@article{cruz2011sequence,
  title={Sequence-based identification of 3D structural modules in {RNA} with RMDetect},
  author={Cruz, Jos{\'e} Almeida and Westhof, Eric},
  journal={Nature methods},
  volume={8},
  number={6},
  pages={513},
  year={2011},
  publisher={Nature Publishing Group}
}

@article{djelloul_automated_2008,
	title = {Automated motif extraction and classification in {RNA} tertiary structures},
	volume = {14},
	issn = {1355-8382, 1469-9001},
	url = {http://rnajournal.cshlp.org/content/14/12/2489},
	doi = {10.1261/rna.1061108},
	abstract = {We used a novel graph-based approach to extract {RNA} tertiary motifs. We cataloged them all and clustered them using an innovative graph similarity measure. We applied our method to three widely studied structures: Haloarcula marismortui 50S (H.m 50S), Escherichia coli 50S (E. coli 50S), and Thermus thermophilus 16S (T.th 16S) RNAs. We identified 10 known motifs without any prior knowledge of their shapes or positions. We additionally identified four putative new motifs.},
	language = {en},
	number = {12},
	urldate = {2018-10-04},
	journal = {RNA},
	author = {Djelloul, Mahassine and Denise, Alain},
	month = jan,
	year = {2008},
	pmid = {18957493},
	keywords = {clustering, graph similarity, {RNA} tertiary structure},
	pages = {2489--2497},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/6PDZZRI6/Djelloul et Denise - 2008 - Automated motif extraction and classification in R.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/28T5ICXG/2489.html:text/html}
}

@article{roll_jar3d_2016,
	title = {{JAR}3D {Webserver}: {Scoring} and aligning {RNA} loop sequences to known 3D motifs},
	volume = {44},
	issn = {0305-1048},
	shorttitle = {{JAR}3D {Webserver}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987954/},
	doi = {10.1093/nar/gkw453},
	abstract = {Many non-coding RNAs have been identified and may function by forming 2D and 3D structures. {RNA} hairpin and internal loops are often represented as unstructured on secondary structure diagrams, but {RNA} 3D structures show that most such loops are structured by non-Watson–Crick basepairs and base stacking. Moreover, different {RNA} sequences can form the same {RNA} 3D motif. JAR3D finds possible 3D geometries for hairpin and internal loops by matching loop sequences to motif groups from the {RNA} 3D Motif Atlas, by exact sequence match when possible, and by probabilistic scoring and edit distance for novel sequences. The scoring gauges the ability of the sequences to form the same pattern of interactions observed in 3D structures of the motif. The JAR3D webserver at http://rna.bgsu.edu/jar3d/ takes one or many sequences of a single loop as input, or else one or many sequences of longer RNAs with multiple loops. Each sequence is scored against all current motif groups. The output shows the ten best-matching motif groups. Users can align input sequences to each of the motif groups found by JAR3D. JAR3D will be updated with every release of the {RNA} 3D Motif Atlas, and so its performance is expected to improve over time.},
	number = {Web Server issue},
	urldate = {2018-10-04},
	journal = {Nucleic Acids Research},
	author = {Roll, James and Zirbel, Craig L. and Sweeney, Blake and Petrov, Anton I. and Leontis, Neocles},
	month = jul,
	year = {2016},
	pmid = {27235417},
	pmcid = {PMC4987954},
	pages = {W320--W327},
	file = {PubMed Central Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/KWD59J5I/Roll et al. - 2016 - JAR3D Webserver Scoring and aligning {RNA} loop seq.pdf:application/pdf}
}

@article{zhong_rnamotifscan:_2010,
	title = {{RNAMotifScan}: automatic identification of {RNA} structural motifs using secondary structural alignment},
	volume = {38},
	issn = {0305-1048},
	shorttitle = {{RNAMotifScan}},
	url = {https://academic.oup.com/nar/article/38/18/e176/1069222},
	doi = {10.1093/nar/gkq672},
	abstract = {Abstract.  Recent studies have shown that {RNA} structural motifs play essential roles in {RNA} folding and interaction with other molecules. Computational identifi},
	language = {en},
	number = {18},
	urldate = {2018-10-04},
	journal = {Nucleic Acids Research},
	author = {Zhong, Cuncong and Tang, Haixu and Zhang, Shaojie},
	month = oct,
	year = {2010},
	pages = {e176--e176},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/ESXL69Q6/Zhong et al. - 2010 - RNAMotifScan automatic identification of {RNA} stru.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/MWTNC94Z/1069222.html:text/html}
}

@inproceedings{tahi_fast_2003,
	title = {A fast algorithm for {RNA} secondary structure prediction including pseudoknots},
	doi = {10.1109/BIBE.2003.1188924},
	abstract = {Many important {RNA} molecules contain pseudoknots, which are generally excluded by the definition of the secondary structure, mainly for computational reasons. Still, most existing algorithms for secondary structure prediction are not satisfactory in results and complexities, even when pseudoknots are not allowed. We present an algorithm, called P-DCFold, for the prediction of {RNA} secondary structures including all kinds of pseudoknots. It is based on the comparative approach. The helices are searched recursively, from more "likely" to less "likely", using the "Divide and Conquer" approach. This approach, which allows to limit the amount of searching, is possible when only non-interleaved helices are searched for. The pseudoknots are therefore searched in several steps, each helix of the pseudoknot being selected in a different step. P-DCFold has been applied to tmRNA and RnaseP sequences. In less than two seconds, their respective secondary structures, including their pseudoknots, have been recovered very efficiently.},
	booktitle = {Third {IEEE} {Symposium} on {Bioinformatics} and {Bioengineering}, 2003. {Proceedings}.},
	author = {Tahi, F. and Engelen, S. and Regnier, M.},
	month = mar,
	year = {2003},
	keywords = {2 s, Bioinformatics, Biological control systems, computational methods, computational reasons, Context modeling, Databases, Evolution (biology), fast algorithm, macromolecules, molecular biophysics, molecular configurations, noninterleaved helices, Predictive models, pseudoknots, recursive searches, RNA, {RNA} secondary structure prediction, RnaseP, Robustness, secondary structures, spatial structures, Stochastic processes, Switches, tmRNA},
	pages = {11--17},
	file = {IEEE Xplore Abstract Record:/nhome/siniac/lbecquey/Zotero/storage/HTHXNSVF/1188924.html:text/html}
}

@article{tahi_p-dcfold_2005,
	title = {P-dcfold or how to predict all kinds of pseudoknots in {RNA} secondary structures},
	volume = {14},
	issn = {0218-2130},
	url = {https://www.worldscientific.com/doi/abs/10.1142/S021821300500234X},
	doi = {10.1142/S021821300500234X},
	abstract = {Pseudoknots play important roles in many RNAs. But for computational reasons, pseudoknots are usually excluded from the definition of {RNA} secondary structures. Indeed, prediction of pseudoknots increase very highly the complexities in time of the algorithms, knowing that all existing algorithms for {RNA} secondary structure prediction have  complexities at least of O(n3). Some algorithms have  been developed for searching pseudoknots, but all of them have very high complexities, and consider generally particular kinds of pseudoknots.We present an algorithm, called P-DCFold based on the comparative approach, for the prediction of {RNA} secondary structures including all kinds of pseudoknots. The helices are searched recursively using the "Divide and Conquer" approach, searching the helices from the "most significant" to the "less significant". A selected  helix subdivide the sequence into two sub-sequences, the internal one and  a concatenation of the two externals. This approach is used to search  non-interleaved helices and allows to limit the space of searching. To  search for pseudoknots, the processing is reiterated. Therefore, each helix  of the pseudoknot is selected in a different step.P-DCFold has been applied to several {RNA} sequences. In less  than two seconds, their respective secondary structures, including their pseudoknots, have been recovered very efficiently.},
	number = {05},
	urldate = {2018-10-02},
	journal = {International Journal on Artificial Intelligence Tools},
	author = {Tahi, Fariza and Stefan, Engelen and Regnier, Mireille},
	month = oct,
	year = {2005},
	pages = {703--716},
	file = {Snapshot:/nhome/siniac/lbecquey/Zotero/storage/GEIBPMJ4/S021821300500234X.html:text/html}
}

@article{tempel_fast_2012,
	title = {A fast ab-initio method for predicting {miRNA} precursors in genomes},
	volume = {40},
	issn = {0305-1048},
	url = {https://academic.oup.com/nar/article/40/11/e80/2409259},
	doi = {10.1093/nar/gks146},
	abstract = {Abstract.   miRNAs are small non coding {RNA} structures which play important roles in biological processes. Finding miRNA precursors in genomes is therefore an i},
	language = {en},
	number = {11},
	urldate = {2018-10-02},
	journal = {Nucleic Acids Research},
	author = {Tempel, Sébastien and Tahi, Fariza},
	month = jun,
	year = {2012},
	pages = {e80--e80},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/BSCSRRL2/Tempel et Tahi - 2012 - A fast ab-initio method for predicting miRNA precu.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/ZTG4GDDT/2409259.html:text/html}
}

@article{reinharz_towards_2012,
	title = {Towards 3D structure prediction of large {RNA} molecules: an integer programming framework to insert local 3D motifs in {RNA} secondary structure},
	volume = {28},
	issn = {1367-4803},
	shorttitle = {Towards 3D structure prediction of large {RNA} molecules},
	url = {https://academic.oup.com/bioinformatics/article/28/12/i207/269345},
	doi = {10.1093/bioinformatics/bts226},
	abstract = {Abstract.  Motivation: The prediction of {RNA} 3D structures from its sequence only is a milestone to {RNA} function analysis and prediction. In recent years, many},
	language = {en},
	number = {12},
	urldate = {2018-10-01},
	journal = {Bioinformatics},
	author = {Reinharz, Vladimir and Major, François and Waldispühl, Jérôme},
	month = jun,
	year = {2012},
	pages = {i207--i214},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/IAYRK53E/Reinharz et al. - 2012 - Towards 3D structure prediction of large {RNA} molec.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/SSMTN6YZ/269345.html:text/html}
}

@article{pan_predicting_nodate,
	title = {Predicting {RNA}–protein binding sites and motifs through combining local and global deep convolutional neural networks},
	url = {https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty364/4990826},
	doi = {10.1093/bioinformatics/bty364},
	abstract = {AbstractMotivation.  RNA-binding proteins (RBPs) take over 5–10\% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulatio},
	language = {en},
	urldate = {2018-06-12},
	journal = {Bioinformatics},
	author = {Pan, Xiaoyong and Shen, Hong-Bin and Valencia, Alfonso},
	file = {10.1093@bioinformatics@bty364.pdf:/nhome/siniac/lbecquey/Zotero/storage/KUQZ2HNN/10.1093@bioinformatics@bty364.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/TLQUYHEU/4990826.html:text/html}
}

@article{yi_brief_2017,
	title = {A {Brief} {Review} of {RNA}–{Protein} {Interaction} {Database} {Resources}},
	volume = {3},
	issn = {2311-553X},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832006/},
	doi = {10.3390/ncrna3010006},
	abstract = {RNA–Protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA–Protein interactions and binding sites from experiments and predictions, RNA–Protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA–Protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA–Protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA–Protein interactions.},
	number = {1},
	urldate = {2018-06-08},
	journal = {Non-Coding RNA},
	author = {Yi, Ying and Zhao, Yue and Huang, Yan and Wang, Dong},
	month = jan,
	year = {2017},
	pmid = {29657278},
	pmcid = {PMC5832006},
	file = {PubMed Central Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/C3ZRG7MJ/Yi et al. - 2017 - A Brief Review of RNA–Protein Interaction Database.pdf:application/pdf}
}

@article{das_automated_2007,
	title = {Automated de novo prediction of native-like {RNA} tertiary structures},
	volume = {104},
	copyright = {© 2007 by The National Academy of Sciences of the USA.                    Freely available online through the PNAS open access option.},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/content/104/37/14664},
	doi = {10.1073/pnas.0703836104},
	abstract = {RNA tertiary structure prediction has been based almost entirely on base-pairing constraints derived from phylogenetic covariation analysis. We describe here a complementary approach, inspired by the Rosetta low-resolution protein structure prediction method, that seeks the lowest energy tertiary structure for a given {RNA} sequence without using evolutionary information. In a benchmark test of 20 {RNA} sequences with known structure and lengths of ≈30 nt, the new method reproduces better than 90\% of Watson–Crick base pairs, comparable with the accuracy of secondary structure prediction methods. In more than half the cases, at least one of the top five models agrees with the native structure to better than 4 Å rmsd over the backbone. Most importantly, the method recapitulates more than one-third of non-Watson–Crick base pairs seen in the native structures. Tandem stacks of “sheared” base pairs, base triplets, and pseudoknots are among the noncanonical features reproduced in the models. In the cases in which none of the top five models were native-like, higher energy conformations similar to the native structures are still sampled frequently but not assigned low energies. These results suggest that modest improvements in the energy function, together with the incorporation of information from phylogenetic covariance, may allow confident and accurate structure prediction for larger and more complex {RNA} chains.},
	language = {en},
	number = {37},
	urldate = {2018-06-04},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Das, Rhiju and Baker, David},
	month = sep,
	year = {2007},
	pmid = {17726102},
	keywords = {ab initio, energy-based, fragment assembly, nucleic acid, Rosetta},
	pages = {14664--14669},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/H79FEN6P/Das and Baker - 2007 - Automated de novo prediction of native-like {RNA} te.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/WH7PUGNW/14664.html:text/html}
}

@article{cao_physics-based_2011,
	title = {Physics-based de novo prediction of {RNA} 3D structures},
	volume = {115},
	issn = {1520-6106},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3072456/},
	doi = {10.1021/jp112059y},
	abstract = {Current experiments on structural determination cannot keep up the pace with the steadily emerging {RNA} sequences and new functions. This underscores the request for an accurate model for {RNA} three-dimensional (3D) structural prediction. Although considerable progress has been made in mechanistic studies, accurate prediction for {RNA} tertiary folding from sequence remains an unsolved problem. The first and most important requirement for the prediction of {RNA} structure from physical principles is an accurate free energy model. A recently developed three-vector virtual bond-based {RNA} folding model (“Vfold”) has allowed us to compute the chain entropy and predict folding free energies and structures for {RNA} secondary structures and simple pseudoknots. Here we develop a free energy-based method to predict larger more complex {RNA} tertiary folds. The approach is based on a multiscaling strategy: from the nucleotide sequence, we predict the two-dimensional (2D) structures (defined by the base pairs and tertiary contacts); based on the 2D structure, we construct a 3D scaffold; with the 3D scaffold as the initial state, we combine AMBER energy minimization and PDB-based fragment search to predict the all-atom structure. A key advantage of the approach is the statistical mechanical calculation for the conformational entropy of {RNA} structures, including those with cross-linked loops. Benchmark tests show that the model leads to significant improvements in {RNA} 3D structure prediction.},
	number = {14},
	urldate = {2018-06-04},
	journal = {The journal of physical chemistry. B},
	author = {Cao, Song and Chen, Shi-Jie},
	month = apr,
	year = {2011},
	pmid = {21413701},
	pmcid = {PMC3072456},
	pages = {4216--4226},
	file = {PubMed Central Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/ZY98QT5K/Cao and Chen - 2011 - Physics-based de novo prediction of {RNA} 3D structu.pdf:application/pdf}
}

@article{bellaousov2010probknot,
  title={ProbKnot: fast prediction of {RNA} secondary structure including pseudoknots},
  author={Bellaousov, Stanislav and Mathews, David H},
  journal={Rna},
  volume={16},
  number={10},
  pages={1870--1880},
  year={2010},
  publisher={Cold Spring Harbor Lab}
}

@article{mccaskill1990equilibrium,
  title={The equilibrium partition function and base pair binding probabilities for {RNA} secondary structure},
  author={McCaskill, John S},
  journal={Biopolymers: Original Research on Biomolecules},
  volume={29},
  number={6-7},
  pages={1105--1119},
  year={1990},
  publisher={Wiley Online Library}
}

@article{bottaro_towards_2015,
	title = {Towards de novo {RNA} 3D {Structure} {Prediction}},
	volume = {2},
	copyright = {Copyright (c) 2015 Sandro Bottaro, Francesco Di Palma, Giovanni Bussi},
	issn = {2375-2467},
	url = {http://www.smartscitech.com/index.php/RD/article/view/544},
	doi = {10.14800/rd.544},
	abstract = {RNA is a fundamental class of biomolecules that mediate a large variety of molecular processes within the cell. Computational algorithms can be of great help in the understanding of {RNA} structure-function relationship. One of the main challenges in this field is the development of structure-prediction algorithms, which aim at the prediction of the three-dimensional (3D) native fold from the sole knowledge of the sequence. In a recent paper, we have introduced a scoring function for {RNA} structure prediction. Here, we analyze in detail the performance of the method, we underline strengths and shortcomings, and we discuss the results with respect to state-of-the-art techniques. These observations provide a starting point for improving current methodologies, thus paving the way to the advances of more accurate approaches for {RNA} 3D structure prediction.},
	language = {en},
	number = {2},
	urldate = {2018-06-04},
	journal = {RNA \& DISEASE},
	author = {Bottaro, Sandro and Palma, Francesco Di and Bussi, Giovanni},
	month = jan,
	year = {2015},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/SWAYZQEV/Bottaro et al. - 2015 - Towards de novo {RNA} 3D Structure Prediction.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/SQMMYVCU/544.html:text/html}
}

@article{mathews2004using,
  title={Using an {RNA} secondary structure partition function to determine confidence in base pairs predicted by free energy minimization},
  author={Mathews, David H},
  journal={Rna},
  volume={10},
  number={8},
  pages={1178--1190},
  year={2004},
  publisher={Cold Spring Harbor Lab}
}

@article{lorenz2011viennarna,
  title={ViennaRNA Package 2.0},
  author={Lorenz, Ronny and Bernhart, Stephan H and Zu Siederdissen, Christian Hoener and Tafer, Hakim and Flamm, Christoph and Stadler, Peter F and Hofacker, Ivo L},
  journal={Algorithms for Molecular Biology},
  volume={6},
  number={1},
  pages={26},
  year={2011},
  publisher={BioMed Central}
}
@article{andronescu2008rna,
  title={RNA STRAND: the {RNA} secondary structure and statistical analysis database},
  author={Andronescu, Mirela and Bereg, Vera and Hoos, Holger H and Condon, Anne},
  journal={BMC bioinformatics},
  volume={9},
  number={1},
  pages={340},
  year={2008},
  publisher={BioMed Central}
}

@article{bindewald_multistrand_2016,
	title = {Multistrand {Structure} {Prediction} of {Nucleic} {Acid} {Assemblies} and {Design} of {RNA} {Switches}},
	volume = {16},
	issn = {1530-6984},
	url = {https://doi.org/10.1021/acs.nanolett.5b04651},
	doi = {10.1021/acs.nanolett.5b04651},
	abstract = {RNA is an attractive material for the creation of molecular logic gates that release programmed functionalities only in the presence of specific molecular interaction partners. Here we present HyperFold, a multistrand RNA/DNA structure prediction approach for predicting nucleic acid complexes that can contain pseudoknots. We show that HyperFold also performs competitively compared to other published folding algorithms. We performed a large variety of RNA/DNA hybrid reassociation experiments for different concentrations, DNA toehold lengths, and G+C content and find that the observed tendencies for reassociation correspond well to computational predictions. Importantly, we apply this method to the design and experimental verification of a two-stranded {RNA} molecular switch that upon binding to a single-stranded {RNA} toehold disease-marker trigger mRNA changes its conformation releasing an shRNA-like Dicer substrate structure. To demonstrate the concept, connective tissue growth factor (CTGF) mRNA and enhanced green fluorescent protein (eGFP) mRNA were chosen as trigger and target sequences, respectively. In vitro experiments confirm the formation of an {RNA} switch and demonstrate that the functional unit is being released when the trigger {RNA} interacts with the switch toehold. The designed {RNA} switch is shown to be functional in MDA-MB-231 breast cancer cells. Several other switches were also designed and tested. We conclude that this approach has considerable potential because, in principle, it allows the release of an siRNA designed against a gene that differs from the gene that is utilized as a biomarker for a disease state.},
	number = {3},
	urldate = {2018-05-31},
	journal = {Nano Letters},
	author = {Bindewald, Eckart and Afonin, Kirill A. and Viard, Mathias and Zakrevsky, Paul and Kim, Taejin and Shapiro, Bruce A.},
	month = mar,
	year = {2016},
	pages = {1726--1735},
	file = {ACS Full Text Snapshot:/nhome/siniac/lbecquey/Zotero/storage/285MME3I/acs.nanolett.html:text/html}
}


@article{parisien2008mc,
  title={The MC-Fold and MC-Sym pipeline infers {RNA} structure from sequence data},
  author={Parisien, Marc and Major, Francois},
  journal={Nature},
  volume={452},
  number={7183},
  pages={51},
  year={2008},
  publisher={Nature Publishing Group}
}

@article{ge_novo_2018,
	title = {De novo discovery of structural motifs in {RNA} 3D structures through clustering},
	volume = {46},
	issn = {0305-1048},
	url = {https://academic.oup.com/nar/article/46/9/4783/4925243},
	doi = {10.1093/nar/gky139},
	abstract = {Abstract.  As functional components in three-dimensional (3D) conformation of an RNA, the {RNA} structural motifs provide an easy way to associate the molecular a},
	language = {en},
	number = {9},
	urldate = {2018-05-31},
	journal = {Nucleic Acids Research},
	author = {Ge, Ping and Islam, Shahidul and Zhong, Cuncong and Zhang, Shaojie},
	month = may,
	year = {2018},
	pages = {4783--4793},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/8YS7LY6E/Ge et al. - 2018 - De novo discovery of structural motifs in {RNA} 3D s.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/4TLRV8ZT/4925243.html:text/html}
}

@article{wang_rna_2018,
	title = {{RNA} 3-dimensional structural motifs as a critical constraint of viroid {RNA} evolution},
	volume = {14},
	issn = {1553-7374},
	url = {http://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1006801},
	doi = {10.1371/journal.ppat.1006801},
	language = {en},
	number = {2},
	urldate = {2018-05-31},
	journal = {PLOS Pathogens},
	author = {Wang, Ying and Zirbel, Craig L. and Leontis, Neocles B. and Ding, Biao},
	month = feb,
	year = {2018},
	keywords = {Non-coding {RNA} sequences, {RNA} structure, {RNA} viruses, Sequence motif analysis, Viral evolution, Viral replication, Viral structure, Viroids},
	pages = {e1006801},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/M3P6GCA7/Wang et al. - 2018 - {RNA} 3-dimensional structural motifs as a critical .pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/3YRBKI96/article.html:text/html}
}

@article{lorenz_viennarna_2011,
	title = {{ViennaRNA} {Package} 2.0},
	volume = {6},
	issn = {1748-7188},
	url = {https://doi.org/10.1186/1748-7188-6-26},
	doi = {10.1186/1748-7188-6-26},
	abstract = {Secondary structure forms an important intermediate level of description of nucleic acids that encapsulates the dominating part of the folding energy, is often well conserved in evolution, and is routinely used as a basis to explain experimental findings. Based on carefully measured thermodynamic parameters, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties.},
	urldate = {2018-05-31},
	journal = {Algorithms for Molecular Biology},
	author = {Lorenz, Ronny and Bernhart, Stephan H. and Höner zu Siederdissen, Christian and Tafer, Hakim and Flamm, Christoph and Stadler, Peter F. and Hofacker, Ivo L.},
	month = nov,
	year = {2011},
	keywords = {Consensus Structure, Folding Algorithm, Matthews Correlation Coefficient, Minimum Free Energy, Partition Function},
	pages = {26},
	annote = {Pages 26 in PDF},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/WFPBA2H8/Lorenz et al. - 2011 - ViennaRNA Package 2.0.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/ADNFF9EA/1748-7188-6-26.html:text/html}
}

@article{janssen_rna_2015,
	title = {The {RNA} shapes studio},
	volume = {31},
	issn = {1367-4803},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4308662/},
	doi = {10.1093/bioinformatics/btu649},
	abstract = {Motivation: Abstract shape analysis, first proposed in 2004, allows one to extract several relevant structures from the folding space of an {RNA} sequence, preferable to focusing in a single structure of minimal free energy. We report recent extensions to this approach., Results: We have rebuilt the original RNAshapes as a repository of components that allows us to integrate several established tools for {RNA} structure analysis: RNAshapes, RNAalishapes and pknotsRG, including its recent extension pKiss. As a spin-off, we obtain heretofore unavailable functionality: e. g. with pKiss, we can now perform abstract shape analysis for structures holding pseudoknots up to the complexity of kissing hairpin motifs. The new tool pAliKiss can predict kissing hairpin motifs from aligned sequences. Along with the integration, the functionality of the tools was also extended in manifold ways., Availability and implementation: As before, the tool is available on the Bielefeld Bioinformatics server at http://bibiserv.cebitec.uni-bielefeld.de/rnashapesstudio., Contact: bibi-help@cebitec.uni-bielefeld.de},
	number = {3},
	urldate = {2018-05-31},
	journal = {Bioinformatics},
	author = {Janssen, Stefan and Giegerich, Robert},
	month = feb,
	year = {2015},
	pmid = {25273103},
	pmcid = {PMC4308662},
	pages = {423--425},
	file = {PubMed Central Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/KFQTVDR3/Janssen and Giegerich - 2015 - The {RNA} shapes studio.pdf:application/pdf}
}

@article{parlea_rna_2016,
	series = {Advances in {RNA} {Structure} {Determination}},
	title = {The {RNA} 3D {Motif} {Atlas}: {Computational} methods for extraction, organization and evaluation of {RNA} motifs},
	volume = {103},
	issn = {1046-2023},
	shorttitle = {The {RNA} 3D {Motif} {Atlas}},
	url = {http://www.sciencedirect.com/science/article/pii/S1046202316301049},
	doi = {10.1016/j.ymeth.2016.04.025},
	abstract = {RNA 3D motifs occupy places in structured {RNA} molecules that correspond to the hairpin, internal and multi-helix junction “loops” of their secondary structure representations. As many as 40\% of the nucleotides of an {RNA} molecule can belong to these structural elements, which are distinct from the regular double helical regions formed by contiguous AU, GC, and GU Watson-Crick basepairs. With the large number of atomic- or near atomic-resolution 3D structures appearing in a steady stream in the PDB/NDB structure databases, the automated identification, extraction, comparison, clustering and visualization of these structural elements presents an opportunity to enhance {RNA} science. Three broad applications are: (1) identification of modular, autonomous structural units for {RNA} nanotechnology, nanobiology and synthetic biology applications; (2) bioinformatic analysis to improve {RNA} 3D structure prediction from sequence; and (3) creation of searchable databases for exploring the binding specificities, structural flexibility, and dynamics of these {RNA} elements. In this contribution, we review methods developed for computational extraction of hairpin and internal loop motifs from a non-redundant set of high-quality {RNA} 3D structures. We provide a statistical summary of the extracted hairpin and internal loop motifs in the most recent version of the {RNA} 3D Motif Atlas. We also explore the reliability and accuracy of the extraction process by examining its performance in clustering recurrent motifs from homologous ribosomal {RNA} (rRNA) structures. We conclude with a summary of remaining challenges, especially with regard to extraction of multi-helix junction motifs.},
	urldate = {2018-05-31},
	journal = {Methods},
	author = {Parlea, Lorena G. and Sweeney, Blake A. and Hosseini-Asanjan, Maryam and Zirbel, Craig L. and Leontis, Neocles B.},
	month = jul,
	year = {2016},
	keywords = {Hairpin loop, Internal loop, Multi-helix junction loop, Non-Watson-Crick basepair, {RNA} 3D Motif, Structured {RNA} molecules},
	pages = {99--119},
	file = {ScienceDirect Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/HM9ZDD83/Parlea et al. - 2016 - The {RNA} 3D Motif Atlas Computational methods for .pdf:application/pdf;ScienceDirect Snapshot:/nhome/siniac/lbecquey/Zotero/storage/ANJRICVQ/S1046202316301049.html:text/html}
}

@article{legendre_bi-objective_2018,
	title = {Bi-objective integer programming for {RNA} secondary structure prediction with pseudoknots},
	volume = {19},
	issn = {1471-2105},
	url = {https://doi.org/10.1186/s12859-018-2007-7},
	doi = {10.1186/s12859-018-2007-7},
	abstract = {RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of {RNA} secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure.},
	urldate = {2018-05-24},
	journal = {BMC Bioinformatics},
	author = {Legendre, Audrey and Angel, Eric and Tahi, Fariza},
	month = jan,
	year = {2018},
	keywords = {RNA, Bi-objective, Integer programming, Optimal solutions, Pseudoknot, Secondary structure, Sub-optimal solutions},
	pages = {13},
	annote = {Pages 13 in PDF},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/4YMW5M4S/Legendre et al. - 2018 - Bi-objective integer programming for {RNA} secondary.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/XP46BMHH/s12859-018-2007-7.html:text/html}
}

@article{sarver_fr3d:_2008,
	title = {{FR}3D: finding local and composite recurrent structural motifs in {RNA} 3D structures},
	volume = {56},
	issn = {1432-1416},
	shorttitle = {{FR}3D},
	url = {https://doi.org/10.1007/s00285-007-0110-x},
	doi = {10.1007/s00285-007-0110-x},
	abstract = {New methods are described for finding recurrent three-dimensional (3D) motifs in {RNA} atomic-resolution structures. Recurrent {RNA} 3D motifs are sets of {RNA} nucleotides with similar spatial arrangements. They can be local or composite. Local motifs comprise nucleotides that occur in the same hairpin or internal loop. Composite motifs comprise nucleotides belonging to three or more different {RNA} strand segments or molecules. We use a base-centered approach to construct efficient, yet exhaustive search procedures using geometric, symbolic, or mixed representations of {RNA} structure that we implement in a suite of MATLAB programs, “Find {RNA} 3D” (FR3D). The first modules of FR3D preprocess structure files to classify base-pair and -stacking interactions. Each base is represented geometrically by the position of its glycosidic nitrogen in 3D space and by the rotation matrix that describes its orientation with respect to a common frame. Base-pairing and base-stacking interactions are calculated from the base geometries and are represented symbolically according to the Leontis/Westhof basepairing classification, extended to include base-stacking. These data are stored and used to organize motif searches. For geometric searches, the user supplies the 3D structure of a query motif which FR3D uses to find and score geometrically similar candidate motifs, without regard to the sequential position of their nucleotides in the {RNA} chain or the identity of their bases. To score and rank candidate motifs, FR3D calculates a geometric discrepancy by rigidly rotating candidates to align optimally with the query motif and then comparing the relative orientations of the corresponding bases in the query and candidate motifs. Given the growing size of the {RNA} structure database, it is impossible to explicitly compute the discrepancy for all conceivable candidate motifs, even for motifs with less than ten nucleotides. The screening algorithm that we describe finds all candidate motifs whose geometric discrepancy with respect to the query motif falls below a user-specified cutoff discrepancy. This technique can be applied to RMSD searches. Candidate motifs identified geometrically may be further screened symbolically to identify those that contain particular basepair types or base-stacking arrangements or that conform to sequence continuity or nucleotide identity constraints. Purely symbolic searches for motifs containing user-defined sequence, continuity and interaction constraints have also been implemented. We demonstrate that FR3D finds all occurrences, both local and composite and with nucleotide substitutions, of sarcin/ricin and kink-turn motifs in the 23S and 5S ribosomal {RNA} 3D structures of the H. marismortui 50S ribosomal subunit and assigns the lowest discrepancy scores to bona fide examples of these motifs. The search algorithms have been optimized for speed to allow users to search the non-redundant {RNA} 3D structure database on a personal computer in a matter of minutes.},
	language = {en},
	number = {1},
	urldate = {2018-10-10},
	journal = {Journal of Mathematical Biology},
	author = {Sarver, Michael and Zirbel, Craig L. and Stombaugh, Jesse and Mokdad, Ali and Leontis, Neocles B.},
	month = jan,
	year = {2008},
	keywords = {05C85, 92C40},
	pages = {215--252}
}

@article{petrov_webfr3dserver_2011,
	title = {{WebFR}3D—a server for finding, aligning and analyzing recurrent {RNA} 3D motifs},
	volume = {39},
	issn = {0305-1048},
	url = {https://academic.oup.com/nar/article/39/suppl_2/W50/2505799},
	doi = {10.1093/nar/gkr249},
	abstract = {Abstract.   WebFR3D is the on-line version of ‘Find {RNA} 3D’ (FR3D), a program for annotating atomic-resolution {RNA} 3D structure files and searching them efficie},
	language = {en},
	number = {suppl\_2},
	urldate = {2018-10-10},
	journal = {Nucleic Acids Research},
	author = {Petrov, Anton I. and Zirbel, Craig L. and Leontis, Neocles B.},
	month = jul,
	year = {2011},
	pages = {W50--W55},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/WVGVFLFH/Petrov et al. - 2011 - WebFR3D—a server for finding, aligning and analyzi.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/8NMZZLKV/2505799.html:text/html}
}

@article{petrov_automated_2013,
	title = {Automated classification of {RNA} 3D motifs and the {RNA} 3D {Motif} {Atlas}},
	volume = {19},
	issn = {1355-8382, 1469-9001},
	url = {http://rnajournal.cshlp.org/content/19/10/1327},
	doi = {10.1261/rna.039438.113},
	abstract = {A monthly journal publishing high-quality, peer-reviewed research on all topics related to {RNA} and its metabolism in all organisms},
	language = {en},
	number = {10},
	urldate = {2018-10-10},
	journal = {RNA},
	author = {Petrov, Anton I. and Zirbel, Craig L. and Leontis, Neocles B.},
	month = jan,
	year = {2013},
	pmid = {23970545},
	pages = {1327--1340},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/GIC5F2CJ/Petrov et al. - 2013 - Automated classification of {RNA} 3D motifs and the .pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/UJFVEML3/1327.full.html:text/html}
}

@article{lu_dssr:_2015,
	title = {{DSSR}: an integrated software tool for dissecting the spatial structure of {RNA}},
	volume = {43},
	issn = {0305-1048},
	shorttitle = {{DSSR}},
	url = {https://academic.oup.com/nar/article/43/21/e142/2468098},
	doi = {10.1093/nar/gkv716},
	abstract = {Abstract.  Insight into the three-dimensional architecture of {RNA} is essential for understanding its cellular functions. However, even the classic transfer RNA},
	language = {en},
	number = {21},
	urldate = {2018-10-09},
	journal = {Nucleic Acids Research},
	author = {Lu, Xiang-Jun and Bussemaker, Harmen J. and Olson, Wilma K.},
	month = dec,
	year = {2015},
	pages = {e142--e142},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/NURMS9UY/Lu et al. - 2015 - DSSR an integrated software tool for dissecting t.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/BTZXBR6S/2468098.html:text/html}
}

@article{antczak_rnapdbeewebserver_2014,
	title = {{RNApdbee}—a webserver to derive secondary structures from pdb files of knotted and unknotted {RNAs}},
	volume = {42},
	issn = {0305-1048},
	url = {https://academic.oup.com/nar/article/42/W1/W368/2435287},
	doi = {10.1093/nar/gku330},
	abstract = {Abstract.  In {RNA} structural biology and bioinformatics an access to correct {RNA} secondary structure and its proper representation is of crucial importance. Thi},
	language = {en},
	number = {W1},
	urldate = {2018-10-09},
	journal = {Nucleic Acids Research},
	author = {Antczak, Maciej and Zok, Tomasz and Popenda, Mariusz and Lukasiak, Piotr and Adamiak, Ryszard W. and Blazewicz, Jacek and Szachniuk, Marta},
	month = jul,
	year = {2014},
	pages = {W368--W372},
	file = {Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/RWXNJQH6/Antczak et al. - 2014 - RNApdbee—a webserver to derive secondary structure.pdf:application/pdf;Snapshot:/nhome/siniac/lbecquey/Zotero/storage/H6CZVH4F/2435287.html:text/html}
}


@article{dirksAlgorithmComputingNucleic2004,
  title = {An Algorithm for Computing Nucleic Acid Base-Pairing Probabilities Including Pseudoknots},
  volume = {25},
  copyright = {Copyright \textcopyright{} 2004 Wiley Periodicals, Inc.},
  issn = {1096-987X},
  doi = {10.1002/jcc.20057},
  abstract = {Given a nucleic acid sequence, a recent algorithm allows the calculation of the partition function over secondary structure space including a class of physically relevant pseudoknots. Here, we present a method for computing base-pairing probabilities starting from the output of this partition function algorithm. The approach relies on the calculation of recursion probabilities that are computed by backtracking through the partition function algorithm, applying a particular transformation at each step. This transformation is applicable to any partition function algorithm that follows the same basic dynamic programming paradigm. Base-pairing probabilities are useful for analyzing the equilibrium ensemble properties of natural and engineered nucleic acids, as demonstrated for a human telomerase {RNA} and a synthetic DNA nanostructure. \textcopyright{} 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1295\textendash{}1304, 2004},
  language = {en},
  number = {10},
  journal = {Journal of Computational Chemistry},
  author = {Dirks, Robert M. and Pierce, Niles A.},
  year = {2004},
  keywords = {pseudoknots,RNA,DNA,base-pairing probabilities,partition function},
  pages = {1295-1304},
  file = {/nhome/siniac/lbecquey/Zotero/storage/LA8RHBVN/jcc.html}
}


@article{laing_computational_2010,
	title = {Computational approaches to 3D modeling of {RNA}},
	volume = {22},
	issn = {0953-8984},
	url = {http://stacks.iop.org/0953-8984/22/i=28/a=283101},
	doi = {10.1088/0953-8984/22/28/283101},
	abstract = {Many exciting discoveries have recently revealed the versatility of {RNA} and its importance in a variety of functions within the cell. Since the structural features of {RNA} are of major importance to their biological function, there is much interest in predicting {RNA} structure, either in free form or in interaction with various ligands, including proteins, metabolites and other molecules. In recent years, an increasing number of researchers have developed novel {RNA} algorithms for predicting {RNA} secondary and tertiary structures. In this review, we describe current experimental and computational advances and discuss recent ideas that are transforming the traditional view of {RNA} folding. To evaluate the performance of the most recent {RNA} 3D folding algorithms, we provide a comparative study in order to test the performance of available 3D structure prediction algorithms for an {RNA} data set of 43 structures of various lengths and motifs. We find that the algorithms vary widely in terms of prediction quality across different {RNA} lengths and topologies; most predictions have very large root mean square deviations from the experimental structure. We conclude by outlining some suggestions for future {RNA} folding research.},
	language = {en},
	number = {28},
	urldate = {2018-10-09},
	journal = {Journal of Physics: Condensed Matter},
	author = {Laing, Christian and Schlick, Tamar},
	year = {2010},
	pages = {283101}
}

@article{dawson_computational_2016,
	series = {Theory and simulation • {Macromolcular} machines},
	title = {Computational modeling of {RNA} 3D structures and interactions},
	volume = {37},
	issn = {0959-440X},
	url = {http://www.sciencedirect.com/science/article/pii/S0959440X15001700},
	doi = {10.1016/j.sbi.2015.11.007},
	abstract = {RNA molecules have key functions in cellular processes beyond being carriers of protein-coding information. These functions are often dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of {RNA} 3D structures is difficult, which has prompted the development of computational methods for structure prediction from sequence. Recent progress in 3D structure modeling of {RNA} and emerging approaches for predicting {RNA} interactions with ions, ligands and proteins have been stimulated by successes in protein 3D structure modeling.},
	urldate = {2018-10-09},
	journal = {Current Opinion in Structural Biology},
	author = {Dawson, Wayne K and Bujnicki, Janusz M},
	month = apr,
	year = {2016},
	pages = {22--28},
	file = {ScienceDirect Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/7V5SQ8PN/Dawson et Bujnicki - 2016 - Computational modeling of {RNA} 3D structures and in.pdf:application/pdf;ScienceDirect Snapshot:/nhome/siniac/lbecquey/Zotero/storage/CDMTYU7W/S0959440X15001700.html:text/html}
}

@article{laing_computational_2011,
	title = {Computational approaches to {RNA} structure prediction, analysis, and design},
	volume = {21},
	issn = {0959-440X},
	url = {http://www.sciencedirect.com/science/article/pii/S0959440X11000674},
	doi = {10.1016/j.sbi.2011.03.015},
	abstract = {RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires {RNA} tertiary structure knowledge. Although modeling approaches for the study of {RNA} structures and dynamics lag behind efforts in protein folding, much progress has been achieved in the past two years. Here, we review recent advances in {RNA} folding algorithms, {RNA} tertiary motif discovery, applications of graph theory approaches to {RNA} structure and function, and in silico generation of {RNA} sequence pools for aptamer design. Advances within each area can be combined to impact many problems in {RNA} structure and function.},
	number = {3},
	urldate = {2018-10-09},
	journal = {Current Opinion in Structural Biology},
	author = {Laing, Christian and Schlick, Tamar},
	month = jun,
	year = {2011},
	pages = {306--318},
	file = {ScienceDirect Snapshot:/nhome/siniac/lbecquey/Zotero/storage/LWS5RU5Z/S0959440X11000674.html:text/html}
}


@article{reinharz2018mining,
  title={Mining for recurrent long-range interactions in {RNA} structures reveals embedded hierarchies in network families},
  author={Reinharz, Vladimir and Soul{\'e}, Antoine and Westhof, Eric and Waldisp{\"u}hl, J{\'e}r{\^o}me and Denise, Alain},
  journal={Nucleic Acids Research},
  volume={46},
  number={8},
  pages={3841--3851},
  year={2018},
  publisher={Oxford University Press}
}

@article{sato_ipknot:_2011,
	title = {{IPknot}: fast and accurate prediction of {RNA} secondary structures with pseudoknots using integer programming},
	volume = {27},
	issn = {1367-4803},
	shorttitle = {{IPknot}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117384/},
	doi = {10.1093/bioinformatics/btr215},
	abstract = {Motivation: Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes. Recent methods for predicting {RNA} secondary structures cover certain classes of pseudoknotted structures, but only a few of them achieve satisfying predictions in terms of both speed and accuracy., Results: We propose IPknot, a novel computational method for predicting {RNA} secondary structures with pseudoknots based on maximizing expected accuracy of a predicted structure. IPknot decomposes a pseudoknotted structure into a set of pseudoknot-free substructures and approximates a base-pairing probability distribution that considers pseudoknots, leading to the capability of modeling a wide class of pseudoknots and running quite fast. In addition, we propose a heuristic algorithm for refining base-paring probabilities to improve the prediction accuracy of IPknot. The problem of maximizing expected accuracy is solved by using integer programming with threshold cut. We also extend IPknot so that it can predict the consensus secondary structure with pseudoknots when a multiple sequence alignment is given. IPknot is validated through extensive experiments on various datasets, showing that IPknot achieves better prediction accuracy and faster running time as compared with several competitive prediction methods., Availability: The program of IPknot is available at http://www.ncrna.org/software/ipknot/. IPknot is also available as a web server at http://rna.naist.jp/ipknot/., Contact: satoken@k.u-tokyo.ac.jp; ykato@is.naist.jp, Supplementary information: Supplementary data are available at Bioinformatics online.},
	number = {13},
	urldate = {2018-10-12},
	journal = {Bioinformatics},
	author = {Sato, Kengo and Kato, Yuki and Hamada, Michiaki and Akutsu, Tatsuya and Asai, Kiyoshi},
	month = jul,
	year = {2011},
	pmid = {21685106},
	pmcid = {PMC3117384},
	pages = {i85--i93},
	file = {Texte intégral:/nhome/siniac/lbecquey/Zotero/storage/EEWT77EA/Sato et al. - 2011 - IPknot fast and accurate prediction of {RNA} second.pdf:application/pdf}
}


@article{zirbel_identifying_2015,
	title = {Identifying novel sequence variants of {RNA} 3D motifs},
	volume = {43},
	issn = {0305-1048},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4551918/},
	doi = {10.1093/nar/gkv651},
	abstract = {Predicting {RNA} 3D structure from sequence is a major challenge in biophysics. An important sub-goal is accurately identifying recurrent 3D motifs from {RNA} internal and hairpin loop sequences extracted from secondary structure (2D) diagrams. We have developed and validated new probabilistic models for 3D motif sequences based on hybrid Stochastic Context-Free Grammars and Markov Random Fields (SCFG/MRF). The SCFG/MRF models are constructed using atomic-resolution {RNA} 3D structures. To parameterize each model, we use all instances of each motif found in the {RNA} 3D Motif Atlas and annotations of pairwise nucleotide interactions generated by the FR3D software. Isostericity relations between non-Watson–Crick basepairs are used in scoring sequence variants. SCFG techniques model nested pairs and insertions, while MRF ideas handle crossing interactions and base triples. We use test sets of randomly-generated sequences to set acceptance and rejection thresholds for each motif group and thus control the false positive rate. Validation was carried out by comparing results for four motif groups to RMDetect. The software developed for sequence scoring (JAR3D) is structured to automatically incorporate new motifs as they accumulate in the {RNA} 3D Motif Atlas when new structures are solved and is available free for download.},
	number = {15},
	urldate = {2018-10-19},
	journal = {Nucleic Acids Research},
	author = {Zirbel, Craig L. and Roll, James and Sweeney, Blake A. and Petrov, Anton I. and Pirrung, Meg and Leontis, Neocles B.},
	month = sep,
	year = {2015},
	pmid = {26130723},
	pmcid = {PMC4551918},
	pages = {7504--7520},
	file = {PubMed Central Full Text PDF:/nhome/siniac/lbecquey/Zotero/storage/C68JKL5J/Zirbel et al. - 2015 - Identifying novel sequence variants of {RNA} 3D moti.pdf:application/pdf}
}


@software{cplex,
	author =   {IBM ILOG},
	title =    {{CPLEX}: {CPLEX} {Optimizer} (Academic license)},
	version = {12.8},
	howpublished = {https://www.ibm.com/analytics/optimization-modeling-interfaces},
	year = {2018}
}

@article{leontis2001geometric,
  title={Geometric nomenclature and classification of {RNA} base pairs},
  author={Leontis, Neocles B and Westhof, Eric},
  journal={Rna},
  volume={7},
  number={4},
  pages={499--512},
  year={2001},
  publisher={Cambridge University Press}
}

@article{sarrazin2019automated,
  title={Automated, customizable and efficient identification of 3D base pair modules with BayesPairing},
  author={Sarrazin-Gendron, Roman and Reinharz, Vladimir and Oliver, Carlos G and Moitessier, Nicolas and Waldisp{\"u}hl, J{\'e}r{\^o}me},
  journal={Nucleic acids research},
  year={2019}
}

@article{schlick2018adventures,
  title={Adventures with {RNA} graphs},
  author={Schlick, Tamar},
  journal={Methods},
  year={2018},
  publisher={Elsevier}
}

@article{chojnowski2014rna,
  title={RNA Bricks—a database of {RNA} 3D motifs and their interactions},
  author={Chojnowski, Grzegorz and Wale{\'n}, Tomasz and Bujnicki, Janusz M},
  journal={Nucleic acids research},
  volume={42},
  number={D1},
  pages={D123--D131},
  year={2014},
  publisher={Oxford University Press}
}

@article{gendron2001quantitative,
  title={Quantitative analysis of nucleic acid three-dimensional structures},
  author={Gendron, Patrick and Lemieux, S{\'e}bastien and Major, Fran{\c{c}}ois},
  journal={Journal of molecular biology},
  volume={308},
  number={5},
  pages={919--936},
  year={2001},
  publisher={Elsevier}
}