Purpose: Retinitis pigmentosa (RP) is a major cause of heritable human blindness with extreme genetic heterogeneity. A large number of causative genes have been defined by next-generation sequencing (NGS). However, due to technical limitations, determining the existence of uncovered or low-depth regions is a fundamental challenge in analyzing NGS data. Therefore, undetected mutations may exist in genomic regions less effectively covered by NGS.
Methods: To address this problem, we tested a complementary approach for identifying previously undetected mutations in NGS data sets. The strategy consisted of coverage-based analysis and additional target screening of low-depth regions. Fifty RP patients were analyzed, and none of the mutations found had previously been identified by NGS.
Results: Coverage-based analysis indicated that, because of a highly repetitive sequence, the RPGR open reading frame (ORF)15 was located in an uncovered or low-depth region. Through additional screening of ORF15, we identified pathogenic mutations in 14% (7/50) of patients, including four novel mutations first described herein.
Conclusion: In brief, we support the need for a complementary approach to identify mutations undetected by NGS, underscoring the power and significance of combining coverage-based analysis with additional target screening of low-depth regions in improving diagnosis of genetic diseases. In addition to its usefulness in RP, this approach is likely applicable to other Mendelian diseases.
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http://dx.doi.org/10.1038/gim.2014.193 | DOI Listing |
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The key step in the entire molecularly imprinted polymer (MIP) preparation process is the formation of the complementary cavities in the polymer matrix through the template removal process. The template is removed using chemical treatments, leaving behind selective binding sites for target molecules within the polymer matrix. Other MIP preparation steps include mixing monomers and template molecules in the appropriate solvent(s), monomer-template complex equilibration, and polymerisation of the monomers around the template.
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Rheumatoid Arthritis (RA) is an autoimmune, chronic, systemic inflammatory disease that causes redness, swelling, stiffness, and joint pain. It is a long-lasting disease that can have a widespread impact on the body, often affecting the hands, feet, and wrists. The immune cells, such as dendritic cells, T cells, B cells, macrophages, and neutrophils, play a significant role in bone degradation and inflammation.
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Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, 110870, China.
The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages.
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