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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.
scPROTEIN is a deep graph contrastive learning framework that can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings under a unified framework.
Long-read sequencing can greatly improve detection of genomic structural variants (SVs), and numerous methods have been developed to identify SVs using long-read data. Here the authors compare the performance of these methods and provide guidelines to aid users in selecting the most suitable tools for various scenarios.
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.
As the number of cloud platforms supporting scientific research grows, there is an increasing need to support interoperability between two or more cloud platforms. A well accepted core concept is to make data in cloud platforms Findable, Accessible, Interoperable and Reusable (FAIR). We introduce a companion concept that applies to cloud-based computing environments that we call a Secure and Authorized FAIR Environment (SAFE). SAFE environments require data and platform governance structures and are designed to support the interoperability of sensitive or controlled access data, such as biomedical data. A SAFE environment is a cloud platform that has been approved through a defined data and platform governance process as authorized to hold data from another cloud platform and exposes appropriate APIs for the two platforms to interoperate.
Volker Springel created the original GADGET code more than 25 years ago. Now it supports some of the largest simulations in astrophysics, and is being developed to do vastly more.