Abstract
In eukaryotic genomes, approximately 90\(\%\) of genes are transcribed genes, of which only 1–2\(\%\) encode proteins, while the majority of transcribed genes are non-coding RNAs. Non-coding RNAs represent one of the most rapidly advancing frontiers in the field of life sciences. They continuously enhance our understanding of the essence of life, lead the deepening of life sciences, and are poised to make significant breakthroughs in modern life science and technology, providing novel ideas and techniques for genetic breeding; intervention, prevention, and treatment of major human diseases; as well as drug research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guo J M, Tang H. Non-coding RNA was associated with the tumor[M]. Bei**g: People’s Health Publishing House, 2014.
DENG L, GUAN J, WEI X, et al. Boosting prediction performance of protein–protein interaction hot spots by using structural neighborhood properties[J]. Journal of Computational Biology, 2013, 20(11): 878–891.
ALTSCHUL S F, MADDEN T L, SCHÄFFER A A, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs[J]. Nucleic Acids Research, 1997, 25(17): 3389–3402.
ASHKENAZY H, EREZ E, MARTZ E, et al. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids[J]. Nucleic Acids Research, 2010, 38(suppl_2): W529-W533.
LAI E C, TOMANCAK P, WILLIAMS R W, et al. Computational identification of Drosophila microRNA genes[J]. Genome Biology, 2003, 4(7): 1–20.
Yang B F, Wang Z G. Non-coding micromolecular RNA with cardiac disease[M]. Bei**g: People’ s Health Publishing House, 2018.
LAI E C, TOMANCAK P, WILLIAMS R W, et al. Computational identification of Drosophila microRNA genes[J]. Genome Biology, 2003, 4(7): 1–20.
HIGA R H, TOZZI C L. Prediction of binding hot spot residues by using structural and evolutionary parameters[J]. Genetics and Molecular Biology, 2009, 32: 626–633.
JOOSTEN R P, TE BEEK T A H, KRIEGER E, et al. A series of PDB related databases for everyday needs[J]. Nucleic Acids Research, 2010, 39(suppl): D411-D419.
SHINGATE P, MANOHARAN M, SUKHWAL A, et al. ECMIS: computational approach for the identification of hotspots at protein-protein interfaces[J]. BMC Bioinformatics, 2014, 15(1): 1–10.
LEE B, RICHARDS F M. The interpretation of protein structures: estimation of static accessibility[J]. Journal of Molecular Biology, 1971, 55(3): 379-IN4.non-coding.
KORTEMME T, KIM D E, BAKER D. Computational alanine scanning of protein-protein interfaces[J]. Science’s STKE, 2004, (219): l2.
YANG Y, ZHAN L, ZHANG W, et al. RNA secondary structure in mutually exclusive splicing[J]. Nature Structural and Molecular Biology, 2011, 18(2): 159–168.
MARTINEZ H M, MAIZEL J V, SHAPIRO B A. RNA2D3D: a program for generating, viewing, and comparing 3-dimensional models of RNA[J]. Journal of Biomolecular Structure and Dynamics, 2008, 25(6): 669–683.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2024 Guangxi Education Publishing House
About this chapter
Cite this chapter
Chen, Q. (2024). Non-Coding RNA Function and Structure. In: Association Analysis Techniques and Applications in Bioinformatics. Springer, Singapore. https://doi.org/10.1007/978-981-99-8251-6_5
Download citation
DOI: https://doi.org/10.1007/978-981-99-8251-6_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8250-9
Online ISBN: 978-981-99-8251-6
eBook Packages: Computer ScienceComputer Science (R0)