Plant Biotechnology Reports

, Volume 13, Issue 5, pp 553–558 | Cite as

Sugarcane ORF finder: the web-application for mining genes from sugarcane genome

  • Yang Jae KangEmail author
Original Article


Sugarcane is one of the most important multi-purpose crops that are native to tropical regions. As commercial sugarcane is an interspecific hybrid between Saccharum officianarum and Saccharum spontaneum, the genome size of sugarcane has been reported around 10 Gbase. From this reason, it has been hard to build the reference level assembly resulting in partial contigs. With the advance of third generation sequencers and assembly strategies, recently, two sugarcane genome assemblies were published representing monoploidy of sugarcane cultivar and whole genome of S. spontaneum. Synergetically, the genome-editing technology is advanced that can eventually facilitate the sugarcane breeding and the necessity of utilizing genome sequence is also arising for sugarcane researchers to find guide-RNA binding site by the machine learning algorithms or human inspection. Here, we built web-application for the researchers to find the open reading frame (ORF) of sugarcane genes easily with a query gene ID and catalog candidate gene list by query sentences ( This enables the researchers to find their genes of interest and directly observe the ORF structure of the query gene and design precise guide RNA for genome editing.



This work was carried out with the support of “Cooperative Research Program for National Agricultural Genome Program (Project No. PJ01347303)” Rural Development Administration, Republic of Korea.


  1. Buels R, Yao E, Diesh CM et al (2016) JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biol 17:66CrossRefGoogle Scholar
  2. Camacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC Bioinformat 10:421CrossRefGoogle Scholar
  3. Cock PJA, Antao T, Chang JT et al (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25:1422–1423CrossRefGoogle Scholar
  4. Crystian D, dos Santos JM, de S Barbosa GV, Almeida C (2018) Genetic diversity trends in sugarcane germplasm: analysis in the germplasm bank of the RB varieties. Crop Breed Appl Biotechnol 18:426–431CrossRefGoogle Scholar
  5. Deren CW (1995) Genetic base of US mainland sugarcane. Crop Sci 35:1195–1199CrossRefGoogle Scholar
  6. Devlin J, Chang M-W, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. arXiv [cs.CL]Google Scholar
  7. Edmé SJ, Miller JD, Glaz B et al (2005) Genetic contribution to yield gains in the Florida sugarcane industry across 33 years. Crop Sci 45:92–97CrossRefGoogle Scholar
  8. Garside AL, Bell MJ, Robotham BG et al (2005) Managing yield decline in sugarcane cropping systems. Int Sugar J 107:16–26Google Scholar
  9. Garsmeur O, Droc G, Antonise R et al (2018) A mosaic monoploid reference sequence for the highly complex genome of sugarcane. Nat Commun 9:2638CrossRefGoogle Scholar
  10. Hoang NV, Furtado A, Botha FC et al (2015) Potential for genetic improvement of sugarcane as a source of biomass for biofuels. Front Bioeng Biotechnol 3:182CrossRefGoogle Scholar
  11. Kang YJ, Lee T, Lee J et al (2016) Translational genomics for plant breeding with the genome sequence explosion. Plant Biotechnol J 14:1057–1069CrossRefGoogle Scholar
  12. Kim C, Wang X, Lee T-H et al (2014) Comparative analysis of Miscanthus and Saccharum reveals a shared whole-genome duplication but different evolutionary fates. Plant Cell 26:2420–2429CrossRefGoogle Scholar
  13. Linnenluecke MK, Nucifora N, Thompson N (2018) Implications of climate change for the sugarcane industry. Wiley Interdiscip Rev Clim Change 9:e498CrossRefGoogle Scholar
  14. Nadkarni PM, Ohno-Machado L, Chapman WW (2011) Natural language processing: an introduction. J Am Med Inform Assoc 18:544–551CrossRefGoogle Scholar
  15. Paterson AH, Wang X, Li J, Tang H (2012) Ancient and recent polyploidy in monocots. In: Soltis PS, Soltis DE (eds) Polyploidy and genome evolution. Springer, Berlin, pp 93–108CrossRefGoogle Scholar
  16. Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  17. Roach BT (1972) Nobilisation of sugarcane. In: Proc Int Soc Sugar Cane Technol., pp 206–216Google Scholar
  18. Stein LD (2013) Using GBrowse 2.0 to visualize and share next-generation sequence data. Brief Bioinform 14:162–171CrossRefGoogle Scholar
  19. Talukdar D, Verma DK, Malik K et al (2017) Sugarcane as a potential biofuel crop. In: Mohan C (ed) Sugarcane biotechnology: challenges and prospects. Springer International Publishing, Cham, pp 123–137CrossRefGoogle Scholar
  20. Thirugnanasambandam PP, Hoang NV, Henry RJ (2018) The challenge of analyzing the sugarcane genome. Front Plant Sci 9:616CrossRefGoogle Scholar
  21. UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515CrossRefGoogle Scholar
  22. Waskom M, Botvinnik O, O’Kane D, et al (2018) mwaskom/seaborn: v0.9.0 (July 2018)Google Scholar
  23. Wu Y, Schuster M, Chen Z, et al (2016) Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv [cs.CL]Google Scholar
  24. Zhang J, Nagai C, Yu Q et al (2012) Genome size variation in three Saccharum species. Euphytica 185:511–519CrossRefGoogle Scholar
  25. Zhang J, Zhang X, Tang H et al (2018a) Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L. Nat Genet 50:1565–1573CrossRefGoogle Scholar
  26. Zhang Y, Massel K, Godwin ID, Gao C (2018b) Applications and potential of genome editing in crop improvement. Genome Biol 19:210CrossRefGoogle Scholar

Copyright information

© Korean Society for Plant Biotechnology 2019

Authors and Affiliations

  1. 1.Division of Applied Life Science Department atGyeongsang National UniversityPMBBRC, JinjuRepublic of Korea
  2. 2.Division of Life Science Department atGyeongsang National UniversityJinjuRepublic of Korea

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