A functional analysis of 180 cancer cell lines reveals conserved intrinsic metabolic programs | Molecular Systems Biology
Cancer cells reprogram their metabolism to support growth and invasion. While previous work has highlighted how single altered reactions and pathways can drive tumorigenesis, it remains unclear how individual changes propagate at the network level and eventually determine global metabolic activity. To characterize the metabolic lifestyle of cancer cells across pathways and genotypes, researchers profiled the intracellular metabolome of 180 pan-cancer cell lines grown in identical conditions. For each cell line, thet estimated activity for 49 pathways spanning the entirety of the metabolic network. Upon clustering, they discovered a convergence into only two major metabolic types.
Deciphering the impact of genetic variation on human polyadenylation using APARENT2 | Genome Biology
3′-end processing by cleavage and polyadenylation is an important and finely tuned regulatory process during mRNA maturation. Researchers introduce a residual neural network model, APARENT2, that can infer 3′-cleavage and polyadenylation from DNA sequence more accurately than any previous model. This model generalizes to the case of alternative polyadenylation (APA) for a variable number of polyadenylation signals.
UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference | Nature Communications
The recent breakthrough of single-cell RNA velocity methods brings attractive promises to reveal directed trajectory on cell differentiation, states transition and response to perturbations. Here, researchers present UniTVelo, a statistical framework of RNA velocity that models the dynamics of spliced and unspliced RNAs via flexible transcription activities. Uniquely, it also supports the inference of a unified latent time across the transcriptome.