Skip to main content

Prediction of Plant mRNA Polyadenylation Sites

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


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

This is a preview of subscription content, access via your institution.

Buying options

USD   49.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-1-4939-2175-1_2
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-1-4939-2175-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.00
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more


  1. Graber JH, Cantor CR, Mohr SC, Smith TF (1999) In silico detection of control signals: mRNA 3′ end-processing sequences in diverse species. Proc Natl Acad Sci U S A 96(24):14055–14060

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  2. Graber JH, Cantor CR, Mohr SC, Smith TF (1999) Genomic detection of new yeast pre-mRNA 3′-end-processing signals. Nucleic Acids Res 27(3):888–894

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  3. Loke JC, Stahlberg EA, Strenski DG, Haas BJ, Wood PC, Li QQ (2005) Compilation of mRNA polyadenylation signals in Arabidopsis revealed a new signal element and potential secondary structures. Plant Physiol 138(3):1457–1468

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  4. Shen Y, Ji G, Haas BJ, Wu X, Zheng J, Reese GJ, Li QQ (2008) Genome level analysis of rice mRNA 3′-end processing signals and alternative polyadenylation. Nucleic Acids Res 36(9):3150–3161

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  5. Shen Y, Liu Y, Liu L, Liang C, Li QQ (2008) Unique features of nuclear mRNA poly(A) signals and alternative polvadenylation in Chlamydomonas reinhardtii. Genetics 179(1):167–176

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  6. Li QQ, Hunt AG (1997) The polyadenylation of RNA in plants. Plant Physiol 115:321–325

    CAS  PubMed Central  PubMed  CrossRef  Google Scholar 

  7. Ji G, Zheng J, Shen Y, Wu X, Jiang R, Lin Y, Loke JC, Davis KM, Reese GJ, Li QQ (2007) Predictive modeling of plant messenger RNA polyadenylation sites. BMC Bioinform 8(43):43

    CrossRef  Google Scholar 

  8. Ji G, Wu X, Shen Y, Huang J, Li QQ (2010) A classification-based prediction model of messenger RNA polyadenylation sites. J Theor Biol 265(3):287–296. doi:10.1016/j.jtbi.2010.05.015

    CAS  PubMed  CrossRef  Google Scholar 

  9. Ji G, Wu X, Li Q, Zheng J (2010) Messenger RNA polyadenylation site recognition in green alga Chlamydomonas Reinhardtii. Lect Notes Comput Sci 6063:17–26. doi:10.1007/978-3-642-13278-0_3

    CrossRef  Google Scholar 

  10. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Elsevier, San Francisco, CA

    Google Scholar 

Download references


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).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Xiaohui Wu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

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.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2174-4

  • Online ISBN: 978-1-4939-2175-1

  • eBook Packages: Springer Protocols