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Nature Papers on Single-Cell RNA-Seq Data Preprocessing, Defective Viral Genomes

A method for improved single-cell RNA sequencing (scRNA-seq) data preprocessing is presented in Nature Biotechnology this week. Preprocessing — the quantification of transcript or gene abundances in individual cells from a scRNA-seq experiment — is hampered by challenges such as the memory requirements for handling increasingly large volumes of data. Aiming to strike a balance between quality and efficiency, a group led by scientists from the California Institute of Technology created a preprocessing workflow, based on the kallisto and bustools preprocessing programs, that they say offers speed with constant memory requirements, providing scalability for arbitrarily large datasets. They demonstrate the workflow's flexibility by showing its use for RNA velocity analyses. 

Defective viral genomes (DVGs) are associated with the severity of respiratory syncytial virus (RSV) and their detection can be used to identify patients at risk for severe disease, according to a new study in Nature Microbiology. DVGs are known to suppress viral replication by competing for viral proteins and by stimulating antiviral immunity. Building on earlier work that discovered DVGs of the copy-back type (cbDVGs) in respiratory secretions from some RSV-infected pediatric patients, a team led by University of Pennsylvania investigators examined the association between cbDVGs and disease severity. They find that the detection of DVGs in pediatric respiratory samples at or around the time of hospital admission associated strongly with more severe disease, higher viral load, and a stronger pro-inflammatory response. In experimentally infected adults, the detection of DVGs late after infection was associated with severe disease, while detection soon after infection was associated with mild disease. The study indicates that the kinetics of DVG accumulation and duration could predict clinical outcome of RSV infection, the authors write.