Sigma-2: Multiple sequence alignment of non-coding DNA via an evolutionary model View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-09-16

AUTHORS

Gayathri Jayaraman, Rahul Siddharthan

ABSTRACT

BACKGROUND: While most multiple sequence alignment programs expect that all or most of their input is known to be homologous, and penalise insertions and deletions, this is not a reasonable assumption for non-coding DNA, which is much less strongly conserved than protein-coding genes. Arguing that the goal of sequence alignment should be the detection of homology and not similarity, we incorporate an evolutionary model into a previously published multiple sequence alignment program for non-coding DNA, Sigma, as a sensitive likelihood-based way to assess the significance of alignments. Version 1 of Sigma was successful in eliminating spurious alignments but exhibited relatively poor sensitivity on synthetic data. Sigma 1 used a p-value (the probability under the "null hypothesis" of non-homology) to assess the significance of alignments, and, optionally, a background model that captured short-range genomic correlations. Sigma version 2, described here, retains these features, but calculates the p-value using a sophisticated evolutionary model that we describe here, and also allows for a transition matrix for different substitution rates from and to different nucleotides. Our evolutionary model takes separate account of mutation and fixation, and can be extended to allow for locally differing functional constraints on sequence. RESULTS: We demonstrate that, on real and synthetic data, Sigma-2 significantly outperforms other programs in specificity to genuine homology (that is, it minimises alignment of spuriously similar regions that do not have a common ancestry) while it is now as sensitive as the best current programs. CONCLUSIONS: Comparing these results with an extrapolation of the best results from other available programs, we suggest that conservation rates in intergenic DNA are often significantly over-estimated. It is increasingly important to align non-coding DNA correctly, in regulatory genomics and in the context of whole-genome alignment, and Sigma-2 is an important step in that direction. More... »

PAGES

464-464

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-11-464

DOI

http://dx.doi.org/10.1186/1471-2105-11-464

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029723621

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20846408


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": "DNA, Intergenic", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Evolution, Molecular", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Genomics", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Likelihood Functions", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Alignment", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Sequence Analysis, DNA", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Software", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India", 
          "id": "http://www.grid.ac/institutes/grid.462414.1", 
          "name": [
            "The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jayaraman", 
        "givenName": "Gayathri", 
        "id": "sg:person.01336147564.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01336147564.03"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India", 
          "id": "http://www.grid.ac/institutes/grid.462414.1", 
          "name": [
            "The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Siddharthan", 
        "givenName": "Rahul", 
        "id": "sg:person.0614124227.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614124227.85"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1471-2105-6-298", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047546418", 
          "https://doi.org/10.1186/1471-2105-6-298"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02193625", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049370355", 
          "https://doi.org/10.1007/bf02193625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01734359", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044065382", 
          "https://doi.org/10.1007/bf01734359"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-7-143", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014283709", 
          "https://doi.org/10.1186/1471-2105-7-143"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00163848", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027539169", 
          "https://doi.org/10.1007/bf00163848"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02101694", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039008589", 
          "https://doi.org/10.1007/bf02101694"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature04979", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004695015", 
          "https://doi.org/10.1038/nature04979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/356168a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003127095", 
          "https://doi.org/10.1038/356168a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1748-7188-3-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027480499", 
          "https://doi.org/10.1186/1748-7188-3-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2105-11-54", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003163452", 
          "https://doi.org/10.1186/1471-2105-11-54"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01731581", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023239976", 
          "https://doi.org/10.1007/bf01731581"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2010-09-16", 
    "datePublishedReg": "2010-09-16", 
    "description": "BACKGROUND: While most multiple sequence alignment programs expect that all or most of their input is known to be homologous, and penalise insertions and deletions, this is not a reasonable assumption for non-coding DNA, which is much less strongly conserved than protein-coding genes. Arguing that the goal of sequence alignment should be the detection of homology and not similarity, we incorporate an evolutionary model into a previously published multiple sequence alignment program for non-coding DNA, Sigma, as a sensitive likelihood-based way to assess the significance of alignments. Version 1 of Sigma was successful in eliminating spurious alignments but exhibited relatively poor sensitivity on synthetic data. Sigma 1 used a p-value (the probability under the \"null hypothesis\" of non-homology) to assess the significance of alignments, and, optionally, a background model that captured short-range genomic correlations. Sigma version 2, described here, retains these features, but calculates the p-value using a sophisticated evolutionary model that we describe here, and also allows for a transition matrix for different substitution rates from and to different nucleotides. Our evolutionary model takes separate account of mutation and fixation, and can be extended to allow for locally differing functional constraints on sequence.\nRESULTS: We demonstrate that, on real and synthetic data, Sigma-2 significantly outperforms other programs in specificity to genuine homology (that is, it minimises alignment of spuriously similar regions that do not have a common ancestry) while it is now as sensitive as the best current programs.\nCONCLUSIONS: Comparing these results with an extrapolation of the best results from other available programs, we suggest that conservation rates in intergenic DNA are often significantly over-estimated. It is increasingly important to align non-coding DNA correctly, in regulatory genomics and in the context of whole-genome alignment, and Sigma-2 is an important step in that direction.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/1471-2105-11-464", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1023786", 
        "issn": [
          "1471-2105"
        ], 
        "name": "BMC Bioinformatics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "11"
      }
    ], 
    "keywords": [
      "non-coding DNA", 
      "significance of alignments", 
      "sequence alignment", 
      "protein-coding genes", 
      "sophisticated evolutionary models", 
      "evolutionary models", 
      "detection of homology", 
      "whole-genome alignments", 
      "multiple sequence alignment", 
      "intergenic DNA", 
      "regulatory genomics", 
      "genomic correlations", 
      "alignment programs", 
      "sequence alignment programs", 
      "substitution rates", 
      "functional constraints", 
      "different substitution rates", 
      "multiple sequence alignment programs", 
      "different nucleotides", 
      "DNA", 
      "homology", 
      "sigma 2", 
      "spurious alignments", 
      "sigma 1", 
      "genomics", 
      "genuine homology", 
      "genes", 
      "nucleotides", 
      "deletion", 
      "mutations", 
      "sequence", 
      "conservation rate", 
      "important step", 
      "alignment", 
      "similarity", 
      "specificity", 
      "insertion", 
      "significance", 
      "available programs", 
      "fixation", 
      "sigma", 
      "step", 
      "data", 
      "results", 
      "rate", 
      "program", 
      "model", 
      "matrix", 
      "sensitivity", 
      "features", 
      "correlation", 
      "detection", 
      "context", 
      "extrapolation", 
      "values", 
      "input", 
      "way", 
      "constraints", 
      "goal", 
      "version 1", 
      "direction", 
      "transition matrix", 
      "assumption", 
      "version 2", 
      "separate accounts", 
      "poor sensitivity", 
      "account", 
      "current programs", 
      "reasonable assumptions", 
      "synthetic data", 
      "better results", 
      "background model", 
      "most multiple sequence alignment programs", 
      "sensitive likelihood-based way", 
      "likelihood-based way", 
      "short-range genomic correlations", 
      "Sigma version 2", 
      "best current programs"
    ], 
    "name": "Sigma-2: Multiple sequence alignment of non-coding DNA via an evolutionary model", 
    "pagination": "464-464", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1029723621"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/1471-2105-11-464"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "20846408"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/1471-2105-11-464", 
      "https://app.dimensions.ai/details/publication/pub.1029723621"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-12-01T19:23", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/article/article_511.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/1471-2105-11-464"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-11-464'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-11-464'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-11-464'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-11-464'


 

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

218 TRIPLES      22 PREDICATES      122 URIs      103 LITERALS      14 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/1471-2105-11-464 schema:about N44ed50c9ac864ecd96515d6cd7d403f4
2 N5cdd779b786f4cd9bfe3b1631860e9ec
3 N7138e9c0e3bc4d0ea0ae8cdaaf6a385e
4 N78204b9360864990ae9fdc6307c008b7
5 Nb22b435308ad4e85809b10858818ed0c
6 Nd422833cd9e64a8b9920b984c2b51208
7 Ndc8d4d2831f148c0bbedb40d46f6c23d
8 anzsrc-for:06
9 anzsrc-for:0604
10 schema:author Nc074ce7e8574431d8d8234d5038de1c5
11 schema:citation sg:pub.10.1007/bf00163848
12 sg:pub.10.1007/bf01731581
13 sg:pub.10.1007/bf01734359
14 sg:pub.10.1007/bf02101694
15 sg:pub.10.1007/bf02193625
16 sg:pub.10.1038/356168a0
17 sg:pub.10.1038/nature04979
18 sg:pub.10.1186/1471-2105-11-54
19 sg:pub.10.1186/1471-2105-6-298
20 sg:pub.10.1186/1471-2105-7-143
21 sg:pub.10.1186/1748-7188-3-6
22 schema:datePublished 2010-09-16
23 schema:datePublishedReg 2010-09-16
24 schema:description BACKGROUND: While most multiple sequence alignment programs expect that all or most of their input is known to be homologous, and penalise insertions and deletions, this is not a reasonable assumption for non-coding DNA, which is much less strongly conserved than protein-coding genes. Arguing that the goal of sequence alignment should be the detection of homology and not similarity, we incorporate an evolutionary model into a previously published multiple sequence alignment program for non-coding DNA, Sigma, as a sensitive likelihood-based way to assess the significance of alignments. Version 1 of Sigma was successful in eliminating spurious alignments but exhibited relatively poor sensitivity on synthetic data. Sigma 1 used a p-value (the probability under the "null hypothesis" of non-homology) to assess the significance of alignments, and, optionally, a background model that captured short-range genomic correlations. Sigma version 2, described here, retains these features, but calculates the p-value using a sophisticated evolutionary model that we describe here, and also allows for a transition matrix for different substitution rates from and to different nucleotides. Our evolutionary model takes separate account of mutation and fixation, and can be extended to allow for locally differing functional constraints on sequence. RESULTS: We demonstrate that, on real and synthetic data, Sigma-2 significantly outperforms other programs in specificity to genuine homology (that is, it minimises alignment of spuriously similar regions that do not have a common ancestry) while it is now as sensitive as the best current programs. CONCLUSIONS: Comparing these results with an extrapolation of the best results from other available programs, we suggest that conservation rates in intergenic DNA are often significantly over-estimated. It is increasingly important to align non-coding DNA correctly, in regulatory genomics and in the context of whole-genome alignment, and Sigma-2 is an important step in that direction.
25 schema:genre article
26 schema:inLanguage en
27 schema:isAccessibleForFree true
28 schema:isPartOf N3c19b90499094187a8cdec52d99e9e3a
29 Nb123847e5d1a4f9f818cbf0855f29938
30 sg:journal.1023786
31 schema:keywords DNA
32 Sigma version 2
33 account
34 alignment
35 alignment programs
36 assumption
37 available programs
38 background model
39 best current programs
40 better results
41 conservation rate
42 constraints
43 context
44 correlation
45 current programs
46 data
47 deletion
48 detection
49 detection of homology
50 different nucleotides
51 different substitution rates
52 direction
53 evolutionary models
54 extrapolation
55 features
56 fixation
57 functional constraints
58 genes
59 genomic correlations
60 genomics
61 genuine homology
62 goal
63 homology
64 important step
65 input
66 insertion
67 intergenic DNA
68 likelihood-based way
69 matrix
70 model
71 most multiple sequence alignment programs
72 multiple sequence alignment
73 multiple sequence alignment programs
74 mutations
75 non-coding DNA
76 nucleotides
77 poor sensitivity
78 program
79 protein-coding genes
80 rate
81 reasonable assumptions
82 regulatory genomics
83 results
84 sensitive likelihood-based way
85 sensitivity
86 separate accounts
87 sequence
88 sequence alignment
89 sequence alignment programs
90 short-range genomic correlations
91 sigma
92 sigma 1
93 sigma 2
94 significance
95 significance of alignments
96 similarity
97 sophisticated evolutionary models
98 specificity
99 spurious alignments
100 step
101 substitution rates
102 synthetic data
103 transition matrix
104 values
105 version 1
106 version 2
107 way
108 whole-genome alignments
109 schema:name Sigma-2: Multiple sequence alignment of non-coding DNA via an evolutionary model
110 schema:pagination 464-464
111 schema:productId N5a5ab45596dc48be94491a05459e64de
112 N5ab84dbbfa6e4254b462f98a8be01ece
113 Nb245fe4a8d4940d2bb9e64b6ec365ead
114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029723621
115 https://doi.org/10.1186/1471-2105-11-464
116 schema:sdDatePublished 2021-12-01T19:23
117 schema:sdLicense https://scigraph.springernature.com/explorer/license/
118 schema:sdPublisher N2d2b269176bb465daee6a5ee5666b746
119 schema:url https://doi.org/10.1186/1471-2105-11-464
120 sgo:license sg:explorer/license/
121 sgo:sdDataset articles
122 rdf:type schema:ScholarlyArticle
123 N2d2b269176bb465daee6a5ee5666b746 schema:name Springer Nature - SN SciGraph project
124 rdf:type schema:Organization
125 N3c19b90499094187a8cdec52d99e9e3a schema:issueNumber 1
126 rdf:type schema:PublicationIssue
127 N44ed50c9ac864ecd96515d6cd7d403f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Genomics
129 rdf:type schema:DefinedTerm
130 N5a5ab45596dc48be94491a05459e64de schema:name dimensions_id
131 schema:value pub.1029723621
132 rdf:type schema:PropertyValue
133 N5ab84dbbfa6e4254b462f98a8be01ece schema:name pubmed_id
134 schema:value 20846408
135 rdf:type schema:PropertyValue
136 N5cdd779b786f4cd9bfe3b1631860e9ec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Likelihood Functions
138 rdf:type schema:DefinedTerm
139 N7138e9c0e3bc4d0ea0ae8cdaaf6a385e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
140 schema:name Software
141 rdf:type schema:DefinedTerm
142 N78204b9360864990ae9fdc6307c008b7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Evolution, Molecular
144 rdf:type schema:DefinedTerm
145 N95ba81ed0e0b44a8843ee29d51613280 rdf:first sg:person.0614124227.85
146 rdf:rest rdf:nil
147 Nb123847e5d1a4f9f818cbf0855f29938 schema:volumeNumber 11
148 rdf:type schema:PublicationVolume
149 Nb22b435308ad4e85809b10858818ed0c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
150 schema:name Sequence Analysis, DNA
151 rdf:type schema:DefinedTerm
152 Nb245fe4a8d4940d2bb9e64b6ec365ead schema:name doi
153 schema:value 10.1186/1471-2105-11-464
154 rdf:type schema:PropertyValue
155 Nc074ce7e8574431d8d8234d5038de1c5 rdf:first sg:person.01336147564.03
156 rdf:rest N95ba81ed0e0b44a8843ee29d51613280
157 Nd422833cd9e64a8b9920b984c2b51208 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
158 schema:name Sequence Alignment
159 rdf:type schema:DefinedTerm
160 Ndc8d4d2831f148c0bbedb40d46f6c23d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
161 schema:name DNA, Intergenic
162 rdf:type schema:DefinedTerm
163 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
164 schema:name Biological Sciences
165 rdf:type schema:DefinedTerm
166 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
167 schema:name Genetics
168 rdf:type schema:DefinedTerm
169 sg:journal.1023786 schema:issn 1471-2105
170 schema:name BMC Bioinformatics
171 schema:publisher Springer Nature
172 rdf:type schema:Periodical
173 sg:person.01336147564.03 schema:affiliation grid-institutes:grid.462414.1
174 schema:familyName Jayaraman
175 schema:givenName Gayathri
176 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01336147564.03
177 rdf:type schema:Person
178 sg:person.0614124227.85 schema:affiliation grid-institutes:grid.462414.1
179 schema:familyName Siddharthan
180 schema:givenName Rahul
181 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0614124227.85
182 rdf:type schema:Person
183 sg:pub.10.1007/bf00163848 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027539169
184 https://doi.org/10.1007/bf00163848
185 rdf:type schema:CreativeWork
186 sg:pub.10.1007/bf01731581 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023239976
187 https://doi.org/10.1007/bf01731581
188 rdf:type schema:CreativeWork
189 sg:pub.10.1007/bf01734359 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044065382
190 https://doi.org/10.1007/bf01734359
191 rdf:type schema:CreativeWork
192 sg:pub.10.1007/bf02101694 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039008589
193 https://doi.org/10.1007/bf02101694
194 rdf:type schema:CreativeWork
195 sg:pub.10.1007/bf02193625 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049370355
196 https://doi.org/10.1007/bf02193625
197 rdf:type schema:CreativeWork
198 sg:pub.10.1038/356168a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003127095
199 https://doi.org/10.1038/356168a0
200 rdf:type schema:CreativeWork
201 sg:pub.10.1038/nature04979 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004695015
202 https://doi.org/10.1038/nature04979
203 rdf:type schema:CreativeWork
204 sg:pub.10.1186/1471-2105-11-54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003163452
205 https://doi.org/10.1186/1471-2105-11-54
206 rdf:type schema:CreativeWork
207 sg:pub.10.1186/1471-2105-6-298 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047546418
208 https://doi.org/10.1186/1471-2105-6-298
209 rdf:type schema:CreativeWork
210 sg:pub.10.1186/1471-2105-7-143 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014283709
211 https://doi.org/10.1186/1471-2105-7-143
212 rdf:type schema:CreativeWork
213 sg:pub.10.1186/1748-7188-3-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027480499
214 https://doi.org/10.1186/1748-7188-3-6
215 rdf:type schema:CreativeWork
216 grid-institutes:grid.462414.1 schema:alternateName The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India
217 schema:name The Institute of Mathematical Sciences, Taramani, Chennai 600 113, India
218 rdf:type schema:Organization
 




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


...