scholarly journals lncRNA_Mdeep: an alignment-free predictor for long non-coding RNAs identification by multimodal deep learning

2020 ◽  
Author(s):  
Xiao-Nan Fan ◽  
Shao-Wu Zhang ◽  
Song-Yao Zhang ◽  
Jin-Jie Ni
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Protein Coding ◽  
Learning Framework ◽  
Alignment Free ◽  
Independent Test ◽  
Non Coding Rnas ◽  
High Level ◽  
Fold Cross Validation ◽  
Human Complex

Abstract Background: Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. Results: In this study, we present an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporates three different input modalities (i.e. OFH modality, k-mer modality, and sequence modality), then a multimodal deep learning framework is built for learning the high-level abstract representations and predicting the probability whether a transcript is lncRNA or not. Conclusions: LncRNA_Mdeep achieves 98.73% prediction accuracy in 10-fold cross-validation test on human. Compared with other eight state-of-the-art methods, lncRNA_Mdeep shows 93.12% prediction accuracy independent test on human, which is 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets show that lncRNA_Mdeep is a powerful predictor for identifying lncRNAs. The source code can be downloaded from https://github.com/NWPU-903PR/lncRNA_Mdeep.

scholarly journals lncRNA_Mdeep: An Alignment-Free Predictor for Distinguishing Long Non-Coding RNAs from Protein-Coding Transcripts by Multimodal Deep Learning

2020 ◽  
Vol 21 (15) ◽  
pp. 5222 ◽  
Author(s):  
Xiao-Nan Fan ◽  
Shao-Wu Zhang ◽  
Song-Yao Zhang ◽  
Jin-Jie Ni
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Protein Coding ◽  
Learning Framework ◽  
Alignment Free ◽  
Independent Test ◽  
Non Coding Rnas ◽  
High Level ◽  
Fold Cross Validation ◽  
Human Complex

Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing the lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. In this study, we presented an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporated three different input modalities, then a multimodal deep learning framework was built for learning the high-level abstract representations and predicting the probability whether a transcript was lncRNA or not. LncRNA_Mdeep achieved 98.73% prediction accuracy in a 10-fold cross-validation test on humans. Compared with other eight state-of-the-art methods, lncRNA_Mdeep showed 93.12% prediction accuracy independent test on humans, which was 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets showed that lncRNA_Mdeep was a powerful predictor for predicting lncRNAs.

scholarly journals lncRNA_Mdeep: an alignment-free predictor for long non-coding RNAs identification by multimodal deep learning

2019 ◽  
Author(s):  
Xiao-Nan Fan ◽  
Shao-Wu Zhang ◽  
Song-Yao Zhang ◽  
Jin-Jie Ni
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Protein Coding ◽  
Learning Framework ◽  
Alignment Free ◽  
Independent Test ◽  
Non Coding Rnas ◽  
High Level ◽  
Fold Cross Validation ◽  
Human Complex

Abstract Background: Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. Results: In this study, we present an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporates three different input modalities (i.e. OFH modality, k-mer modality, and sequence modality), then a multimodal deep learning framework is built for learning the high-level abstract representations and predicting the probability whether a transcript is lncRNA or not.Conclusions: LncRNA_Mdeep achieves 98.73% prediction accuracy in 10-fold cross-validation test on human. Compared with other eight state-of-the-art methods, lncRNA_Mdeep shows 93.12% prediction accuracy independent test on human, which is 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets show that lncRNA_Mdeep is a powerful predictor for identifying lncRNAs. The source code can be downloaded from https://github.com/NWPU-903PR/lncRNA_Mdeep.

scholarly journals Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species

2021 ◽  
Vol 17 (2) ◽  
pp. e1008767
Author(s):  
Zutan Li ◽  
Hangjin Jiang ◽  
Lingpeng Kong ◽  
Yuanyuan Chen ◽  
Kun Lang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Biological Processes ◽  
Biological Functions ◽  
Learning Framework ◽  
Wide Range ◽  
Genomic Scale ◽  
Downstream Gene Expression ◽  
Fold Cross Validation

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA’s biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

scholarly journals 6mAPred-MSFF: A Deep Learning Model for Predicting DNA N6-Methyladenine Sites across Species Based on a Multi-Scale Feature Fusion Mechanism

2021 ◽  
Vol 11 (16) ◽  
pp. 7731
Author(s):  
Rao Zeng ◽  
Minghong Liao
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Feature Fusion ◽  
Experimental Comparison ◽  
Scale Feature ◽  
Learning Framework ◽  
Multi Scale ◽  
Fold Cross Validation

DNA methylation is one of the most extensive epigenetic modifications. DNA N6-methyladenine (6mA) plays a key role in many biology regulation processes. An accurate and reliable genome-wide identification of 6mA sites is crucial for systematically understanding its biological functions. Some machine learning tools can identify 6mA sites, but their limited prediction accuracy and lack of robustness limit their usability in epigenetic studies, which implies the great need of developing new computational methods for this problem. In this paper, we developed a novel computational predictor, namely the 6mAPred-MSFF, which is a deep learning framework based on a multi-scale feature fusion mechanism to identify 6mA sites across different species. In the predictor, we integrate the inverted residual block and multi-scale attention mechanism to build lightweight and deep neural networks. As compared to existing predictors using traditional machine learning, our deep learning framework needs no prior knowledge of 6mA or manually crafted sequence features and sufficiently capture better characteristics of 6mA sites. By benchmarking comparison, our deep learning method outperforms the state-of-the-art methods on the 5-fold cross-validation test on the seven datasets of six species, demonstrating that the proposed 6mAPred-MSFF is more effective and generic. Specifically, our proposed 6mAPred-MSFF gives the sensitivity and specificity of the 5-fold cross-validation on the 6mA-rice-Lv dataset as 97.88% and 94.64%, respectively. Our model trained with the rice data predicts well the 6mA sites of other five species: Arabidopsis thaliana, Fragaria vesca, Rosa chinensis, Homo sapiens, and Drosophila melanogaster with a prediction accuracy 98.51%, 93.02%, and 91.53%, respectively. Moreover, via experimental comparison, we explored performance impact by training and testing our proposed model under different encoding schemes and feature descriptors.

scholarly journals Deep6mA: a deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species

2019 ◽  
Author(s):  
Zutan Li ◽  
Hangjin Jiang ◽  
Lingpeng Kong ◽  
Yuanyuan Chen ◽  
Liangyun Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Biological Processes ◽  
Biological Functions ◽  
Learning Framework ◽  
Wide Range ◽  
Genomic Scale ◽  
Downstream Gene Expression ◽  
Fold Cross Validation

ABSTRACTN6-methyladenin(6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for understanding of 6mA’s biological functions. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca, and Rosa chinensis, with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

scholarly journals circDeep: deep learning approach for circular RNA classification from other long non-coding RNA

2019 ◽  
Vol 36 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Mohamed Chaabane ◽  
Robert M Williams ◽  
Austin T Stephens ◽  
Juw Won Park
Keyword(s):  
Deep Learning ◽  
Circular Rna ◽  
Supplementary Information ◽  
Circular Rnas ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Gene Transcripts ◽  
Non Coding Rnas ◽  
Increase In Accuracy ◽  
High Level

Abstract Motivation Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is vital to understanding their biogenesis and purpose. Prediction of circular RNA can be achieved in three steps: distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs and predicting circular RNAs from other long non-coding RNAs (lncRNAs). However, the available tools are less than 80 percent accurate for distinguishing circular RNAs from other lncRNAs due to difficulty of classification. Therefore, the availability of a more accurate and fast machine learning method for the identification of circular RNAs, which considers the specific features of circular RNA, is essential to the development of systematic annotation. Results Here we present an End-to-End deep learning framework, circDeep, to classify circular RNA from other lncRNA. circDeep fuses an RCM descriptor, ACNN-BLSTM sequence descriptor and a conservation descriptor into high level abstraction descriptors, where the shared representations across different modalities are integrated. The experiments show that circDeep is not only faster than existing tools but also performs at an unprecedented level of accuracy by achieving a 12 percent increase in accuracy over the other tools. Availability and implementation https://github.com/UofLBioinformatics/circDeep. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals LINC01106 drives colorectal cancer growth and stemness through a positive feedback loop to regulate the Gli family factors

2020 ◽  
Vol 11 (10) ◽  
Author(s):  
Kun Guo ◽  
Wenbin Gong ◽  
Qin Wang ◽  
Guosheng Gu ◽  
Tao Zheng ◽  
...  
Keyword(s):  
Feedback Loop ◽  
Colon Adenocarcinoma ◽  
Family Factors ◽  
Transcription Activator ◽  
Normal Colon ◽  
Protein Coding ◽  
Non Coding Rnas ◽  
High Level ◽  
Ucsc Database

Abstract Long non-coding RNAs (lncRNAs) are essential contributors to the progression of various human cancers. Long intergenic non-protein coding RNA 1106 is a member of lncRNAs family. Until now, the specific role of LINC01106 in CRC remains undefined. The aim the current study was to unveil the functions of LINC01106 and explore its potential molecular mechanism in CRC. Based on the data of online database GEPIA, we determined that LINC01106 was expressed at a high level in colon adenocarcinoma (COAD) tissues compared to normal colon tissues. More importantly, high level of LINC01106 had negative correlation with the overall survival of COAD patients. Additionally, we also determined the low level of LINC01106 in normal colon tissues based on UCSC database. Through qRT-PCR, we identified that LINC01106 was highly expressed in CRC tissues compared to adjacent normal ones. Similarly, we detected the expression of LINC01106 and confirmed that LINC01106 was expressed higher in CRC cells than that in normal cells. Subsequently, LINC01106 was mainly distributed in the cytoplasm. LINC01106 induced the proliferation, migration, and stem-like phenotype of CRC cells. Mechanistically, cytoplasmic LINC01106 positively modulated Gli4 in CRC cells by serving as a miR-449b-5p sponge. Furthermore, nuclear LINC01106 could activate the transcription of Gli1 and Gli2 through recruiting FUS to Gli1 and Gli2 promoters. Mechanism of investigation unveiled that Gli2 was a transcription activator of LINC01106. In conclusion, Gli2-induced upregulation of LINC01106 aggravates CRC progression through upregulating Gli2, Gli2, and Gli4.

scholarly journals BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information

2019 ◽  
Vol 20 (4) ◽  
pp. 978 ◽  
Author(s):  
Zhao-Hui Zhan ◽  
Li-Na Jia ◽  
Yong Zhou ◽  
Li-Ping Li ◽  
Hai-Cheng Yi
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Prediction Accuracy ◽  
Sparse Matrices ◽  
Protein Sequences ◽  
Biological Research ◽  
Sequence Information ◽  
Feature Extraction Method ◽  
Cellular Processes ◽  
High Level

The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.

scholarly journals DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding

2021 ◽  
Author(s):  
Min Zeng ◽  
Yifan Wu ◽  
Chengqian Lu ◽  
Fuhao Zhang ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Subcellular Localization ◽  
Order Information ◽  
Embedding Method ◽  
Rna Molecules ◽  
Complete Representation ◽  
Learning Framework ◽  
Non Coding Rna ◽  
High Level ◽  
Long Non Coding Rna

Abstract Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.

Sign in / Sign up

Export Citation Format

Share Document