Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou’s 5-steps Rule and General Pseudo Components

Author(s): Zhe Ju*, Shi-Yun Wang

Journal Name: Current Genomics

Volume 20 , Issue 8 , 2019

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Graphical Abstract:


Introduction: Neddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation.

Objective: As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites.

Methods: In this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction.

Results: Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy.

Conclusion: Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at

Keywords: Post-translational modification, neddylation, feature extraction, fuzzy support vector machine, chou’s 5-steps rule, pseudo components.

Jones, J.; Wu, K.; Yang, Y.; Guerrero, C.; Nillegoda, N.; Pan, Z.Q.; Huang, L. A targeted proteomic analysis of the ubiquitin-like modifier nedd8 and associated proteins. J. Proteome Res., 2008, 7(3), 1274-1287.
[] [PMID: 18247557]
Rabut, G.; Peter, M. Function and regulation of protein neddylation. ‘Protein modifications: beyond the usual suspects’ review series. EMBO Rep., 2008, 9(10), 969-976.
[] [PMID: 18802447]
Herrmann, J.; Lerman, L.O.; Lerman, A. Ubiquitin and ubiquitin-like proteins in protein regulation. Circ. Res., 2007, 100(9), 1276-1291.
[] [PMID: 17495234]
Xirodimas, D.P. Novel substrates and functions for the ubiquitin-like molecule NEDD8. Biochem. Soc. Trans., 2008, 36(Pt 5), 802-806.
[] [PMID: 18793140]
Yao, W.T.; Wu, J.F.; Yu, G.Y.; Wang, R.; Wang, K.; Li, L.H.; Chen, P.; Jiang, Y.N.; Cheng, H.; Lee, H.W.; Yu, J.; Qi, H.; Yu, X.J.; Wang, P.; Chu, Y.W.; Yang, M.; Hua, Z.C.; Ying, H.Q.; Hoffman, R.M.; Jeong, L.S.; Jia, L.J. Suppression of tumor angiogenesis by targeting the protein neddylation pathway. Cell Death Dis., 2014, 5(2)e1059
[] [PMID: 24525735]
Chen, Y.; Neve, R.L.; Liu, H. Neddylation dysfunction in Alzheimer’s disease. J. Cell. Mol. Med., 2012, 16(11), 2583-2591.
[] [PMID: 22805479]
Choo, Y.S.; Vogler, G.; Wang, D.; Kalvakuri, S.; Iliuk, A.; Tao, W.A.; Bodmer, R.; Zhang, Z. Regulation of parkin and PINK1 by neddylation. Hum. Mol. Genet., 2012, 21(11), 2514-2523.
[] [PMID: 22388932]
Akbar, S.; Hayat, M. iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences. J. Theor. Biol., 2018, 455, 205-211.
[] [PMID: 30031793]
Ju, Z.; Wang, S.Y. Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou’s general pseudo amino acid composition. Gene, 2018, 664, 78-83.
[] [PMID: 29694908]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. 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., 2019, 568, 14-23.
[] [PMID: 30593778]
Li, F.; Zhang, Y.; Purcell, A.W.; Webb, G.I.; Chou, K.C.; Lithgow, T.; Li, C.; Song, J. Positive-unlabelled learning of glycosylation sites in the human proteome. BMC Bioinformatics, 2019, 20(1), 112.
[] [PMID: 30841845]
Wang, L.; Zhang, R.; Mu, Y. Fu-SulfPred: Identification of protein s-sulfenylation sites by fusing forests via Chou’s general PseAAC. J. Theor. Biol., 2019, 461, 51-58.
[] [PMID: 30365947]
Yavuz, A.S.; Sözer, N.B.; Sezerman, O.U. Prediction of neddylation sites from protein sequences and sequence-derived properties. BMC Bioinformatics, 2015, 16(Suppl. 18), S9.
[] [PMID: 26679222]
Hussain, W.; Khan, Y.D.; Rasool, N.; Khan, S.A.; Chou, K.C. 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., 2019, 468, 1-11.
[] [PMID: 30768975]
Xiao, X.; Min, J.L.; Lin, W.Z.; Liu, Z.; Cheng, X.; Chou, K.C. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach. J. Biomol. Struct. Dyn., 2015, 33(10), 2221-2233.
[] [PMID: 25513722]
Liu, B.; Fang, L.; Wang, S.; Wang, X.; Li, H.; Chou, K.C. Identification of microRNA precursor with the degenerate K-tuple or Kmer strategy. J. Theor. Biol., 2015, 385, 153-159.
[] [PMID: 26362104]
Liu, Z.; Xiao, X.; Yu, D.J.; Jia, J.; Qiu, W.R.; Chou, K.C. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties. Anal. Biochem., 2016, 497, 60-67.
[] [PMID: 26748145]
Chen, W.; Feng, P.M.; Lin, H.; Chou, K.C. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res., 2013, 41(6)e68
[] [PMID: 23303794]
Chen, W.; Feng, P.M.; Deng, E.Z.; Lin, H.; Chou, K.C. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal. Biochem., 2014, 462, 76-83.
[] [PMID: 25016190]
Chen, W.; Feng, P.; Yang, H.; Ding, H.; Lin, H.; Chou, K.C. iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences. Oncotarget, 2017, 8(3), 4208-4217.
[] [PMID: 27926534]
Lin, H.; Deng, E.Z.; Ding, H.; Chen, W.; Chou, K.C. iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res., 2014, 42(21), 12961-12972.
[] [PMID: 25361964]
Feng, P.M.; Chen, W.; Lin, H.; Chou, K.C. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem., 2013, 442(1), 118-125.
[] [PMID: 23756733]
Ding, H.; Deng, E.Z.; Yuan, L.F.; Liu, L.; Lin, H.; Chen, W.; Chou, K.C. iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels. BioMed Res. Int., 2014, 2014286419
[] [PMID: 24991545]
Khan, Y.D.; Jamil, M.; Hussain, W.; Rasool, N.; Khan, S.A.; Chou, K.C. pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J. Theor. Biol., 2019, 463, 47-55.
[] [PMID: 30550863]
Jia, J.; Li, X.; Qiu, W.; Xiao, X.; Chou, K.C. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. J. Theor. Biol., 2019, 460, 195-203.
[] [PMID: 30312687]
Chou, K.C. Some remarks on protein attribute prediction and pseudo amino acid composition. J. Theor. Biol., 2011, 273(1), 236-247.
[] [PMID: 21168420]
Zhang, C.T.; Chou, K.C. An optimization approach to predicting protein structural class from amino acid composition. Protein Sci., 1992, 1(3), 401-408.
[] [PMID: 1304347]
Chou, K.C.; Elrod, D.W. Bioinformatical analysis of G-protein-coupled receptors. J. Proteome Res., 2002, 1(5), 429-433.
[] [PMID: 12645914]
Chou, K.C.; Cai, Y.D. Prediction and classification of protein subcellular location-sequence-order effect and pseudo amino acid composition. J. Cell. Biochem., 2003, 90(6), 1250-1260.
[] [PMID: 14635197]
Hu, L.; Huang, T.; Shi, X.; Lu, W.C.; Cai, Y.D.; Chou, K.C. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One, 2011, 6(1)e14556
[] [PMID: 21283518]
Cai, Y.D.; Feng, K.Y.; Lu, W.C.; Chou, K.C. Using LogitBoost classifier to predict protein structural classes. J. Theor. Biol., 2006, 238(1), 172-176.
[] [PMID: 16043193]
Chou, K.C. Impacts of bioinformatics to medicinal chemistry. Med. Chem., 2015, 11(3), 218-234.
[] [PMID: 25548930]
Chou, K.C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics, 2005, 21(1), 10-19.
[] [PMID: 15308540]
Dehzangi, A.; Heffernan, R.; Sharma, A.; Lyons, J.; Paliwal, K.; Sattar, A. Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC. J. Theor. Biol., 2015, 364, 284-294.
[] [PMID: 25264267]
Behbahani, M.; Mohabatkar, H.; Nosrati, M. Analysis and comparison of lignin peroxidases between fungi and bacteria using three different modes of Chou’s general pseudo amino acid composition. J. Theor. Biol., 2016, 411, 1-5.
[] [PMID: 27615149]
Kabir, M.; Hayat, M. iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Mol. Genet. Genomics, 2016, 291(1), 285-296.
[] [PMID: 26319782]
Meher, P.K.; Sahu, T.K.; Saini, V.; Rao, A.R. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci. Rep., 2017, 7, 42362.
[] [PMID: 28205576]
Ju, Z.; He, J.J. Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou’s PseAAC. J. Mol. Graph. Model., 2017, 76, 356-363.
[] [PMID: 28763688]
Yu, B.; Li, S.; Qiu, W.Y.; Chen, C.; Chen, R.X.; Wang, L.; Wang, M.H.; Zhang, Y. Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising. Oncotarget, 2017, 8(64), 107640-107665.
[] [PMID: 29296195]
Ahmad, J.; Hayat, M. MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components. J. Theor. Biol., 2019, 463, 99-109.
[] [PMID: 30562500]
Contreras-Torres, E. Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC. J. Theor. Biol., 2018, 454, 139-145.
[] [PMID: 29870696]
Zhang, S.; Liang, Y. Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC. J. Theor. Biol., 2018, 457, 163-169.
[] [PMID: 30179589]
Tahir, M.; Hayat, M.; Khan, S.A. iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition. Mol. Genet. Genomics, 2019, 294(1), 199-210.
[] [PMID: 30291426]
Chou, K.C. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr. Top. Med. Chem., 2017, 17(21), 2337-2358.
[] [PMID: 28413951]
Shen, H.B.; Chou, K.C. PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem., 2008, 373(2), 386-388.
[] [PMID: 17976365]
Du, P.; Wang, X.; Xu, C.; Gao, Y. PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions. Anal. Biochem., 2012, 425(2), 117-119.
[] [PMID: 22459120]
Cao, D.S.; Xu, Q.S.; Liang, Y.Z. propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics, 2013, 29(7), 960-962.
[] [PMID: 23426256]
Du, P.; Gu, S.; Jiao, Y. PseAAC-General: fast building various modes of general form of Chou’s pseudo-amino acid composition for large-scale protein datasets. Int. J. Mol. Sci., 2014, 15(3), 3495-3506.
[] [PMID: 24577312]
Chou, K.C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteomics, 2009, 6(4), 262-274.
Chen, W.; Lei, T.Y.; Jin, D.C.; Lin, H.; Chou, K.C. PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. Anal. Biochem., 2014, 456, 53-60.
[] [PMID: 24732113]
Chen, W.; Feng, P.; Ding, H.; Lin, H.; Chou, K.C. iRNA-Methyl: Identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal. Biochem., 2015, 490, 26-33.
[] [PMID: 26314792]
Liu, B.; Yang, F.; Huang, D.S.; Chou, K.C. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC. Bioinformatics, 2018, 34(1), 33-40.
[] [PMID: 28968797]
Tahir, M.; Tayara, H.; Chong, K.T. iRNA-PseKNC(2methyl): Identify RNA 2′-O-methylation sites by convolution neural network and Chou’s pseudo components. J. Theor. Biol., 2019, 465, 1-6.
[] [PMID: 30590059]
Liu, B.; Liu, F.; Wang, X.; Chen, J.; Fang, L.; Chou, K.C. Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res., 2015, 43(W1)W65-71
[] [PMID: 25958395]
Liu, B.; Wu, H. Pse-in-One 2.0: An improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nat. Sci., 2017, 9, 67-91.
Shao, J.; Xu, D.; Tsai, S.N.; Wang, Y.; Ngai, S.M. Computational identification of protein methylation sites through bi-profile Bayes feature extraction. PLoS One, 2009, 4(3)e4920
[] [PMID: 19290060]
Song, J.; Tan, H.; Shen, H.; Mahmood, K.; Boyd, S.E.; Webb, G.I.; Akutsu, T.; Whisstock, J.C. Cascleave: towards more accurate prediction of caspase substrate cleavage sites. Bioinformatics, 2010, 26(6), 752-760.
[] [PMID: 20130033]
Wang, Y.; Zhang, Q.; Sun, M.A.; Guo, D. High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles. Bioinformatics, 2011, 27(6), 777-784.
[] [PMID: 21233168]
Jia, C.; Liu, T.; Chang, A.K.; Zhai, Y. Prediction of mitochondrial proteins of malaria parasite using bi-profile Bayes feature extraction. Biochimie, 2011, 93(4), 778-782.
[] [PMID: 21281691]
Jia, C.Z.; Liu, T.; Wang, Z.P. O-GlcNAcPRED: a sensitive predictor to capture protein O-GlcNAcylation sites. Mol. Biosyst., 2013, 9(11), 2909-2913.
[] [PMID: 24056994]
Xu, Y.; Ding, Y.X.; Ding, J.; Lei, Y.H.; Wu, L.Y.; Deng, N.Y. iSuc-PseAAC: predicting lysine succinylation in proteins by incorporating peptide position-specific propensity. Sci. Rep., 2015, 5, 10184.
[] [PMID: 26084794]
Xu, Y.; Li, L.; Ding, J.; Wu, L.Y.; Mai, G.; Zhou, F. Gly-PseAAC: Identifying protein lysine glycation through sequences. Gene, 2017, 602, 1-7.
[] [PMID: 27845204]
Qiu, W.R.; Sun, B.Q.; Tang, H.; Huang, J.; Lin, H. Identify and analysis crotonylation sites in histone by using support vector machines. Artif. Intell. Med., 2017, 83, 75-81. d
[] [PMID: 28283358]
Lin, C.F.; Wang, S.D. Fuzzy support vector machines. IEEE Trans. Neural Netw., 2002, 13(2), 464-471.
[] [PMID: 18244447]
Batuwita, R.; Palade, V. Class imbalance learning methods for support vector machines. Imbalanced Learning: Foundations, Algorithms, and Applications; He, H; Ma, Y., Ed.; John Wiley Hoboken, NJ, 2013, pp. 83-96.
Chang, C.C.; Lin, C.J. LIBSVM: A library for support vector machines. Acm T. Intel. Syst. Tec., 2011, 2(3), 1-27.
Chou, K.C. Prediction of protein signal sequences and their cleavage sites. Proteins, 2001, 42(1), 136-139.
[<136:AID-PROT130>3.0.CO;2-F] [PMID: 11093267]
Chou, K.C. Using subsite coupling to predict signal peptides. Protein Eng., 2001, 14(2), 75-79.
[] [PMID: 11297664]
Chou, K.C. Prediction of signal peptides using scaled window. Peptides, 2001, 22(12), 1973-1979.
[] [PMID: 11786179]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Mol. Biosyst., 2017, 13(9), 1722-1727.
[] [PMID: 28702580]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mVirus: predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene, 2017, 628, 315-321.
[] [PMID: 28728979]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mGneg: predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics, 2017, 110(4), 231-239.
[] [PMID: 28989035]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics, 2018, 110(1), 50-58.
[] [PMID: 28818512]
Cheng, X.; Zhao, S.G.; Lin, W.Z.; Xiao, X.; Chou, K.C. pLoc-m Animal: predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics, 2017, 33(22), 3524-3531.
[] [PMID: 29036535]
Xiao, X.; Cheng, X.; Su, S.; Nao, Q. pLoc-mGpos: incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat. Sci., 2017, 9, 331-349.
Cheng, X.; Zhao, S.G.; Xiao, X.; Chou, K.C. iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics, 2017, 33(3), 341-346.
[] [PMID: 28172617]
Cheng, X.; Zhao, S.G.; Xiao, X.; Chou, K.C. iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget, 2017, 8(35), 58494-58503.
[] [PMID: 28938573]
Chou, K.C. Some remarks on predicting multi-label attributes in molecular biosystems. Mol. Biosyst., 2013, 9(6), 1092-1100.
[] [PMID: 23536215]
Nakashima, H.; Nishikawa, K. Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J. Mol. Biol., 1994, 238(1), 54-61.
[] [PMID: 8145256]
Wan, S.; Mak, M.W.; Kung, S.Y. Ensemble linear neighborhood propagation forpredicting subchloro plast localization of multi-location proteins. J. Proteome Res., 2016, 15(12), 4755-4762.
[] [PMID: 27766879]
Atchley, W.R.; Zhao, J.; Fernandes, A.D.; Drüke, T. Solving the protein sequence metric problem. Proc. Natl. Acad. Sci. USA, 2005, 102(18), 6395-6400.
[] [PMID: 15851683]
Sagara, J.I.; Shimizu, S.; Kawabata, T.; Nakamura, S.; Ikeguchi, M.; Shimizu, K. The use of sequence comparison to detect ‘identities’ in tRNA genes. Nucleic Acids Res., 1998, 26(8), 1974-1979.
[] [PMID: 9518491]
Chen, Y.Z.; Tang, Y.R.; Sheng, Z.Y.; Zhang, Z. Prediction of mucin-type O-glycosylation sites in mammalian proteins using the composition of k-spaced amino acid pairs. BMC Bioinformatics, 2008, 9(1), 101.
[] [PMID: 18282281]
Chou, K.C.; Shen, H.B. Recent advances in developing web-servers for predicting protein attributes. Nat. Sci., 2009, 1(2), 63-92.
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics, 2018, 34(9), 1448-1456.
[] [PMID: 29106451]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. J. Theor. Biol., 2018, 458, 92-102. d
[] [PMID: 30201434]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc_bal-mPlant: predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. Curr. Pharm. Des., 2018, 24(34), 4013-4022. e
[] [PMID: 30451108]
Chou, K.C.; Cheng, X.; Xiao, X. pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics, 2019, 111(6), 1274-1282.
[] [PMID: 30179658]
Xiao, X.; Cheng, X.; Chen, G.; Mao, Q.; Chou, K.C. pLoc_bal-mVirus: predict subcellular localization of multi-label virus proteins by PseAAC and IHTS treatment to balance training dataset. Med. Chem., 2019, 15(5), 496-509.
[] [PMID: 30556503]
Chen, Y.W.; Lin, C.J. Combining svms with various feature selection strategies. Feature Extraction; Guyon, I.; Nikravesh, N.; Gunn, S; Zadeh, L., Ed.; Springer: Berlin, Germany, 2006, pp. 315-324.
Vacic, V.; Iakoucheva, L.M.; Radivojac, P. Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments. Bioinformatics, 2006, 22(12), 1536-1537.
[] [PMID: 16632492]

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Year: 2019
Published on: 23 January, 2020
Page: [592 - 601]
Pages: 10
DOI: 10.2174/1389202921666191223154629
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