads, sequences generated from short inserts, such as those obtained from fossil and degraded material, are increasingly expected to contain adapter sequences. Efficient adapter trimming algorithms are also needed to process the growing amount of data generated per sequencing run. FINDINGS: We introduce AdapterRemoval v2, a major revision of AdapterRemoval v1, which introduces (i) striking improvements in throughput, through the use of single instruction, multiple data (SIMD; SSE1 and SSE2) instructions and multi-threading support, (ii) the ability to handle datasets containing reads or read-pairs with different adapters or adapter pairs, (iii) simultaneous demultiplexing and adapter trimming, (iv) the ability to reconstruct adapter sequences from paired-end reads for poorly documented data sets, and (v) native gzip and bzip2 support. CONCLUSIONS: We show that AdapterRemoval v2 compares favorably with existing tools, while offering superior throughput to most alternatives examined here, both for single and multi-threaded operations. More... »

PAGES

88

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13104-016-1900-2

DOI

http://dx.doi.org/10.1186/s13104-016-1900-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040653018

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26868221


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": "High-Throughput Nucleotide Sequencing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Copenhagen", 
          "id": "https://www.grid.ac/institutes/grid.5254.6", 
          "name": [
            "Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350, Copenhagen, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schubert", 
        "givenName": "Mikkel", 
        "id": "sg:person.0774220504.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774220504.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Copenhagen", 
          "id": "https://www.grid.ac/institutes/grid.5254.6", 
          "name": [
            "Department of Biology, Section for Computational and RNA Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200, Copenhagen, Denmark", 
            "Carlsberg Research Laboratory, Gamle Carlsberg Vej 4-10, 1799, Copenhagen, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lindgreen", 
        "givenName": "Stinus", 
        "id": "sg:person.0635177211.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0635177211.38"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Copenhagen", 
          "id": "https://www.grid.ac/institutes/grid.5254.6", 
          "name": [
            "Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350, Copenhagen, Denmark", 
            "Laboratoire AMIS, Universit\u00e9 de Toulouse, University Paul Sabatier (UPS), CNRS UMR 5288, 37 All\u00e9es Jules Guesde, 31000, Toulouse, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Orlando", 
        "givenName": "Ludovic", 
        "id": "sg:person.01201152047.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201152047.48"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.ymeth.2013.06.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006047051"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ymeth.2013.06.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006047051"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btt593", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010435278"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ygeno.2013.07.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011945276"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gku699", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017404686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1756-0500-5-337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017672942", 
          "https://doi.org/10.1186/1756-0500-5-337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1756-0500-5-337", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017672942", 
          "https://doi.org/10.1186/1756-0500-5-337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/biology1030895", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018168136"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-61779-516-9_23", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018322075", 
          "https://doi.org/10.1007/978-1-61779-516-9_23"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nprot.2014.063", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020179577", 
          "https://doi.org/10.1038/nprot.2014.063"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bts563", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025288251"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nrg3935", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028996959", 
          "https://doi.org/10.1038/nrg3935"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-15-182", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030198285", 
          "https://doi.org/10.1186/1471-2105-15-182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btr507", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031241489"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/bts187", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039045908"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/bioinformatics/btu170", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042720804"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-16-s1-s2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048032234", 
          "https://doi.org/10.1186/1471-2105-16-s1-s2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/mec.12680", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052203348"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.14806/ej.17.1.200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1067372670"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2174/1875036201307010001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069237740"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-12", 
    "datePublishedReg": "2016-12-01", 
    "description": "BACKGROUND: As high-throughput sequencing platforms produce longer and longer reads, sequences generated from short inserts, such as those obtained from fossil and degraded material, are increasingly expected to contain adapter sequences. Efficient adapter trimming algorithms are also needed to process the growing amount of data generated per sequencing run.\nFINDINGS: We introduce AdapterRemoval v2, a major revision of AdapterRemoval v1, which introduces (i) striking improvements in throughput, through the use of single instruction, multiple data (SIMD; SSE1 and SSE2) instructions and multi-threading support, (ii) the ability to handle datasets containing reads or read-pairs with different adapters or adapter pairs, (iii) simultaneous demultiplexing and adapter trimming, (iv) the ability to reconstruct adapter sequences from paired-end reads for poorly documented data sets, and (v) native gzip and bzip2 support.\nCONCLUSIONS: We show that AdapterRemoval v2 compares favorably with existing tools, while offering superior throughput to most alternatives examined here, both for single and multi-threaded operations.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s13104-016-1900-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1039457", 
        "issn": [
          "1756-0500"
        ], 
        "name": "BMC Research Notes", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "9"
      }
    ], 
    "name": "AdapterRemoval v2: rapid adapter trimming, identification, and read merging", 
    "pagination": "88", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c1a7b05e51f78de96b416b84aa6ac4e8f603f946f0d065abc66df6030db2a2fc"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "26868221"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101462768"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s13104-016-1900-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1040653018"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s13104-016-1900-2", 
      "https://app.dimensions.ai/details/publication/pub.1040653018"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:16", 
    "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/v90/0000000348_0000000348/records_54301_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs13104-016-1900-2"
  }
]
 

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/s13104-016-1900-2'

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/s13104-016-1900-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s13104-016-1900-2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13104-016-1900-2'


 

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

157 TRIPLES      21 PREDICATES      50 URIs      24 LITERALS      12 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s13104-016-1900-2 schema:about N24baa133cded42ebb71c3f68a17bcb16
2 N423b39b378b34d26920003f2039adda3
3 Ne0a756aef2a8472dba2692dab69de39f
4 anzsrc-for:06
5 anzsrc-for:0604
6 schema:author N3f5dfa9f85f342e2b6d0d6db51079c6a
7 schema:citation sg:pub.10.1007/978-1-61779-516-9_23
8 sg:pub.10.1038/nprot.2014.063
9 sg:pub.10.1038/nrg3935
10 sg:pub.10.1186/1471-2105-15-182
11 sg:pub.10.1186/1471-2105-16-s1-s2
12 sg:pub.10.1186/1756-0500-5-337
13 https://doi.org/10.1016/j.ygeno.2013.07.011
14 https://doi.org/10.1016/j.ymeth.2013.06.027
15 https://doi.org/10.1093/bioinformatics/btr507
16 https://doi.org/10.1093/bioinformatics/bts187
17 https://doi.org/10.1093/bioinformatics/bts563
18 https://doi.org/10.1093/bioinformatics/btt593
19 https://doi.org/10.1093/bioinformatics/btu170
20 https://doi.org/10.1093/nar/gku699
21 https://doi.org/10.1111/mec.12680
22 https://doi.org/10.14806/ej.17.1.200
23 https://doi.org/10.2174/1875036201307010001
24 https://doi.org/10.3390/biology1030895
25 schema:datePublished 2016-12
26 schema:datePublishedReg 2016-12-01
27 schema:description BACKGROUND: As high-throughput sequencing platforms produce longer and longer reads, sequences generated from short inserts, such as those obtained from fossil and degraded material, are increasingly expected to contain adapter sequences. Efficient adapter trimming algorithms are also needed to process the growing amount of data generated per sequencing run. FINDINGS: We introduce AdapterRemoval v2, a major revision of AdapterRemoval v1, which introduces (i) striking improvements in throughput, through the use of single instruction, multiple data (SIMD; SSE1 and SSE2) instructions and multi-threading support, (ii) the ability to handle datasets containing reads or read-pairs with different adapters or adapter pairs, (iii) simultaneous demultiplexing and adapter trimming, (iv) the ability to reconstruct adapter sequences from paired-end reads for poorly documented data sets, and (v) native gzip and bzip2 support. CONCLUSIONS: We show that AdapterRemoval v2 compares favorably with existing tools, while offering superior throughput to most alternatives examined here, both for single and multi-threaded operations.
28 schema:genre research_article
29 schema:inLanguage en
30 schema:isAccessibleForFree true
31 schema:isPartOf Na24d690db1bb4bd6abe79c8d03705550
32 Ncdb781542a9e4c3f877fc147067e6215
33 sg:journal.1039457
34 schema:name AdapterRemoval v2: rapid adapter trimming, identification, and read merging
35 schema:pagination 88
36 schema:productId N1ddcf268bc084f48b4c26866543a3e86
37 N2418fce25edf4547ace2bfaa7ede4639
38 N6c09ffad72e447fab49b2ef109b0e9c5
39 Nc3ac3fdeadc0488284e287e2c59f6adb
40 Nf093dfa534744f73a188e0ad61d77915
41 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040653018
42 https://doi.org/10.1186/s13104-016-1900-2
43 schema:sdDatePublished 2019-04-11T10:16
44 schema:sdLicense https://scigraph.springernature.com/explorer/license/
45 schema:sdPublisher N39ea085651f64c1b87b01fd3a00354ce
46 schema:url https://link.springer.com/10.1186%2Fs13104-016-1900-2
47 sgo:license sg:explorer/license/
48 sgo:sdDataset articles
49 rdf:type schema:ScholarlyArticle
50 N1ddcf268bc084f48b4c26866543a3e86 schema:name readcube_id
51 schema:value c1a7b05e51f78de96b416b84aa6ac4e8f603f946f0d065abc66df6030db2a2fc
52 rdf:type schema:PropertyValue
53 N2418fce25edf4547ace2bfaa7ede4639 schema:name pubmed_id
54 schema:value 26868221
55 rdf:type schema:PropertyValue
56 N24baa133cded42ebb71c3f68a17bcb16 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
57 schema:name High-Throughput Nucleotide Sequencing
58 rdf:type schema:DefinedTerm
59 N39ea085651f64c1b87b01fd3a00354ce schema:name Springer Nature - SN SciGraph project
60 rdf:type schema:Organization
61 N3f5dfa9f85f342e2b6d0d6db51079c6a rdf:first sg:person.0774220504.34
62 rdf:rest N469f281a914d4201ae23e6b76b91a7c9
63 N423b39b378b34d26920003f2039adda3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
64 schema:name Base Sequence
65 rdf:type schema:DefinedTerm
66 N469f281a914d4201ae23e6b76b91a7c9 rdf:first sg:person.0635177211.38
67 rdf:rest Ncee0b2f767eb4aaab59732befa6bbc8f
68 N6c09ffad72e447fab49b2ef109b0e9c5 schema:name doi
69 schema:value 10.1186/s13104-016-1900-2
70 rdf:type schema:PropertyValue
71 Na24d690db1bb4bd6abe79c8d03705550 schema:issueNumber 1
72 rdf:type schema:PublicationIssue
73 Nc3ac3fdeadc0488284e287e2c59f6adb schema:name dimensions_id
74 schema:value pub.1040653018
75 rdf:type schema:PropertyValue
76 Ncdb781542a9e4c3f877fc147067e6215 schema:volumeNumber 9
77 rdf:type schema:PublicationVolume
78 Ncee0b2f767eb4aaab59732befa6bbc8f rdf:first sg:person.01201152047.48
79 rdf:rest rdf:nil
80 Ne0a756aef2a8472dba2692dab69de39f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
81 schema:name Algorithms
82 rdf:type schema:DefinedTerm
83 Nf093dfa534744f73a188e0ad61d77915 schema:name nlm_unique_id
84 schema:value 101462768
85 rdf:type schema:PropertyValue
86 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
87 schema:name Biological Sciences
88 rdf:type schema:DefinedTerm
89 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
90 schema:name Genetics
91 rdf:type schema:DefinedTerm
92 sg:journal.1039457 schema:issn 1756-0500
93 schema:name BMC Research Notes
94 rdf:type schema:Periodical
95 sg:person.01201152047.48 schema:affiliation https://www.grid.ac/institutes/grid.5254.6
96 schema:familyName Orlando
97 schema:givenName Ludovic
98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01201152047.48
99 rdf:type schema:Person
100 sg:person.0635177211.38 schema:affiliation https://www.grid.ac/institutes/grid.5254.6
101 schema:familyName Lindgreen
102 schema:givenName Stinus
103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0635177211.38
104 rdf:type schema:Person
105 sg:person.0774220504.34 schema:affiliation https://www.grid.ac/institutes/grid.5254.6
106 schema:familyName Schubert
107 schema:givenName Mikkel
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774220504.34
109 rdf:type schema:Person
110 sg:pub.10.1007/978-1-61779-516-9_23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018322075
111 https://doi.org/10.1007/978-1-61779-516-9_23
112 rdf:type schema:CreativeWork
113 sg:pub.10.1038/nprot.2014.063 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020179577
114 https://doi.org/10.1038/nprot.2014.063
115 rdf:type schema:CreativeWork
116 sg:pub.10.1038/nrg3935 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028996959
117 https://doi.org/10.1038/nrg3935
118 rdf:type schema:CreativeWork
119 sg:pub.10.1186/1471-2105-15-182 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030198285
120 https://doi.org/10.1186/1471-2105-15-182
121 rdf:type schema:CreativeWork
122 sg:pub.10.1186/1471-2105-16-s1-s2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048032234
123 https://doi.org/10.1186/1471-2105-16-s1-s2
124 rdf:type schema:CreativeWork
125 sg:pub.10.1186/1756-0500-5-337 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017672942
126 https://doi.org/10.1186/1756-0500-5-337
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.ygeno.2013.07.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011945276
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.ymeth.2013.06.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006047051
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1093/bioinformatics/btr507 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031241489
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1093/bioinformatics/bts187 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039045908
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1093/bioinformatics/bts563 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025288251
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1093/bioinformatics/btt593 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010435278
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1093/bioinformatics/btu170 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042720804
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1093/nar/gku699 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017404686
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1111/mec.12680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052203348
145 rdf:type schema:CreativeWork
146 https://doi.org/10.14806/ej.17.1.200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1067372670
147 rdf:type schema:CreativeWork
148 https://doi.org/10.2174/1875036201307010001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069237740
149 rdf:type schema:CreativeWork
150 https://doi.org/10.3390/biology1030895 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018168136
151 rdf:type schema:CreativeWork
152 https://www.grid.ac/institutes/grid.5254.6 schema:alternateName University of Copenhagen
153 schema:name Carlsberg Research Laboratory, Gamle Carlsberg Vej 4-10, 1799, Copenhagen, Denmark
154 Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350, Copenhagen, Denmark
155 Department of Biology, Section for Computational and RNA Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200, Copenhagen, Denmark
156 Laboratoire AMIS, Université de Toulouse, University Paul Sabatier (UPS), CNRS UMR 5288, 37 Allées Jules Guesde, 31000, Toulouse, France
157 rdf:type schema:Organization
 




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


...