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

Zhang, Shijie; He, Yukun; Liu, Huanhuan; Zhai, Haoyu; Huang, Dandan; Yi, Xianfu; Dong, Xiaobao; Wang, Zhao; Zhao, Ke; Zhou, Yao; Wang, Jianhua; Yao, Hongcheng; Xu, Hang; Yang, Zhenglu; Sham, Pak C.; Chen, Kexin; Plewczynski, Dariusz

doi:10.1093/nar/gkz774

Cited by 52 publications

(70 citation statements)

References 76 publications

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“…Ensemble learning methods generally surpass unsupervised methods when high-quality training data of appropriate type and quantity are available ( 37 ). Consistent with recent study on prediction of non-coding regulatory variants ( 46 ), ensemble learning methods including XGBoost, RF and GBT exhibit better performance than conventional SVM classifier in all training datasets. Moreover, the model trained by XGBoost algorithm shows the best prediction performance.…”

Section: Discussionsupporting

confidence: 84%

“…Each DT comprises a series of rules that semi-optimally split the training data. Its sparsity-aware split search approach makes it suitable for our dataset where missing values commonly appear ( 46 ). Successive trees that ‘correct’ the errors in the initial tree were learned to improve the classification of positive and negative training examples.…”

Section: Methodsmentioning

confidence: 99%

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AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

Wang

Zhao

et al. 2020

NAR Genomics and Bioinformatics

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The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.

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Section: Discussionsupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

Wang

Zhao

et al. 2020

NAR Genomics and Bioinformatics

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“…To determine which of the CFTR introns could be involved in gene regulation, we used the GWAS3D score proposed by Li et al [ 38 ]. This score is based on different functional information of regulation, such as Dnase-seq, TF ChIP-Seq, histone modifications and 5C data [ 39 , 40 ], and a higher score is predictive of a more important functional impact of the variant. We compared the distribution of the scores of variants in the 1000 Genomes project [ 41 ] between all CFTR introns with the hypothesis that an intron showing more variants with high GWAS3D scores has a stronger functional effect.…”

Section: Resultsmentioning

confidence: 99%

“…To investigate this question, we used the GWAS3D score [ 38 ] to identify introns enriched in important functional variants in the CFTR gene regulation in the general population. This score is based on different functional information such as DNA-seq, TF ChIP-Seq, histone modifications and 5C data [ 40 ], and enables us to rank variants according to their predicted functional impact. We identify four introns, introns 26, 24, 1 and 12, in order of importance ( Figure 1 ) showing GWAS3D scores significantly higher than the other introns.…”

Section: Discussionmentioning

confidence: 99%

CFTR Cooperative Cis-Regulatory Elements in Intestinal Cells

Collobert

Bocher

Nabec

et al. 2021

IJMS

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About 8% of the human genome is covered with candidate cis-regulatory elements (cCREs). Disruptions of CREs, described as “cis-ruptions” have been identified as being involved in various genetic diseases. Thanks to the development of chromatin conformation study techniques, several long-range cystic fibrosis transmembrane conductance regulator (CFTR) regulatory elements were identified, but the regulatory mechanisms of the CFTR gene have yet to be fully elucidated. The aim of this work is to improve our knowledge of the CFTR gene regulation, and to identity factors that could impact the CFTR gene expression, and potentially account for the variability of the clinical presentation of cystic fibrosis as well as CFTR-related disorders. Here, we apply the robust GWAS3D score to determine which of the CFTR introns could be involved in gene regulation. This approach highlights four particular CFTR introns of interest. Using reporter gene constructs in intestinal cells, we show that two new introns display strong cooperative effects in intestinal cells. Chromatin immunoprecipitation analyses further demonstrate fixation of transcription factors network. These results provide new insights into our understanding of the CFTR gene regulation and allow us to suggest a 3D CFTR locus structure in intestinal cells. A better understand of regulation mechanisms of the CFTR gene could elucidate cases of patients where the phenotype is not yet explained by the genotype. This would thus help in better diagnosis and therefore better management. These cis-acting regions may be a therapeutic challenge that could lead to the development of specific molecules capable of modulating gene expression in the future.

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“…Nevertheless, a new model with a novel experimental approach, CRISPRi-FlowFISH, has been proposed for interpreting the functions of variants in non-coding regions [ 98 ]. In addition, various in silico prediction tools for non-coding regions are being developed, including regBase [ 99 ], RegSNPs-intron [ 100 ], and GRAM [ 101 ]. Overall, a better understanding of variants in coding and non-coding regions and single variant annotation across whole genome would take advantage of population-based sequencing data to provide great benefits to human health.…”

Section: Future Perspectivesmentioning

confidence: 99%

Unique roles of rare variants in the genetics of complex diseases in humans

2020

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Genome-wide association studies have identified >10,000 genetic variants associated with various phenotypes and diseases. Although the majority are common variants, rare variants with >0.1% of minor allele frequency have been investigated by imputation and using disease-specific custom SNP arrays. Rare variants sequencing analysis mainly revealed have played unique roles in the genetics of complex diseases in humans due to their distinctive features, in contrast to common variants. Unique roles are hypothesis-free evidence for gene causality, a precise target of functional analysis for understanding disease mechanisms, a new favorable target for drug development, and a genetic marker with high disease risk for personalized medicine. As whole-genome sequencing continues to identify more rare variants, the roles associated with rare variants will also increase. However, a better estimation of the functional impact of rare variants across whole genome is needed to enhance their contribution to improvements in human health.

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

Cited by 52 publications

References 76 publications

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

CFTR Cooperative Cis-Regulatory Elements in Intestinal Cells

Unique roles of rare variants in the genetics of complex diseases in humans

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