LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs
Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing me...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en_US |
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Cold Spring Harbor Laboratory Press
2014
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| Online Access: | http://hdl.handle.net/1721.1/88441 https://orcid.org/0000-0003-2672-5264 |
| _version_ | 1826210830188806144 |
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| author | Joshi, T. Hofacker, Ivo L. Stadler, P. F. Backofen, Rolf Will, Sebastian |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Joshi, T. Hofacker, Ivo L. Stadler, P. F. Backofen, Rolf Will, Sebastian |
| author_sort | Joshi, T. |
| collection | MIT |
| description | Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used ncRNA gene finder RNAz by a factor of 3 from a median deviation of 47 to 13 nt. Post-processing RNAz predictions, LocARNA-P's STAR score allows much stronger discrimination between true- and false-positive predictions than RNAz's own evaluation. The improved accuracy, in this scenario increased from AUC 0.71 to AUC 0.87, significantly reduces the cost of successive analysis steps. The ready-to-use software tool LocARNA-P produces structure-based multiple RNA alignments with associated columnwise STARs and predicts ncRNA boundaries. We provide additional results, a web server for LocARNA/LocARNA-P, and the software package, including documentation and a pipeline for refining screens for structural ncRNA, at http://www.bioinf.uni-freiburg.de/Supplements/LocARNA-P/. |
| first_indexed | 2024-09-23T14:56:06Z |
| format | Article |
| id | mit-1721.1/88441 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:56:06Z |
| publishDate | 2014 |
| publisher | Cold Spring Harbor Laboratory Press |
| record_format | dspace |
| spelling | mit-1721.1/884412022-10-01T23:25:57Z LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs Joshi, T. Hofacker, Ivo L. Stadler, P. F. Backofen, Rolf Will, Sebastian Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Mathematics Massachusetts Institute of Technology. Department of Physics Will, Sebastian Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used ncRNA gene finder RNAz by a factor of 3 from a median deviation of 47 to 13 nt. Post-processing RNAz predictions, LocARNA-P's STAR score allows much stronger discrimination between true- and false-positive predictions than RNAz's own evaluation. The improved accuracy, in this scenario increased from AUC 0.71 to AUC 0.87, significantly reduces the cost of successive analysis steps. The ready-to-use software tool LocARNA-P produces structure-based multiple RNA alignments with associated columnwise STARs and predicts ncRNA boundaries. We provide additional results, a web server for LocARNA/LocARNA-P, and the software package, including documentation and a pipeline for refining screens for structural ncRNA, at http://www.bioinf.uni-freiburg.de/Supplements/LocARNA-P/. 2014-07-18T14:47:23Z 2014-07-18T14:47:23Z 2012-03 2011-07 Article http://purl.org/eprint/type/JournalArticle 1355-8382 1469-9001 http://hdl.handle.net/1721.1/88441 Will, S., T. Joshi, I. L. Hofacker, P. F. Stadler, and R. Backofen. “LocARNA-P: Accurate Boundary Prediction and Improved Detection of Structural RNAs.” RNA 18, no. 5 (May 1, 2012): 900–914. https://orcid.org/0000-0003-2672-5264 en_US http://dx.doi.org/10.1261/rna.029041.111 RNA Article is available under a Creative Commons license; see publisher’s site for details. http://creativecommons.org/ application/pdf Cold Spring Harbor Laboratory Press Cold Spring Harbor Laboratory Press |
| spellingShingle | Joshi, T. Hofacker, Ivo L. Stadler, P. F. Backofen, Rolf Will, Sebastian LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs |
| title | LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs |
| title_full | LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs |
| title_fullStr | LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs |
| title_full_unstemmed | LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs |
| title_short | LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs |
| title_sort | locarna p accurate boundary prediction and improved detection of structural rnas |
| url | http://hdl.handle.net/1721.1/88441 https://orcid.org/0000-0003-2672-5264 |
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