Reviews & Analysis

We developed Tapioca, an integrative ensemble machine learning-based framework, to accurately predict global protein–protein interaction network dynamics. Tapioca enabled the characterization of host regulation during reactivation from latency of an oncogenic virus. Introducing an interactome homology analysis method, we identified a proviral host factor with broad relevance for herpesviruses.

This Perspective presents a reliable and comprehensive source of information on pitfalls related to validation metrics in image analysis, with an emphasis on biomedical imaging.

Metrics Reloaded is a comprehensive framework for guiding researchers in the problem-aware selection of metrics for common tasks in biomedical image analysis.

sc-SPORT offers a way to probe RNA structure at the single-cell level. It reveals cell-to-cell heterogeneity in RNA folding.

We pinpoint PCR artifacts as the primary source of inaccurate quantification in both short- and long-read RNA sequencing, a problem that intensifies with an increase in PCR cycles in both bulk and single-cell sequencing contexts. To overcome this challenge, we engineered a novel unique molecular identifier (UMI) barcode composed of homotrimer nucleotide blocks. This design facilitates accurate quantification of RNA molecules, substantially improving molecular counting.

We developed a prime editing (PE) strategy by incorporating a 5′–3′ exonuclease activity, which enhanced the efficacy and precision of ≥30-nucleotide DNA insertions without a secondary nick. Our optimization of the PE complex revealed that recruiting the exonuclease via an RNA aptamer outperformed direct protein fusions.

Intrinsically disordered regions of proteins are prevalent across the kingdoms of life; however, biophysical characterization is expensive, requiring specialized expertise and equipment and time-consuming sample preparation. By combining simulations and deep learning, we have developed a method to predict their average ensemble properties directly from sequence.

ARTR-seq uses antibody-guided in situ reverse transcription to efficiently and accurately identify RNA-binding protein target sites in as few as 20 cells, or in a formaldehyde-fixed tissue section. The high temporal resolution of ARTR-seq opens opportunities for the investigation of dynamic RNA-binding protein–RNA interactions.