Sequence-specific flexibility organization of splicing flanking sequence and prediction of splice sites in the human genome

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

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 (χ²-DT) for donor splice site prediction.

Results

Using a short window size of 11 bp, χ²-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, χ²-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). χ²-DT also exhibits good independent test accuracy (92.40%), when validated with BG-570 mutated sequences with frameshift errors (nucleotide insertions and deletions). Moreover, χ²-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.

DNA physical properties outperform sequence compositional information in classifying nucleosome-enriched and -depleted regions

The nucleosome is the fundamental structural unit of eukaryotic chromatin and plays an essential role in the epigenetic regulation of cellular processes, such as DNA replication, recombination, and transcription. Hence, it is important to identify nucleosome positions in the genome. Our previous model based on DNA deformation energy, in which a set of DNA physical descriptors was used, performed well in predicting nucleosome dyad positions and occupancy. In this study, we established a machine-learning model for predicting nucleosome occupancy in order to further verify the physical descriptors. Results showed that (1) our model outperformed several other sequence compositional information-based models, indicating a stronger dependence of nucleosome positioning on DNA physical properties; (2) nucleosome-enriched and -depleted regions have distinct features in terms of DNA physical descriptors like sequence-dependent flexibility and equilibrium structure parameters; (3) gene transcription start sites and termination sites can be well characterized with the distribution patterns of the physical descriptors, indicating the regulatory role of DNA physical properties in gene transcription. In addition, we developed a web server for the model, which is freely accessible at http://lin-group.cn/server/iNuc-force/.

Sequence-based predictive modeling to identify cancerlectins

Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools.

The OsCYP19-4 Gene Is Expressed as Multiple Alternatively Spliced Transcripts Encoding Isoforms with Distinct Cellular Localizations and PPIase Activities under Cold Stress

Alternative splicing (AS) is an important molecular mechanism by which single genes can generate multiple mRNA isoforms. We reported previously that, in Oryza sativa, the cyclophilin 19-4 (OsCYP19-4.1) transcript was significantly upregulated in response to cold stress, and that transgenic plants were cold tolerant. Here we show that, under cold stress, OsCYP19-4 produces eight transcript variants by intron retention and exon skipping, resulting in production of four distinct protein isoforms. The OsCYP19-4 AS isoforms exhibited different cellular localizations in the epidermal cells: in contrast to OsCYP19-4.1, the OsCYP19-4.2 and OsCYP19-4.3 proteins were primarily targeted to guard and subsidiary cells, whereas OsCYP19-4.5, which consists largely of an endoplasmic reticulum (ER) targeting signal, was co-localized with the RFP-BiP marker in the ER. In OsCYP19-4.2, the key residues of the PPIase domain are altered; consistent with this, recombinant OsCYP19-4.2 had significantly lower PPIase activity than OsCYP19-4.1 in vitro. Specific protein-protein interactions between OsCYP19-4.2/3 and AtRCN1 were verified in yeast two-hybrid (Y2H) and bimolecular fluoresence complementation (BiFC assays), although the OsCYP19-4 isoforms could not bind each other. Based on these results, we propose that two OsCYP19-4 AS isoforms, OsCYP19-4.2 and OsCYP19-4.3, play roles linking auxin transport and cold stress via interactions with RCN1.