PyHLA: tests for the association between HLA alleles and diseases View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-02-06

AUTHORS

Yanhui Fan, You-Qiang Song

ABSTRACT

BackgroundRecently, several tools have been designed for human leukocyte antigen (HLA) typing using single nucleotide polymorphism (SNP) array and next-generation sequencing (NGS) data. These tools provide high-throughput and cost-effective approaches for identifying HLA types. Therefore, tools for downstream association analysis are highly desirable. Although several tools have been designed for multi-allelic marker association analysis, they were designed only for microsatellite markers and do not scale well with increasing data volumes, or they were designed for large-scale data but provided a limited number of tests.ResultsWe have developed a Python package called PyHLA, which implements several methods for HLA association analysis, to fill the gap. PyHLA is a tailor-made, easy to use, and flexible tool designed specifically for the association analysis of the HLA types imputed from genome-wide genotyping and NGS data. PyHLA provides functions for association analysis, zygosity tests, and interaction tests between HLA alleles and diseases. Monte Carlo permutation and several methods for multiple testing corrections have also been implemented.ConclusionsPyHLA provides a convenient and powerful tool for HLA analysis. Existing methods have been integrated and desired methods have been added in PyHLA. Furthermore, PyHLA is applicable to small and large sample sizes and can finish the analysis in a timely manner on a personal computer with different platforms. PyHLA is implemented in Python. PyHLA is a free, open source software distributed under the GPLv2 license. The source code, tutorial, and examples are available at https://github.com/felixfan/PyHLA. More... »

PAGES

90

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-017-1496-0

DOI

http://dx.doi.org/10.1186/s12859-017-1496-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1083687269

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28166716


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/2008/06", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Biological Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Alleles", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computational Biology", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genome, Human", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genotyping Techniques", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "HLA Antigens", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "High-Throughput Nucleotide Sequencing", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Linear Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Logistic Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Microsatellite Repeats", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Theoretical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Polymorphism, Single Nucleotide", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Cancer Genomics, LemonData Biotech (Shenzhen) Ltd., Shenzhen, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, Hong Kong", 
            "Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong, Hong Kong", 
            "Department of Cancer Genomics, LemonData Biotech (Shenzhen) Ltd., Shenzhen, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fan", 
        "givenName": "Yanhui", 
        "id": "sg:person.01056350153.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01056350153.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong, Hong Kong", 
          "id": "http://www.grid.ac/institutes/grid.194645.b", 
          "name": [
            "School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, Hong Kong", 
            "Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong, Hong Kong"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Song", 
        "givenName": "You-Qiang", 
        "id": "sg:person.01043333627.37", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01043333627.37"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/s13073-015-0145-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008310987", 
          "https://doi.org/10.1186/s13073-015-0145-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s13073-014-0122-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033649267", 
          "https://doi.org/10.1186/s13073-014-0122-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-15-81", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025010426", 
          "https://doi.org/10.1186/1471-2164-15-81"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-11-724", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005310527", 
          "https://doi.org/10.1186/1471-2164-11-724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-16-s2-s7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024548741", 
          "https://doi.org/10.1186/1471-2164-16-s2-s7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2164-9-516", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042702003", 
          "https://doi.org/10.1186/1471-2164-9-516"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-02-06", 
    "datePublishedReg": "2017-02-06", 
    "description": "BackgroundRecently, several tools have been designed for human leukocyte antigen (HLA) typing using single nucleotide polymorphism (SNP) array and next-generation sequencing (NGS) data. These tools provide high-throughput and cost-effective approaches for identifying HLA types. Therefore, tools for downstream association analysis are highly desirable. Although several tools have been designed for multi-allelic marker association analysis, they were designed only for microsatellite markers and do not scale well with increasing data volumes, or they were designed for large-scale data but provided a limited number of tests.ResultsWe have developed a Python package called PyHLA, which implements several methods for HLA association analysis, to fill the gap. PyHLA is a tailor-made, easy to use, and flexible tool designed specifically for the association analysis of the HLA types imputed from genome-wide genotyping and NGS data. PyHLA provides functions for association analysis, zygosity tests, and interaction tests between HLA alleles and diseases. Monte Carlo permutation and several methods for multiple testing corrections have also been implemented.ConclusionsPyHLA provides a convenient and powerful tool for HLA analysis. Existing methods have been integrated and desired methods have been added in PyHLA. Furthermore, PyHLA is applicable to small and large sample sizes and can finish the analysis in a timely manner on a personal computer with different platforms. PyHLA is implemented in Python. PyHLA is a free, open source software distributed under the GPLv2 license. The source code, tutorial, and examples are available at https://github.com/felixfan/PyHLA.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12859-017-1496-0", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7186252", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.7430435", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "18"
      }
    ], 
    "keywords": [
      "downstream association analysis", 
      "Monte Carlo permutations", 
      "Python package", 
      "flexible tool", 
      "existing methods", 
      "large-scale data", 
      "GPLv2 license", 
      "powerful tool", 
      "sample size", 
      "zygosity tests", 
      "permutations", 
      "personal computer", 
      "next-generation sequencing data", 
      "data volume", 
      "tool", 
      "open source software", 
      "limited number", 
      "multiple testing correction", 
      "tutorial", 
      "computer", 
      "larger sample size", 
      "package", 
      "source code", 
      "code", 
      "analysis", 
      "correction", 
      "function", 
      "approach", 
      "Python", 
      "array", 
      "data", 
      "number", 
      "association analysis", 
      "source software", 
      "types", 
      "gap", 
      "software", 
      "NGS data", 
      "timely manner", 
      "size", 
      "different platforms", 
      "sequencing data", 
      "marker association analysis", 
      "manner", 
      "test", 
      "cost-effective approach", 
      "volume", 
      "genome-wide genotyping", 
      "single nucleotide polymorphism array", 
      "platform", 
      "nucleotide polymorphism array", 
      "microsatellite markers", 
      "polymorphism array", 
      "license", 
      "interaction test", 
      "alleles", 
      "genotyping", 
      "markers", 
      "disease", 
      "typing", 
      "association", 
      "HLA analysis", 
      "ResultsWe", 
      "HLA alleles", 
      "antigen typing", 
      "example", 
      "method", 
      "human leukocyte antigen (HLA) typing", 
      "HLA types", 
      "HLA"
    ], 
    "name": "PyHLA: tests for the association between HLA alleles and diseases", 
    "pagination": "90", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1083687269"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12859-017-1496-0"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "28166716"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12859-017-1496-0", 
      "https://app.dimensions.ai/details/publication/pub.1083687269"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-08-04T17:04", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_731.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12859-017-1496-0"
  }
]
 

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/s12859-017-1496-0'

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/s12859-017-1496-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12859-017-1496-0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12859-017-1496-0'


 

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

223 TRIPLES      21 PREDICATES      114 URIs      100 LITERALS      20 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12859-017-1496-0 schema:about N11a2ac37826d48be845837c0a427b0f1
2 N4440fb079bf745b68657962bd0dd1a7c
3 N57af15b6ca7e4856849ac24dc52b327b
4 N64e075559bdf40b693555223d83b954c
5 N65bf430ce59b439dbc4ae91e20454585
6 N712b352f064d46bdb570a2cec35a6188
7 N7415a79a7e8b48b3b2270a73c9503899
8 N7ca3cda43a524f17a10c4dfcfb1ddb73
9 N7d78c418ee794f77b05c7972eb72951c
10 Na646ba77ae6c4ddc9ec5a98b333d910a
11 Nad3a4d49b3004d8f8fea8763f5eaea04
12 Nbbd7d25df97a41f99e5b76cb85fc8185
13 Nc4ba4dc38a9b47589e81896685772745
14 anzsrc-for:06
15 anzsrc-for:0604
16 schema:author N17d6d670b4c546d1ba286c2338fcb709
17 schema:citation sg:pub.10.1186/1471-2164-11-724
18 sg:pub.10.1186/1471-2164-15-81
19 sg:pub.10.1186/1471-2164-16-s2-s7
20 sg:pub.10.1186/1471-2164-9-516
21 sg:pub.10.1186/s13073-014-0122-2
22 sg:pub.10.1186/s13073-015-0145-3
23 schema:datePublished 2017-02-06
24 schema:datePublishedReg 2017-02-06
25 schema:description BackgroundRecently, several tools have been designed for human leukocyte antigen (HLA) typing using single nucleotide polymorphism (SNP) array and next-generation sequencing (NGS) data. These tools provide high-throughput and cost-effective approaches for identifying HLA types. Therefore, tools for downstream association analysis are highly desirable. Although several tools have been designed for multi-allelic marker association analysis, they were designed only for microsatellite markers and do not scale well with increasing data volumes, or they were designed for large-scale data but provided a limited number of tests.ResultsWe have developed a Python package called PyHLA, which implements several methods for HLA association analysis, to fill the gap. PyHLA is a tailor-made, easy to use, and flexible tool designed specifically for the association analysis of the HLA types imputed from genome-wide genotyping and NGS data. PyHLA provides functions for association analysis, zygosity tests, and interaction tests between HLA alleles and diseases. Monte Carlo permutation and several methods for multiple testing corrections have also been implemented.ConclusionsPyHLA provides a convenient and powerful tool for HLA analysis. Existing methods have been integrated and desired methods have been added in PyHLA. Furthermore, PyHLA is applicable to small and large sample sizes and can finish the analysis in a timely manner on a personal computer with different platforms. PyHLA is implemented in Python. PyHLA is a free, open source software distributed under the GPLv2 license. The source code, tutorial, and examples are available at https://github.com/felixfan/PyHLA.
26 schema:genre article
27 schema:isAccessibleForFree true
28 schema:isPartOf Nd524318eb22b4daf927841ca9c0f04a5
29 Nf7ad0a20cc05491cbecf5a4001e20e7e
30 sg:journal.1023786
31 schema:keywords GPLv2 license
32 HLA
33 HLA alleles
34 HLA analysis
35 HLA types
36 Monte Carlo permutations
37 NGS data
38 Python
39 Python package
40 ResultsWe
41 alleles
42 analysis
43 antigen typing
44 approach
45 array
46 association
47 association analysis
48 code
49 computer
50 correction
51 cost-effective approach
52 data
53 data volume
54 different platforms
55 disease
56 downstream association analysis
57 example
58 existing methods
59 flexible tool
60 function
61 gap
62 genome-wide genotyping
63 genotyping
64 human leukocyte antigen (HLA) typing
65 interaction test
66 large-scale data
67 larger sample size
68 license
69 limited number
70 manner
71 marker association analysis
72 markers
73 method
74 microsatellite markers
75 multiple testing correction
76 next-generation sequencing data
77 nucleotide polymorphism array
78 number
79 open source software
80 package
81 permutations
82 personal computer
83 platform
84 polymorphism array
85 powerful tool
86 sample size
87 sequencing data
88 single nucleotide polymorphism array
89 size
90 software
91 source code
92 source software
93 test
94 timely manner
95 tool
96 tutorial
97 types
98 typing
99 volume
100 zygosity tests
101 schema:name PyHLA: tests for the association between HLA alleles and diseases
102 schema:pagination 90
103 schema:productId N13053bab738d4bac8e9ff14d79b89d67
104 N9053ffa219c542cea13211dff36a3770
105 Nff976c9841194a5db85598383e4022a1
106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083687269
107 https://doi.org/10.1186/s12859-017-1496-0
108 schema:sdDatePublished 2022-08-04T17:04
109 schema:sdLicense https://scigraph.springernature.com/explorer/license/
110 schema:sdPublisher N972127791ed246999b2f371023b2b875
111 schema:url https://doi.org/10.1186/s12859-017-1496-0
112 sgo:license sg:explorer/license/
113 sgo:sdDataset articles
114 rdf:type schema:ScholarlyArticle
115 N11a2ac37826d48be845837c0a427b0f1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
116 schema:name Genotyping Techniques
117 rdf:type schema:DefinedTerm
118 N13053bab738d4bac8e9ff14d79b89d67 schema:name doi
119 schema:value 10.1186/s12859-017-1496-0
120 rdf:type schema:PropertyValue
121 N17d6d670b4c546d1ba286c2338fcb709 rdf:first sg:person.01056350153.28
122 rdf:rest Neb7e50c6c06642db9275849e4b862474
123 N4440fb079bf745b68657962bd0dd1a7c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name HLA Antigens
125 rdf:type schema:DefinedTerm
126 N57af15b6ca7e4856849ac24dc52b327b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name High-Throughput Nucleotide Sequencing
128 rdf:type schema:DefinedTerm
129 N64e075559bdf40b693555223d83b954c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Software
131 rdf:type schema:DefinedTerm
132 N65bf430ce59b439dbc4ae91e20454585 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
133 schema:name Polymorphism, Single Nucleotide
134 rdf:type schema:DefinedTerm
135 N712b352f064d46bdb570a2cec35a6188 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Models, Theoretical
137 rdf:type schema:DefinedTerm
138 N7415a79a7e8b48b3b2270a73c9503899 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
139 schema:name Alleles
140 rdf:type schema:DefinedTerm
141 N7ca3cda43a524f17a10c4dfcfb1ddb73 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
142 schema:name Logistic Models
143 rdf:type schema:DefinedTerm
144 N7d78c418ee794f77b05c7972eb72951c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
145 schema:name Humans
146 rdf:type schema:DefinedTerm
147 N9053ffa219c542cea13211dff36a3770 schema:name pubmed_id
148 schema:value 28166716
149 rdf:type schema:PropertyValue
150 N972127791ed246999b2f371023b2b875 schema:name Springer Nature - SN SciGraph project
151 rdf:type schema:Organization
152 Na646ba77ae6c4ddc9ec5a98b333d910a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
153 schema:name Computational Biology
154 rdf:type schema:DefinedTerm
155 Nad3a4d49b3004d8f8fea8763f5eaea04 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
156 schema:name Genome, Human
157 rdf:type schema:DefinedTerm
158 Nbbd7d25df97a41f99e5b76cb85fc8185 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
159 schema:name Microsatellite Repeats
160 rdf:type schema:DefinedTerm
161 Nc4ba4dc38a9b47589e81896685772745 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Linear Models
163 rdf:type schema:DefinedTerm
164 Nd524318eb22b4daf927841ca9c0f04a5 schema:issueNumber 1
165 rdf:type schema:PublicationIssue
166 Neb7e50c6c06642db9275849e4b862474 rdf:first sg:person.01043333627.37
167 rdf:rest rdf:nil
168 Nf7ad0a20cc05491cbecf5a4001e20e7e schema:volumeNumber 18
169 rdf:type schema:PublicationVolume
170 Nff976c9841194a5db85598383e4022a1 schema:name dimensions_id
171 schema:value pub.1083687269
172 rdf:type schema:PropertyValue
173 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
174 schema:name Biological Sciences
175 rdf:type schema:DefinedTerm
176 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
177 schema:name Genetics
178 rdf:type schema:DefinedTerm
179 sg:grant.7186252 http://pending.schema.org/fundedItem sg:pub.10.1186/s12859-017-1496-0
180 rdf:type schema:MonetaryGrant
181 sg:grant.7430435 http://pending.schema.org/fundedItem sg:pub.10.1186/s12859-017-1496-0
182 rdf:type schema:MonetaryGrant
183 sg:journal.1023786 schema:issn 1471-2105
184 schema:name BMC Bioinformatics
185 schema:publisher Springer Nature
186 rdf:type schema:Periodical
187 sg:person.01043333627.37 schema:affiliation grid-institutes:grid.194645.b
188 schema:familyName Song
189 schema:givenName You-Qiang
190 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01043333627.37
191 rdf:type schema:Person
192 sg:person.01056350153.28 schema:affiliation grid-institutes:None
193 schema:familyName Fan
194 schema:givenName Yanhui
195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01056350153.28
196 rdf:type schema:Person
197 sg:pub.10.1186/1471-2164-11-724 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005310527
198 https://doi.org/10.1186/1471-2164-11-724
199 rdf:type schema:CreativeWork
200 sg:pub.10.1186/1471-2164-15-81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025010426
201 https://doi.org/10.1186/1471-2164-15-81
202 rdf:type schema:CreativeWork
203 sg:pub.10.1186/1471-2164-16-s2-s7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024548741
204 https://doi.org/10.1186/1471-2164-16-s2-s7
205 rdf:type schema:CreativeWork
206 sg:pub.10.1186/1471-2164-9-516 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042702003
207 https://doi.org/10.1186/1471-2164-9-516
208 rdf:type schema:CreativeWork
209 sg:pub.10.1186/s13073-014-0122-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033649267
210 https://doi.org/10.1186/s13073-014-0122-2
211 rdf:type schema:CreativeWork
212 sg:pub.10.1186/s13073-015-0145-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008310987
213 https://doi.org/10.1186/s13073-015-0145-3
214 rdf:type schema:CreativeWork
215 grid-institutes:None schema:alternateName Department of Cancer Genomics, LemonData Biotech (Shenzhen) Ltd., Shenzhen, China
216 schema:name Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong, Hong Kong
217 Department of Cancer Genomics, LemonData Biotech (Shenzhen) Ltd., Shenzhen, China
218 School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, Hong Kong
219 rdf:type schema:Organization
220 grid-institutes:grid.194645.b schema:alternateName Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong, Hong Kong
221 schema:name Centre for Genomic Sciences, The University of Hong Kong, 5 Sassoon Road, Pokfulam, Hong Kong, Hong Kong
222 School of Biomedical Sciences, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, Hong Kong
223 rdf:type schema:Organization
 




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


...