LipoCLEAN: A Machine Learning Filter to Improve Untargeted Lipid Identification Confidence.

In untargeted lipidomics experiments, putative lipid identifications generated by automated analysis software require substantial manual filtering to arrive at usable high-confidence data. However, identification software tools do not make full use of the available data to assess the quality of lipid identifications. Here, we present a machine-learning-based model to provide coherent, holistic quality scores based on multiple lines of evidence.

Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome.

In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms.

PRIMPOL-Mediated Adaptive Response Suppresses Replication Fork Reversal in BRCA-Deficient Cells.

Acute treatment with replication-stalling chemotherapeutics causes reversal of replication forks. BRCA proteins protect reversed forks from nucleolytic degradation, and their loss leads to chemosensitivity. Here, we show that fork degradation is no longer detectable in BRCA1-deficient cancer cells exposed to multiple cisplatin doses, mimicking a clinical treatment regimen.