Pseudogenes and Their Genome-Wide Prediction in Plants

Xiao, Jin; Sekhwal, Manoj Kumar; Li, Pingchuan; Ragupathy, Raja; Cloutier, Sylvie; Wang, Xiue; You, Frank M.

doi:10.3390/ijms17121991

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Pseudogenes and Their Genome-Wide Prediction in Plants

by

Jin Xiao

^1,2,

Manoj Kumar Sekhwal

^1,3,

Pingchuan Li

¹,

Raja Ragupathy

⁴,

Sylvie Cloutier

⁵,

Xiue Wang

² and

Frank M. You

^1,*

¹

Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada

²

Department of Agronomy, Nanjing Agricultural University, Nanjing 210095, China

³

Department of Soil Science, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada

⁴

Department of Plant Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada

⁵

Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada

^*

Author to whom correspondence should be addressed.

Int. J. Mol. Sci. 2016, 17(12), 1991; https://doi.org/10.3390/ijms17121991

Submission received: 15 October 2016 / Revised: 20 November 2016 / Accepted: 22 November 2016 / Published: 28 November 2016

(This article belongs to the Section Molecular Plant Sciences)

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Abstract

Pseudogenes are paralogs generated from ancestral functional genes (parents) during genome evolution, which contain critical defects in their sequences, such as lacking a promoter, having a premature stop codon or frameshift mutations. Generally, pseudogenes are functionless, but recent evidence demonstrates that some of them have potential roles in regulation. The majority of pseudogenes are generated from functional progenitor genes either by gene duplication (duplicated pseudogenes) or retro-transposition (processed pseudogenes). Pseudogenes are primarily identified by comparison to their parent genes. Bioinformatics tools for pseudogene prediction have been developed, among which PseudoPipe, PSF and Shiu’s pipeline are publicly available. We compared these three tools using the well-annotated Arabidopsis thaliana genome and its known 924 pseudogenes as a test data set. PseudoPipe and Shiu’s pipeline identified ~80% of A. thaliana pseudogenes, of which 94% were shared, while PSF failed to generate adequate results. A need for improvement of the bioinformatics tools for pseudogene prediction accuracy in plant genomes was thus identified, with the ultimate goal of improving the quality of genome annotation in plants.

Keywords: pseudogenes; processed; duplicated; bioinformatics tools; plants; genome-wide

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MDPI and ACS Style

Xiao, J.; Sekhwal, M.K.; Li, P.; Ragupathy, R.; Cloutier, S.; Wang, X.; You, F.M. Pseudogenes and Their Genome-Wide Prediction in Plants. Int. J. Mol. Sci. 2016, 17, 1991. https://doi.org/10.3390/ijms17121991

AMA Style

Xiao J, Sekhwal MK, Li P, Ragupathy R, Cloutier S, Wang X, You FM. Pseudogenes and Their Genome-Wide Prediction in Plants. International Journal of Molecular Sciences. 2016; 17(12):1991. https://doi.org/10.3390/ijms17121991

Chicago/Turabian Style

Xiao, Jin, Manoj Kumar Sekhwal, Pingchuan Li, Raja Ragupathy, Sylvie Cloutier, Xiue Wang, and Frank M. You. 2016. "Pseudogenes and Their Genome-Wide Prediction in Plants" International Journal of Molecular Sciences 17, no. 12: 1991. https://doi.org/10.3390/ijms17121991

APA Style

Xiao, J., Sekhwal, M. K., Li, P., Ragupathy, R., Cloutier, S., Wang, X., & You, F. M. (2016). Pseudogenes and Their Genome-Wide Prediction in Plants. International Journal of Molecular Sciences, 17(12), 1991. https://doi.org/10.3390/ijms17121991

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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