LncADeep: an<i>ab initio</i>lncRNA identification and functional annotation tool based on deep learning

Yang, Cheng; Yang, Longshu; Zhou, Man; Xie, Haoling; Zhang, Chengjiu; Wang, May D.; Zhu, Huaiqiu

doi:10.1093/bioinformatics/bty428

Cited by 120 publications

(94 citation statements)

References 47 publications

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“…For trans acting lncRNAs potential protein-interacting lncRNAs were predicted with LncADeep tool 72 and sequences of proteins with at least evidence at transcript level or from homology were downloaded from UniProt. For more confident results, interactions were only predicted for proteins from genes in the same co-expression modules and a probability of 0.9 was set as threshold.…”

Section: Weighted Gene Co-expression Network Analysis a Weighted Genmentioning

confidence: 99%

Whole transcriptome approach to evaluate the effect of aluminium hydroxide in ovine encephalon

Varela-Martínez

Bilbao-Arribas

Abendaño

et al. 2020

Sci Rep

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Aluminium hydroxide adjuvants are crucial for livestock and human vaccines. Few studies have analysed their effect on the central nervous system in vivo. In this work, lambs received three different treatments of parallel subcutaneous inoculations during 16 months with aluminium-containing commercial vaccines, an equivalent dose of aluminium hydroxide or mock injections. Brain samples were sequenced by RNA-seq and miRNA-seq for the expression analysis of mRNAs, long non-coding RNAs and microRNAs and three expression comparisons were made. Although few differentially expressed genes were identified, some dysregulated genes by aluminium hydroxide alone were linked to neurological functions, the lncRNA TUNA among them, or were enriched in mitochondrial energy metabolism related functions. In the same way, the miRNA expression was mainly disrupted by the adjuvant alone treatment. Some differentially expressed miRNAs had been previously linked to neurological diseases, oxidative stress and apoptosis. In brief, in this study aluminium hydroxide alone altered the transcriptome of the encephalon to a higher degree than commercial vaccines that present a milder effect. The expression changes in the animals inoculated with aluminium hydroxide suggest mitochondrial disfunction. Further research is needed to elucidate to which extent these changes could have pathological consequences.

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Section: Weighted Gene Co-expression Network Analysis a Weighted Genmentioning

confidence: 99%

Whole transcriptome approach to evaluate the effect of aluminium hydroxide in ovine encephalon

Varela-Martínez

Bilbao-Arribas

Abendaño

et al. 2020

Sci Rep

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“…There is a reasonable deduction that the EDP phase space contains bias between the cluster of lncRNA sequences on different subcellular organelles [ 36 ]. Thus, the entropy density profile of 2-mer can be described as:

where

is the Shannon entropy, and

is the abundance of the i th 2-mer [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion

Feng

et al. 2020

IJMS

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Recent studies uncover that subcellular location of long non-coding RNAs (lncRNAs) can provide significant information on its function. Due to the lack of experimental data, the number of lncRNAs is very limited, experimentally verified subcellular localization, and the numbers of lncRNAs located in different organelle are wildly imbalanced. The prediction of subcellular location of lncRNAs is actually a multi-classification small sample imbalance problem. The imbalance of data results in the poor recognition effect of machine learning models on small data subsets, which is a puzzling and challenging problem in the existing research. In this study, we integrate multi-source features to construct a sequence-based computational tool, lncLocation, to predict the subcellular location of lncRNAs. Autoencoder is used to enhance part of the features, and the binomial distribution-based filtering method and recursive feature elimination (RFE) are used to filter some of the features. It improves the representation ability of data and reduces the problem of unbalanced multi-classification data. By comprehensive experiments on different feature combinations and machine learning models, we select the optimal features and classifier model scheme to construct a subcellular location prediction tool, lncLocation. LncLocation can obtain an 87.78% accuracy using 5-fold cross validation on the benchmark data, which is higher than the state-of-the-art tools, and the classification performance, especially for small class sets, is improved significantly.

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“…To predict the potential lncRNA-protein interactions, we used the pre-trained LncADeep (Yang et al, 2018) model, a deep learning model, and utilized the sequences of differentially expressed lncRNAs and proteins to predict their interactions. In addition, we also conducted Pearson correlation analysis between the lncRNAs and proteins, with a threshold of 0.3 for Pearson correlation coefficients (PCC).…”

Section: Lncrna-protein Interaction Analysismentioning

confidence: 99%

Identification of the Prognosis-Related lncRNAs and Genes in Gastric Cancer

Zhang

Yang

et al. 2020

Front. Genet.

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Gastric cancer is a common malignant tumor with high occurrence and recurrence and is the leading cause of death worldwide. However, the prognostic value of protein-coding and non-coding RNAs in stage III gastric cancer has not been systematically analyzed. In this study, using TCGA data, we identified 585 long noncoding RNAs (lncRNAs) and 927 protein-coding genes (PCGs) correlated with the overall survival rate of gastric cancer. Functional enrichment analysis revealed that the prognostic genes positively correlated with death rates were enriched in pathways, including gap junction, focal adhesion, cell adhesion molecules (CAMs), and neuroactive ligand-receptor interaction, that are involved in the tumor microenvironment and cell-cell communications, suggesting that their dysregulation may promote the tumor progression. To evaluate the performance of the prognostic genes in risk prediction, we built three multivariable Cox models based on prognostic genes selected from the prognostic PCGs and lncRNAs. The performance of the three models based on features from only PCGs or lncRNAs or from all prognostic genes were systematically compared, which revealed that the features selected from all the prognostic genes showed higher performance than the features selected only from lncRNAs or PCGs. Furthermore, the multivariable Cox regression analysis revealed that the stratification with the highest performance was an independent prognostic factor in stage III gastric cancer. In addition, we explored the underlying mechanism of the prognostic lncRNAs in the Cox model by predicting the lncRNA and protein interaction. Specifically, CTD-2218G20.2 was predicted to interact with PSG4, PSG5, and PSG7, which could also interact with cancer-related proteins, including KISS1, TIMP2, MMP11, IGFBP1, EGFR, and CDKN1C, suggesting that CTD-2218G20.2 might participate in the cancer progression via these cancer-related proteins. In summary, the systematic analysis of the prognostic lncRNAs and PCGs was of great importance to the understanding of the progression of stage III gastric cancer.

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LncADeep: anab initiolncRNA identification and functional annotation tool based on deep learning

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 120 publications

References 47 publications

Whole transcriptome approach to evaluate the effect of aluminium hydroxide in ovine encephalon

Whole transcriptome approach to evaluate the effect of aluminium hydroxide in ovine encephalon

LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion

Identification of the Prognosis-Related lncRNAs and Genes in Gastric Cancer

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