Open Access Highly Accessed Methodology article

Characterization of statistical features for plant microRNA prediction

Vivek Thakur12*, Samart Wanchana12, Mercedes Xu1, Richard Bruskiewich2, William Paul Quick2, Axel Mosig1 and Xin-Guang Zhu1*

Author Affiliations

1 Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Key Laboratory of Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, PR China

2 International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, Philippines

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BMC Genomics 2011, 12:108  doi:10.1186/1471-2164-12-108

Published: 16 February 2011

Additional files

Additional file 1:

Figure 1. Mean MFE as a function of (precursor) length. The vertical bars display the standard deviation. The best fit linear curve has a slope of 0.48.

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Additional file 2:

Figure 1. Comparison of cumulative frequency distributions of (length normalized) MFE of real and background precursors from dicot species. These precursors are of length 260-290 nt. Bgr: background.

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Additional file 3:

Figure 1. Best fit curve of Gumbel distribution (minimum) for the cumulative distributions of MFE of real and background precursors (length: 260-290 nt). Except for small range of normalized MFE values, largely in middle, the corresponding curves do not fit well.

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Additional file 4:

Figure 1. Cumulative frequency distribution of p-value of background precursors of five species (size = 300 nt). Those from dicot species have higher stability, this, rendering them less distinguishable from the real precursors.

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Additional file 5:

Text. Key changes made in the syntax of miRDeep to incorporate plant-specific parameters.

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