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Single-cell RNA sequencing data analysis is limited by noise and high dimensionality. Here, authors present scLENS, a tool that automates accurate signal detection without manual input, particularly in complex datasets.
BEAN is a Bayesian approach for analyzing base editing screens with improved effect size quantification and variant classification. Applied to low-density lipoprotein (LDL)-associated common variants and saturation base editing of LDLR, BEAN identifies new LDL uptake genes and offers insights into variant structure–pathogenicity mechanisms.
Wearable sweat sensors could be used to monitor patients with heart failure, providing a route to personalized and automated patient management in hospitals and at home.
Diploid assembly is a difficult task that requires several types of genomic sequencing data, including — but not limited to — HiFi reads and parental sequences. Hypo-assembler, an assembly algorithm, uses high quality solid k-mers extracted from Illumina data alongside Nanopore reads to produce a high-quality diploid assembly using only Nanopore and Illumina data.
Spatial omics enables the molecular profiling of cells with the tissue context preserved. A new analytic approach shows how cellular neighborhood analysis and feature augmentation can spatially connect and cluster millions of cells into higher-order functional units.