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Mashup Score: 3
Background Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. Results Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reve
Source: genomebiology.biomedcentral.comCategories: General Medicine News, NeurologyTweet
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Mashup Score: 1NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads - Genome Biology - 9 day(s) ago
Long-read sequencing data, particularly those derived from the Oxford Nanopore sequencing platform, tend to exhibit high error rates. Here, we present NextDenovo, an efficient error correction and assembly tool for noisy long reads, which achieves a high level of accuracy in genome assembly. We apply NextDenovo to assemble 35 diverse human genomes from around the world using Nanopore long-read data. These genomes allow us to identify the landscape of segmental duplication and gene copy number variation in modern human populations. The use of NextDenovo should pave the way for population-scale long-read assembly using Nanopore long-read data.
Source: genomebiology.biomedcentral.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 0Single Cell Atlas: a single-cell multi-omics human cell encyclopedia - Genome Biology - 16 day(s) ago
Single-cell sequencing datasets are key in biology and medicine for unraveling insights into heterogeneous cell populations with unprecedented resolution. Here, we construct a single-cell multi-omics map of human tissues through in-depth characterizations of datasets from five single-cell omics, spatial transcriptomics, and two bulk omics across 125 healthy adult and fetal tissues. We construct its complement web-based platform, the Single Cell Atlas (SCA, www.singlecellatlas.org ), to enable vast interactive data exploration of deep multi-omics signatures across human fetal and adult tissues. The atlas resources and database queries aspire to serve as a one-stop, comprehensive, and time-effective resource for various omics studies.
Source: genomebiology.biomedcentral.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 0Scoary2: rapid association of phenotypic multi-omics data with microbial pan-genomes - Genome Biology - 23 day(s) ago
Unraveling bacterial gene function drives progress in various areas, such as food production, pharmacology, and ecology. While omics technologies capture high-dimensional phenotypic data, linking them to genomic data is challenging, leaving 40–60% of bacterial genes undescribed. To address this bottleneck, we introduce Scoary2, an ultra-fast microbial genome-wide association studies (mGWAS) software. With its data exploration app and improved performance, Scoary2 is the first tool to enable the study of large phenotypic datasets using mGWAS. As proof of concept, we explore the metabolome of yogurts, each produced with a different Propionibacterium reichii strain and discover two genes affecting carnitine metabolism.
Source: genomebiology.biomedcentral.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 1SuperCellCyto: enabling efficient analysis of large scale cytometry datasets - Genome Biology - 29 day(s) ago
Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).
Source: genomebiology.biomedcentral.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 2FixNCut: single-cell genomics through reversible tissue fixation and dissociation - Genome Biology - 1 month(s) ago
The use of single-cell technologies for clinical applications requires disconnecting sampling from downstream processing steps. Early sample preservation can further increase robustness and reproducibility by avoiding artifacts introduced during specimen handling. We present FixNCut, a methodology for the reversible fixation of tissue followed by dissociation that overcomes current limitations. We applied FixNCut to human and mouse tissues to demonstrate the preservation of RNA integrity, sequencing library complexity, and cellular composition, while diminishing stress-related artifacts. Besides single-cell RNA sequencing, FixNCut is compatible with multiple single-cell and spatial technologies, making it a versatile tool for robust and flexible study designs.
Source: genomebiology.biomedcentral.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 1DANCE: a deep learning library and benchmark platform for single-cell analysis - Genome Biology - 2 month(s) ago
DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.
Source: genomebiology.biomedcentral.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 0scGIST: gene panel design for spatial transcriptomics with prioritized gene sets - Genome Biology - 2 month(s) ago
A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST’s efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST’s algorithmic toolbox.
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Mashup Score: 0SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data - Genome Biology - 2 month(s) ago
Multi-omic single-cell technologies, which simultaneously measure the transcriptional and epigenomic state of the same cell, enable understanding epigenetic mechanisms of gene regulation. However, noisy and sparse data pose fundamental statistical challenges to extract biological knowledge from complex datasets. SHARE-Topic, a Bayesian generative model of multi-omic single cell data using topic models, aims to address these challenges. SHARE-Topic identifies common patterns of co-variation between different omic layers, providing interpretable explanations for the data complexity. Tested on data from different technological platforms, SHARE-Topic provides low dimensional representations recapitulating known biology and defines associations between genes and distal regulators in individual cells.
Source: genomebiology.biomedcentral.comCategories: General Medicine News, General HCPsTweet
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Mashup Score: 19Structural variation and DNA methylation shape the centromere-proximal meiotic crossover landscape in Arabidopsis - Genome Biology - 3 month(s) ago
Background Centromeres load kinetochore complexes onto chromosomes, which mediate spindle attachment and allow segregation during cell division. Although centromeres perform a conserved cellular function, their underlying DNA sequences are highly divergent within and between species. Despite variability in DNA sequence, centromeres are also universally suppressed for meiotic crossover recombination, across eukaryotes. However, the genetic and epigenetic factors responsible for suppression of centromeric crossovers remain to be completely defined. Results To explore the centromere-proximal meiotic recombination landscape, we map 14,397 crossovers against fully assembled Arabidopsis thaliana (A. thaliana) genomes. A. thaliana centromeres comprise megabase satellite repeat arrays that load nucleosomes containing the CENH3 histone variant. Each chromosome contains a structurally polymorphic region of ~3–4 megabases, which lack crossovers and include the satellite arrays. This polymorphic r
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“Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes” by Jonathan P. Bradfield et al. Genome Biology https://t.co/RmTMlX6hT1 3/3