Abstract
In this paper, we address the problem of discovering novel non-coding RNA (ncRNA) using primary sequence, and secondary structure conservation, focusing on ncRNA families with pseudo-knotted structures. Our main technical result is an efficient algorithm for computing an optimum structural alignment of an RNA sequence against a genomic substring. This algorithm finds two applications. First, by scanning a genome, we can identify novel (homologous) pseudoknotted ncRNA, and second, we can infer the secondary structure of the target aligned sequence. We test an implementation of our algorithm (Pal), and show that it has near-perfect behavior for predicting the structure of many known pseudoknots. Additionally, it can detect the true homologs with high sensitivity and specificity in controlled tests. We also use Pal to search entire viral genome and mouse genome for novel homologs of some viral, and eukaryotic pseudoknots respectively. In each case, we have found strong support for novel homologs.
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Keywords
- Structural Alignment
- Recursive Structure
- Pseudoknot Structure
- Ribosomal Frameshifting
- Tree Adjoining Grammar
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Dost, B., Han, B., Zhang, S., Bafna, V. (2006). Structural Alignment of Pseudoknotted RNA. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_13
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DOI: https://doi.org/10.1007/11732990_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33295-4
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