Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-03-27

AUTHORS

Tobias Kind, Oliver Fiehn

ABSTRACT

BackgroundStructure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas.ResultsAn algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries.ConclusionThe seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website. More... »

PAGES

105

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-8-105

DOI

http://dx.doi.org/10.1186/1471-2105-8-105

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1022661426

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17389044


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/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "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"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Algorithms", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Biopolymers", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Mass Spectrometry", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Chemical", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Organic Chemicals", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of California Davis, Genome Center, 451 E. Health Sci. Dr., 95616, Davis, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.27860.3b", 
          "name": [
            "University of California Davis, Genome Center, 451 E. Health Sci. Dr., 95616, Davis, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kind", 
        "givenName": "Tobias", 
        "id": "sg:person.0604176630.24", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0604176630.24"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California Davis, Genome Center, 451 E. Health Sci. Dr., 95616, Davis, CA, USA", 
          "id": "http://www.grid.ac/institutes/grid.27860.3b", 
          "name": [
            "University of California Davis, Genome Center, 451 E. Health Sci. Dr., 95616, Davis, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fiehn", 
        "givenName": "Oliver", 
        "id": "sg:person.0615142477.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0615142477.79"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1016/s1044-0305(99)00047-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042808879", 
          "https://doi.org/10.1016/s1044-0305(99)00047-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-6-180", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003217589", 
          "https://doi.org/10.1186/1471-2105-6-180"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-234", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045172077", 
          "https://doi.org/10.1186/1471-2105-7-234"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01569759", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045792473", 
          "https://doi.org/10.1007/bf01569759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1016/s1044-0305(99)00089-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038779198", 
          "https://doi.org/10.1016/s1044-0305(99)00089-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1016/j.jasms.2005.12.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019417430", 
          "https://doi.org/10.1016/j.jasms.2005.12.001"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1016/j.jasms.2004.10.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020784349", 
          "https://doi.org/10.1016/j.jasms.2004.10.001"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/2001202a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017184614", 
          "https://doi.org/10.1038/2001202a0"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007-03-27", 
    "datePublishedReg": "2007-03-27", 
    "description": "BackgroundStructure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas.ResultsAn algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80\u201399% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries.ConclusionThe seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65\u201381%. Corresponding software and supplemental data are available for downloads from the authors' website.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/1471-2105-8-105", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2503754", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2439931", 
        "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": "8"
      }
    ], 
    "keywords": [
      "molecular formula", 
      "mass spectrometry", 
      "Fourier transform ion cyclotron resonance mass spectrometry", 
      "transform ion cyclotron resonance mass spectrometry", 
      "ion cyclotron resonance mass spectrometry", 
      "cyclotron resonance mass spectrometry", 
      "resonance mass spectrometry", 
      "elemental composition", 
      "accurate mass spectrometry", 
      "correct molecular formula", 
      "ppm mass accuracy", 
      "hydrogen/carbon ratio", 
      "unknown small molecules", 
      "isotopic patterns", 
      "public chemical databases", 
      "correct elemental composition", 
      "flight mass spectrometry", 
      "probable elemental compositions", 
      "mass accuracy", 
      "chemical rules", 
      "trimethylsilylated compound", 
      "possible candidate structures", 
      "exemplary compounds", 
      "chemical databases", 
      "compound databases", 
      "small molecules", 
      "unique compounds", 
      "spectrometry", 
      "novel compounds", 
      "compounds", 
      "carbon ratio", 
      "natural compounds", 
      "candidate structures", 
      "top hits", 
      "heuristic filtering", 
      "molecules", 
      "sulfur", 
      "pharmaceuticals", 
      "P formula", 
      "phosphors", 
      "composition", 
      "oxygen", 
      "carbon", 
      "elucidation", 
      "hits", 
      "Lewis", 
      "nitrogen", 
      "presence", 
      "first crucial step", 
      "crucial step", 
      "DrugBank", 
      "structure", 
      "ratio", 
      "element ratios", 
      "gas", 
      "correct formula", 
      "DA", 
      "step", 
      "formula", 
      "instrumentation", 
      "ratio deviation", 
      "database entries", 
      "golden rule", 
      "advances", 
      "deviation", 
      "TSCA", 
      "resolution", 
      "experiments", 
      "elements", 
      "order", 
      "number of elements", 
      "challenges", 
      "number", 
      "automatic exclusion", 
      "thousands", 
      "data acquisition", 
      "patterns", 
      "data", 
      "rules", 
      "Supplemental data", 
      "cases", 
      "complement", 
      "exclusion", 
      "accuracy", 
      "low number", 
      "entry", 
      "probability", 
      "restriction", 
      "database", 
      "consistency", 
      "complete resolution", 
      "software", 
      "heuristic rules", 
      "author's website", 
      "acquisition", 
      "download", 
      "database queries", 
      "filtering", 
      "algorithm", 
      "websites", 
      "queries"
    ], 
    "name": "Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry", 
    "pagination": "105", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1022661426"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-8-105"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "17389044"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-8-105", 
      "https://app.dimensions.ai/details/publication/pub.1022661426"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-08-04T16:56", 
    "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_442.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/1471-2105-8-105"
  }
]
 

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.10.1186/1471-2105-8-105'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-105'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-105'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-8-105'


 

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

232 TRIPLES      21 PREDICATES      141 URIs      124 LITERALS      13 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-8-105 schema:about N04c8e4089e4749a9940377ae7a925ef3
2 N1f17d389c9274a0e89e22cb5ff68c87d
3 N30c4e325c90a4eee8314253fcdd4ea88
4 N52ec07a1938d4a83b6b028bb004cbf49
5 N655774351d5749d997dae76f229ed684
6 Nc3e392ed325e4a4fb79200c4cd499295
7 anzsrc-for:01
8 anzsrc-for:06
9 anzsrc-for:08
10 schema:author N912225d7dd5740138d048ea64742fb2e
11 schema:citation sg:pub.10.1007/bf01569759
12 sg:pub.10.1016/j.jasms.2004.10.001
13 sg:pub.10.1016/j.jasms.2005.12.001
14 sg:pub.10.1016/s1044-0305(99)00047-1
15 sg:pub.10.1016/s1044-0305(99)00089-6
16 sg:pub.10.1038/2001202a0
17 sg:pub.10.1186/1471-2105-6-180
18 sg:pub.10.1186/1471-2105-7-234
19 schema:datePublished 2007-03-27
20 schema:datePublishedReg 2007-03-27
21 schema:description BackgroundStructure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas.ResultsAn algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries.ConclusionThe seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website.
22 schema:genre article
23 schema:isAccessibleForFree true
24 schema:isPartOf N4c09ace131d7412187f327260c8a14c2
25 N69019ec8e60e43cf8844fc0633405d81
26 sg:journal.1023786
27 schema:keywords DA
28 DrugBank
29 Fourier transform ion cyclotron resonance mass spectrometry
30 Lewis
31 P formula
32 Supplemental data
33 TSCA
34 accuracy
35 accurate mass spectrometry
36 acquisition
37 advances
38 algorithm
39 author's website
40 automatic exclusion
41 candidate structures
42 carbon
43 carbon ratio
44 cases
45 challenges
46 chemical databases
47 chemical rules
48 complement
49 complete resolution
50 composition
51 compound databases
52 compounds
53 consistency
54 correct elemental composition
55 correct formula
56 correct molecular formula
57 crucial step
58 cyclotron resonance mass spectrometry
59 data
60 data acquisition
61 database
62 database entries
63 database queries
64 deviation
65 download
66 element ratios
67 elemental composition
68 elements
69 elucidation
70 entry
71 exclusion
72 exemplary compounds
73 experiments
74 filtering
75 first crucial step
76 flight mass spectrometry
77 formula
78 gas
79 golden rule
80 heuristic filtering
81 heuristic rules
82 hits
83 hydrogen/carbon ratio
84 instrumentation
85 ion cyclotron resonance mass spectrometry
86 isotopic patterns
87 low number
88 mass accuracy
89 mass spectrometry
90 molecular formula
91 molecules
92 natural compounds
93 nitrogen
94 novel compounds
95 number
96 number of elements
97 order
98 oxygen
99 patterns
100 pharmaceuticals
101 phosphors
102 possible candidate structures
103 ppm mass accuracy
104 presence
105 probability
106 probable elemental compositions
107 public chemical databases
108 queries
109 ratio
110 ratio deviation
111 resolution
112 resonance mass spectrometry
113 restriction
114 rules
115 small molecules
116 software
117 spectrometry
118 step
119 structure
120 sulfur
121 thousands
122 top hits
123 transform ion cyclotron resonance mass spectrometry
124 trimethylsilylated compound
125 unique compounds
126 unknown small molecules
127 websites
128 schema:name Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry
129 schema:pagination 105
130 schema:productId N3d078cc96d364223a4e71560d13c3a2e
131 N61f0587850ed4f43a95cc5104e7a0ef9
132 N6abeaac18efb43a1b37ad912c56542bf
133 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022661426
134 https://doi.org/10.1186/1471-2105-8-105
135 schema:sdDatePublished 2022-08-04T16:56
136 schema:sdLicense https://scigraph.springernature.com/explorer/license/
137 schema:sdPublisher N377b4083102640edac9493d672d1a095
138 schema:url https://doi.org/10.1186/1471-2105-8-105
139 sgo:license sg:explorer/license/
140 sgo:sdDataset articles
141 rdf:type schema:ScholarlyArticle
142 N04c8e4089e4749a9940377ae7a925ef3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Algorithms
144 rdf:type schema:DefinedTerm
145 N18061dce617e41daa5a49a7b79d8f9c2 rdf:first sg:person.0615142477.79
146 rdf:rest rdf:nil
147 N1f17d389c9274a0e89e22cb5ff68c87d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
148 schema:name Organic Chemicals
149 rdf:type schema:DefinedTerm
150 N30c4e325c90a4eee8314253fcdd4ea88 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
151 schema:name Models, Chemical
152 rdf:type schema:DefinedTerm
153 N377b4083102640edac9493d672d1a095 schema:name Springer Nature - SN SciGraph project
154 rdf:type schema:Organization
155 N3d078cc96d364223a4e71560d13c3a2e schema:name doi
156 schema:value 10.1186/1471-2105-8-105
157 rdf:type schema:PropertyValue
158 N4c09ace131d7412187f327260c8a14c2 schema:volumeNumber 8
159 rdf:type schema:PublicationVolume
160 N52ec07a1938d4a83b6b028bb004cbf49 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
161 schema:name Computer Simulation
162 rdf:type schema:DefinedTerm
163 N61f0587850ed4f43a95cc5104e7a0ef9 schema:name dimensions_id
164 schema:value pub.1022661426
165 rdf:type schema:PropertyValue
166 N655774351d5749d997dae76f229ed684 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Biopolymers
168 rdf:type schema:DefinedTerm
169 N69019ec8e60e43cf8844fc0633405d81 schema:issueNumber 1
170 rdf:type schema:PublicationIssue
171 N6abeaac18efb43a1b37ad912c56542bf schema:name pubmed_id
172 schema:value 17389044
173 rdf:type schema:PropertyValue
174 N912225d7dd5740138d048ea64742fb2e rdf:first sg:person.0604176630.24
175 rdf:rest N18061dce617e41daa5a49a7b79d8f9c2
176 Nc3e392ed325e4a4fb79200c4cd499295 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
177 schema:name Mass Spectrometry
178 rdf:type schema:DefinedTerm
179 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
180 schema:name Mathematical Sciences
181 rdf:type schema:DefinedTerm
182 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
183 schema:name Biological Sciences
184 rdf:type schema:DefinedTerm
185 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
186 schema:name Information and Computing Sciences
187 rdf:type schema:DefinedTerm
188 sg:grant.2439931 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-8-105
189 rdf:type schema:MonetaryGrant
190 sg:grant.2503754 http://pending.schema.org/fundedItem sg:pub.10.1186/1471-2105-8-105
191 rdf:type schema:MonetaryGrant
192 sg:journal.1023786 schema:issn 1471-2105
193 schema:name BMC Bioinformatics
194 schema:publisher Springer Nature
195 rdf:type schema:Periodical
196 sg:person.0604176630.24 schema:affiliation grid-institutes:grid.27860.3b
197 schema:familyName Kind
198 schema:givenName Tobias
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0604176630.24
200 rdf:type schema:Person
201 sg:person.0615142477.79 schema:affiliation grid-institutes:grid.27860.3b
202 schema:familyName Fiehn
203 schema:givenName Oliver
204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0615142477.79
205 rdf:type schema:Person
206 sg:pub.10.1007/bf01569759 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045792473
207 https://doi.org/10.1007/bf01569759
208 rdf:type schema:CreativeWork
209 sg:pub.10.1016/j.jasms.2004.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020784349
210 https://doi.org/10.1016/j.jasms.2004.10.001
211 rdf:type schema:CreativeWork
212 sg:pub.10.1016/j.jasms.2005.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019417430
213 https://doi.org/10.1016/j.jasms.2005.12.001
214 rdf:type schema:CreativeWork
215 sg:pub.10.1016/s1044-0305(99)00047-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042808879
216 https://doi.org/10.1016/s1044-0305(99)00047-1
217 rdf:type schema:CreativeWork
218 sg:pub.10.1016/s1044-0305(99)00089-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038779198
219 https://doi.org/10.1016/s1044-0305(99)00089-6
220 rdf:type schema:CreativeWork
221 sg:pub.10.1038/2001202a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017184614
222 https://doi.org/10.1038/2001202a0
223 rdf:type schema:CreativeWork
224 sg:pub.10.1186/1471-2105-6-180 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003217589
225 https://doi.org/10.1186/1471-2105-6-180
226 rdf:type schema:CreativeWork
227 sg:pub.10.1186/1471-2105-7-234 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045172077
228 https://doi.org/10.1186/1471-2105-7-234
229 rdf:type schema:CreativeWork
230 grid-institutes:grid.27860.3b schema:alternateName University of California Davis, Genome Center, 451 E. Health Sci. Dr., 95616, Davis, CA, USA
231 schema:name University of California Davis, Genome Center, 451 E. Health Sci. Dr., 95616, Davis, CA, USA
232 rdf:type schema:Organization
 




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


...