| Title: |
Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence |
| Authors: |
Kuipers, Jack; Singer, Jochen; Beerenwinkel, Niko |
| Contributors: |
Schwartz, Russell; Swiss National Science Foundation |
| Source: |
Bioinformatics ; volume 38, issue 20, page 4713-4719 ; ISSN 1367-4803 1367-4811 |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2022 |
| Description: |
Motivation Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient’s tumour composition and evolutionary history is key for personalized therapies. Single-cell sequencing (SCS) now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, but brings with it challenges related to the particular noise profiles of the sequencing protocols as well as the complexity of the underlying evolutionary process. Results By modelling the noise processes and allowing mutations to be lost or to reoccur during tumour evolution, we present a method to jointly call mutations in each cell, reconstruct the phylogenetic relationship between cells, and determine the locations of mutational losses and recurrences. Our Bayesian approach allows us to accurately call mutations as well as to quantify our certainty in such predictions. We show the advantages of allowing mutational loss or recurrence with simulated data and present its application to tumour SCS data. Availability and implementation SCIΦN is available at https://github.com/cbg-ethz/SCIPhIN. Supplementary information Supplementary data are available at Bioinformatics online. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/bioinformatics/btac577 |
| DOI: |
10.1093/bioinformatics/btac577/45739765/btac577.pdf |
| Availability: |
https://doi.org/10.1093/bioinformatics/btac577; https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btac577/45739765/btac577.pdf; https://academic.oup.com/bioinformatics/article-pdf/38/20/4713/46535125/btac577.pdf |
| Rights: |
https://creativecommons.org/licenses/by-nc/4.0/ |
| Accession Number: |
edsbas.667B44C4 |
| Database: |
BASE |