ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

doi:10.1093/nar/gkv491

. 2015 Jul 1;43(W1):W85-90.

doi: 10.1093/nar/gkv491. Epub 2015 May 14.

ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

Zhi-Gang Hua¹, Yan Lin², Ya-Zhou Yuan¹, De-Chang Yang¹, Wen Wei¹, Feng-Biao Guo³

Affiliations

¹ Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, China Health Big Data Science Research Center, Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China.
² Department of Physics, Tianjin University, Tianjin 300072, China Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin 300072, China Collaborative Innovation Center of Chemical Science and Engineering, Tianjin 300072, China.
³ Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, China Health Big Data Science Research Center, Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China fbguo@uestc.edu.cn.

PMID: 25977299
PMCID: PMC4489317
DOI: 10.1093/nar/gkv491

ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

Zhi-Gang Hua et al. Nucleic Acids Res. 2015.

. 2015 Jul 1;43(W1):W85-90.

doi: 10.1093/nar/gkv491. Epub 2015 May 14.

Authors

Zhi-Gang Hua¹, Yan Lin², Ya-Zhou Yuan¹, De-Chang Yang¹, Wen Wei¹, Feng-Biao Guo³

Affiliations

¹ Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, China Health Big Data Science Research Center, Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China.
² Department of Physics, Tianjin University, Tianjin 300072, China Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin 300072, China Collaborative Innovation Center of Chemical Science and Engineering, Tianjin 300072, China.
³ Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 610054, China Health Big Data Science Research Center, Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China Key Laboratory for NeuroInformation of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu 610054, China fbguo@uestc.edu.cn.

PMID: 25977299
PMCID: PMC4489317
DOI: 10.1093/nar/gkv491

Abstract

In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions.

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Figures

**Figure 1.**
Submission interface (left top panel), option menu (left bottom panel), and an example of output information (right panel) using the *Portiera aleyrodidarum* genome.

**Figure 2.**
Sketch of the procedure for the joint application of ZCURVE 3.0 and Glimmer 3.02. (A) A simulated example using the *Pyrococcus abyssi* GE5 genome. (B) Suggested pipeline when using a newly sequenced genome.

See this image and copyright information in PMC

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References

1. Xu Y., Gogarten J.P. Computational Methods for Understanding Archaeal and Bacterial Genomes. London: Imperial College Press; 2008. pp. 39–74.
1. Richardson E.J., Watson M. The automatic annotation of bacterial genomes. Brief Bioinform. 2013;14:1–12. - PMC - PubMed
1. Guo F.B., Ou H.Y., Zhang C.T. ZCURVE: a new system for recognizing protein-coding genes in bacterial and archaeal genomes. Nucleic Acids Res. 2003;31:1780–1789. - PMC - PubMed
1. Zhang R., Zhang C.T. Z curves, an intuitive tool for visualizing and analyzing the DNA sequences. J. Biomol. Struct. Dyn. 1994;11:767–782. - PubMed
1. Guo F.B., Lin Y., Chen L.L. Recognition of protein-coding genes based on Z-curve algorithms. Curr. Genomics. 2014;15:95–103. - PMC - PubMed

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[1] Xu Y., Gogarten J.P. Computational Methods for Understanding Archaeal and Bacterial Genomes. London: Imperial College Press; 2008. pp. 39–74.

[2] Xu Y., Gogarten J.P. Computational Methods for Understanding Archaeal and Bacterial Genomes. London: Imperial College Press; 2008. pp. 39–74.

[3] Richardson E.J., Watson M. The automatic annotation of bacterial genomes. Brief Bioinform. 2013;14:1–12. - PMC - PubMed

[4] Richardson E.J., Watson M. The automatic annotation of bacterial genomes. Brief Bioinform. 2013;14:1–12. - PMC - PubMed

[5] Guo F.B., Ou H.Y., Zhang C.T. ZCURVE: a new system for recognizing protein-coding genes in bacterial and archaeal genomes. Nucleic Acids Res. 2003;31:1780–1789. - PMC - PubMed

[6] Guo F.B., Ou H.Y., Zhang C.T. ZCURVE: a new system for recognizing protein-coding genes in bacterial and archaeal genomes. Nucleic Acids Res. 2003;31:1780–1789. - PMC - PubMed

[7] Zhang R., Zhang C.T. Z curves, an intuitive tool for visualizing and analyzing the DNA sequences. J. Biomol. Struct. Dyn. 1994;11:767–782. - PubMed

[8] Zhang R., Zhang C.T. Z curves, an intuitive tool for visualizing and analyzing the DNA sequences. J. Biomol. Struct. Dyn. 1994;11:767–782. - PubMed

[9] Guo F.B., Lin Y., Chen L.L. Recognition of protein-coding genes based on Z-curve algorithms. Curr. Genomics. 2014;15:95–103. - PMC - PubMed

[10] Guo F.B., Lin Y., Chen L.L. Recognition of protein-coding genes based on Z-curve algorithms. Curr. Genomics. 2014;15:95–103. - PMC - PubMed

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ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

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ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

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