Dataset Information

RegBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.

ABSTRACT: Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

SUBMITTER: Zhang S

PROVIDER: S-EPMC6868349 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.

Zhang Shijie S He Yukun Y Liu Huanhuan H Zhai Haoyu H Huang Dandan D Yi Xianfu X Dong Xiaobao X Wang Zhao Z Zhao Ke K Zhou Yao Y Wang Jianhua J Yao Hongcheng H Xu Hang H Yang Zhenglu Z Sham Pak Chung PC Chen Kexin K Li Mulin Jun MJ

Nucleic acids research 20191201 21

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we ...[more]

PMID: 31511901

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Project description:BackgroundLong intergenic non-coding RNAs (lincRNAs) can act as regulators of expression of protein-coding genes. Trans-natural antisense transcripts (trans-NATs) are a type of lincRNAs that contain sequence complementary to mRNA from other loci. The regulatory potential of trans-NATs has been poorly studied in eukaryotes and no example of trans-NATs regulating gene expression in plants are reported. The goal of this study was to identify lincRNAs, and particularly trans-NATs, in Arabidopsis thaliana that have a potential to regulate expression of target genes in trans at the transcriptional or translational level.ResultsWe identified 1001 lincRNAs using an RNAseq dataset from total polyA+ and polysome-associated RNA of seedlings grown under high and low phosphate, or shoots and roots treated with different phytohormones, of which 550 were differentially regulated. Approximately 30% of lincRNAs showed conservation amongst Brassicaceae and 25% harbored transposon element (TE) sequences. Gene co-expression network analysis highlighted a group of lincRNAs associated with the response of roots to low phosphate. A total of 129 trans-NATs were predicted, of which 88 were significantly differentially expressed under at least one pairwise comparison. Five trans-NATs showed a positive correlation between their expression and target mRNA steady-state levels, and three showed a negative correlation. Expression of four trans-NATs positively correlated with a change in target mRNA polysome association. The regulatory potential of these trans-NATs did not implicate miRNA mimics nor siRNAs. We also looked for lincRNAs that could regulate gene expression in trans by Watson-Crick DNA:RNA base pairing with target protein-encoding loci. We identified 100 and 81 with a positive or negative correlation, respectively, with steady-state level of their predicted target. The regulatory potential of one such candidate lincRNA harboring a SINE TE sequence was validated in a protoplast assay on three distinct genes containing homologous TE sequence in their promoters. Construction of networks highlighted other putative lincRNAs with multiple predicted target loci for which expression was positively correlated with target gene expression.ConclusionsThis study identified lincRNAs in Arabidopsis with potential in regulating target gene expression in trans by both RNA:RNA and RNA:DNA base pairing and highlights lincRNAs harboring TE sequences in such activity.

Project description:SummaryInterpreting genetic variants of unknown significance (VUS) is essential in clinical applications of genome sequencing for diagnosis and personalized care. Non-coding variants remain particularly difficult to interpret, despite making up a large majority of trait associations identified in genome-wide association studies (GWAS) analyses. Predicting the regulatory effects of non-coding variants on candidate genes is a key step in evaluating their clinical significance. Here, we develop a machine-learning algorithm, Inference of Connected expression quantitative trait loci (eQTLs) (IRT), to predict the regulatory targets of non-coding variants identified in studies of eQTLs. We assemble datasets using eQTL results from the Genotype-Tissue Expression (GTEx) project and learn to separate positive and negative pairs based on annotations characterizing the variant, gene and the intermediate sequence. IRT achieves an area under the receiver operating characteristic curve (ROC-AUC) of 0.799 using random cross-validation, and 0.700 for a more stringent position-based cross-validation. Further evaluation on rare variants and experimentally validated regulatory variants shows a significant enrichment in IRT identifying the true target genes versus negative controls. In gene-ranking experiments, IRT achieves a top-1 accuracy of 50% and top-3 accuracy of 90%. Salient features, including GC-content, histone modifications and Hi-C interactions are further analyzed and visualized to illustrate their influences on predictions. IRT can be applied to any VUS of interest and each candidate nearby gene to output a score reflecting the likelihood of regulatory effect on the expression level. These scores can be used to prioritize variants and genes to assist in patient diagnosis and GWAS follow-up studies.Availability and implementationCodes and data used in this work are available at https://github.com/miaecle/eQTL_Trees.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

RegBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.

Publications

regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.

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