Amplicon sequencing approach is commonly employed in microbiome studies and sequencing depth is considered as a major factor influencing the outcome of data analyses. As of now, the effect of amplicon sequencing depth in environmental microbiome analyses is not explicitly illustrated. In this study, microbiome data of nine aquatic samples from Sundarbans mangrove region, obtained from SRA, were analyzed to explain the influence of sequencing depth variation in environmental microbiome data analyses. Briefly, four groups based on number of reads (NOR) were created comprising of, total NOR, 75 k, 50 k and 25 k, followed by data analyses. The results showed that the observed ASVs among four groups were significantly different (P value 1.094e-06). The Bray-Curtis dissimilarity analysis showed differences in microbiome composition and also, each group exhibited slightly different core-microbiome structure. Importantly, the variation in sequencing depth was found to affect the predictions of environmental drivers associated with microbiome composition. Thus, this study emphasizes that the microbiome data are compositional and the NOR in the data could affect the microbial composition. In summary, this study demonstrates the consequences of sequencing depth variation on microbiome data analyses and suggests the researchers to take proper cautions to avoid misleading results due to sequencing depth variation.
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http://dx.doi.org/10.1007/s00284-021-02345-8 | DOI Listing |
Epilepsia
January 2025
Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.
Objective: Somatic variants causing epilepsy are challenging to detect, as they are only present in a subset of brain cells (e.g., mosaic), resulting in low variant allele frequencies.
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December 2024
Huashan Hospital, Fudan University, Shanghai, Shanghai, China.
Background: Cognition and its two critical proxies, socioeconomic status (SES) and educational attainment (EA), contribute substantially to human health and are heritable. Elucidating the genetic characteristics of SES/EA/Cognition not only helps to understand the innate individual differences in cognition, but also aids in unraveling the biological mechanisms of complex cognitive-related disorders such as Alzheimer's disease (AD). Here, we explored the rare and common protein-coding variants impacting the comprehensive cognition phenotypic spectrum by leveraging large-scale exomes.
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December 2024
Columbia University Irving Medical Center, New York, NY, USA.
Background: African Americans (AA) are disproportionally burdened by Alzheimer's disease (AD), but there is a scarcity of research focusing on understanding the neuroimmune component of AD pathogenesis in this population. It is generally accepted that microglia would be an ideal therapeutic target for AD and that genetic, lifestyle, societal and environmental factors and stressors have the potential to shape microglia phenotypes and their contribution to neurodegenerative processes. The overarching goal of the current study is to establish the population structure of microglia in older AAs and to investigate the relationship of the different microglia subsets with histopathological hallmarks of brain aging and AD in AAs.
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December 2024
McLaughlin Research Institute, Great Falls, MT, USA.
Background: Apolipoprotein E (ApoE) is a lipid cargo binding protein that has three variants in humans, ApoE 2, 3, and 4. The ApoE 4 allele is the greatest known genetic factor for sporadic Alzheimer's Disease. The gut microbiome (GMB) is a key essential to health, and bacterial dysbiosis can lead to poorer outcomes for disease states and an increase in microbiota and their metabolites in the peripheral.
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December 2024
Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Background: Single nucleus RNA sequencing (snRNA-seq) has revolutionized our ability to dissect transcriptional profiles in specific cell types. While nuclear sequencing enhances analysis robustness, it captures only 20-50% of the cellular transcriptional information, limiting our comprehensive understanding of the cellular transcriptional ensemble. Therefore, we propose a computational approach to extract the cellular signal from bulk transcriptomic data from brain tissue, allowing us to investigate cell type-specific transcriptomic programs underlying neurodegeneration.
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