RT Journal Article
SR Electronic
T1 gpps: An ILP-based approach for inferring cancer progression with mutation losses from single cell data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 365635
DO 10.1101/365635
A1 Ciccolella, Simone
A1 Gomez, Mauricio Soto
A1 Patterson, Murray
A1 Vedova, Gianluca Della
A1 Hajirasouliha, Iman
A1 Bonizzoni, Paola
YR 2018
UL http://biorxiv.org/content/early/2018/09/04/365635.abstract
AB Motivation In recent years, the well-known Infinite Sites Assumption (ISA) has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progression where mutations are accumulated through histories. However, some recent studies leveraging Single Cell Sequencing (SCS) techniques have shown evidence of mutation losses in several tumor samples [19], making the inference problem harder.Results We present a new tool, gpps, that reconstructs a tumor phylogeny from single cell data, allowing each mutation to be lost at most a fixed number of times.Availability The General Parsimony Phylogeny from Single cell (gpps) tool is open source and available at https://github.com/AlgoLab/gppf.