iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens

BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities

RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding RNA sequence into numerical data play a crucial role in developing robust models for predicting modification sites. However, existing techniques suffer from limitations, including inadequate information representation, challenges in effectively integrating positional and sequential information, and the generation of irrelevant or redundant features when combining multiple approaches. These deficiencies hinder the effectiveness of machine learning models in addressing the performance challenges associated with predicting RNA modification sites. Here, we introduce a novel RNA sequence feature representation method, named BiPSTP, which utilizes bidirectional trinucleotide position-specific propensities. We employ the parameter ξ to denote the interval between the current nucleotide and its adjacent forward or backward dinucleotide, enabling the extraction of positional and sequential information from RNA sequences. Leveraging the BiPSTP method, we have developed the prediction model mRNAPred using support vector machine classifier to identify multiple types of RNA modification sites. We evaluate the performance of our BiPSTP method and mRNAPred model across 12 distinct RNA modification types. Our experimental results demonstrate the superiority of the mRNAPred model compared to state-of-art models in the domain of RNA modification sites identification. Importantly, our BiPSTP method enhances the robustness and generalization performance of prediction models. Notably, it can be applied to feature extraction from DNA sequences to predict other biological modification sites.

m6A-TCPred: a web server to predict tissue-conserved human m6A sites using machine learning approach

BACKGROUND

N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level.

RESULTS

We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively.

CONCLUSION

Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .

DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple sequence-derived features

Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA. Methods: To identify D in epitranscriptome, we provided a prediction framework named "DPred_3S" based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time. Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae, Escherichia coli, and Schizosaccharomyces pombe, respectively. The performances of different machine learning algorithms were also compared in this study. Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA

2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors.

MRT-ModSeq - Rapid Detection of RNA Modifications with MarathonRT

Chemical modifications are essential regulatory elements that modulate the behavior and function of cellular RNAs. Despite recent advances in sequencing-based RNA modification mapping, methods combining accuracy and speed are still lacking. Here, we introduce MRT-ModSeq for rapid, simultaneous detection of multiple RNA modifications using MarathonRT. MRT-ModSeq employs distinct divalent cofactors to generate 2-D mutational profiles that are highly dependent on nucleotide identity and modification type. As a proof of concept, we use the MRT fingerprints of well-studied rRNAs to implement a general workflow for detecting RNA modifications. MRT-ModSeq rapidly detects positions of diverse modifications across a RNA transcript, enabling assignment of m1acp3Y, m1A, m3U, m7G and 2'-OMe locations through mutation-rate filtering and machine learning. m1A sites in sparsely modified targets, such as MALAT1 and PRUNE1 could also be detected. MRT-ModSeq can be trained on natural and synthetic transcripts to expedite detection of diverse RNA modification subtypes across targets of interest.

EMDL_m6Am: identifying N6,2'-O-dimethyladenosine sites based on stacking ensemble deep learning

BACKGROUND

N6, 2'-O-dimethyladenosine (m6Am) is an abundant RNA methylation modification on vertebrate mRNAs and is present in the transcription initiation region of mRNAs. It has recently been experimentally shown to be associated with several human disorders, including obesity genes, and stomach cancer, among others. As a result, N6,2'-O-dimethyladenosine (m6Am) site will play a crucial part in the regulation of RNA if it can be correctly identified.

RESULTS

This study proposes a novel deep learning-based m6Am prediction model, EMDL_m6Am, which employs one-hot encoding to expressthe feature map of the RNA sequence and recognizes m6Am sites by integrating different CNN models via stacking. Including DenseNet, Inflated Convolutional Network (DCNN) and Deep Multiscale Residual Network (MSRN), the sensitivity (Sn), specificity (Sp), accuracy (ACC), Mathews correlation coefficient (MCC) and area under the curve (AUC) of our model on the training data set reach 86.62%, 88.94%, 87.78%, 0.7590 and 0.8778, respectively, and the prediction results on the independent test set are as high as 82.25%, 79.72%, 80.98%, 0.6199, and 0.8211.

CONCLUSIONS

In conclusion, the experimental results demonstrated that EMDL_m6Am greatly improved the predictive performance of the m6Am sites and could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDL-m6Am .

M6ATMR: identifying N6-methyladenosine sites through RNA sequence similarity matrix reconstruction guided by Transformer

Numerous studies have focused on the classification of N6-methyladenosine (m6A) modification sites in RNA sequences, treating it as a multi-feature extraction task. In these studies, the incorporation of physicochemical properties of nucleotides has been applied to enhance recognition efficacy. However, the introduction of excessive supplementary information may introduce noise to the RNA sequence features, and the utilization of sequence similarity information remains underexplored. In this research, we present a novel method for RNA m6A modification site recognition called M6ATMR. Our approach relies solely on sequence information, leveraging Transformer to guide the reconstruction of the sequence similarity matrix, thereby enhancing feature representation. Initially, M6ATMR encodes RNA sequences using 3-mers to generate the sequence similarity matrix. Meanwhile, Transformer is applied to extract sequence structure graphs for each RNA sequence. Subsequently, to capture low-dimensional representations of similarity matrices and structure graphs, we introduce a graph self-correlation convolution block. These representations are then fused and reconstructed through the local-global fusion block. Notably, we adopt iteratively updated sequence structure graphs to continuously optimize the similarity matrix, thereby constraining the end-to-end feature extraction process. Finally, we employ the random forest (RF) algorithm for identifying m6A modification sites based on the reconstructed features. Experimental results demonstrate that M6ATMR achieves promising performance by solely utilizing RNA sequences for m6A modification site identification. Our proposed method can be considered an effective complement to existing RNA m6A modification site recognition approaches.

Prediction of Multiple Types of RNA Modifications via Biological Language Model

It has been demonstrated that RNA modifications play essential roles in multiple biological processes. Accurate identification of RNA modifications in the transcriptome is critical for providing insights into the biological functions and mechanisms. Many tools have been developed for predicting RNA modifications at single-base resolution, which employ conventional feature engineering methods that focus on feature design and feature selection processes that require extensive biological expertise and may introduce redundant information. With the rapid development of artificial intelligence technologies, end-to-end methods are favorably received by researchers. Nevertheless, each well-trained model is only suitable for a specific RNA methylation modification type for nearly all of these approaches. In this study, we present MRM-BERT by feeding task-specific sequences into the powerful BERT (Bidirectional Encoder Representations from Transformers) model and implementing fine-tuning, which exhibits competitive performance to the state-of-the-art methods. MRM-BERT avoids repeated de novo training of the model and can predict multiple RNA modifications such as pseudouridine, m6A, m5C, and m1A in Mus musculus, Arabidopsis thaliana, and Saccharomyces cerevisiae. In addition, we analyse the attention heads to provide high attention regions for the prediction, and conduct saturated in silico mutagenesis of the input sequences to discover potential changes of RNA modifications, which can better assist researchers in their follow-up research.

m6A-TSHub: Unveiling the Context-specific m6A Methylation and m6A-affecting Mutations in 23 Human Tissues

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.

MRT-ModSeq - Rapid detection of RNA modifications with MarathonRT

Chemical modifications are essential regulatory elements that modulate the behavior and function of cellular RNAs. Despite recent advances in sequencing-based RNA modification mapping, methods combining accuracy and speed are still lacking. Here, we introduce MRT- ModSeq for rapid, simultaneous detection of multiple RNA modifications using MarathonRT. MRT-ModSeq employs distinct divalent cofactors to generate 2-D mutational profiles that are highly dependent on nucleotide identity and modification type. As a proof of concept, we use the MRT fingerprints of well-studied rRNAs to implement a general workflow for detecting RNA modifications. MRT-ModSeq rapidly detects positions of diverse modifications across a RNA transcript, enabling assignment of m1acp3Y, m1A, m3U, m7G and 2'-OMe locations through mutation-rate filtering and machine learning. m1A sites in sparsely modified targets, such as MALAT1 and PRUNE1 could also be detected. MRT-ModSeq can be trained on natural and synthetic transcripts to expedite detection of diverse RNA modification subtypes across targets of interest.

BERT2OME: Prediction of 2'-O-Methylation Modifications From RNA Sequence by Transformer Architecture Based on BERT

Recent work on language models has resulted in state-of-the-art performance on various language tasks. Among these, Bidirectional Encoder Representations from Transformers (BERT) has focused on contextualizing word embeddings to extract context and semantics of the words. On the other hand, post-transcriptional 2'-O-methylation (Nm) RNA modification is important in various cellular tasks and related to a number of diseases. The existing high-throughput experimental techniques take longer time to detect these modifications, and costly in exploring these functional processes. Here, to deeply understand the associated biological processes faster, we come up with an efficient method Bert2Ome to infer 2'-O-methylation RNA modification sites from RNA sequences. Bert2Ome combines BERT-based model with convolutional neural networks (CNN) to infer the relationship between the modification sites and RNA sequence content. Unlike the methods proposed so far, Bert2Ome assumes each given RNA sequence as a text and focuses on improving the modification prediction performance by integrating the pretrained deep learning-based language model BERT. Additionally, our transformer-based approach could infer modification sites across multiple species. According to 5-fold cross-validation, human and mouse accuracies were 99.15% and 94.35% respectively. Similarly, ROC AUC scores were 0.99, 0.94 for the same species. Detailed results show that Bert2Ome reduces the time consumed in biological experiments and outperforms the existing approaches across different datasets and species over multiple metrics. Additionally, deep learning approaches such as 2D CNNs are more promising in learning BERT attributes than more conventional machine learning methods.

i2OM: Toward a better prediction of 2'-O-methylation in human RNA

2'-O-methylation (2OM) is an omnipresent post-transcriptional modification in RNAs. It is important for the regulation of RNA stability, mRNA splicing and translation, as well as innate immunity. With the increase in publicly available 2OM data, several computational tools have been developed for the identification of 2OM sites in human RNA. Unfortunately, these tools suffer from the low discriminative power of redundant features, unreasonable dataset construction or overfitting. To address those issues, based on four types of 2OM (2OM-adenine (A), cytosine (C), guanine (G), and uracil (U)) data, we developed a two-step feature selection model to identify 2OM. For each type, the one-way analysis of variance (ANOVA) combined with mutual information (MI) was proposed to rank sequence features for obtaining the optimal feature subset. Subsequently, four predictors based on eXtreme Gradient Boosting (XGBoost) or support vector machine (SVM) were presented to identify the four types of 2OM sites. Finally, the proposed model could produce an overall accuracy of 84.3 % on the independent set. To provide a convenience for users, an online tool called i2OM was constructed and can be freely access at i2om.lin-group.cn. The predictor may provide a reference for the study of the 2OM.

Bioinformatic tools for epitranscriptomics

The epitranscriptome, defined as RNA modifications that do not involve alterations in the nucleotide sequence, is a popular topic in the genomic sciences. Because we need massive computational techniques to identify epitranscriptomes within individual transcripts, many tools have been developed to infer epitranscriptomic sites as well as to process datasets using high-throughput sequencing. In this review, we summarize recent developments in epitranscriptome spatial detection and data analysis and discuss their progression.

Self-attention enabled deep learning of dihydrouridine (D) modification on mRNAs unveiled a distinct sequence signature from tRNAs

Dihydrouridine (D) is a modified pyrimidine nucleotide universally found in viral, prokaryotic, and eukaryotic species. It serves as a metabolic modulator for various pathological conditions, and its elevated levels in tumors are associated with a series of cancers. Precise identification of D sites on RNA is vital for understanding its biological function. A number of computational approaches have been developed for predicting D sites on tRNAs; however, none have considered mRNAs. We present here DPred, the first computational tool for predicting D on mRNAs in yeast from the primary RNA sequences. Built on a local self-attention layer and a convolutional neural network (CNN) layer, the proposed deep learning model outperformed classic machine learning approaches (random forest, support vector machines, etc.) and achieved reasonable accuracy and reliability with areas under the curve of 0.9166 and 0.9027 in jackknife cross-validation and on an independent testing dataset, respectively. Importantly, we showed that distinct sequence signatures are associated with the D sites on mRNAs and tRNAs, implying potentially different formation mechanisms and putative divergent functionality of this modification on the two types of RNA. DPred is available as a user-friendly Web server.

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation

As the most pervasive epigenetic mark present on mRNA and lncRNA, N⁶-methyladenosine (m⁶A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3′UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m⁶A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m⁶A sites, and improves m⁶A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m⁶A but also N¹-methyladenosine (m¹A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.

iPseU-TWSVM: Identification of RNA pseudouridine sites based on TWSVM

Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.

Deepm5C: A deep learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C), plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models have been developed using small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we propose Deepm5C, a bioinformatics method to identify RNA m5C sites in the throughout human genome. To develop Deepm5C, we constructed a novel benchmarking dataset and investigated a mixture of three conventional feature encoding algorithms and a feature derived from word embedding approaches. Afterwards, four variants of deep learning classifiers and four commonly used conventional classifiers were employed and trained with the four encodings, ultimately obtaining 32 baseline models. A stacking strategy is effectively utilized by integrating the predicted output of the optimal baseline models and trained with a 1-D convolutional neural network. As a result, the Deepm5C predictor achieved excellent performance during cross-validation with a Matthews correlation coefficient and accuracy of 0.697 and 0.855, respectively. The corresponding metrics during the independent test were 0.691 and 0.852, respectively. Overall, Deepm5C achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, Deepm5C is expected to assist community-wide efforts in identifying putative m5Cs and formulate the novel testable biological hypothesis.

BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches

N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are costly and time-consuming. To detect the 6mA and complement these shortcomings of experimental methods, we proposed a novel, deep leaning approach called BERT6mA. To compare the BERT6mA with other deep learning approaches, we used the benchmark datasets including 11 species. The BERT6mA presented the highest AUCs in eight species in independent tests. Furthermore, BERT6mA showed higher and comparable performance with the state-of-the-art models while the BERT6mA showed poor performances in a few species with a small sample size. To overcome this issue, pretraining and fine-tuning between two species were applied to the BERT6mA. The pretrained and fine-tuned models on specific species presented higher performances than other models even for the species with a small sample size. In addition to the prediction, we analyzed the attention weights generated by BERT6mA to reveal how the BERT6mA model extracts critical features responsible for the 6mA prediction. To facilitate biological sciences, the BERT6mA online web server and its source codes are freely accessible at https://github.com/kuratahiroyuki/BERT6mA.git, respectively.

Biological Sequence Classification: A Review on Data and General Methods

With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website ( http://lab.malab.cn/~acy/BioseqData/home.html ), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.

m6A-Maize: Weakly supervised prediction of m6A-carrying transcripts and m6A-affecting mutations in maize (Zea mays)

With the rapid development of high-throughput sequencing techniques nowadays, extensive attention has been paid to epitranscriptomics, which covers more than 150 distinct chemical modifications to date. Among that, N6-methyladenosine (m⁶A) modification has the most abundant existence, and it is also significantly related to varieties of biological processes. Meanwhile, maize is the most important food crop and cultivated throughout the world. Therefore, the study of m⁶A modification in maize has both economic and academic value. In this research, we proposed a weakly supervised learning model to predict the situation of m⁶A modification in maize. The proposed model learns from low-resolution epitranscriptome datasets (e.g., MeRIP-seq), which predicts the m⁶A methylation status of given fragments or regions. By taking advantage of our prediction model, we further identified traits-associated SNPs that may affect (add or remove) m⁶A modifications in maize, which may provide potential regulatory mechanisms at epitranscriptome layer. Additionally, a centralized online-platform was developed for m⁶A study in maize, which contains 58,838 experimentally validated maize m⁶A-containing regions including training and testing datasets, and a database for 2,578 predicted traits-associated m⁶A-affecting maize mutations. Furthermore, the online web server based on proposed weakly supervised model is available for predicting putative m⁶A sites from user-uploaded maize sequences, as well as accessing the epitranscriptome impact of user-interested maize SNPs on m⁶A modification. In all, our work provided a useful resource for the study of m⁶A RNA methylation in maize species. It is freely accessible at www.xjtlu.edu.cn/biologicalsciences/maize.

Machine learning applications in RNA modification sites prediction

Ribonucleic acid (RNA) modifications are post-transcriptional chemical composition changes that have a fundamental role in regulating the main aspect of RNA function. Recently, large datasets have become available thanks to the recent development in deep sequencing and large-scale profiling. This availability of transcriptomic datasets has led to increased use of machine learning based approaches in epitranscriptomics, particularly in identifying RNA modifications. In this review, we comprehensively explore machine learning based approaches used for the prediction of 11 RNA modification types, namely,m 1 A ,m 6 A ,m 5 C , 5 hmC , ψ , 2 ' - O - Me , ac 4 C ,m 7 G , A - to - I ,m 2 G , and D . This review covers the life cycle of machine learning methods to predict RNA modification sites including available benchmark datasets, feature extraction, and classification algorithms. We compare available methods in terms of datasets, target species, approach, and accuracy for each RNA modification type. Finally, we discuss the advantages and limitations of the reviewed approaches and suggest future perspectives.

4mC-RF: Improving the prediction of 4mC sites using composition and position relative features and statistical moment

N4-methylcytosine (4 mC) is an important epigenetic modification that occurs enzymatically by the action of DNA methyltransferases. 4 mC sites exist in prokaryotes and eukaryotes while playing a vital role in regulating gene expression, DNA replication, and cell cycle. The efficient and accurate prediction of 4 mC sites has a significant role in the insight of 4 mC biological properties and functions. Therefore, a sequence-based predictor is proposed, namely 4 mC-RF, for identifying 4 mC sites through the integration of statistical moments along with position, and composition-dependent features. Relative and absolute position-based features are computed to extract optimal features. A popular machine learning classifier Random Forest was used for training the model. Validation results were obtained through rigorous processes of self-consistency, 10-fold cross-validation, Independent set testing, and Jackknife yielding 95.1%, 95.2%, 97.0%, and 94.7% accuracies, respectively. Our proposed model depicts the highest prediction accuracies as compared to existing models. Subsequently, the developed 4 mC-RF model was constructed into a web server. A significant and more accurate predictor of 4 mC Methylcytosine sites helps experimental scientists to gather faster, efficient, and cost-effective results.

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.

Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review

Protein palmitoylation is a fundamental and reversible post-translational lipid modification that involves a series of biological processes. Although a large number of experimental studies have explored the molecular mechanism behind the palmitoylation process, the computational methods has attracted much attention for its good performance in predicting palmitoylation sites compared with expensive and time-consuming biochemical experiments. The prediction of protein palmitoylation sites is helpful to reveal its biological mechanism. Therefore, the research on the application of machine learning methods to predict palmitoylation sites has become a hot topic in bioinformatics and promoted the development in the related fields. In this review, we briefly introduced the recent development in predicting protein palmitoylation sites by using machine learningbased methods and discussed their benefits and drawbacks. The perspective of machine learning-based methods in predicting palmitoylation sites was also provided. We hope the review could provide a guide in related fields.

iDNA6mA-Rice-DL: A local web server for identifying DNA N6-methyladenine sites in rice genome by deep learning method

Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational model to identify the 6mA sites on a genomic scale, especially for plant genomes. In view of this, we proposed a model called iDNA6mA-Rice-DL for the effective identification of 6mA sites in rice genome, which is an intelligent computing model based on deep learning method. Traditional machine learning methods assume the preparation of the features for analysis. However, our proposed model automatically encodes and extracts key DNA features through an embedded layer and several groups of dense layers. We use an independent dataset to evaluate the generalization ability of our model. An area under the receiver operating characteristic curve (auROC) of 0.98 with an accuracy of 95.96% was obtained. The experiment results demonstrate that our model had good performance in predicting 6mA sites in the rice genome. A user-friendly local web server has been established. The Docker image of the local web server can be freely downloaded at https://hub.docker.com/r/his1server/idna6ma-rice-dl.

WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction

The primary sequences of DNA, RNA and protein have been used as the dominant information source of existing machine learning tools, especially for contexts not fully explored by wet-experimental approaches. Since molecular markers are profoundly orchestrated in the living organisms, those markers that cannot be unambiguously recovered from the primary sequence often help to predict other biological events. To the best of our knowledge, there is no current tool to build and deploy machine learning models that consider genomic evidence. We therefore developed the WHISTLE server, the first machine learning platform based on genomic coordinates. It features convenient covariate extraction and model web deployment with 46 distinct genomic features integrated along with the conventional sequence features. We showed that, when predicting m⁶A sites from SRAMP project, the model integrating genomic features substantially outperformed those based on only sequence features. The WHISTLE server should be a useful tool for studying biological attributes specifically associated with genomic coordinates, and is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/whi2.

Identification of gene signature associated with type 2 diabetes mellitus by integrating mutation and expression data

Type 2 diabetes mellitus (T2DM) is a chronic disease. The molecular diagnosis should be helpful for the treatment of T2DM patients. With the development of sequencing technology, a large number of differentially expressed genes were identified from expression data. However, the method of machine learning can only identify the local optimal solution as the signature. The mutation information obtained by inheritance can better reflect the relationship between genes and diseases. Therefore, we need to integrate mutation information to more accurately identify the signature. To this end, we integrated genome-wide association study (GWAS) data and expression data, combined with expression quantitative trait loci (eQTL) technology to get T2DM predictive signature (T2DMSig-10). Firstly, we used GWAS data to obtain a list of T2DM susceptible loci. Then, we used eQTL technology to obtain risk single nucleotide polymorphisms (SNPs), and combined with the pancreatic β-cells gene expression data to obtain 10 protein-coding genes. Next, we combined these genes with equal weights. After receiver operating characteristic (ROC), single-gene removal and increase method, gene ontology function enrichment and protein-protein interaction network were used to verify the results that showed that T2DMSig-10 had an excellent predictive effect on T2DM (AUC=0.99), and was highly robust. In short, we obtained the predictive signature of T2DM, and further verified it.

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m⁶A, m¹A, m⁵C, m⁵U, m⁶Am, m⁷G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms.

RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.

RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features

N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.

ConsRM: collection and large-scale prediction of the evolutionarily conserved RNA methylation sites, with implications for the functional epitranscriptome

Motivation N6-methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. Evidence increasingly demonstrates its crucial importance in essential molecular mechanisms and various diseases. With recent advances in sequencing techniques, tens of thousands of m6A sites are identified in a typical high-throughput experiment, posing a key challenge to distinguish the functional m6A sites from the remaining 'passenger' (or 'silent') sites. Results: We performed a comparative conservation analysis of the human and mouse m6A epitranscriptomes at single site resolution. A novel scoring framework, ConsRM, was devised to quantitatively measure the degree of conservation of individual m6A sites. ConsRM integrates multiple information sources and a positive-unlabeled learning framework, which integrated genomic and sequence features to trace subtle hints of epitranscriptome layer conservation. With a series validation experiments in mouse, fly and zebrafish, we showed that ConsRM outperformed well-adopted conservation scores (phastCons and phyloP) in distinguishing the conserved and unconserved m6A sites. Additionally, the m6A sites with a higher ConsRM score are more likely to be functionally important. An online database was developed containing the conservation metrics of 177 998 distinct human m6A sites to support conservation analysis and functional prioritization of individual m6A sites. And it is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/con.

DeepOMe: A Web Server for the Prediction of 2'-O-Me Sites Based on the Hybrid CNN and BLSTM Architecture

2'-O-methylations (2'-O-Me or Nm) are one of the most important layers of regulatory control over gene expression. With increasing attentions focused on the characteristics, mechanisms and influences of 2'-O-Me, a revolutionary technique termed Nm-seq were established, allowing the identification of precise 2'-O-Me sites in RNA sequences with high sensitivity. However, as the costs and complexities involved with this new method, the large-scale detection and in-depth study of 2'-O-Me is still largely limited. Therefore, the development of a novel computational method to identify 2'-O-Me sites with adequate reliability is urgently needed at the current stage. To address the above issue, we proposed a hybrid deep-learning algorithm named DeepOMe that combined Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory (BLSTM) to accurately predict 2'-O-Me sites in human transcriptome. Validating under 4-, 6-, 8-, and 10-fold cross-validation, we confirmed that our proposed model achieved a high performance (AUC close to 0.998 and AUPR close to 0.880). When testing in the independent data set, DeepOMe was substantially superior to NmSEER V2.0. To facilitate the usage of DeepOMe, a user-friendly web-server was constructed, which can be freely accessed at http://deepome.renlab.org.

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning

Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.

The Assessment of Interleukin-18 on the Risk of Coronary Heart Disease

BACKGROUND

Observational studies support the inflammation hypothesis in coronary heart disease (CHD). As a pleiotropic proinflammatory cytokine, Interleukin-18 (IL-18), has also been found to be associated with the risk of CHD. However, to our knowledge, the method of Mendelian Randomization has not been used to explore the causal effect of IL-18 on CHD.

OBJECTIVE

To assess the causal effect of IL-18 on the risk of CHD.

METHODS AND RESULTS

Genetic variant instruments for IL-18 were obtained from information of the CHS and InCHIANTI cohort, and consisted of the per-allele difference in mean IL-18 for 16 independent variants that reached genome-wide significance. The per-allele difference in log-odds of CHD for each of these variants was estimated from CARDIoGRAMplusC4D, a two-stage meta -analysis. Two-sample Mendelian Randomization (MR) was then performed. Various MR analyses were used, including weighted inverse-variance, MR-Egger regression, robust regression, and penalized regression. The OR of elevated IL-18 associated with CHD was only 0.005 (95%CI -0.105~0.095; P-value=0.927). Similar results were obtained with the use of MR-Egger regression, suggesting that directional pleiotropy was unlikely biasing these results (intercept -0.050, P-value=0.220). Moreover, results from the robust regression and penalized regression analyses also revealed essentially similar findings.

CONCLUSION

Our findings indicate that, by itself, IL-18 is unlikely to represent even a modest causal factor for CHD risk.

Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families

Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, reproduction, homeostasis, and metabolism. According to the alignments of the conserved domains, NRs are classified and assigned the following seven subfamilies or eight subfamilies: (1) NR1: thyroid hormone like (thyroid hormone, retinoic acid, RAR-related orphan receptor, peroxisome proliferator activated, vitamin D3- like), (2) NR2: HNF4-like (hepatocyte nuclear factor 4, retinoic acid X, tailless-like, COUP-TFlike, USP), (3) NR3: estrogen-like (estrogen, estrogen-related, glucocorticoid-like), (4) NR4: nerve growth factor IB-like (NGFI-B-like), (5) NR5: fushi tarazu-F1 like (fushi tarazu-F1 like), (6) NR6: germ cell nuclear factor like (germ cell nuclear factor), and (7) NR0: knirps like (knirps, knirpsrelated, embryonic gonad protein, ODR7, trithorax) and DAX like (DAX, SHP), or dividing NR0 into (7) NR7: knirps like and (8) NR8: DAX like. Different NRs families have different structural features and functions. Since the function of a NR is closely correlated with which subfamily it belongs to, it is highly desirable to identify NRs and their subfamilies rapidly and effectively. The knowledge acquired is essential for a proper understanding of normal and abnormal cellular mechanisms. With the advent of the post-genomics era, huge amounts of sequence-known proteins have increased explosively. Conventional methods for accurately classifying the family of NRs are experimental means with high cost and low efficiency. Therefore, it has created a greater need for bioinformatics tools to effectively recognize NRs and their subfamilies for the purpose of understanding their biological function. In this review, we summarized the application of machine learning methods in the prediction of NRs from different aspects. We hope that this review will provide a reference for further research on the classification of NRs and their families.

m6AmPred: Identifying RNA N6, 2'-O-dimethyladenosine (m6Am) sites based on sequence-derived information

N6,2'-O-dimethyladenosine (m⁶A_m) is a reversible modification widely occurred on varied RNA molecules. The biological function of m⁶A_m is yet to be known though recent studies have revealed its influences in cellular mRNA fate. Precise identification of m⁶A_m sites on RNA is vital for the understanding of its biological functions. We present here m6AmPred, the first web server for in silico identification of m⁶A_m sites from the primary sequences of RNA. Built upon the eXtreme Gradient Boosting with Dart algorithm (XgbDart) and EIIP-PseEIIP encoding scheme, m6AmPred achieved promising prediction performance with the AUCs greater than 0.954 when tested by 10-fold cross-validation and independent testing datasets. To critically test and validate the performance of m6AmPred, the experimentally verified m⁶A_m sites from two data sources were cross-validated. The m6AmPred web server is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/m6am, and it should make a useful tool for the researchers who are interested in N6,2'-O-dimethyladenosine RNA modification.

RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis

Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m⁶A, m⁵C, m¹A, m⁵U, Ψ, m⁶Am, m⁷G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.

Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis

With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning capacity has been applied in diagnosing different types of diseases. This study reviews the application of expert systems, neural networks, and deep learning used by AI technology in disease diagnosis. This paper also gives a glimpse of the intelligent diagnosis and treatment of digestive system diseases, respiratory system diseases, and osteoporosis by AI technology.

Identification of Sub-Golgi protein localization by use of deep representation learning features

Motivation

The Golgi apparatus has a key functional role in protein biosynthesis within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases. For a better understanding of the Golgi apparatus, it is essential to identification of sub-Golgi protein localization. Although some machine learning methods have been used to identify sub-Golgi localization proteins by sequence representation fusion, more accurate sub-Golgi protein identification is still challenging by existing methodology.

Results

we developed a protein sub-Golgi localization identification protocol using deep representation learning features with 107 dimensions. By this protocol, we demonstrated that instead of multi-type protein sequence feature representation fusion as in previous state-of-the-art sub-Golgi-protein localization classifiers, it is sufficient to exploit only one type of feature representation for more accurately identification of sub-Golgi proteins. Compared with independent testing results for benchmark datasets, our protocol is able to perform generally, reliably and robustly for sub-Golgi protein localization prediction.

Availabilityand implementation

A use-friendly webserver is freely accessible at http://isGP-DRLF.aibiochem.net and the prediction code is accessible at https://github.com/zhibinlv/isGP-DRLF.

Supplementary information

Supplementary data are available at Bioinformatics online.

Analysis of Oncogene Protein Structure Using Small World Network Concept

Background:

The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges.

Objective:

Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept.

Methods:

Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming.

Results:

Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted.

Conclusion:

Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

Prediction of bio-sequence modifications and the associations with diseases

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.

Classifying the superfamily of small heat shock proteins by using g-gap dipeptide compositions

Small heat shock protein (sHSP) is a superfamily of molecular chaperone and is found from archaea to human. Recent researches have demonstrated that sHSPs participate in a series of biological processes and are even closely associated with serious diseases. Since sHSP is a very large superfamily and members from different superfamilies exhibit distinct functions, accurate classification of the subfamily of sHSP will be helpful for unrevealing its functions. In the present work, a support vector machine-based method was proposed to classify the subfamily of sHSPs. In the 10-fold cross validation test, an overall accuracy of 93.25% was obtained for classifying the subfamily of sHSPs. The superiority of the proposed method was also demonstrated by comparing it with the other methods. It is anticipated that the proposed method will become a useful tool for classifying the subfamily of sHSPs.

Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions

Background:

Long non-coding RNAs (lncRNAs) are transcripts with a length more than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown that the biological functions of lncRNAs are intimately related to their subcellular localizations. Therefore, it is very important to confirm the lncRNA subcellular localization.

Methods:

In this paper, we proposed a novel method to predict the subcellular localization of lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer nucleotide composition and sequence order correlated factors of lncRNA to formulate lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support vector machine (SVM) to perform the prediction.

Results:

The AUC value of the proposed method can reach 0.9695, which indicated the proposed predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore, the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross validation, which clearly outperforms the existing state-of- the-art method.

Conclusion:

It is demonstrated that the proposed predictor is feasible and powerful for the prediction of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the source code at https://github.com/NicoleYXF/lncRNA.

Identification of Cancerlectins By Using Cascade Linear Discriminant Analysis and Optimal g-gap Tripeptide Composition

Background:

Lectins are a diverse group of glycoproteins or glycoconjugate proteins that can be extracted from plants, invertebrates and higher animals. Cancerlectins, a kind of lectins, which play a key role in the process of tumor cells interacting with each other and are being employed as therapeutic agents. A full understanding of cancerlectins is significant because it provides a tool for the future direction of cancer therapy.

Objective:

To develop an accurate and practically useful timesaving tool to identify cancerlectins. A novel sequence-based method is proposed along with a correlative webserver to access the proposed tool.

Methods:

Firstly, protein features were extracted in a newly feature building way termed, g-gap tripeptide composition. After which a proposed cascade linear discriminant analysis (Cascade LDA) is used to alleviate the high dimensional difficulties with the Analysis Of Variance (ANOVA) as a feature importance criterion. Finally, Support Vector Machine (SVM) is used as the classifier to identify cancerlectins.

Results:

The proposed method achieved an accuracy of 91.34% with sensitivity of 89.89%, specificity of 92.48% and an 0.8318 Mathew’s correlation coefficient based on only 13 fusion features in jackknife cross validation, the result of which is superior to other published methods in this domain.

Conclusion:

In this study, a new method based only on primary structure of protein is proposed and experimental results show that it could be a promising tool to identify cancerlectins. An openaccess webserver is made available in this work to facilitate other related works.

iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications

MOTIVATION

RNA modifications play critical roles in a series of cellular and developmental processes. Knowledge about the distributions of RNA modifications in the transcriptomes will provide clues to revealing their functions. Since experimental methods are time consuming and laborious for detecting RNA modifications, computational methods have been proposed for this aim in the past five years. However, there are some drawbacks for both experimental and computational methods in simultaneously identifying modifications occurred on different nucleotides.

RESULTS

To address such a challenge, in this article, we developed a new predictor called iMRM, which is able to simultaneously identify m6A, m5C, m1A, ψ and A-to-I modifications in Homo sapiens, Mus musculus and Saccharomyces cerevisiae. In iMRM, the feature selection technique was used to pick out the optimal features. The results from both 10-fold cross-validation and jackknife test demonstrated that the performance of iMRM is superior to existing methods for identifying RNA modifications.

AVAILABILITY AND IMPLEMENTATION

A user-friendly web server for iMRM was established at http://www.bioml.cn/XG_iRNA/home. The off-line command-line version is available at https://github.com/liukeweiaway/iMRM.

CONTACT

greatchen@ncst.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC

Background:

Carboxylation is one of the most biologically important post-translational modifications and occurs on lysine, arginine, and glutamine residues of a protein. Among all these three, the covalent attachment of the carboxyl group with the lysine side chain is the most frequent and biologically important type of carboxylation. For studying such biological functions, it is essential to correctly determine the lysine sites sensitive to carboxylation.

Objective:

Herein, we present a computational model for the prediction of the carboxylysine site which is based on machine learning.

Methods:

Various position and composition relative features have been incorporated into the Pse- AAC for construction of feature vectors and a neural network is employed as a classifier. The model is validated by jackknife, cross-validation, self-consistency, and independent testing.

Results:

The results of the self-consistency test elaborated that model has 99.76% Acc, 99.76% Sp, 99.76% Sp, and 0.99 MCC. Using the jackknife method, prediction model validation gave 97.07% Acc, while for 10-fold cross-validation, prediction model validation gave 95.16% Acc.

Conclusion:

The results of independent dataset testing were 94.3% which illustrated that the proposed model has better performance as compared to the existing model PreLysCar; however, the accuracy can be improved further, in the future, due to the increasing number of carboxylysine sites in proteins.

m5UPred: A Web Server for the Prediction of RNA 5-Methyluridine Sites from Sequences

As one of the widely occurring RNA modifications, 5-methyluridine (m⁵U) has recently been shown to play critical roles in various biological functions and disease pathogenesis, such as under stress response and during breast cancer development. Precise identification of m⁵U sites on RNA is vital for the understanding of the regulatory mechanisms of RNA life. We present here m5UPred, the first web server for in silico identification of m⁵U sites from the primary sequences of RNA. Built upon the support vector machine (SVM) algorithm and the biochemical encoding scheme, m5UPred achieved reasonable prediction performance with the area under the receiver operating characteristic curve (AUC) greater than 0.954 by 5-fold cross-validation and independent testing datasets. To critically test and validate the performance of our newly proposed predictor, the experimentally validated m⁵U sites were further separated by high-throughput sequencing techniques (miCLIP-Seq and FICC-Seq) and cell types (HEK293 and HAP1). When tested on cross-technique and cross-cell-type validation using independent datasets, m5UPred achieved an average AUC of 0.922 and 0.926 under mature mRNA mode, respectively, showing reasonable accuracy and reliability. The m5UPred web server is freely accessible now and it should make a useful tool for the researchers who are interested in m⁵U RNA modification.

NeuroCS: A Tool to Predict Cleavage Sites of Neuropeptide Precursors

BACKGROUND

Neuropeptides are a class of bioactive peptides produced from neuropeptide precursors through a series of extremely complex processes, mediating neuronal regulations in many aspects. Accurate identification of cleavage sites of neuropeptide precursors is of great significance for the development of neuroscience and brain science.

OBJECTIVE

With the explosive growth of neuropeptide precursor data, it is pretty much needed to develop bioinformatics methods for predicting neuropeptide precursors' cleavage sites quickly and efficiently.

METHODS

We started with processing the neuropeptide precursor data from SwissProt and NueoPedia into two sets of data, training dataset and testing dataset. Subsequently, six feature extraction schemes were applied to generate different feature sets and then feature selection methods were used to find the optimal feature subset of each. Thereafter the support vector machine was utilized to build models for different feature types. Finally, the performance of models were evaluated with the independent testing dataset.

RESULTS

Six models are built through support vector machine. Among them the enhanced amino acid composition-based model reaches the highest accuracy of 91.60% in the 5-fold cross validation. When evaluated with independent testing dataset, it also showed an excellent performance with a high accuracy of 90.37% and Area under Receiver Operating Characteristic curve up to 0.9576.

CONCLUSION

The performance of the developed model was decent. Moreover, for users' convenience, an online web server called NeuroCS is built, which is freely available at http://i.uestc.edu.cn/NeuroCS/dist/index.html#/. NeuroCS can be used to predict neuropeptide precursors' cleavage sites effectively.