Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses.

摘要: MOTIVATION In recent years, the well-known Infinite Sites Assumption has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, studies leveraging single-cell sequencing (SCS) techniques have shown evidence widespread recurrence and, especially, loss mutations in several samples. While there exist established that infer with mutation losses, remain some advancements to be made. RESULTS We present Simulated Annealing Single-Cell inference (SASC): new robust approach based on simulated annealing progression from SCS datasets. particular, we introduce an extension model evolution where are only accumulated, by allowing also limited amount evolutionary history tumor: Dollo-k model. demonstrate SASC achieves high levels accuracy when tested both real datasets comparison other available methods. AVAILABILITY AND IMPLEMENTATION The tool is open source at https://github.com/sciccolella/sasc. SUPPLEMENTARY INFORMATION Supplementary data Bioinformatics online.