Location:Home / Journals / Article Detail

Journal of Applied Mathematics and Computation

DOI:http://dx.doi.org/10.26855/jamc.2020.12.007

PDF Download

Recent Progresses for Computationally Identifying N6-methyladenosine Sites in Saccharomyces cerevisiae

Kuo-Chen Chou

Gordon Life Science Institute, Boston, MA 02478, USA.

*Corresponding author: Kuo-Chen Chou

Date: November 10,2020 Hits: 2016

Abstract

N6-methyladenosine (m6A) plays critical roles in a broad set of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A methylations, they are still labor intensive, costly and time consuming. As good complements to experimen-tal methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites in Saccharomyces cerevisiae. In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to give a comprehensive review and comparison on existing computational methods. In this review, we summarized the current progresses in computational prediction of m6A sites and also assessed the performance of computational methods for identifying m6A sites on an independent dataset. Finally, challenges and future directions of computationally identifying m6A sites were presented as well. Taken together, we anticipate that this review will provide an important guide for future computational analysis of m6A and other RNA modifications.

References

[1] H. Ding, E. Z. Deng, L. F. Yuan, L. Liu, H. Lin, W. Chen, K. C. Chou. (2014). iCTX-Type: A sequence-based pre-dictor for identifying the types of conotoxins in targeting ion channels, BioMed Research International (BMRI), 2014(2014), 286419.

[2] G. L. Fan, X. Y. Zhang, Y. L. Liu, Y. Nang, H. Wang. (2015). DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou’s pseudo amino acid patterns. J. Comput. Chem., 36(2015), 2317-2327.

[3] L. Pan, W. Zhao, J. Lai, D. Ding, Q. Zhang, X. Yang, M. Huang, S. Jin, Y. Xu, S. Zeng, J. J. Chou, S. Chen. (2017). Sortase A-Generated Highly Potent Anti-CD20-MMAE Conjugates for Efficient Elimination of B-Lineage Lym-phomas, Small, 13(2017).

[4] K. Oxenoid, Y. S. Dong, C. Cao, T. Cui, Y. Sancak, A. L. Markhard, Z. Grabarek, L. Kong, Z. Liu, B. Ouyang, Y. Cong, V. K. Mootha, J. J. Chou. (2016). Architecture of the Mitochondrial Calcium Uniporter, Nature, 533(2016), 269-273.

[5] K. C. Chou. (2011). Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anni-versary Year Review, 5-steps rule). J. Theor. Biol., 273(2011), 236-247.

[6] K. C. Chou, H. B. Shen. (2008). Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols, 3(2008), 153-162.

[7] K. C. Chou, H. B. Shen. (2010). Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular lo-calization of proteins in various organisms. Natural Science, 2(2010), 1090-1103.

[8] K. C. Chou, H. B. Shen. (2007). Recent progresses in protein subcellular location prediction. Anal. Biochem., 370(2007), 1-16.

[9] K. C. Chou, D. W. Elrod. (2002). Bioinformatical analysis of G-protein-coupled receptors. Journal of Proteome Research, 1(2002), 429-433.

[10] W. Chen, H. Lin, P. M. Feng, C. Ding, Y. C. Zuo, K. C. Chou. (2012). iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties. PLoS ONE, 7(2012), e47843.

[11] Y. D. Cai, K. C. Chou. (2004). Predicting subcellular localization of proteins in a hybridization space. Bioinformat-ics, 20(2004), 1151-1156.

[12] K. C. Chou, Y. D. Cai. (2006). Prediction of protease types in a hybridization space. Biochem Biophys Res Comm (BBRC), 339(2006), 1015-1020.

[13] P. M. Feng, W. Chen, H. Lin, K. C. Chou. (2013). iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem., 442(2013), 118-125.

[14] W. Chen, P. M. Feng, H. Lin, K. C. Chou. (2013). iRSpot-PseDNC: identify recombination spots with pseudo di-nucleotide composition. Nucleic Acids Research, 41(2013), e68.

[15] W. Z. Lin, J. A. Fang, X. Xiao, K. C. Chou. (2011). iDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model. PLoS ONE, 6(2011), e24756.

[16] J. Jia, Z. Liu, X. Xiao, B. Liu, K. C. Chou. (2016). pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J. Theor. Biol., 394(2016), 223-230.

[17] K. C. Chou. (2015). Impacts of bioinformatics to medicinal chemistry. Medicinal Chemistry, 11(2015), 218-234.

[18] K. C. Chou. (2001). Prediction of protein cellular attributes using pseudo amino acid composition, PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol. 44, 60), 43(2001), 246-255.

[19] K. C. Chou. (2005). Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioin-formatics, 21(2005), 10-19.

[20] K. C. Chou, Y. D. Cai. (2003). Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition, Journal of Cellular Biochemistry (Addendum, ibid. 2004, 91, 1085), 90(2003) 1250-1260.

[21] M. Arif, M. Hayat, Z. Jan. (2018). iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou's pseudo amino acid composition. J. Theor. Biol., 442(2018), 11-21.

[22] J. Mei, J. Zhao. (2018). Prediction of HIV-1 and HIV-2 proteins by using Chou’s pseudo amino acid compositions and different classifiers. Sci Rep, 8(2018), 2359.

[23] J. Mei, J. Zhao. (2018). Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou’s general pseudo amino acid composition and motif features. J. Theor. Biol., 427(2018), 147-153.

[24] M. S. Krishnan. (2018). Using Chou’s general PseAAC to analyze the evolutionary relationship of receptor asso-ciated proteins (RAP) with various folding patterns of protein domains. J. Theor. Biol., 445(2018), 62-74.

[25] L. Zhang, L. Kong. (2018). iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleo-tide product model into Chou’s pseudo components. J. Theor. Biol., 441(2018), 1-8.

[26] S. Zhang, X. Duan. (2018). Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC. J. Theor. Biol., 437(2018), 239-250.

[27] A. H. Butt, N. Rasool, Y. D. Khan. (2018). Predicting membrane proteins and their types by extracting various se-quence features into Chou’s general PseAAC. Molecular biology reports, 18(2018), 39-58.

[28] E. Contreras-Torres. (2018). Predicting structural classes of proteins by incorporating their global and local physi-cochemical and conformational properties into general Chou’s PseAAC. J. Theor. Biol., 454(2018), 139-145.

[29] F. Javed, M. Hayat. (2018). Predicting subcellular localizations of multi-label proteins by incorporating the se-quence features into Chou’s PseAAC. Genomics, 17(2018), 793-821.

[30] Z. Ju, S. Y. Wang. (2018). Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition. Gene, 664(2018), 78-83.

[31] Y. Liang, S. Zhang. (2018). Identify Gram-negative bacterial secreted protein types by incorporating different mod-es of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence. J. Theor. Biol., 454(2018), 22-29.

[32] J. Mei, Y. Fu, J. Zhao. (2018). Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition. J. Theor. Biol., 456(2018), 41-48.

[33] M. Mousavizadegan, H. Mohabatkar. (2018). Computational prediction of antifungal peptides via Chou’s PseAAC and SVM. Journal of bioinformatics and computational biology, (2018), 1850016.

[34] W. Qiu, S. Li, X. Cui, Z. Yu, M. Wang, J. Du, Y. Peng, B. Yu. (2018). Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid com-position. J. Theor. Biol., 450(2018), 86-103.

[35] S. M. Rahman, S. Shatabda, S. Saha, M. Kaykobad, M. Sohel Rahman. (2018). DPP-PseAAC: A DNA-binding Protein Prediction model using Chou’s general PseAAC. J. Theor. Biol., 452(2018), 22-34.

[36] E. S. Sankari, D. D. Manimegalai. (2018). Predicting membrane protein types by incorporating a novel feature set into Chou’s general PseAAC. J. Theor. Biol., 455(2018), 319-328.

[37] A. Srivastava, R. Kumar, M. Kumar. (2018). BlaPred: predicting and classifying beta-lactamase using a 3-tier pre-diction system via Chou’s general PseAAC. J. Theor. Biol., 457(2018), 29-36.

[38] S. Zhang, Y. Liang. (2018). Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC. J. Theor. Biol., 457(2018), 163-169.

[39] W. Zhao, L. Wang, T. X. Zhang, Z. N. Zhao, P. F. Du. (2018). A brief review on software tools in generating Chou's pseudo-factor representations for all types of biological sequences. Protein Pept Lett, 25(2018), 822-829.

[40] S. Akbar, M. Hayat. (2018). iMethyl-STTNC: Identification of N(6)-methyladenosine sites by extending the Idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J. Theor. Biol., 455(2018), 205-211.

[41] M. A. Al Maruf, S. Shatabda. (2018). iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou’s Pseudo components. Genomics, 18(2018), 63-82.

[42] Y. Pan, S. Wang, Q. Zhang, Q. Lu, D. Su, Y. Zuo, L. Yang. (2019). Analysis and prediction of animal toxins by various Chou’s pseudo components and reduced amino acid compositions. J. Theor. Biol., 462(2019), 221-229.

[43] M. Tahir, M. Hayat, S. A. Khan. (2019). iNuc-ext-PseTNC: an efficient ensemble model for identification of nuc-leosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition, Molecular genetics and genomics: MGG, 294(2019), 199-210.

[44] M. Tahir, H. Tayara, K. T. Chong. (2019). iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by con-volution neural network and Chou’s pseudo components. J. Theor. Biol., 465(2019), 1-6.

[45] B. Tian, X. Wu, C. Chen, W. Qiu, Q. Ma, B. Yu. (2019). Predicting protein-protein interactions by fusing various Chou’s pseudo components and using wavelet denoising approach. J. Theor. Biol., 462(2019), 329-346.

[46] K. C. Chou. (2017). An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Current Topics in Medicinal Chemistry, 17(2017), 2337-2358.

[47] H. B. Shen, K. C. Chou. (2008). PseAAC: a flexible web-server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem., 373(2008), 386-388.

[48] P. Du, X. Wang, C. Xu, Y. Gao. (2012). PseAAC-Builder: A cross-platform stand-alone program for generating various special Chou’s pseudo amino acid compositions, Anal. Biochem., 425(2012), 117-119.

[49] D. S. Cao, Q. S. Xu, Y. Z. Liang. (2013). Propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics, 29(2013), 960-962.

[50] P. Du, S. Gu, Y. Jiao. (2014). PseAAC-General: Fast building various modes of general form of Chou’s pseudo amino acid composition for large-scale protein datasets. International Journal of Molecular Sciences, 15(2014), 3495-3506.

[51] K. C. Chou. (2009). Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics, 6(2009), 262-274.

[52] W. Chen, T. Y. Lei, D. C. Jin, H. Lin, K. C. Chou. (2014). PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition, Anal. Biochem., 456(2014), 53-60.

[53] W. Chen, H. Lin, K. C. Chou. (2015). Pseudo nucleotide composition or PseKNC: an effective formulation for ana-lyzing genomic sequences, Mol BioSyst, 11(2015), 2620-2634.

[54] W. Chen, H. Tang, J. Ye, H. Lin, K. C. Chou. (2016). iRNA-PseU: Identifying RNA pseudouridine sites Molecular Therapy - Nucleic Acids, 5(2016), e332.

[55] B. Liu, L. Fang, R. Long, X. Lan, K. C. Chou. (2016). iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics, 32(2016), 362-369.

[56] B. Liu, R. Long, K. C. Chou. (2016). iDHS-EL: Identifying DNase I hypersensi-tivesites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. Bioinformatics, 32(2016), 2411-2418.

[57] P. Feng, H. Ding, H. Yang, W. Chen, H. Lin, K. C. Chou. (2017). iRNA-PseColl: Identifying the occurrence sites of different RNA modifications by incorporating collective effects of nucleotides into PseKNC. Molecular Therapy - Nucleic Acids, 7(2017), 155-163.

[58] B. Liu, S. Wang, R. Long, K. C. Chou. (2017). iRSpot-EL: identify recombination spots with an ensemble learning approach. Bioinformatics, 33(2017), 35-41.

[59] B. Liu, F. Yang, K. C. Chou. (2017). 2L-piRNA: A two-layer ensemble classifier for identifying piwi-interacting RNAs and their function. Molecular Therapy - Nucleic Acids, 7(2017), 267-277.

[60] M. F. Sabooh, N. Iqbal, M. Khan, M. Khan, H. F. Maqbool. (2018). Identifying 5-methylcytosine sites in RNA se-quence using composite encoding feature into Chou’s PseKNC. J. Theor. Biol., 452(2018), 1-9.

[61] B. Liu, F. Liu, X. Wang, J. Chen, L. Fang, K. C. Chou. (2015). Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res., 43(2015), W65-W71.

[62] B. Liu, H. Wu, K. C. Chou. (2017). Pse-in-One 2.0: An improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences. Natural Science, 9(2017), 67-91.

[63] Z. D. Su, Y. Huang, Z. Y. Zhang, Y. W. Zhao, D. Wang, W. Chen, K. C. Chou, H. Lin. (2018). iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics, 34(2018), 4196-4204.

[64] W. Chen, P. Feng, H. Yang, H. Ding, H. Lin, K.C. Chou. (2018). iRNA-3typeA: identifying 3-types of modification at RNA’s adenosine sites. Molecular Therapy: Nucleic Acid, 11(2018), 468-474.

[65] H. Yang, W. R. Qiu, G. Liu, F. B. Guo, W. Chen, K.C. Chou, H. Lin. (2018). iRSpot-Pse6NC: Identifying recom-bination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Interna-tional Journal of Biological Sciences, 14(2018), 883-891.

[66] K. C. Chou, Y. D. Cai. (2002). Using functional domain composition and support vector machines for prediction of protein subcellular location. J. Biol. Chem., 277(2002), 45765-45769.

[67] Y. D. Cai, G. P. Zhou, K. C. Chou. (2003). Support vector machines for predicting membrane protein types by using functional domain composition. Biophys. J., 84(2003), 3257-3263.

[68] N. Cristianini, J. Shawe-Taylor. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Chapter 3, Cambridge University Press.

[69] L. Breiman, Random Forests. (2001). Machine learning, 45(2001), 5-32.

[70] K. K. Kandaswamy, K. C. Chou, T. Martinetz, S. Moller, P. N. Suganthan, S. Sridharan, G. Pugalenthi. (2011). AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J. Theor. Biol., 270(2011), 56-62.

[71] G. Pugalenthi, K. K. Kandaswamy, K. C. Chou, S. Vivekanandan, P. Kolatkar. (2012). RSARF: Prediction of Resi-due Solvent Accessibility from Protein Sequence Using Random Forest Method. Protein & Peptide Letters, 19(2012), 50-56.

[72] Y. Xu, J. Ding, L. Y. Wu, K. C. Chou. (2013). iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition PLoS ONE, 8(2013), e55844.

[73] J. Jia, Z. Liu, X. Xiao, K. C. Chou. (2015). iPPI-Esml: an ensemble classifier for identifying the interactions of pro-teins by incorporating their physicochemical properties and wavelet transforms into PseAAC. J. Theor. Biol., 377(2015), 47-56.

[74] J. Jia, Z. Liu, X. Xiao, B. Liu, K. C. Chou. (2016). Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC). J Biomol Struct Dyn (JBSD), 34(2016), 1946-1961.

[75] J. Jia, Z. Liu, X. Xiao, B. Liu, K. C. Chou. (2016). iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset. Anal. Biochem., 497(2016), 48-56.

[76] J. Jia, Z. Liu, X. Xiao, B. Liu, K. C. Chou. (2016). iCar-PseCp: identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget, 7(2016), 34558-34570.

[77] W. R. Qiu, B. Q. Sun, X. Xiao, Z. C. Xu, K. C. Chou. (2016). iHyd-PseCp: Identify hydroxyproline and hydroxyly-sine in proteins by incorporating sequence-coupled effects into general PseAAC. Oncotarget, 7(2016), 44310-44321.

[78] W. R. Qiu, B. Q. Sun, X. Xiao, Z. C. Xu, K. C. Chou. (2016). iPTM-mLys: identifying multiple lysine PTM sites and their different types. Bioinformatics, 32(2016), 3116-3123.

[79] W. R. Qiu, X. Xiao, Z. C. Xu, K. C. Chou. (2016). iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier. Oncotarget, 7(2016), 51270-51283.

[80] X. Xiao, H. X. Ye, Z. Liu, J. H. Jia, K. C. Chou. (2016). iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition. Oncotarget, 7(2016), 34180-34189.

[81] W. R. Qiu, B. Q. Sun, X. Xiao, D. Xu, K. C. Chou. (2017). iPhos-PseEvo: Identifying human phosphorylated pro-teins by incorporating evolutionary information into general PseAAC via grey system theory. Molecular Informatics, 36(2017), UNSP 1600010.

[82] B. Liu, F. Yang, D. S. Huang, K. C. Chou. (2018). iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics, 34(2018), 33-40.

[83] J. Jia, X. Li, W. Qiu, X. Xiao, K. C. Chou. (2019). iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. Journal of Theoretical Biology, 460(2019), 195-203.

[84] J. Chen, H. Liu, J. Yang, K. C. Chou. (2007). Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids, 33(2007), 423-428.

[85] K. C. Chou. (2001). Prediction of signal peptides using scaled window, Peptides, 22(2001), 1973-1979.

[86] Y. Xu, X. J. Shao, L. Y. Wu, N. Y. Deng, K. C. Chou. (2013). iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins, PeerJ, 1(2013), e171.

[87] Y. Xu, X. Wen, X. J. Shao, N. Y. Deng, K. C. Chou. (2014). iHyd-PseAAC: Predicting hydroxyproline and hy-droxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition, Int. J. Mol. Sci., 15(2014), 7594-7610.

[88] W. R. Qiu, X. Xiao, W. Z. Lin, K. C. Chou. (2014). iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach, Biomed Res Int (BMRI), 2014(2014), 947416.

[89] H. Lin, E. Z. Deng, H. Ding, W. Chen, K. C. Chou. (2014). iPro54-PseKNC: a sequence-based predictor for identi-fying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition, Nucleic Acids Res., 42 (2014) 12961-12972.

[90] Y. Xu, X. Wen, L. S. Wen, L. Y. Wu, N. Y. Deng, K. C. Chou. (2014). iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition, PLoS ONE, 9(2014), e105018.

[91] X. Xiao, J. L. Min, W. Z. Lin, Z. Liu, X. Cheng, K. C. Chou. (2015). iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via the benchmark dataset optimization approach, J Biomol Struct Dyn (JBSD), 33(2015), 2221-2233.

[92] C. J. Zhang, H. Tang, W. C. Li, H. Lin, W. Chen, K. C. Chou. (2016). iOri-Human: identify human origin of repli-cation by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition, Oncotarget, 7(2016), 69783-69793.

[93] Z. Liu, X. Xiao, D.J. Yu, J. Jia, W.R. Qiu, K.C. Chou, pRNAm-PC: Predicting N-methyladenosine sites in RNA sequences via physical-chemical properties, Anal. Biochem., 497 (2016), 60-67.

[94] W. Chen, H. Ding, P. Feng, H. Lin, K.C. Chou, iACP: a sequence-based tool for identifying anticancer peptides, Oncotarget, 7 (2016), 16895-16909.

[95] J. Jia, Z. Liu, X. Xiao, B. Liu, K. C. Chou. (2016). iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets, Molecules, 21(2016), E95.

[96] W. R. Qiu, S. Y. Jiang, B. Q. Sun, X. Xiao, X. Cheng, K. C. Chou. (2017). iRNA-2methyl: identify RNA 2′-O-methylation sites by incorporating sequence-coupled effects into general PseKNC and ensemble classifier, Me-dicinal Chemistry, 13(2017), 734-743.

[97] W. Chen, P. Feng, H. Yang, H. Ding, H. Lin, K. C. Chou. (2017). iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences, Oncotarget, 8(2017), 4208-4217.

[98] P. K. Meher, T. K. Sahu, V. Saini, A. R. Rao. (2017). Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC, Sci Rep, 7(2017), 42362.

[99] J. Jia, L. Zhang, Z. Liu, X. Xiao, K. C. Chou. (2016). pSumo-CD: Predicting sumoylation sites in proteins with co-variance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC, Bioinformatics, 32(2016), 3133-3141.

[100] A. Ehsan, K. Mahmood, Y. D. Khan, S. A. Khan, K. C. Chou. (2018). A Novel Modeling in Mathematical Biology for Classification of Signal Peptides, Scientific Reports, 8(2018), 1039.

[101] J. Wang, J. Li, B. Yang, R. Xie, T. T. Marquez-Lago, A. Leier, M. Hayashida, T. Akutsu, Y. Zhang, K. C. Chou, J. Selkrig, T. Zhou, J. Song, T. Lithgow. (2018). Bastion3: a two-layer approach for identifying type III secreted ef-fectors using ensemble learning, Bioinformatics, 35 (2018), 2017-2028.

[102] F. Li, C. Li, T. T. Marquez-Lago, A. Leier, T. Akutsu, A. W. Purcell, A. I. Smith, T. Lightow, R. J. Daly, J. Song, K. C. Chou. (2018). Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome, Bioinformatics, 34(2018), 4223-4231.

[103] F. Li, Y. Wang, C. Li, T. T. Marquez-Lago, A. Leier, N. D. Rawlings, G. Haffari, J. Revote, T. Akutsu, K. C. Chou, A. W. Purcell, R. N. Pike, G. I. Webb, A. Ian Smith, T. Lithgow, R. J. Daly, J. C. Whisstock, J. Song. (2018). Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods, Brief in Bioinform, doi:10.1093/bib/bby077 (2018).

[104] J. Song, Y. Wang, F. Li, T. Akutsu, N. D. Rawlings, G. I. Webb, K. C. Chou. (2018). iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites, Brief in Bioinform, 20(2018), 638-658.

[105] W. Chen, H. Ding, X. Zhou, H. Lin, K. C. Chou. (2018). iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition, Anal. Biochem., 561-562(2018), 59-65.

[106] Y. D. Khan, M. Jamil, W. Hussain, N. Rasool, S. A. Khan, K. C. Chou. (2019). pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments, J. Theor. Biol., 463(2019), 47-55.

[107] X. Cheng, W. Z. Lin, X. Xiao, K. C. Chou. (2019). pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC, Bioinformatics, 35(2019), 398-406.

[108] K. C. Chou. (2019). Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs, Current Medicinal Chemistry, 26(2019), 4918-4943.

[109] K. C. Chou, X. Cheng, X. Xiao. (2019). pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset, Med Chem, 15(2019), 472-485.

[110] A. Ehsan, M.K. Mahmood, Y. D. Khan, O. M. Barukab, S. A. Khan, K. C. Chou. (2019). iHyd-PseAAC (EPSV): Identify hydroxylation sites in proteins by extracting enhanced position and sequence variant feature via Chou's 5-step rule and general pseudo amino acid composition, Current Genomics, 20(2019), 124-133.

[111] P. Feng, H. Yang, H. Ding, H. Lin, W. Chen, K. C. Chou. (2019). iDNA6mA-PseKNC: Identifying DNA N(6)-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC, Genomics, 111(2019), 96-102.

[112] W. Hussain, S. D. Khan, N. Rasool, S. A. Khan, K. C. Chou. (2019). SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins, Anal. Biochem., 568(2019), 14-23.

[113] W. Hussain, Y. D. Khan, N. Rasool, S. A. Khan, K. C. Chou. (2019). SPrenylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins, J. Theor. Biol., 468(2019), 1-11.

[114] Y. Lu, S. Wang, J. Wang, G. Zhou, Q. Zhang, X. Zhou, B. Niu, Q. Chen, K. C. Chou. (2019). An Epidemic Avian Influenza Prediction Model Based on Google Trends, Letters in Organic Chemistry, 16(2019), 303-310.

[115] H. B. Shen, K. C. Chou. (2010). Gneg-mPLoc: A top-down strategy to enhance the quality of predicting subcellu-lar localization of Gram-negative bacterial proteins, Journal of Theoretical Biology, 264(2010), 326-333.

[116] K. C. Chou, H. B. Shen. (2010). Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization, PLoS ONE, 5(2010), e11335.

[117] H. B. Shen, K. C. Chou. (2009). A top-down approach to enhance the power of predicting human protein subcel-lular localization: Hum-mPLoc 2.0, Anal. Biochem., 394(2009), 269-274.

[118] Z. C. Wu, X. Xiao, K. C. Chou. (2012). iLoc-Gpos: A Multi-Layer Classifier for Predicting the Subcellular Loca-lization of Singleplex and Multiplex Gram-Positive Bacterial Proteins, Protein & Peptide Letters, 19(2012), 4-14.

[119] W. Z. Lin, J. A. Fang, X. Xiao, K. C. Chou. (2013). iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins Molecular BioSystems, 9(2013), 634-644.

[120] Z. C. Wu, X. Xiao, K. C. Chou. (2011). iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites, Molecular BioSystems, 7(2011), 3287-3297.

[121] X. Cheng, X. Xiao, K. C. Chou. (2017). pLoc-mPlant: predict subcellular localization of multi-location plant pro-teins via incorporating the optimal GO information into general PseAAC, Molecular BioSystems, 13(2017), 1722-1727.

[122] X. Cheng, X. Xiao, K. C. Chou. (2017). pLoc-mVirus: predict subcellular localization of multi-location virus pro-teins via incorporating the optimal GO information into general PseAAC, Gene (Erratum: ibid., 2018, Vol.644, 156-156), 628(2017), 315-321.

[123] X. Cheng, S. G. Zhao, W. Z. Lin, X. Xiao, K. C. Chou. (2017). pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites, Bioinformatics, 33(2017), 3524-3531.

[124] X. Xiao, X. Cheng, S. Su, Q. Nao, K. C. Chou. (2017). pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins, Natural Science, 9(2017), 330-349.

[125] X. Cheng, X. Xiao, K. C. Chou. (2018). pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC, Genomics, 110(2018), 50-58.

[126] X. Cheng, X. Xiao, K. C. Chou. (2018). pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC, Genomics, 110(2018), 231-239.

[127] X. Cheng, X. Xiao, K. C. Chou. (2018). pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information, Bioinformatics, 34(2018), 1448-1456.

[128] E. Pacharawongsakda, T. Theeramunkong. (2013). Predict Subcellular Locations of Singleplex and Multiplex Proteins by Semi-Supervised Learning and Dimension-Reducing General Mode of Chou's PseAAC, IEEE Trans-actions on Nanobioscience, 12(2013), 311-320.

[129] J. Z. Cao, W. Q. Liu, H. Gu. (2012). Predicting Viral Protein Subcellular Localization with Chou's Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm, Protein and  Peptide Letters, 19(2012), 1163-1169.

[130] L. Q. Li, Y. Zhang, L. Y. Zou, Y. Zhou, X. Q. Zheng. (2012). Prediction of Protein Subcellular Multi-Localization Based on the General form of Chou's Pseudo Amino Acid Composition, Protein & Peptide Letters, 19(2012), 375-387.

[131] S. Mei. (2012). Predicting plant protein subcellular multi-localization by Chou’s PseAAC formulation based mul-ti-label homolog knowledge transfer learning, J. Theor. Biol., 310(2012), 80-87.

[132] C. Huang, J. Yuan. (2013). Using radial basis function on the general form of Chou's pseudo amino acid composition and PSSM to predict subcellular locations of proteins with both single and multiple sites, Biosystems, 113(2013), 50-57.

[133] X. Wang, G. Z. Li, W. C. Lu. (2013). Virus-ECC-mPLoc: a multi-label predictor for predicting the subcellular localization of virus proteins with both single and multiple sites based on a general form of Chou’s pseudo amino acid composition, Protein & Peptide Letters, 20(2013), 309-317.

[134] M. Mandal, A. Mukhopadhyay, U. Maulik. (2015). Prediction of protein subcellular localization by incorporating multiobjective PSO-based feature subset selection into the general form of Chou’s PseAAC, Medical & biological engineering & computing, 53(2015), 331-344.

[135] X. Cheng, S. G. Zhao, X. Xiao, K. C. Chou. (2017). iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals, Bioinformatics (Corrigendum, ibid., 2017, Vol. 33, 2610), 33(2017), 341-346.

[136] X. Cheng, S. G. Zhao, X. Xiao, K. C. Chou. (2017). iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals, Oncotarget, 8(2017), 58494-58503.

[137] X. Xiao, P. Wang, W. Z. Lin, J. H. Jia, K. C. Chou. (2013). iAMP-2L: A two-level multi-label classifier for iden-tifying antimicrobial peptides and their functional types, Anal. Biochem.

[138] K. C. Chou. (2013). Some remarks on predicting multi-label attributes in molecular biosystems, Molecular Bio-systems, 9(2013), 1092-1100.

[139] K. C. Chou, C. T. Zhang. (1995). Review: Prediction of protein structural classes, Crit. Rev. Biochem. Mol. Biol., 30(1995), 275-349.

[140] H. Mohabatkar. (2010). Prediction of cyclin proteins using Chou's pseudo amino acid composition, Protein & Peptide Letters, 17(2010), 1207-1214.

[141] G. P. Zhou, K. Doctor. (2003). Subcellular location prediction of apoptosis proteins, Proteins: Struct., Funct., Ge-net., 50(2003), 44-48.

[142] S. S. Sahu, G. Panda. (2010). A novel feature representation method based on Chou’s pseudo amino acid compo-sition for protein structural class prediction, Computational Biology and Chemistry, 34(2010), 320-327.

[143] Zia-ur-Rehman, A. Khan. (2012). Identifying GPCRs and their Types with Chou's Pseudo Amino Acid Composi-tion: An Approach from Multi-scale Energy Representation and Position Specific Scoring Matrix, Protein & Pep-tide Letters, 19(2012), 890-903.

[144] G. L. Fan, Q. Z. Li. (2013). Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition, J. Theor. Biol., 334(2013), 45-51.

[145] C. Huang, J. Q. Yuan. (2013). Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions, J. Theor. Biol., 335(2013), 205-212.

[146] Z. Hajisharifi, M. Piryaiee, M. Mohammad Beigi, M. Behbahani, H. Mohabatkar. (2014). Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via Ames test, J. Theor. Biol., 341(2014), 34-40.

[147] K. C. Chou, H. B. Shen. (2009). Recent advances in developing web-servers for predicting protein attributes Nat-ural Science, 1(2009), 63-92. 

[148] H. B. Shen, K. C. Chou. (2008). HIVcleave: a web-server for predicting HIV protease cleavage sites in proteins, Anal. Biochem., 375(2008), 388-390.

[149] W. R. Qiu, X. Xiao, K. C. Chou. (2014). iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components, Int J Mol Sci (IJMS), 15(2014), 1746-1766.

[150] B. Liu, L. Fang, F. Liu, X. Wang, J. Chen, K. C. Chou. (2015). Identification of real microRNA precursors with a pseudo structure status composition approach, PLoS ONE, 10(2015), e0121501.

[151] J. Wang, B. Yang, J. Revote, A. Leier, T. T. Marquez-Lago, G. Webb, J. Song, K. C. Chou, T. Lithgow. (2017).  POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles, Bioinformatics, 33(2017), 2756-2758.

[152] Z. Chen, P. Y. Zhao, F. Li, Leier A, T. T. Marquez-Lago, Y. Wang, G. I. Webb, A. I. Smith, R. J. Daly, K. C. Chou, J. Song. (2018). iFeature: a python package and web server for features extraction and selection from protein and peptide sequences, Bioinformatics, 34(2018), 2499-2502.

[153] J. Song, F. Li, A. Leier, T. T. Marquez-Lago, T. Akutsu, G. Haffari, K. C. Chou, G. I. Webb, R. N. Pike. (2018). PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy, Bioinformatics, 34(2018), 684-687.

[154] J. Song, F. Li, K. Takemoto, G. Haffari, T. Akutsu, K. C. Chou, G. I. Webb. (2018). PREvaIL, an integrative ap-proach for inferring catalytic residues using sequence, structural and network features in a machine learning framework, Journal of Theoretical  Biology, 443(2018), 125-137.

[155] W. R. Qiu, B. Q. Sun, X. Xiao, Z. C. Xu, J. H. Jia, K. C. Chou. (2018). iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier, Genomics, 110(2018), 239-246.

[156] L. M. Liu, Y. Xu, K. C. Chou, iPGK-PseAAC: identify lysine phosphoglycerylation sites in proteins by incorpo-rating four different tiers of amino acid pairwise coupling information into the general PseAAC, Med Chem, 13(2017), 552-559.

[157] W. R. Qiu, S. Y. Jiang, Z. C. Xu, X. Xiao, K. C. Chou. (2017). iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition, On-cotarget, 8(2017), 41178-41188.

[158] J. Wang, B. Yang, A. Leier, T. T. Marquez-Lago, M. Hayashida, A. Rocker, Z. Yanju, T. Akutsu, K. C. Chou, R. A. Strugnell, J. Song, T. Lithgow. (2018). Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors, Bioinformatics, 34(2018), 2546-2555.

[159] Y. Xu, C. Li, K. C. Chou. (2017). iPreny-PseAAC: identify C-terminal cysteine prenylation sites in proteins by incorporating two tiers of sequence couplings into PseAAC, Med Chem, 13(2017), 544-551.

[160] B. Liu, H. Wu, D. Zhang, X. Wang, K. C. Chou. (2017). Pse-Analysis: a python package for DNA/RNA and pro-tein/peptide sequence analysis based on pseudo components and kernel methods, Oncotarget, 8(2017), 13338-13343.

[161] K. C. Chou, S. P. Jiang, W. M. Liu, C. H. Fee. (1979). Graph theory of enzyme kinetics: 1. Steady-state reaction system, Scientia Sinica, 22(1979), 341-358.

[162] K. C. Chou, S. Forsen. (1980). Graphical rules for enzyme-catalyzed rate laws, Biochem. J., 187(1980), 829-835.

[163] K. C. Chou, S. Forsen, G. Q. Zhou. (1980). Three schematic rules for deriving apparent rate constants, Chemica Scripta, 16(1980), 109-113.

[164] K. C. Chou, R. E. Carter, S. Forsen. (1981). A new graphical method for deriving rate equations for complicated mechanisms, Chemica Scripta, 18(1981), 82-86.

[165] K. C. Chou, S. Forsen. (1981). Graphical rules of steady-state reaction systems, Can. J. Chem., 59(1981), 737-755.

[166] G. P. Zhou, M. H. Deng. (1984). An extension of Chou’s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways, Biochem. J., 222(1984), 169-176.

[167] K. C. Chou. (1989). Graphic rules in steady and non-steady enzyme kinetics, J. Biol. Chem., 264(1989), 12074-12079.

[168] I. W. Althaus, J. J. Chou, A. J. Gonzales, M. R. Diebel, K. C. Chou, F. J. Kezdy, D. L. Romero, P. A. Aristoff, W. G. Tarpley, F. Reusser. (1993). Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E, J. Biol. Chem., 268(1993), 6119-6124.

[169] K. C. Chou. (1990). Review: Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems, Biophysical Chemistry, 35(1990), 1-24.

[170] I. W. Althaus, A. J. Gonzales, J. J. Chou, M. R. Diebel, K. C. Chou, F. J. Kezdy, D. L. Romero, P. A. Aristoff, W. G. Tarpley, F. Reusser. (1993). The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase, J. Biol. Chem., 268(1993), 14875-14880.

[171] K. C. Chou. (2010). Graphic rule for drug metabolism systems, Current Drug Metabolism, 11(2010), 369-378.

[172] G. P. Zhou. (2011). The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism, J. Theor. Biol., 284(2011), 142-148.

[173] I. W. Althaus, J. J. Chou, A. J. Gonzales, M. R. Diebel, K. C. Chou, F. J. Kezdy, D. L. Romero, P. A. Aristoff, W. G. Tarpley, F. Reusser. (1993). Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E, Biochemistry, 32(1993), 6548-6554.

[174] K. C. Chou, W. Z. Lin, X. Xiao. (2011). Wenxiang: a web-server for drawing wenxiang diagrams Natural Science, 3(2011), 862-865 

[175] K. C. Chou, S. Forsen. (1980). Diffusion-controlled effects in reversible enzymatic fast reaction system: Critical spherical shell and proximity rate constants, Biophysical Chemistry, 12(1980), 255-263.

[176] K. C. Chou, T. T. Li, S. Forsen. (1980). The critical spherical shell in enzymatic fast reaction systems, Biophysical Chemistry, 12(1980), 265-269.

[177] H. B. Shen, J. N. Song, K. C. Chou. (2009). Prediction of protein folding rates from primary sequence by fusing multiple sequential features Journal of Biomedical Science and Engineering (JBiSE), 2(2009), 136-143.

[178] K. C. Chou, N. Y. Chen, S. Forsen. (1981). The biological functions of low-frequency phonons: 2. Cooperative effects, Chemica Scripta, 18(1981), 126-132.

[179] K. C. Chou. (1988). Review: Low-frequency collective motion in biomacromolecules and its biological functions, Biophysical Chemistry, 30(1988), 3-48.

[180] K. C. Chou, F. J. Kezdy, F. Reusser. (1994). Review: Kinetics of processive nucleic acid polymerases and nuc-leases, Anal. Biochem., 221(1994), 217-230.

[181] X. Xiao, S. Shao, Y. Ding, Z. Huang, X. Chen, K. C. Chou. (2005). Using cellular automata to generate Image representation for biological sequences, Amino Acids, 28(2005), 29-35.

[182] Z. C. Wu, X. Xiao, K. C. Chou. (2010). 2D-MH: A web-server for generating graphic representation of protein sequences based on the physicochemical properties of their constituent amino acids, J. Theor. Biol., 267(2010), 29-34.


Full-Text HTML

Recent Progresses for Computationally Identifying N6-methyladenosine Sites in Saccharomyces cerevisiae

How to cite this paper: Kuo-Chen Chou. (2020) Recent Progresses for Computationally Identifying N6-methyladenosine Sites in Saccharomyces cerevisiae. Journal of Applied Mathematics and Computation, 4(4), 153-173.

DOI: http://dx.doi.org/10.26855/jamc.2020.12.007