N 6-methyladenosine (m6A) and N6, 2′-O-Dimethyladenosine (m6Am) modifications (m6A/m) of messenger RNA mediate diverse cellular functions. Oncogenic Kaposi’s sarcoma-associated herpesvirus (KSHV) has latent and lytic replication phases that are essential for the development of KSHV-associated cancers. To date, the role of m6A/m in KSHV replication and tumorigenesis is unclear. Here, we provide mechanistic insights by examining the viral and cellular m6A/m epitranscriptomes during KSHV latent and lytic infection. KSHV transcripts contain abundant m6A/m modifications during latent and lytic replication, and these modifications are highly conserved among different cell types and infection systems. Knockdown of YTHDF2 enhanced lytic replication by impeding KSHV RNA degradation. YTHDF2 binds to viral transcripts and differentially mediates their stability. KSHV latent infection induces 5′UTR hypomethylation and 3′UTR hypermethylation of the cellular epitranscriptome, regulating oncogenic and epithelial-mesenchymal transition pathways. KSHV lytic replication induces dynamic reprograming of epitranscriptome, regulating pathways that control lytic replication. These results reveal a critical role of m6A/m modifications in KSHV lifecycle and provide rich resources for future investigations.

BackgroundThe study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors.ResultsWe proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies.ConclusionsHere we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. A knowledge base with the systematic collection and curation of context specific transcriptome-wide methylations is critical for elucidating their biological functions as well as for developing bioinformatics tools. Since its inception in 2014, the Met-DB (Liu, H., Flores, M.A., Meng, J., Zhang, L., Zhao, X., Rao, M.K., Chen, Y. and Huang, Y. (2015) MeT-DB: a database of transcriptome methylation in mammalian cells. Nucleic Acids Res., 43, D197–D203), has become an important resource for methyltranscriptome, especially in the N6-methyl-adenosine (m6A) research community. Here, we report Met-DB v2.0, the significantly improved second version of Met-DB, which is entirely redesigned to focus more on elucidating context-specific m6A functions. Met-DB v2.0 has a major increase in context-specific m6A peaks and single-base sites predicted from 185 samples for 7 species from 26 independent studies. Moreover, it is also integrated with a new database for targets of m6A readers, erasers and writers and expanded with more collections of functional data. The redesigned Met-DB v2.0 web interface and genome browser provide more friendly, powerful, and informative ways to query and visualize the data. More importantly, MeT-DB v2.0 offers for the first time a series of tools specifically designed for understanding m6A functions. Met-DB V2.0 will be a valuable resource for m6A methyltranscriptome research. The Met-DB V2.0 database is available at http://compgenomics.utsa.edu/MeTDB/ and http://www.xjtlu.edu.cn/metdb2.

BackgroundBioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists’ capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter-gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets.ResultsIn this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets’ ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets.ConclusionsUsing autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0642-2) contains supplementary material, which is available to authorized users.

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N6-methyladenosine (m 6 A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m 6 A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m 6 A levels are controlled and whether and how regulation of m 6 A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m 6 A-regulated genes and m 6 A-associated disease, which includes Deep-m 6 A, the first model for detecting condition-specific m 6 A sites from MeRIP-Seq data with a single base resolution using deep learning and a new network-based pipeline that prioritizes functional significant m 6 A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m 6 A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m 6 A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m 6 A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway.The m 6 A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m 6 A regulatory functions and its roles in diseases.Keywords: N6-methyladenosine (m 6 A), methylated RNA immunoprecipitation sequencing (MeRIP-Seq ), convolutional neural networks, m 6 A site prediction, m 6 A functional prediction, m 6 A-disease association Author summaryThe goal of this work is to identify functional significant m 6 A-regulated genes and m 6 A-associated diseases from analyzing an extensive collection of MeRIP-seq data. To achieve this, we first developed Deep-m 6 A, a CNN model for single-base m 6 A prediction. To our knowledge, this is the first condition-specific single-base m 6 A site prediction model that combines mRNA sequence feature and MeRIP-Seq data. The 10-fold cross-validation and test on an independent dataset show that Deep-m 6 A outperformed two sequence-based models. We applied Deep-m 6 A followed by network-based analysis using HotNet2 and RWRH to 75 human MeRIP-Seq samples from various cells and tissue under different conditions to globally detect m 6 A-regulated genes and further predict m 6 A mediated functions and associated diseases. This is also to our knowledge the first attempt to predict m 6 A functions and associated diseases using only computational methods in a global manner on a large number of human MeRIP-Seq samples. The predicted functions and diseases show considerable consistent with those reported in the literature, which demonstrated the power of our proposed pipe...

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Cerebral cortex development undergoes a variety of processes, which provide valuable information for the study of the developmental mechanism of cortical folding as well as its relationship to brain structural architectures and brain functions. Despite the variability in the anatomy-function relationship on the higher-order cortex, recent studies have succeeded in identifying typical cortical landmarks, such as sulcal pits, that bestow specific functional and cognitive patterns and remain invariant across subjects and ages with their invariance being related to a gene-mediated proto-map. Inspired by the success of these studies, we aim in this study at defining and identifying novel cortical landmarks, termed gyral peaks, which are the local highest foci on gyri. By analyzing data from 156 MRI scans of 32 macaque monkeys with the age spanned from 0 to 36 months, we identified 39 and 37 gyral peaks on the left and right hemispheres,