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. 2016 Jun 2;44(10):e91.
doi: 10.1093/nar/gkw104. Epub 2016 Feb 20.

SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features

Yuan Zhou  1 , Pan Zeng  2 , Yan-Hui Li  2 , Ziding Zhang  3 , Qinghua Cui  4
Affiliations

SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features

Yuan Zhou et al. Nucleic Acids Res. .

Abstract

N(6)-methyladenosine (m(6)A) is a prevalent RNA methylation modification involved in the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. High-throughput experiments have demonstrated that only a small fraction of the m(6)A consensus motifs in mammalian transcriptomes are modified. Therefore, accurate identification of RNA m(6)A sites becomes emergently important. For the above purpose, here a computational predictor of mammalian m(6)A site named SRAMP is established. To depict the sequence context around m(6)A sites, SRAMP combines three random forest classifiers that exploit the positional nucleotide sequence pattern, the K-nearest neighbor information and the position-independent nucleotide pair spectrum features, respectively. SRAMP uses either genomic sequences or cDNA sequences as its input. With either kind of input sequence, SRAMP achieves competitive performance in both cross-validation tests and rigorous independent benchmarking tests. Analyses of the informative features and overrepresented rules extracted from the random forest classifiers demonstrate that nucleotide usage preferences at the distal positions, in addition to those at the proximal positions, contribute to the classification. As a public prediction server, SRAMP is freely available at http://www.cuilab.cn/sramp/.

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Figures

Figure 1.
Figure 1.
The computational framework of SRAMP. Two prediction modes have been built in SRAMP, i.e. the full transcript mode and the mature mRNA mode. Both prediction modes adopt the same computational framework. First, for a DRACH motif presented in the query sequence, its flanking sequence window is extracted and represented using the three sequence-based encodings. Then the encoded features will be submitted to the corresponding random forest classifiers. Each random forest classifier summarizes the output scores from 10 sub-classifiers, which were trained on all positive samples and a distinct subset of negative samples in the training dataset. Finally, the prediction scores of the random forest classifiers are combined through weighted summing formula. Four stringency thresholds correspond to the 99%, 95%, 90% and 85% specificities in 5-fold cross-validation test that are used to judge the classification and associated confidence. If analysing secondary structure function is enabled, the secondary structure context of the predicted m6A sites will be also provided.
Figure 2.
Figure 2.
The overall performances of the full transcript mode classifiers based on the results from 5-fold cross-validation tests. The performances are illustrated by the ROC curves (A) and the precision-recall curves (B).
Figure 3.
Figure 3.
The performances of different m6A site predictors on the gold standard dataset and the benchmarking datasets. (A) Prediction results on the golden standard dataset. The gene identifiers and site positions are in lines with the original publication by Liu et al. (42). Experimental reference sites and predicted sites are denoted in the E and P columns, respectively. Experimentally verified m6A sites and non-m6A sites are indicated by deep red and grey boxes, respectively. Predicted very high confidence m6A sites, high confidence m6A sites and non-m6A sites are indicated by red, purple and grey boxes, respectively. (B) The ROC curve illustrating the performances on the mammalian benchmarking dataset. (C) The precision-recall curve illustrating the performances on the mammalian benchmarking dataset. (D) The ROC curve illustrating the performances on the yeast benchmarking dataset. (E) The precision-recall curve illustrating the performances on the yeast benchmarking dataset.
Figure 4.
Figure 4.
A sample SRAMP prediction result. The genomic sequence of a representative APRT transcript (ENST00000378364) was used as the input to the full transcript mode predictor, and the analysis of secondary structures was enabled. (A) The overview of result page. The exhibition of the query sequences is truncated, and only the detailed results for the first predicted m6A site and those near the pathogenic mutation site (G2246->C) are shown. The H, M, I, B, P in the secondary structure strings mean hairpin loop, multiple loop, interior loop, bulged loop and paired residues, respectively. In addition to such string, a graphical representation of the local secondary structure will be generated when click on the ‘draw’ button. (B) A graphical representation of the local secondary structure context around the mutation site. This graphical representation was generated by SRAMP server exploiting the VARNA structural visualization tool. We focused on the local secondary structure in proximal to the mutation site for clarity.
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