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Nucleic Acids Res. 2013 Sep;41(17):8220-36. doi: 10.1093/nar/gkt596. Epub 2013 Jul 11.

Widespread purifying selection on RNA structure in mammals.

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RNA Biology and Plasticity Laboratory, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, NSW 2010 Australia, Genomics and Computational Biology Division, Institute for Molecular Bioscience, 306 Carmody Rd, University of Queensland, Brisbane, 4067 Australia, Department of Structural and Computational Biology; and Center for Integrative Bioinformatics Vienna (CIBIV), Max F. Perutz Laboratories (MFPL), University of Vienna, Medical University of Vienna, Dr. Bohr-Gasse 9, A-1030 Vienna, Austria, Bioinformatics Group, Department of Computer Science; and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany, Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany, Center for Non-coding RNA in Technology and Health, Department of Basic Veterinary and Animal Sciences, Faculty of Life Sciences University of Copenhagen, Grønnegårdsvej 3, 1870 Frederiksberg C Denmark, Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA and St Vincent's Clinical School, University of New South Wales, Level 5, de Lacy, Victoria St, St Vincent's Hospital, Sydney, NSW 2010 Australia.


Evolutionarily conserved RNA secondary structures are a robust indicator of purifying selection and, consequently, molecular function. Evaluating their genome-wide occurrence through comparative genomics has consistently been plagued by high false-positive rates and divergent predictions. We present a novel benchmarking pipeline aimed at calibrating the precision of genome-wide scans for consensus RNA structure prediction. The benchmarking data obtained from two refined structure prediction algorithms, RNAz and SISSIz, were then analyzed to fine-tune the parameters of an optimized workflow for genomic sliding window screens. When applied to consistency-based multiple genome alignments of 35 mammals, our approach confidently identifies >4 million evolutionarily constrained RNA structures using a conservative sensitivity threshold that entails historically low false discovery rates for such analyses (5-22%). These predictions comprise 13.6% of the human genome, 88% of which fall outside any known sequence-constrained element, suggesting that a large proportion of the mammalian genome is functional. As an example, our findings identify both known and novel conserved RNA structure motifs in the long noncoding RNA MALAT1. This study provides an extensive set of functional transcriptomic annotations that will assist researchers in uncovering the precise mechanisms underlying the developmental ontologies of higher eukaryotes.

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