Similar Datasets
Project description:Most DNA variants associated with common complex diseases fall outside the protein-coding regions of the genome, making them hard to detect and relate to a function. Although many computational tools are available for prioritizing functional disease risk variants outside the protein-coding regions of the genome, the precision of prediction of these tools is mostly unreliable and hence not close to cancer risk prediction. This study brings to light a novel way to improve prediction accuracy of publicly available tools by integrating the impact of cis-overlapping binding sites of opposing cancer proteins, such as P53 and cMYC, in their analysis to filter out deleterious DNA variants outside the protein-coding regions of the human genome. Using a biology-based statistical approach, DNA variants within cis-overlapping motifs impacting the binding affinity of opposing transcription factors can significantly alter the expression of target genes and regulatory networks. This study brings us closer to developing a generally applicable approach capable of filtering etiological non-coding variations in co-occupied genomic regions of P53 and cMYC family members to improve disease risk assessment.
2025-06-27 | GSE236240 | GEO
Project description:Most DNA variants associated with common complex diseases fall outside the protein-coding regions of the genome, making them hard to detect and relate to a function. Although many computational tools are available for prioritizing functional disease risk variants outside the protein-coding regions of the genome, the precision of prediction of these tools is mostly unreliable and hence not close to cancer risk prediction. This study brings to light a novel way to improve prediction accuracy of publicly available tools by integrating the impact of cis-overlapping binding sites of opposing cancer proteins, such as P53 and cMYC, in their analysis to filter out deleterious DNA variants outside the protein-coding regions of the human genome. Using a biology-based statistical approach, DNA variants within cis-overlapping motifs impacting the binding affinity of opposing transcription factors can significantly alter the expression of target genes and regulatory networks. This study brings us closer to developing a generally applicable approach capable of filtering etiological non-coding variations in co-occupied genomic regions of P53 and cMYC family members to improve disease risk assessment.
2025-06-27 | GSE235999 | GEO
Project description:Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases
| PRJNA989553 | ENA
Project description:Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases [RNA-seq]
| PRJNA988153 | ENA
Project description:Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases [ChIP-seq]
| PRJNA989557 | ENA
Project description:Non-coding DNA variations play a critical role in increasing the risk for development of common complex diseases, and account for the majority of SNPs highly associated with cancer. However, it remains a challenge to identify etiologic variants and to predict their pathological effects on target gene expression for clinical purposes. Cis-overlapping motifs (COMs) are elements of enhancer regions that impact gene expression by enabling competitive binding and switching between transcription factors. Mutations within COMs are especially important when the involved transcription factors have opposing effects on gene regulation, like P53 tumor suppressor and cMYC proto-oncogene. In this study, genome-wide analysis of ChIP-seq data from human cancer and mouse embryonic cells identified a significant number of putative regulatory elements with signals for both P53 and cMYC. Each co-occupied element contains, on average, two COMs, and one common SNP every two COMs. Gene ontology of predicted target genes for COMs showed that the majority are involved in DNA damage, apoptosis, cell cycle regulation, and RNA processing. EMSA results showed that both cMYC and P53 bind to cis-overlapping motifs within a ChIP-seq co-occupied region in Chr12. In vitro functional analysis of selected co-occupied elements verified enhancer activity, and also showed that the occurrence of SNPs within three COMs significantly altered enhancer activity. We identified a list of COM-associated functional SNPs that are in close proximity to SNPs associated with common diseases in large population studies. These results suggest a potential molecular mechanism to identify etiologic regulatory mutations associated with common diseases.
| S-EPMC6281388 | biostudies-literature
Project description:BackgroundWith the recent growth of information on sequence variations in the human genome, predictions regarding the functional effects and relevance to disease phenotypes of coding sequence variations are becoming increasingly important. The aims of this study were to catalog protein-coding sequence variations (CVs) occurring in genetic variation databases and to use bioinformatic programs to analyze CVs. In addition, we aim to provide insight into the functionality of the reference databases.Methodology and findingsTo catalog CVs on a genome-wide scale with regard to protein function and disease, we investigated three representative databases; the Human Gene Mutation Database (HGMD), the Single Nucleotide Polymorphisms database (dbSNP), and the Haplotype Map (HapMap). Using these three databases, we analyzed CVs at the protein function level with bioinformatic programs. We proposed a combinatorial approach using the Support Vector Machine (SVM) to increase the performance of the prediction programs. By cataloging the coding sequence variations using these databases, we found that 4.36% of CVs from HGMD are concurrently registered in dbSNP (8.11% of CVs from dbSNP are concurrent in HGMD). The pattern of substitutions and functional consequences predicted by three bioinformatic programs was significantly different among concurrent CVs, and CVs occurring solely in HGMD or in dbSNP. The experimental results showed that the proposed SVM combination noticeably outperformed the individual prediction programs.ConclusionsThis is the first study to compare human sequence variations in HGMD, dbSNP and HapMap at the genome-wide level. We found that a significant proportion of CVs in HGMD and dbSNP overlap, and we emphasize the need to use caution when interpreting the phenotypic relevance of these concurrent CVs. Combining bioinformatic programs can be helpful in predicting the functional consequences of CVs because it improved the performance of functional predictions.
| S-EPMC2570488 | biostudies-literature
Project description: Not available
| S-EPMC9066700 | biostudies-literature
Project description:BackgroundStructural variation (SV) represents a significant, yet poorly understood contribution to an individual's genetic makeup. Advanced next-generation sequencing technologies are widely used to discover such variations, but there is no single detection tool that is considered a community standard. In an attempt to fulfil this need, we developed an algorithm, SoftSearch, for discovering structural variant breakpoints in Illumina paired-end next-generation sequencing data. SoftSearch combines multiple strategies for detecting SV including split-read, discordant read-pair, and unmated pairs. Co-localized split-reads and discordant read pairs are used to refine the breakpoints.ResultsWe developed and validated SoftSearch using real and synthetic datasets. SoftSearch's key features are 1) not requiring secondary (or exhaustive primary) alignment, 2) portability into established sequencing workflows, and 3) is applicable to any DNA-sequencing experiment (e.g. whole genome, exome, custom capture, etc.). SoftSearch identifies breakpoints from a small number of soft-clipped bases from split reads and a few discordant read-pairs which on their own would not be sufficient to make an SV call.ConclusionsWe show that SoftSearch can identify more true SVs by combining multiple sequence features. SoftSearch was able to call clinically relevant SVs in the BRCA2 gene not reported by other tools while offering significantly improved overall performance.
| S-EPMC3865185 | biostudies-literature
Project description:Asthma, atopic dermatitis and chronic rhinoconjunctivitis are highly heterogeneous. However, epidemiologic associations exist between phenotypic groups of patients. Atopic march is one such association but is not the only common point. Indeed, beyond such phenotypes, hallmarks of type 2 immunity have been found in these diseases involving immune dysregulation as well as environmental triggers and epithelial dysfunction. From the canonical Th2 cytokines (IL-4, IL-5, IL-13), new cellular and molecular actors arise, from the epithelium's alarmins to new innate immune cells. Their interactions are now better understood across the different environmental barriers, and slight differences appeared. In parallel, the development of type 2-targeting biotherapies not only raised hope to treat those diseases but also raised new questions regarding their true pathophysiological involvement. Here, we review the place of type 2 immunity in the different phenotypes of asthma, chronic rhinitis, chronic rhinosinusitis and atopic dermatitis, highlighting nuances between them. New hypotheses rising from the use of biotherapies will be discussed along with the uncertainties and unmet needs of this field.
| S-EPMC9292742 | biostudies-literature