PT  - JOURNAL ARTICLE
AU  - Simone Ciccolella
AU  - Mauricio Soto Gomez
AU  - Murray Patterson
AU  - Gianluca Della Vedova
AU  - Iman Hajirasouliha
AU  - Paola Bonizzoni
TI  - gpps: An ILP-based approach for inferring cancer progression with mutation losses from single cell data
AID  - 10.1101/365635
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 365635
4099  - http://biorxiv.org/content/early/2018/09/04/365635.short
4100  - http://biorxiv.org/content/early/2018/09/04/365635.full
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.