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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Perspective

Essential Non-coding Genes: A New Playground of Bioinformatics

Author(s): Ying-Ying Zhang and Pu-Feng Du*

Volume 18, Issue 2, 2023

Published on: 31 January, 2023

Page: [105 - 108] Pages: 4

DOI: 10.2174/1574893618666230102105652

Abstract

The essentiality of a gene can be defined at different levels and is context-dependent. Essential protein-coding genes have been well studied. However, the essentiality of non-coding genes is not well characterized. Although experimental technologies, like CRISPR-Cas9, can provide insights into the essentiality of non-coding regions of the genome, scoring the essentiality of noncoding genes in different contexts is still challenging. With machine learning algorithms, the essentiality of protein-coding genes can be estimated well. But the development of these algorithms for non-coding genes was very early. Based on several recent studies, we believe the essentiality of noncoding genes will be a new and fertile ground in bioinformatics. We pointed out some possible research topics in this perspective article.

Keywords: Essential protein-coding genes, non-coding genes, CRISPR-Cas9, machine learning algorithms, bioinformatics, miRNAs.

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[1]
Nurk S, Koren S, Rhie A, et al. The complete sequence of a human genome. Science 2022; 376(6588): 44-53.
[http://dx.doi.org/10.1126/science.abj6987] [PMID: 35357919]
[2]
Schaukowitch K, Kim TK. Emerging epigenetic mechanisms of long non-coding RNAs. Neuroscience 2014; 264: 25-38.
[http://dx.doi.org/10.1016/j.neuroscience.2013.12.009] [PMID: 24342564]
[3]
Baba T, Ara T, Hasegawa M, et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: The Keio collection. Mol Syst Biol 2006; 2006: 8.
[4]
Gerdes SY, Scholle MD, Campbell JW, et al. Experimental determination and system level analysis of essential genes in Escherichia coli MG1655. J Bacteriol 2003; 185(19): 5673-84.
[http://dx.doi.org/10.1128/JB.185.19.5673-5684.2003] [PMID: 13129938]
[5]
Giaever G, Chu AM, Ni L, et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 2002; 418(6896): 387-91.
[http://dx.doi.org/10.1038/nature00935] [PMID: 12140549]
[6]
Hutchison CA III, Peterson SN, Gill SR, et al. Global transposon mutagenesis and a minimal Mycoplasma genome. Science 1999; 286(5447): 2165-9.
[http://dx.doi.org/10.1126/science.286.5447.2165] [PMID: 10591650]
[7]
Hutchison CA III, Chuang RY, Noskov VN, et al. Design and synthesis of a minimal bacterial genome. Science 2016; 351(6280): aad6253.
[http://dx.doi.org/10.1126/science.aad6253] [PMID: 27013737]
[8]
Maniloff J. The minimal cell genome: “on being the right size”. Proc Natl Acad Sci USA 1996; 93(19): 10004-6.
[http://dx.doi.org/10.1073/pnas.93.19.10004] [PMID: 8816738]
[9]
Bartha I, di Iulio J, Venter JC, Telenti A. Human gene essentiality. Nat Rev Genet 2018; 19(1): 51-62.
[http://dx.doi.org/10.1038/nrg.2017.75] [PMID: 29082913]
[10]
Rancati G, Moffat J, Typas A, Pavelka N. Emerging and evolving concepts in gene essentiality. Nat Rev Genet 2018; 19(1): 34-49.
[http://dx.doi.org/10.1038/nrg.2017.74] [PMID: 29033457]
[11]
Boone C, Andrews BJ. The indispensable genome. Science 2015; 350(6264): 1028-9.
[http://dx.doi.org/10.1126/science.aad7925] [PMID: 26612934]
[12]
Liu SJ, Horlbeck MA, Cho SW, et al. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 2017; 355(6320): eaah7111.
[http://dx.doi.org/10.1126/science.aah7111] [PMID: 27980086]
[13]
Blomen VA, Májek P, Jae LT, et al. Gene essentiality and synthetic lethality in haploid human cells. Science 2015; 350(6264): 1092-6.
[http://dx.doi.org/10.1126/science.aac7557] [PMID: 26472760]
[14]
Wang T, Birsoy K, Hughes NW, et al. Identification and characterization of essential genes in the human genome. Science 2015; 350(6264): 1096-101.
[http://dx.doi.org/10.1126/science.aac7041] [PMID: 26472758]
[15]
Gurumayum S, Jiang P, Hao X, et al. OGEE v3: Online gene essentiality database with increased coverage of organisms and human cell lines. Nucleic Acids Res 2021; 49(D1): D998-D1003.
[http://dx.doi.org/10.1093/nar/gkaa884] [PMID: 33084874]
[16]
Tu J, Tian G, Cheung HH, Wei W, Lee T. Gas5 is an essential lncRNA regulator for self-renewal and pluripotency of mouse embryonic stem cells and induced pluripotent stem cells. Stem Cell Res Ther 2018; 9(1): 71.
[http://dx.doi.org/10.1186/s13287-018-0813-5] [PMID: 29562912]
[17]
Statello L, Guo CJ, Chen LL, Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol 2021; 22(2): 96-118.
[http://dx.doi.org/10.1038/s41580-020-00315-9] [PMID: 33353982]
[18]
Iyer MK, Niknafs YS, Malik R, et al. The landscape of long noncoding RNAs in the human transcriptome. Nat Genet 2015; 47(3): 199-208.
[http://dx.doi.org/10.1038/ng.3192] [PMID: 25599403]
[19]
Djebali S, Davis CA, Merkel A, et al. Landscape of transcription in human cells. Nature 2012; 489(7414): 101-8.
[http://dx.doi.org/10.1038/nature11233] [PMID: 22955620]
[20]
Sauvageau M, Goff LA, Lodato S, et al. Multiple knockout mouse models reveal lincRNAs are required for life and brain development. Elife 2013; 2: e01749.
[21]
Bartel DP. Metazoan microRNAs. Cell 2018; 173(1): 20-51.
[http://dx.doi.org/10.1016/j.cell.2018.03.006] [PMID: 29570994]
[22]
Ru X, Cao P, Li L, Zou Q. Selecting essential MicroRNAs using a novel voting method. Mol Ther Nucleic Acids 2019; 18: 16-23.
[http://dx.doi.org/10.1016/j.omtn.2019.07.019] [PMID: 31479921]
[23]
Min H, Xin XH, Gao CQ, Wang L, Du PF. XGEM: Predicting essential miRNAs by the ensembles of various sequence-based classifiers with XGBoost algorithm. Front Genet 2022; 13: 877409.
[24]
Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery 2016; pp. 785-94.
[http://dx.doi.org/10.1145/2939672.2939785]
[25]
Song F, Cui C, Gao L, Cui Q. miES: Predicting the essentiality of miRNAs with machine learning and sequence features. Bioinformatics 2019; 35(6): 1053-4.
[http://dx.doi.org/10.1093/bioinformatics/bty738] [PMID: 30165607]
[26]
Yan C, Wu FX, Wang J, Duan G. PESM: Predicting the essentiality of miRNAs based on gradient boosting machines and sequences. BMC Bioinformatics 2020; 21(1): 111.
[http://dx.doi.org/10.1186/s12859-020-3426-9] [PMID: 32183740]
[27]
Le NQK. Potential of deep representative learning features to interpret the sequence information in proteomics. Proteomics 2022; 22(1-2): 2100232.
[http://dx.doi.org/10.1002/pmic.202100232] [PMID: 34730875]
[28]
Le NQK, Do DT, Nguyen TTD, Le QA. A sequence-based prediction of Kruppel-like factors proteins using XGBoost and optimized features. Gene 2021; 787: 145643.
[http://dx.doi.org/10.1016/j.gene.2021.145643] [PMID: 33848577]
[29]
Zeng P, Chen J, Meng Y, Zhou Y, Yang J, Cui Q. Defining essentiality score of protein-coding genes and long noncoding RNAs. Front Genet 2018; 9: 380.
[http://dx.doi.org/10.3389/fgene.2018.00380] [PMID: 30356729]
[30]
Xin XH, Zhang YY, Gao CQ, Min H, Wang L, Du PF. SGII: Systematic identification of essential lncRNAs in mouse and human genome with lncRNA-protein-protein heterogeneous interaction network. Front Genet 2022; 13: 864564.
[http://dx.doi.org/10.3389/fgene.2022.864564] [PMID: 35386279]
[31]
He X, Kuang L, Chen Z, Tan Y, Wang L. Method for identifying essential proteins by key features of proteins in a novel protein-domain network. Front Genet 2021; 12: 708162.
[http://dx.doi.org/10.3389/fgene.2021.708162] [PMID: 34267785]
[32]
Wang J, Li M, Wang H, et al. Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans Comput Biol Bioinform 2012; 9(4): 1070-80.
[33]
Zhang Z, Luo Y, Hu S, Li X, Wang L, Zhao B. A novel method to predict essential proteins based on tensor and HITS algorithm. Hum Genomics 2020; 14(1): 14.
[http://dx.doi.org/10.1186/s40246-020-00263-7] [PMID: 32252824]
[34]
Zhang YY, Zhang WY, Xin XH, Du PF. dbEssLnc: A manually curated database of human and mouse essential lncRNA genes. Comput Struct Biotechnol J 2022; 20: 2657-63.
[http://dx.doi.org/10.1016/j.csbj.2022.05.043] [PMID: 35685362]
[35]
Zhang R, Lin Y. DEG 5.0, A database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res 2009; 37(Database): D455-8.
[http://dx.doi.org/10.1093/nar/gkn858] [PMID: 18974178]
[36]
Vo TH, Nguyen NTK, Kha QH, Le NQK. On the road to explainable AI in drug-drug interactions prediction: A systematic review. Comput Struct Biotechnol J 2022; 20: 2112-23.
[http://dx.doi.org/10.1016/j.csbj.2022.04.021] [PMID: 35832629]

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