Interpretation of the multitude of variants obtained from next generation sequencing (NGS) is labor intensive and complex. Web-based interfaces such as Galaxy streamline the generation of variant lists but lack flexibility in the downstream annotation and filtering that are necessary to identify causative variants in medical genomics. To this end, we built VariantDB, a web-based interactive annotation and filtering platform that automatically annotates variants with allele frequencies, functional impact, pathogenicity predictions and pathway information. VariantDB allows filtering by all annotations, under dominant, recessive or de novo inheritance models and is freely available at http://www.biomina.be/app/variantdb/.
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http://dx.doi.org/10.1186/s13073-014-0074-6 | DOI Listing |
BMC Genomics
January 2025
Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 610225, China.
Background: Microsatellites are highly polymorphic repeat sequences ubiquitously interspersed throughout almost all genomes which are widely used as powerful molecular markers in diverse fields. Microsatellite expansions play pivotal roles in gene expression regulation and are implicated in various neurological diseases and cancers. Although much effort has been devoted to developing efficient tools for microsatellite identification, there is still a lack of a powerful tool for large-scale microsatellite analysis.
View Article and Find Full Text PDFJ Clin Med
January 2025
Centre of Excellence for Sustainable Living and Working (SustAInLivWork), 51423 Kaunas, Lithuania.
: This study focuses on the critical task of blood vessel segmentation in medical image analysis, essential for diagnosing cardiovascular diseases and enabling effective treatment planning. Although deep learning architectures often produce very high segmentation results in medical images, coronary computed tomography angiography (CTA) images are more challenging than invasive coronary angiography (ICA) images due to noise and the complexity of vessel structures. : Classical architectures for medical images, such as U-Net, achieve only moderate accuracy, with an average Dice score of 0.
View Article and Find Full Text PDFBioengineering (Basel)
January 2025
School of Computer Science and Engineering, Sun-Yat sen University, Guanghzou 510006, China.
The consistency regularization method is a widely used semi-supervised method that uses regularization terms constructed from unlabeled data to improve model performance. Poor-quality target predictions in regularization terms produce noisy gradient flows during training, resulting in a degradation in model performance. Recent semi-supervised methods usually filter out low-confidence target predictions to alleviate this problem, but also prevent the model from learning features from unlabeled data in low-confidence regions.
View Article and Find Full Text PDFBMC Genomics
January 2025
International Institute of Molecular and Cell Biology in Warsaw, Laboratory of Zebrafish Developmental Genomics, Księcia Trojdena 4, Warsaw, 02-109, Poland.
Congenital heart disease (CHD) is a prevalent condition characterized by defective heart development, causing premature death and stillbirths among infants. Genome-wide association studies (GWASs) have provided insights into the role of genetic variants in CHD pathogenesis through the identification of a comprehensive set of single-nucleotide polymorphisms (SNPs). Notably, 90-95% of these variants reside in the noncoding genome, complicating the understanding of their underlying mechanisms.
View Article and Find Full Text PDFMed Image Anal
January 2025
School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Big DataBased Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; National Key Laboratory of Kidney Diseases, Beijing, 100853, China. Electronic address:
Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance.
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