Genetic variants in or near miRNA genes can have profound effects on miRNA expression and targeting. As user-friendly software for the impact prediction of miRNA variants on a large scale is still lacking, we created a tool called miRVaS. miRVaS automates this prediction by annotating the location of the variant relative to functional regions within the miRNA hairpin (seed, mature, loop, hairpin arm, flanks) and by annotating all predicted structural changes within the miRNA due to the variant. In addition, the tool defines the most important region that is predicted to have structural changes and calculates a conservation score that is indicative of the reliability of the structure prediction. The output is presented in a tab-separated file, which enables fast screening, and in an html file, which allows visual comparison between wild-type and variant structures. All separate images are provided for downstream use. Finally, we tested two different approaches on a small test set of published functionally validated genetic variants for their capacity to predict the impact of variants on miRNA expression.
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http://dx.doi.org/10.1093/nar/gkv921 | DOI Listing |
Orphanet J Rare Dis
January 2025
The Genetics and Prenatal Diagnosis Center, The Department of Obstetrics and Gynecology, The First Affiliated Hospital of Zhengzhou University, Jianshe Rd, Erqi District, Zhengzhou, 450052, Henan, China.
Objective: Spinal muscular atrophy (SMA) is a motor neuron disorder encompassing 5q and non-5q forms, causing muscle weakness and atrophy due to spinal cord cell degeneration. Understanding its genetic basis is crucial for genetic counseling and personalized treatment options.
Methods: This study retrospectively analyzed families of patients suspected of SMA at our institution from February 2006 to March 2024.
Orphanet J Rare Dis
January 2025
Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
Background: Inclusion Body Myositis is an acquired muscle disease. Its pathogenesis is unclear due to the co-existence of inflammation, muscle degeneration and mitochondrial dysfunction. We aimed to provide a more advanced understanding of the disease by combining multi-omics analysis with prior knowledge.
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January 2025
The Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90069, USA.
Background: With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.
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