GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

Abstract

Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.

High throughput miRNA sequencing and bioinformatics analysis identify the mesenchymal cell proliferation and apoptosis related miRNAs during fetal mice palate development

“…20 The identified protein coding genes only account for a mall part of the total genetic risk relate to palate formation, and more than 80% of the related genes are outside the protein coding genes, which indicates that the non-coding genome plays a very important role in the palatal shelf formation progression. 21 miRNA is involved in the regulation of palatal cell proliferation, adhesion, migration, apoptosis and EMT by targeting a variety of mRNA that are very important for palate development. 22 In mice palatal mesenchymal cells, the miR-17-92 cluster controls cell proliferation and collagen formation by targeting the TGFB pathway.…”

“…With the development of gene detection technology, many susceptible genes related to palate formation have been reported, such as IRF6, VAX1, FGFR2, etc 20 . The identified protein coding genes only account for a mall part of the total genetic risk relate to palate formation, and more than 80% of the related genes are outside the protein coding genes, which indicates that the non‐coding genome plays a very important role in the palatal shelf formation progression 21 …”

The contribution of de novo coding mutations to meningomyelocele

“…Additional populational allele frequency information (gnomAD genome v3.1.2) was added and SpliceAI (v1.3.1) 58 was utilized (score > 0.5) to predict splice disrupting variants. We annotated noncoding variants with GREEN-VARAN 59 (GREEN-DB schema 2.5) to annotate DNMs in noncoding regions. For copy number variant analysis (CNV), CNVpytor (1.3.1) 60 was utilized with a window size of 100 kbp, and manually curated by confirming the read depths of all father and mother.…”

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses

“…To facilitate a complex genome study, another technology that is chromosome-specific has helped in developing Bacterial Artificial Chromosome (BAC) libraries to help in studying the complex genome. Mapping of 1 Gb chromosome of wheat has been possible with the help of the chromosome-by-chromosome approach only [ 76 ]. Mapping compiles genetic mappings into physical contigs as well as providing a framework for the assembly of sequences into the whole genome, and in the absence of a reference genome sequence, this BAC-end shotgun sequence gives details of genome evolution as well as structure [ 77 , 78 ].…”