Computational enhancement of single-cell sequences for inferring tumor evolution.

摘要: Motivation Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing assessment single nucleotide variation (SNV) at highest cellular resolution. However, state-of-the-art technologies produce data many missing bases (MBs) and incorrect base designations lead to false-positive (FP) false-negative (FN) detection somatic mutations. While computational methods available make biological inferences in presence these errors, accuracy imputed MBs corrected FPs FNs remains unknown. Results Using computer simulated datasets, we assessed robustness performance four existing (OncoNEM, SCG, SCITE SiFit) one new method (BEAM). BEAM is a Bayesian evolution-aware improves quality sequences by using intrinsic evolutionary information molecular phylogenetic framework. Overall, performed best. Most high accuracy, but effective correction challenge, especially for small datasets. Analysis empirical dataset shows can improve both tumor their utility inference. In conclusion, cells descend from pre-existing cells, which creates continuity This enables other correctly impute assignments, challenging when number SNVs sampled relative sequenced. Availability implementation on web https://github.com/SayakaMiura/BEAM.