Quantifying variances in comparative RNA secondary structure prediction

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Quantifying variances in comparative RNA secondary structure prediction. / Anderson, James Wj; Novák, Adám; Sükösd, Zsuzsanna; Golden, Michael; Arunapuram, Preeti; Edvardsson, Ingolfur; Hein, Jotun.

In: B M C Bioinformatics, Vol. 14, 149, 01.05.2013.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Anderson, JW, Novák, A, Sükösd, Z, Golden, M, Arunapuram, P, Edvardsson, I & Hein, J 2013, 'Quantifying variances in comparative RNA secondary structure prediction', B M C Bioinformatics, vol. 14, 149. https://doi.org/10.1186/1471-2105-14-149

APA

Anderson, J. W., Novák, A., Sükösd, Z., Golden, M., Arunapuram, P., Edvardsson, I., & Hein, J. (2013). Quantifying variances in comparative RNA secondary structure prediction. B M C Bioinformatics, 14, [149]. https://doi.org/10.1186/1471-2105-14-149

CBE

Anderson JW, Novák A, Sükösd Z, Golden M, Arunapuram P, Edvardsson I, Hein J. 2013. Quantifying variances in comparative RNA secondary structure prediction. B M C Bioinformatics. 14:Article 149. https://doi.org/10.1186/1471-2105-14-149

MLA

Vancouver

Anderson JW, Novák A, Sükösd Z, Golden M, Arunapuram P, Edvardsson I et al. Quantifying variances in comparative RNA secondary structure prediction. B M C Bioinformatics. 2013 May 1;14. 149. https://doi.org/10.1186/1471-2105-14-149

Author

Anderson, James Wj ; Novák, Adám ; Sükösd, Zsuzsanna ; Golden, Michael ; Arunapuram, Preeti ; Edvardsson, Ingolfur ; Hein, Jotun. / Quantifying variances in comparative RNA secondary structure prediction. In: B M C Bioinformatics. 2013 ; Vol. 14.

Bibtex

@article{1de14774f211469ab0e8bb8b5c4f7f70,
title = "Quantifying variances in comparative RNA secondary structure prediction",
abstract = "BACKGROUND: With the advancement of next-generation sequencing and transcriptomics technologies, regulatory effects involving RNA, in particular RNA structural changes are being detected. These results often rely on RNA secondary structure predictions. However, current approaches to RNA secondary structure modelling produce predictions with a high variance in predictive accuracy, and we have little quantifiable knowledge about the reasons for these variances. RESULTS: In this paper we explore a number of factors which can contribute to poor RNA secondary structure prediction quality. We establish a quantified relationship between alignment quality and loss of accuracy. Furthermore, we define two new measures to quantify uncertainty in alignment-based structure predictions. One of the measures improves on the {"}reliability score{"} reported by PPfold, and considers alignment uncertainty as well as base-pair probabilities. The other measure considers the information entropy for SCFGs over a space of input alignments. CONCLUSIONS: Our predictive accuracy improves on the PPfold reliability score. We can successfully characterize many of the underlying reasons for and variances in poor prediction. However, there is still variability unaccounted for, which we therefore suggest comes from the RNA secondary structure predictive model itself.",
author = "Anderson, {James Wj} and Ad{\'a}m Nov{\'a}k and Zsuzsanna S{\"u}k{\"o}sd and Michael Golden and Preeti Arunapuram and Ingolfur Edvardsson and Jotun Hein",
year = "2013",
month = may,
day = "1",
doi = "10.1186/1471-2105-14-149",
language = "English",
volume = "14",
journal = "B M C Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central Ltd.",

}

RIS

TY - JOUR

T1 - Quantifying variances in comparative RNA secondary structure prediction

AU - Anderson, James Wj

AU - Novák, Adám

AU - Sükösd, Zsuzsanna

AU - Golden, Michael

AU - Arunapuram, Preeti

AU - Edvardsson, Ingolfur

AU - Hein, Jotun

PY - 2013/5/1

Y1 - 2013/5/1

N2 - BACKGROUND: With the advancement of next-generation sequencing and transcriptomics technologies, regulatory effects involving RNA, in particular RNA structural changes are being detected. These results often rely on RNA secondary structure predictions. However, current approaches to RNA secondary structure modelling produce predictions with a high variance in predictive accuracy, and we have little quantifiable knowledge about the reasons for these variances. RESULTS: In this paper we explore a number of factors which can contribute to poor RNA secondary structure prediction quality. We establish a quantified relationship between alignment quality and loss of accuracy. Furthermore, we define two new measures to quantify uncertainty in alignment-based structure predictions. One of the measures improves on the "reliability score" reported by PPfold, and considers alignment uncertainty as well as base-pair probabilities. The other measure considers the information entropy for SCFGs over a space of input alignments. CONCLUSIONS: Our predictive accuracy improves on the PPfold reliability score. We can successfully characterize many of the underlying reasons for and variances in poor prediction. However, there is still variability unaccounted for, which we therefore suggest comes from the RNA secondary structure predictive model itself.

AB - BACKGROUND: With the advancement of next-generation sequencing and transcriptomics technologies, regulatory effects involving RNA, in particular RNA structural changes are being detected. These results often rely on RNA secondary structure predictions. However, current approaches to RNA secondary structure modelling produce predictions with a high variance in predictive accuracy, and we have little quantifiable knowledge about the reasons for these variances. RESULTS: In this paper we explore a number of factors which can contribute to poor RNA secondary structure prediction quality. We establish a quantified relationship between alignment quality and loss of accuracy. Furthermore, we define two new measures to quantify uncertainty in alignment-based structure predictions. One of the measures improves on the "reliability score" reported by PPfold, and considers alignment uncertainty as well as base-pair probabilities. The other measure considers the information entropy for SCFGs over a space of input alignments. CONCLUSIONS: Our predictive accuracy improves on the PPfold reliability score. We can successfully characterize many of the underlying reasons for and variances in poor prediction. However, there is still variability unaccounted for, which we therefore suggest comes from the RNA secondary structure predictive model itself.

U2 - 10.1186/1471-2105-14-149

DO - 10.1186/1471-2105-14-149

M3 - Journal article

C2 - 23634662

VL - 14

JO - B M C Bioinformatics

JF - B M C Bioinformatics

SN - 1471-2105

M1 - 149

ER -