Ontology type: schema:ScholarlyArticle Open Access: True
2008-12
AUTHORSStephan H Bernhart, Ivo L Hofacker, Sebastian Will, Andreas R Gruber, Peter F Stadler
ABSTRACTBACKGROUND: The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach. RESULTS: We show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets. CONCLUSION: The new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers. More... »
PAGES474
http://scigraph.springernature.com/pub.10.1186/1471-2105-9-474
DOIhttp://dx.doi.org/10.1186/1471-2105-9-474
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1041611297
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/19014431
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/2008/0104",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Statistics",
"type": "DefinedTerm"
},
{
"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"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Algorithms",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Amino Acid Sequence",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Computational Biology",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Models, Chemical",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Nucleic Acid Conformation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "RNA",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Sequence Alignment",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Software",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Leipzig University",
"id": "https://www.grid.ac/institutes/grid.9647.c",
"name": [
"Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, H\u00e4rtelstrasse 16-18, D-04107, Leipzig, Germany"
],
"type": "Organization"
},
"familyName": "Bernhart",
"givenName": "Stephan H",
"id": "sg:person.0753447607.17",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0753447607.17"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Vienna",
"id": "https://www.grid.ac/institutes/grid.10420.37",
"name": [
"Institute for Theoretical Chemistry, University of Vienna, W\u00e4hringerstrasse 17, A-1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Hofacker",
"givenName": "Ivo L",
"id": "sg:person.01222322364.52",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01222322364.52"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Freiburg",
"id": "https://www.grid.ac/institutes/grid.5963.9",
"name": [
"Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-K\u00f6hler-Allee, Geb. 106, D-79110, Freiburg, Germany"
],
"type": "Organization"
},
"familyName": "Will",
"givenName": "Sebastian",
"id": "sg:person.01172262765.67",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01172262765.67"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Vienna",
"id": "https://www.grid.ac/institutes/grid.10420.37",
"name": [
"Institute for Theoretical Chemistry, University of Vienna, W\u00e4hringerstrasse 17, A-1090, Vienna, Austria"
],
"type": "Organization"
},
"familyName": "Gruber",
"givenName": "Andreas R",
"id": "sg:person.01275342251.18",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01275342251.18"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Santa Fe Institute",
"id": "https://www.grid.ac/institutes/grid.209665.e",
"name": [
"Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, H\u00e4rtelstrasse 16-18, D-04107, Leipzig, Germany",
"Institute for Theoretical Chemistry, University of Vienna, W\u00e4hringerstrasse 17, A-1090, Vienna, Austria",
"RNomics Group, Fraunhofer Institut for Cell Therapy and Immunology (IZI) Perlickstrasse 1, D-04103, Leipzig, Germany",
"The Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, New Mexico"
],
"type": "Organization"
},
"familyName": "Stadler",
"givenName": "Peter F",
"id": "sg:person.0664150133.70",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0664150133.70"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1002/jez.b.21130",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000919862"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.0712329105",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002389179"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/9.1.133",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004222010"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature05874",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005913886",
"https://doi.org/10.1038/nature05874"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-8-366",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005997041",
"https://doi.org/10.1186/1471-2105-8-366"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.sbi.2006.05.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006869749"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/gkg500",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007055430"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/bti577",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007274626"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1261/rna.215407",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007388076"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/bti550",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009861484"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-7-400",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010914847",
"https://doi.org/10.1186/1471-2105-7-400"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btl636",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013478171"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0022-2836(02)00308-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013627034"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pcbi.0030193",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014962641"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/gkg614",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015204321"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.0409169102",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016701541"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btl142",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025361639"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-9-248",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025378257",
"https://doi.org/10.1186/1471-2105-9-248"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-9-122",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027407930",
"https://doi.org/10.1186/1471-2105-9-122"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-9-122",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027407930",
"https://doi.org/10.1186/1471-2105-9-122"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/gkh065",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029580198"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1073/pnas.90.19.8777",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029804271"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.jmb.2004.07.018",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030952580"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00818163",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032495505",
"https://doi.org/10.1007/bf00818163"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0097-8485(99)00010-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032574287"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-9-340",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036166865",
"https://doi.org/10.1186/1471-2105-9-340"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/gki081",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036286749"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-9-219",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036743347",
"https://doi.org/10.1186/1471-2105-9-219"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-9-219",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036743347",
"https://doi.org/10.1186/1471-2105-9-219"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0097-8485(99)00013-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038438236"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btl023",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042367741"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1371/journal.pcbi.0030065",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042599038"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1261/rna.2164906",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042607648"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btm223",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044824599"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-8-130",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044824773",
"https://doi.org/10.1186/1471-2105-8-130"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-8-130",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044824773",
"https://doi.org/10.1186/1471-2105-8-130"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-5-140",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045744598",
"https://doi.org/10.1186/1471-2105-5-140"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-5-140",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045744598",
"https://doi.org/10.1186/1471-2105-5-140"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s002490050023",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047249964",
"https://doi.org/10.1007/s002490050023"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-6-73",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048566346",
"https://doi.org/10.1186/1471-2105-6-73"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2105-4-44",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048914023",
"https://doi.org/10.1186/1471-2105-4-44"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/nar/gkn544",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051720598"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1126/science.1112014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052847323"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/bioinformatics/btk008",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053620023"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1137/0145048",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062840393"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/csb.2003.1227315",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1077183453"
],
"type": "CreativeWork"
}
],
"datePublished": "2008-12",
"datePublishedReg": "2008-12-01",
"description": "BACKGROUND: The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.\nRESULTS: We show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets.\nCONCLUSION: The new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers.",
"genre": "research_article",
"id": "sg:pub.10.1186/1471-2105-9-474",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.7580380",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1023786",
"issn": [
"1471-2105"
],
"name": "BMC Bioinformatics",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "9"
}
],
"name": "RNAalifold: improved consensus structure prediction for RNA alignments",
"pagination": "474",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"c638c9394bf4d0a6e2d8a1cb352bc6a5e2e7d0a6577a03d825f9e578ebd5d6b2"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"19014431"
]
},
{
"name": "nlm_unique_id",
"type": "PropertyValue",
"value": [
"100965194"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1186/1471-2105-9-474"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1041611297"
]
}
],
"sameAs": [
"https://doi.org/10.1186/1471-2105-9-474",
"https://app.dimensions.ai/details/publication/pub.1041611297"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T14:59",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8663_00000507.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1186%2F1471-2105-9-474"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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.10.1186/1471-2105-9-474'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-9-474'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-9-474'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-9-474'
This table displays all metadata directly associated to this object as RDF triples.
281 TRIPLES
21 PREDICATES
79 URIs
29 LITERALS
17 BLANK NODES