Ambivalent covariance models

Janssen S, Giegerich R (2015)
BMC Bioinformatics 16(1): 178.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Background Evolutionary variations let us define a set of similar nucleic acid sequences as a family if these different molecules execute a common function. Capturing their sequence variation by using e. g. position specific scoring matrices significantly improves sensitivity of detection tools. Members of a functional (non‐coding) RNA family are affected by these variations not only on the sequence, but also on the structural level. For example, some transfer‐RNAs exhibit a fifth helix in addition to the typical cloverleaf structure. Current covariance models – the unrivaled homology search approach for structured RNA – do not benefit from structural variation within a family, but rather penalize it. This leads to artificial subdivision of families and loss of information in the RFAM database. Results We propose an extension to the fundamental architecture of covariance models to allow for several, compatible consensus structures. The resulting models are called ambivalent covariance models. Evaluation on several RFAM families shows that coalescence of structural variation within a family by using ambivalent consensus models is superior to subdividing the family into multiple classical covariance models. Conclusion A prototype and source code is available at http://bibiserv.cebitec.uni‐bielefeld.de/acms.
Stichworte
RNA homology search; Covariance model; Consensus structure
Erscheinungsjahr
2015
Zeitschriftentitel
BMC Bioinformatics
Band
16
Ausgabe
1
Art.-Nr.
178
ISSN
1471-2105
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2764888

Zitieren

Janssen S, Giegerich R. Ambivalent covariance models. BMC Bioinformatics. 2015;16(1): 178.
Janssen, S., & Giegerich, R. (2015). Ambivalent covariance models. BMC Bioinformatics, 16(1), 178. doi:10.1186/s12859-015-0569-1
Janssen, Stefan, and Giegerich, Robert. 2015. “Ambivalent covariance models”. BMC Bioinformatics 16 (1): 178.
Janssen, S., and Giegerich, R. (2015). Ambivalent covariance models. BMC Bioinformatics 16:178.
Janssen, S., & Giegerich, R., 2015. Ambivalent covariance models. BMC Bioinformatics, 16(1): 178.
S. Janssen and R. Giegerich, “Ambivalent covariance models”, BMC Bioinformatics, vol. 16, 2015, : 178.
Janssen, S., Giegerich, R.: Ambivalent covariance models. BMC Bioinformatics. 16, : 178 (2015).
Janssen, Stefan, and Giegerich, Robert. “Ambivalent covariance models”. BMC Bioinformatics 16.1 (2015): 178.
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1 Zitation in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

RNAlien - Unsupervised RNA family model construction.
Eggenhofer F, Hofacker IL, Höner Zu Siederdissen C., Nucleic Acids Res 44(17), 2016
PMID: 27330139

27 References

Daten bereitgestellt von Europe PubMed Central.


AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Pure multiple RNA secondary structure alignments: a progressive profile approach.
Hochsmann M, Voss B, Giegerich R., IEEE/ACM Trans Comput Biol Bioinform 1(1), 2004
PMID: 17048408
RNA sequence analysis using covariance models.
Eddy SR, Durbin R., Nucleic Acids Res. 22(11), 1994
PMID: 8029015

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Infernal 1.1: 100-fold faster RNA homology searches.
Nawrocki EP, Eddy SR., Bioinformatics 29(22), 2013
PMID: 24008419
Sequence-based heuristics for faster annotation of non-coding RNA families.
Weinberg Z, Ruzzo WL., Bioinformatics 22(1), 2005
PMID: 16267089
Query-dependent banding (QDB) for faster RNA similarity searches.
Nawrocki EP, Eddy SR., PLoS Comput. Biol. 3(3), 2007
PMID: 17397253
Semantics and ambiguity of stochastic RNA family models.
Giegerich R, Honer zu Siederdissen C., IEEE/ACM Trans Comput Biol Bioinform 8(2), 2011
PMID: 21233528
Volume changes in protein evolution.
Gerstein M, Sonnhammer EL, Chothia C., J. Mol. Biol. 236(4), 1994
PMID: 8120887
Maximum entropy weighting of aligned sequences of proteins or DNA.
Krogh A, Mitchison G., Proc Int Conf Intell Syst Mol Biol 3(), 1995
PMID: 7584440

AUTHOR UNKNOWN, 0
A discipline of dynamic programming over sequence data,
Giegerich R, Meyer C, Steffen P., 2004
Versatile and declarative dynamic programming using pair algebras.
Steffen P, Giegerich R., BMC Bioinformatics 6(), 2005
PMID: 16156887
Bellman's GAP--a language and compiler for dynamic programming in sequence analysis.
Sauthoff G, Mohl M, Janssen S, Giegerich R., Bioinformatics 29(5), 2013
PMID: 23355290
Methods for evaluating gene expression from Affymetrix microarray datasets.
Jiang N, Leach LJ, Hu X, Potokina E, Jia T, Druka A, Waugh R, Kearsey MJ, Luo ZW., BMC Bioinformatics 9(), 2008
PMID: 18559105
Evolution of spliceosomal snRNA genes in metazoan animals.
Marz M, Kirsten T, Stadler PF., J. Mol. Evol. 67(6), 2008
PMID: 19030770

AUTHOR UNKNOWN, 0
The RNA structure alignment ontology.
Brown JW, Birmingham A, Griffiths PE, Jossinet F, Kachouri-Lafond R, Knight R, Lang BF, Leontis N, Steger G, Stombaugh J, Westhof E., RNA 15(9), 2009
PMID: 19622678
Structural modeling of RNase P RNA of the hyperthermophilic archaeon Pyrococcus horikoshii OT3.
Zwieb C, Nakao Y, Nakashima T, Takagi H, Goda S, Andersen ES, Kakuta Y, Kimura M., Biochem. Biophys. Res. Commun. 414(3), 2011
PMID: 21968019
Discriminatory power of RNA family models.
Honer zu Siederdissen C, Hofacker IL., Bioinformatics 26(18), 2010
PMID: 20823307
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