disease gene
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2021 ◽  
pp. gr.275723.121
Author(s):  
Jill E Moore ◽  
Xiao-Ou Zhang ◽  
Shaimae I Elhajjajy ◽  
Kaili Fan ◽  
Henry E Pratt ◽  
...  

Accurate transcription start site (TSS) annotations are essential for understanding transcriptional regulation and its role in human disease. Gene collections such as GENCODE contain annotations for tens of thousands of TSSs, but not all of these annotations are experimentally validated, nor do they contain information on cell type-specific usage. Therefore, we sought to generate a collection of experimentally validated TSSs by integrating RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression (RAMPAGE) data from 115 cell and tissue types, which resulted in a collection of approximately 50 thousand representative RAMPAGE peaks. These peaks were primarily proximal to GENCODE-annotated TSSs and were concordant with other transcription assays. Because RAMPAGE uses paired-end reads, we were then able to connect peaks to transcripts by analyzing the genomic positions of the 3' ends of read mates. Using this paired-end information, we classified the vast majority (37 thousand) of our RAMPAGE peaks as verified TSSs, updating TSS annotations for 20% of GENCODE genes. We also found that these updated TSS annotations were supported by epigenomic and other transcriptomic datasets. To demonstrate the utility of this RAMPAGE rPeak collection, we intersected it with the NHGRI/EBI genome-wide association studies (GWAS) catalog and identified new candidate GWAS genes. Overall, our work demonstrates the importance of integrating experimental data to further refine TSS annotations and provides a valuable resource for the biological community.


2021 ◽  
Vol 8 ◽  
Author(s):  
Zhehao Dai ◽  
Seitaro Nomura

Cardiovascular diseases are among the leading causes of morbidity and mortality worldwide. Although the spectrum of the heart from development to disease has long been studied, it remains largely enigmatic. The emergence of single-cell omics technologies has provided a powerful toolbox for defining cell heterogeneity, unraveling previously unknown pathways, and revealing intercellular communications, thereby boosting biomedical research and obtaining numerous novel findings over the last 7 years. Not only cell atlases of normal and developing hearts that provided substantial research resources, but also some important findings regarding cell-type-specific disease gene program, could never have been established without single-cell omics technologies. Herein, we briefly describe the latest technological advances in single-cell omics and summarize the major findings achieved by such approaches, with a focus on development and homeostasis of the heart, myocardial infarction, and heart failure.


2021 ◽  
Author(s):  
Dhouha Grissa ◽  
Alexander Junge ◽  
Tudor I Oprea ◽  
Lars Juhl Jensen

The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease–gene associations from curated databases, genome-wide association studies (GWAS), and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, TIGA, which considerably increased the number of GWAS-derived disease–gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/TCRD, and the Cytoscape stringApp. All data in DISEASES is updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses.


2021 ◽  
pp. gr.275889.121
Author(s):  
Taylor Weiskittel ◽  
Choong Yong Ung ◽  
Cristina Correia ◽  
Cheng Zhang ◽  
Hu Li

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline which collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo which enable us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations, and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies which were highly varied across patients demonstrating the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nizar Y. Saad ◽  
Mustafa Al-Kharsan ◽  
Sara E. Garwick-Coppens ◽  
Gholamhossein Amini Chermahini ◽  
Madison A. Harper ◽  
...  

AbstractFacioscapulohumeral muscular dystrophy (FSHD) is a potentially devastating myopathy caused by de-repression of the DUX4 gene in skeletal muscles. Effective therapies will likely involve DUX4 inhibition. RNA interference (RNAi) is one powerful approach to inhibit DUX4, and we previously described a RNAi gene therapy to achieve DUX4 silencing in FSHD cells and mice using engineered microRNAs. Here we report a strategy to direct RNAi against DUX4 using the natural microRNA miR-675, which is derived from the lncRNA H19. Human miR-675 inhibits DUX4 expression and associated outcomes in FSHD cell models. In addition, miR-675 delivery using gene therapy protects muscles from DUX4-associated death in mice. Finally, we show that three known miR-675-upregulating small molecules inhibit DUX4 and DUX4-activated FSHD biomarkers in FSHD patient-derived myotubes. To our knowledge, this is the first study demonstrating the use of small molecules to suppress a dominant disease gene using an RNAi mechanism.


2021 ◽  
Vol 17 (S6) ◽  
Author(s):  
Rebecca Kjærgaard Hendel ◽  
Marie Nathalie Nickelsen Hellem ◽  
Lena Elisabeth Hjermind ◽  
Jørgen Erik Nielsen ◽  
Asmus Vogel

Author(s):  
Lakshmi K. S. ◽  
Vadivu G.

High throughput analysis and large scale integration of biological data led to leading researches in the field of bioinformatics. Recent years witnessed the development of various methods for disease associated gene prediction and disease comorbidity predictions. Most of the existing techniques use network-based approaches and similarity-based approaches for these predictions. Even though network-based approaches have better performance, these methods rely on text data from OMIM records and PubMed abstracts. In this method, a novel algorithm (HDCDGP) is proposed for disease comorbidity prediction and disease associated gene prediction. Disease comorbidity network and disease gene network were constructed using data from gene ontology (GO), human phenotype ontology (HPO), protein-protein interaction (PPI) and pathway dataset. Modified random walk restart algorithm was applied on these networks for extracting novel disease-gene associations. Experimental results showed that the hybrid approach has better performance compared to existing systems with an overall accuracy around 85%.


2021 ◽  
Vol 34 (4) ◽  
pp. 295-302
Author(s):  
Rebecca K. Hendel ◽  
Marie N.N. Hellem ◽  
Lena E. Hjermind ◽  
Jørgen E. Nielsen ◽  
Asmus Vogel

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