A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples

Abstract

Background Splice sites prediction has been a long-standing problem in bioinformatics. Although many computational approaches developed for splice site prediction have achieved satisfactory accuracy, further improvement in predictive accuracy is significant, for it is contributing to predict gene structure more accurately. Determining a proper window size before prediction is necessary. Overly long window size may introduce some irrelevant features, which would reduce predictive accuracy, while the use of short window size with maximum information may performs better in terms of predictive accuracy and time cost. Furthermore, the number of false splice sites following the GT–AG rule far exceeds that of true splice sites, accurate and rapid prediction of splice sites using imbalanced large samples has always been a challenge. Therefore, based on the short window size and imbalanced large samples, we developed a new computational method named chi-square decision table (χ 2 -DT) for donor splice site prediction. Results Using a short window size of 11 bp, χ 2 -DT extracts the improved positional features and compositional features based on chi-square test, then introduces features one by one based on information gain, and constructs a balanced decision table aimed at implementing imbalanced pattern classification. With a 2000:271,132 (true sites:false sites) training set, χ 2 -DT achieves the highest independent test accuracy (93.34%) when compared with three classifiers (random forest, artificial neural network, and relaxed variable kernel density estimator) and takes a short computation time (89 s). χ 2 -DT also exhibits good independent test accuracy (92.40%), when validated with BG-570 mutated sequences with frameshift errors (nucleotide insertions and deletions). Moreover, χ 2 -DT is compared with the long-window size-based methods and the short-window size-based methods, and is found to perform better than all of them in terms of predictive accuracy. Conclusions Based on short window size and imbalanced large samples, the proposed method not only achieves higher predictive accuracy than some existing methods, but also has high computational speed and good robustness against nucleotide insertions and deletions. Reviewers This article was reviewed by Ryan McGinty, Ph.D. and Dirk Walther. Electronic supplementary material The online version of this article (10.1186/s13062-019-0236-y) contains supplementary material, which is available to authorized users.

Spliceator: multi-species splice site prediction using convolutional neural networks

“…The sequence segments upstream and downstream of the SS dinucleotide contain information allowing the discrimination of SS and non-SS, such as the BPS, polypyrimidine tract (PPT) or regulatory cis -elements including exon/intron splicing enhancers or silencers (ESE/ISE or ESS/ISS) [ 49 ]. Determining a pertinent sequence length is important because too short genomic regions would prevent the model from using important discriminatory sites, while too large genomic regions may introduce noise-inducing features and loss of accuracy [ 50 ]. We then built CNN prediction models for donor and acceptor SS, using these different sequence lengths.…”

iSuc-ChiDT: a computational method for identifying succinylation sites using statistical difference table encoding and the chi-square decision table classifier

“…For each position in donor site-containing sequences, a 2 × 4 contingency table can be built by counting the frequencies of 4 bases in the positive and negative samples. Following on from ChiMIC, Zeng et al [26] compressed the 2 × 4 table of each position into a 2 × l (2 ≤ l ≤ 4) table using local chi-square test, and developed a highperformance approach to predict donor splice sites based on this compression strategy.…”

A Chi-MIC Based Adaptive Multi-Branch Decision Tree

“…This algorithm could search the approximate optimal split by unequal interval optimizing and can capture a wide range of associations, both linear and nonlinear. In this paper, we employed the improved MIC algorithm, Chi-MIC [18], [19], to find suitable splitting points for the numeric attributes.…”