Exon skipping event prediction based on histone modifications

The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review

Background:

Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification.

Methods:

Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification.

Results:

We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs.

Conclusion:

In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.

Combinatorial Pattern of Histone Modifications in Exon Skipping Event

Histone modifications are associated with alternative splicing. It has been suggested that histone modifications act in combinational patterns in gene expression regulation. However, how they interact with each other and what is their casual relationships in the process of RNA splicing remain unclear. In this study, the combinatorial patterns of 38 kinds of histone modifications in the exon skipping event of the CD4⁺ T cell were analyzed by constructing Bayesian networks. Distinct combinatorial patterns of histone modifications that illustrating their casual relationships were observed in excluded/included exons and the surrounding intronic regions. The Bayesian networks also indicate that some histone modifications directly correlate with RNA splicing. We anticipate that this work could provide novel insights into the effects of histone modifications on RNA splicing regulation.

Classifying Included and Excluded Exons in Exon Skipping Event Using Histone Modifications

Alternative splicing (AS) not only ensures the diversity of gene expression products, but also closely correlated with genetic diseases. Therefore, knowledge about regulatory mechanisms of AS will provide useful clues for understanding its biological functions. In the current study, a random forest based method was developed to classify included and excluded exons in exon skipping event. In this method, the samples in the dataset were encoded by using optimal histone modification features which were optimized by using the Maximum Relevance Maximum Distance (MRMD) feature selection technique. The proposed method obtained an accuracy of 72.91% in 10-fold cross validation test and outperformed existing methods. Meanwhile, we also systematically analyzed the distribution of histone modifications between included and excluded exons and discovered their preference in both kinds of exons, which might provide insights into researches on the regulatory mechanisms of alternative splicing.

Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest

In bioinformatics, exon skipping (ES) event prediction is an essential part of alternative splicing (AS) event analysis. Although many methods have been developed to predict ES events, a solution has yet to be found. In this study, given the limitations of machine learning algorithms with RNA-Seq data or genome sequences, a new feature, called RS (RNA-seq and sequence) features, was constructed. These features include RNA-Seq features derived from the RNA-Seq data and sequence features derived from genome sequences. We propose a novel Rotation Forest classifier to predict ES events with the RS features (RotaF-RSES). To validate the efficacy of RotaF-RSES, a dataset from two human tissues was used, and RotaF-RSES achieved an accuracy of 98.4%, a specificity of 99.2%, a sensitivity of 94.1%, and an area under the curve (AUC) of 98.6%. When compared to the other available methods, the results indicate that RotaF-RSES is efficient and can predict ES events with RS features.

Histone modifications influence skipped exons inclusion

Alternative splicing (AS), by which individual genes can produce multiple mRNA, associates with genomic complexity, disease, and development. Histone modifications show important roles in both transcription initiation and mRNA splicing. Here, we intended to find the link between AS and histone modifications in flanking regions through analyzing publicly available data in two human cell lines, GM12878 and K562 cell lines. According to exon inclusion levels, exons were classified into three types, included skipped exons, excluded skipped exons and expressed constitutive exons. We revealed that the inclusion levels of skipped exons (SEs) were negatively correlated with the enrichment of active histone marks in SEs, indicating a role of histone modifications in AS. We also found that active histone modifications were enriched in the upstream exons of SEs, especially around 5[Formula: see text] splicing sites. We inferred that the histone modifications around the 5[Formula: see text] splicing sites in upstream exon of the SEs could help RNA Polymerase II complex to recruit the effector proteins and facilitate AS. It was indicated that nucleosome occupancy had little influence on the inclusion levels of SEs. At last, we proposed an integrated model that describe how histone modifications affected the pre-mRNA splicing.

Identifying 2'-O-methylationation sites by integrating nucleotide chemical properties and nucleotide compositions

2'-O-methylationation is an important post-transcriptional modification and plays important roles in many biological processes. Although experimental technologies have been proposed to detect 2'-O-methylationation sites, they are cost-ineffective. As complements to experimental techniques, computational methods will facilitate the identification of 2'-O-methylationation sites. In the present study, we proposed a support vector machine-based method to identify 2'-O-methylationation sites. In this method, RNA sequences were formulated by nucleotide chemical properties and nucleotide compositions. In the jackknife cross-validation test, the proposed method obtained an accuracy of 95.58% for identifying 2'-O-methylationation sites in the human genome. Moreover, the model was also validated by identifying 2'-O-methylation sites in the Mus musculus and Saccharomyces cerevisiae genomes, and the obtained accuracies are also satisfactory. These results indicate that the proposed method will become a useful tool for the research on 2'-O-methylation.

Survey of Programs Used to Detect Alternative Splicing Isoforms from Deep Sequencing Data In Silico

Next-generation sequencing techniques have been rapidly emerging. However, the massive sequencing reads hide a great deal of unknown important information. Advances have enabled researchers to discover alternative splicing (AS) sites and isoforms using computational approaches instead of molecular experiments. Given the importance of AS for gene expression and protein diversity in eukaryotes, detecting alternative splicing and isoforms represents a hot topic in systems biology and epigenetics research. The computational methods applied to AS prediction have improved since the emergence of next-generation sequencing. In this study, we introduce state-of-the-art research on AS and then compare the research methods and software tools available for AS based on next-generation sequencing reads. Finally, we discuss the prospects of computational methods related to AS.