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Prediction of Plant mRNA Polyadenylation Sites

Part of the Methods in Molecular Biology book series (MIMB,volume 1255)

Abstract

Messenger RNA polyadenylation is one of the essential processing steps during eukaryotic gene expression. The site of polyadenylation [poly(A) site] marks the end of a transcript, which is also the end of a gene in most cases. A computation program that is able to recognize poly(A) sites would not only be useful for genome annotation in finding genes ends, but also for predicting alternative poly(A) sites. PASS [Poly(A) Site Sleuth] and PAC [Poly(A) site Classifier] were developed to predict poly(A) sites in plants. PASS was built based on the Generalized Hidden Markov Model (GHMM), which consists of four functional modules: input model, poly(A) site recognition module, graphic process module, and output module. PAC is a classification model, integrating several features that define the poly(A) sites including K-gram pattern, Z-curve, position-specific scoring matrix, and first-order inhomogeneous Markov sub-model. PAC can be used to predict poly(A) sites from species whose polyadenylation profile is unknown. The result of PASS and PAC is an output of a few files with one of them containing the score or probability of being a poly(A) site for each position of a given sequence. While the models were built mostly based on poly(A) profile data from Arabidopsis, it is also functional in other higher plants since their profiles are quite similar.

Key words

  • Classification based modeling
  • Polyadenylation
  • Predictive modeling
  • GHMM
  • PASS
  • PAC

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Acknowledgement

Funding supports for this work were from the National Natural Science Foundation of China (Nos. 61174161 and 61304141), the Natural Science Foundation of Fujian Province of China (No. 2012J01154), the specialized Research Fund for the Doctoral Program of Higher Education of China (Nos. 20130121130004 and 20120121120038), and the Fundamental Research Funds for the Central Universities in China (Xiamen University: No. 2013121025), Xiamen Shuangbai Talent Plan (to QQL), and US National Science Foundation (grant nos. IOS–0817829 and IOS-1353354 to QQL).

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Correspondence to Xiaohui Wu .

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Wu, X., Ji, G., Li, Q.Q. (2015). Prediction of Plant mRNA Polyadenylation Sites. In: Hunt, A., Li, Q. (eds) Polyadenylation in Plants. Methods in Molecular Biology, vol 1255. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2175-1_2

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  • DOI: https://doi.org/10.1007/978-1-4939-2175-1_2

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  • Publisher Name: Humana Press, New York, NY

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