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Journal articles on the topic 'RNA bioinformatics'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Backofen, Rolf, Fabian Amman, Fabrizio Costa, Sven Findeiß, Andreas S. Richter, and Peter F. Stadler. "Bioinformatics of prokaryotic RNAs." RNA Biology 11, no. 5 (2014): 470–83. http://dx.doi.org/10.4161/rna.28647.

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2

Ji, Fei, and Ruslan I. Sadreyev. "RNA-seq: Basic Bioinformatics Analysis." Current Protocols in Molecular Biology 124, no. 1 (2018): e68. http://dx.doi.org/10.1002/cpmb.68.

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3

Marz, Manja, Niko Beerenwinkel, Christian Drosten, et al. "Challenges in RNA virus bioinformatics." Bioinformatics 30, no. 13 (2014): 1793–99. http://dx.doi.org/10.1093/bioinformatics/btu105.

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4

Zok, Tomasz. "BioCommons: a robust java library for RNA structural bioinformatics." Bioinformatics 37, no. 17 (2021): 2766–67. http://dx.doi.org/10.1093/bioinformatics/btab069.

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Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

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5

Muckstein, U., H. Tafer, J. Hackermuller, S. H. Bernhart, P. F. Stadler, and I. L. Hofacker. "Thermodynamics of RNA-RNA binding." Bioinformatics 22, no. 10 (2006): 1177–82. http://dx.doi.org/10.1093/bioinformatics/btl024.

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6

Menzel, Peter, Stefan E. Seemann, and Jan Gorodkin. "RILogo: visualizing RNA–RNA interactions." Bioinformatics 28, no. 19 (2012): 2523–26. http://dx.doi.org/10.1093/bioinformatics/bts461.

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7

Huntley, Rachael P., Barbara Kramarz, Tony Sawford, et al. "Expanding the horizons of microRNA bioinformatics." RNA 24, no. 8 (2018): 1005–17. http://dx.doi.org/10.1261/rna.065565.118.

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8

Gawronski, Alexander R., Michael Uhl, Yajia Zhang, et al. "MechRNA: prediction of lncRNA mechanisms from RNA–RNA and RNA–protein interactions." Bioinformatics 34, no. 18 (2018): 3101–10. http://dx.doi.org/10.1093/bioinformatics/bty208.

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9

Evers, D., and R. Giegerich. "RNA movies: visualizing RNA secondary structure spaces." Bioinformatics 15, no. 1 (1999): 32–37. http://dx.doi.org/10.1093/bioinformatics/15.1.32.

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10

Sühnel, Jürgen. "Bioinformatik. Methoden zur Vorhersage von RNA- und Proteinstrukturen (Bioinformatics. Methods for RNA and protein structure prediction)." Bioelectrochemistry 61, no. 1-2 (2003): 110–11. http://dx.doi.org/10.1016/j.bioelechem.2003.09.003.

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11

Tafer, Hakim, Fabian Amman, Florian Eggenhofer, Peter F. Stadler, and Ivo L. Hofacker. "Fast accessibility-based prediction of RNA–RNA interactions." Bioinformatics 27, no. 14 (2011): 1934–40. http://dx.doi.org/10.1093/bioinformatics/btr281.

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12

Maragkakis, Manolis, Panagiotis Alexiou, Tadashi Nakaya, and Zissimos Mourelatos. "CLIPSeqTools—a novel bioinformatics CLIP-seq analysis suite." RNA 22, no. 1 (2015): 1–9. http://dx.doi.org/10.1261/rna.052167.115.

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13

Busch, A., and R. Backofen. "INFO-RNA--a fast approach to inverse RNA folding." Bioinformatics 22, no. 15 (2006): 1823–31. http://dx.doi.org/10.1093/bioinformatics/btl194.

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14

Tafer, Hakim, and Ivo L. Hofacker. "RNAplex: a fast tool for RNA–RNA interaction search." Bioinformatics 24, no. 22 (2008): 2657–63. http://dx.doi.org/10.1093/bioinformatics/btn193.

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15

Li, Andrew X., Manja Marz, Jing Qin, and Christian M. Reidys. "RNA–RNA interaction prediction based on multiple sequence alignments." Bioinformatics 27, no. 4 (2010): 456–63. http://dx.doi.org/10.1093/bioinformatics/btq659.

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16

Cordero, Pablo, Julius B. Lucks, and Rhiju Das. "An RNA Mapping DataBase for curating RNA structure mapping experiments." Bioinformatics 28, no. 22 (2012): 3006–8. http://dx.doi.org/10.1093/bioinformatics/bts554.

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17

Forster, Samuel C., Alexander M. Finkel, Jodee A. Gould, and Paul J. Hertzog. "RNA-eXpress annotates novel transcript features in RNA-seq data." Bioinformatics 29, no. 6 (2013): 810–12. http://dx.doi.org/10.1093/bioinformatics/btt034.

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18

Reeder, Jens, Matthias Höchsmann, Marc Rehmsmeier, Björn Voss, and Robert Giegerich. "Beyond Mfold: Recent advances in RNA bioinformatics." Journal of Biotechnology 124, no. 1 (2006): 41–55. http://dx.doi.org/10.1016/j.jbiotec.2006.01.034.

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19

Holmqvist, Isak, Alan Bäckerholm, Yarong Tian, Guojiang Xie, Kaisa Thorell, and Ka-Wei Tang. "FLAME: long-read bioinformatics tool for comprehensive spliceome characterization." RNA 27, no. 10 (2021): 1127–39. http://dx.doi.org/10.1261/rna.078800.121.

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Comprehensive characterization of differentially spliced RNA transcripts with nanopore sequencing is limited by bioinformatics tools that are reliant on existing annotations. We have developed FLAME, a bioinformatics pipeline for alternative splicing analysis of gene-specific or transcriptome-wide long-read sequencing data. FLAME is a Python-based tool aimed at providing comprehensible quantification of full-length splice variants, reliable de novo recognition of splice sites and exons, and representation of consecutive exon connectivity in the form of a weighted adjacency matrix. Notably, this workflow circumvents issues related to inadequate reference annotations and allows for incorporation of short-read sequencing data to improve the confidence of nanopore sequencing reads. In this study, the Epstein-Barr virus long noncoding RNA RPMS1 was used to demonstrate the utility of the pipeline. RPMS1 is ubiquitously expressed in Epstein-Barr virus associated cancer and known to undergo ample differential splicing. To fully resolve the RPMS1 spliceome, we combined gene-specific nanopore sequencing reads from a primary gastric adenocarcinoma and a nasopharyngeal carcinoma cell line with matched publicly available short-read sequencing data sets. All previously reported splice variants, including putative ORFs, were detected using FLAME. In addition, 32 novel exons, including two intron retentions and a cassette exon, were discovered within the RPMS1 gene.

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20

Huang, Fenix W. D., Jing Qin, Christian M. Reidys, and Peter F. Stadler. "Partition function and base pairing probabilities for RNA–RNA interaction prediction." Bioinformatics 25, no. 20 (2009): 2646–54. http://dx.doi.org/10.1093/bioinformatics/btp481.

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21

Zhang, Yang, Tianyuan Liu, Liqun Chen, et al. "RIscoper: a tool for RNA–RNA interaction extraction from the literature." Bioinformatics 35, no. 17 (2019): 3199–202. http://dx.doi.org/10.1093/bioinformatics/btz044.

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Abstract Motivation Numerous experimental and computational studies in the biomedical literature have provided considerable amounts of data on diverse RNA–RNA interactions (RRIs). However, few text mining systems for RRIs information extraction are available. Results RNA Interactome Scoper (RIscoper) represents the first tool for full-scale RNA interactome scanning and was developed for extracting RRIs from the literature based on the N-gram model. Notably, a reliable RRI corpus was integrated in RIscoper, and more than 13 300 manually curated sentences with RRI information were recruited. RIscoper allows users to upload full texts or abstracts, and provides an online search tool that is connected with PubMed (PMID and keyword input), and these capabilities are useful for biologists. RIscoper has a strong performance (90.4% precision and 93.9% recall), integrates natural language processing techniques and has a reliable RRI corpus. Availability and implementation The standalone software and web server of RIscoper are freely available at www.rna-society.org/riscoper/. Supplementary information Supplementary data are available at Bioinformatics online.

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22

DeLuca, David S., Joshua Z. Levin, Andrey Sivachenko, et al. "RNA-SeQC: RNA-seq metrics for quality control and process optimization." Bioinformatics 28, no. 11 (2012): 1530–32. http://dx.doi.org/10.1093/bioinformatics/bts196.

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23

Schnattinger, T., U. Schoning, A. Marchfelder, and H. A. Kestler. "RNA-Pareto: interactive analysis of Pareto-optimal RNA sequence-structure alignments." Bioinformatics 29, no. 23 (2013): 3102–4. http://dx.doi.org/10.1093/bioinformatics/btt536.

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24

Huang, Fenix W. D., Jing Qin, Christian M. Reidys, and Peter F. Stadler. "Target prediction and a statistical sampling algorithm for RNA–RNA interaction." Bioinformatics 26, no. 2 (2009): 175–81. http://dx.doi.org/10.1093/bioinformatics/btp635.

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25

Giulietti, M., S. A. Milantoni, T. Armeni, G. Principato, and F. Piva. "ExportAid: database of RNA elements regulating nuclear RNA export in mammals." Bioinformatics 31, no. 2 (2014): 246–51. http://dx.doi.org/10.1093/bioinformatics/btu620.

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26

Warren, Andrew S., Cristina Aurrecoechea, Brian Brunk, et al. "RNA-Rocket: an RNA-Seq analysis resource for infectious disease research." Bioinformatics 31, no. 9 (2015): 1496–98. http://dx.doi.org/10.1093/bioinformatics/btv002.

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27

DiChiacchio, Laura, Michael F. Sloma, and David H. Mathews. "AccessFold: predicting RNA–RNA interactions with consideration for competing self-structure." Bioinformatics 32, no. 7 (2015): 1033–39. http://dx.doi.org/10.1093/bioinformatics/btv682.

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28

Yamasaki, Satoshi, and Kazuhiko Fukui. "2P266 Tertiary structure prediction of RNA-RNA complex structures using secondary structure information(22A. Bioinformatics: Structural genomics,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S203. http://dx.doi.org/10.2142/biophys.53.s203_1.

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29

Janssen, S., and R. Giegerich. "The RNA shapes studio." Bioinformatics 31, no. 3 (2014): 423–25. http://dx.doi.org/10.1093/bioinformatics/btu649.

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30

Cloonan, Nicole, Qinying Xu, Geoffrey J. Faulkner, et al. "RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data." Bioinformatics 25, no. 19 (2009): 2615–16. http://dx.doi.org/10.1093/bioinformatics/btp459.

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31

Miladi, Milad, Soheila Montaseri, Rolf Backofen, and Martin Raden. "Integration of accessibility data from structure probing into RNA–RNA interaction prediction." Bioinformatics 35, no. 16 (2018): 2862–64. http://dx.doi.org/10.1093/bioinformatics/bty1029.

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Abstract Summary Experimental structure probing data has been shown to improve thermodynamics-based RNA secondary structure prediction. To this end, chemical reactivity information (as provided e.g. by SHAPE) is incorporated, which encodes whether or not individual nucleotides are involved in intra-molecular structure. Since inter-molecular RNA–RNA interactions are often confined to unpaired RNA regions, SHAPE data is even more promising to improve interaction prediction. Here, we show how such experimental data can be incorporated seamlessly into accessibility-based RNA–RNA interaction prediction approaches, as implemented in IntaRNA. This is possible via the computation and use of unpaired probabilities that incorporate the structure probing information. We show that experimental SHAPE data can significantly improve RNA–RNA interaction prediction. We evaluate our approach by investigating interactions of a spliceosomal U1 snRNA transcript with its target splice sites. When SHAPE data is incorporated, known target sites are predicted with increased precision and specificity. Availability and implementation https://github.com/BackofenLab/IntaRNA Supplementary information Supplementary data are available at Bioinformatics online.

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32

Picardi, Ernesto, Mattia D'Antonio, Danilo Carrabino, Tiziana Castrignanò, and Graziano Pesole. "ExpEdit: a webserver to explore human RNA editing in RNA-Seq experiments." Bioinformatics 27, no. 9 (2011): 1311–12. http://dx.doi.org/10.1093/bioinformatics/btr117.

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33

Kato, Yuki, Kengo Sato, Michiaki Hamada, Yoshihide Watanabe, Kiyoshi Asai, and Tatsuya Akutsu. "RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming." Bioinformatics 26, no. 18 (2010): i460—i466. http://dx.doi.org/10.1093/bioinformatics/btq372.

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34

Zhang, Zhaojun, and Wei Wang. "RNA-Skim: a rapid method for RNA-Seq quantification at transcript level." Bioinformatics 30, no. 12 (2014): i283—i292. http://dx.doi.org/10.1093/bioinformatics/btu288.

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35

Kerpedjiev, Peter, Stefan Hammer, and Ivo L. Hofacker. "Forna (force-directed RNA): Simple and effective online RNA secondary structure diagrams." Bioinformatics 31, no. 20 (2015): 3377–79. http://dx.doi.org/10.1093/bioinformatics/btv372.

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36

Fricke, Markus, and Manja Marz. "Prediction of conserved long-range RNA-RNA interactions in full viral genomes." Bioinformatics 32, no. 19 (2016): 2928–35. http://dx.doi.org/10.1093/bioinformatics/btw323.

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37

Li, Musheng, Xueying Xie, Jing Zhou, et al. "Quantifying circular RNA expression from RNA-seq data using model-based framework." Bioinformatics 33, no. 14 (2017): 2131–39. http://dx.doi.org/10.1093/bioinformatics/btx129.

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38

Yan, Yumeng, and Sheng-You Huang. "RRDB: a comprehensive and non-redundant benchmark for RNA–RNA docking and scoring." Bioinformatics 34, no. 3 (2017): 453–58. http://dx.doi.org/10.1093/bioinformatics/btx615.

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39

Wenzel, Anne, Erdinç Akbaşli, and Jan Gorodkin. "RIsearch: fast RNA–RNA interaction search using a simplified nearest-neighbor energy model." Bioinformatics 28, no. 21 (2012): 2738–46. http://dx.doi.org/10.1093/bioinformatics/bts519.

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40

Iwakiri, Junichi, Tomoshi Kameda, Kiyoshi Asai, and Michiaki Hamada. "Analysis of base-pairing probabilities of RNA molecules involved in protein–RNA interactions." Bioinformatics 29, no. 20 (2013): 2524–28. http://dx.doi.org/10.1093/bioinformatics/btt453.

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41

Yan, Zichao, William L. Hamilton, and Mathieu Blanchette. "Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions." Bioinformatics 36, Supplement_1 (2020): i276—i284. http://dx.doi.org/10.1093/bioinformatics/btaa456.

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Abstract Motivation RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor. Results In this study, we introduce RPI-Net, a graph neural network approach for RNA-protein interaction prediction. RPI-Net learns and exploits a graph representation of RNA molecules, yielding significant performance gains over existing state-of-the-art approaches. We also introduce an approach to rectify an important type of sequence bias caused by the RNase T1 enzyme used in many CLIP-Seq experiments, and we show that correcting this bias is essential in order to learn meaningful predictors and properly evaluate their accuracy. Finally, we provide new approaches to interpret the trained models and extract simple, biologically interpretable representations of the learned sequence and structural motifs. Availability and implementation Source code can be accessed at https://www.github.com/HarveyYan/RNAonGraph. Supplementary information Supplementary data are available at Bioinformatics online.

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42

Bernard, Elsa, Laurent Jacob, Julien Mairal, and Jean-Philippe Vert. "Efficient RNA isoform identification and quantification from RNA-Seq data with network flows." Bioinformatics 30, no. 17 (2014): 2447–55. http://dx.doi.org/10.1093/bioinformatics/btu317.

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43

Hamilton, Russell S., and Ilan Davis. "RNA localization signals: Deciphering the message with bioinformatics." Seminars in Cell & Developmental Biology 18, no. 2 (2007): 178–85. http://dx.doi.org/10.1016/j.semcdb.2007.02.001.

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44

Sczyrba, A. "RNA-related tools on the Bielefeld Bioinformatics Server." Nucleic Acids Research 31, no. 13 (2003): 3767–70. http://dx.doi.org/10.1093/nar/gkg576.

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45

Wong, T. K. F., K. L. Wan, B. Y. Hsu, et al. "RNASAlign: RNA Structural Alignment System." Bioinformatics 27, no. 15 (2011): 2151–52. http://dx.doi.org/10.1093/bioinformatics/btr338.

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46

Howe, E. A., R. Sinha, D. Schlauch, and J. Quackenbush. "RNA-Seq analysis in MeV." Bioinformatics 27, no. 22 (2011): 3209–10. http://dx.doi.org/10.1093/bioinformatics/btr490.

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47

Lorenz, Ronny, Ivo L. Hofacker, and Stephan H. Bernhart. "Folding RNA/DNA hybrid duplexes." Bioinformatics 28, no. 19 (2012): 2530–31. http://dx.doi.org/10.1093/bioinformatics/bts466.

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48

Bryant, D. W., R. Shen, H. D. Priest, W. K. Wong, and T. C. Mockler. "Supersplat--spliced RNA-seq alignment." Bioinformatics 26, no. 12 (2010): 1500–1505. http://dx.doi.org/10.1093/bioinformatics/btq206.

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49

Vinogradov, A. E. "Silent DNA: speaking RNA language?" Bioinformatics 19, no. 17 (2003): 2167–70. http://dx.doi.org/10.1093/bioinformatics/btg320.

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50

Kawamura, Yumi, Shinsuke Koyama, and Ryo Yoshida. "Statistical inference of the rate of RNA polymerase II elongation by total RNA sequencing." Bioinformatics 35, no. 11 (2018): 1877–84. http://dx.doi.org/10.1093/bioinformatics/bty886.

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