Nowadays, single-cell DNA (sc-DNA) sequencing is showing up to be a valuable instrument to investigate intra and inter-tumor heterogeneity and infer its evolutionary dynamics, by using the high-resolution data it produces. That is why the demand for analytical tools to manage this kind of data is increasing. Here we propose a pipeline capable of producing multi-sample copy-number variation (CNV) analysis on large-scale single-cell DNA sequencing data and investigate spatial and temporal tumor heterogeneity.
Single-cell DNA Sequencing Data: a Pipeline for Multi-Sample Analysis / Marilisa, Montemurro; Grassi, Elena; Urgese, Gianvito; Emanuele, Parisi; Gabriele Pizzino, Carmelo; Bertotti, Andrea; Ficarra, Elisa. - (2019). (Intervento presentato al convegno CIBB 2019 - 16th International Conference on Computational Intelligence in Bioinformatics and Biostatistics tenutosi a Bergamo nel 4-6 September 2019).
Single-cell DNA Sequencing Data: a Pipeline for Multi-Sample Analysis
Elisa Ficarra
2019
Abstract
Nowadays, single-cell DNA (sc-DNA) sequencing is showing up to be a valuable instrument to investigate intra and inter-tumor heterogeneity and infer its evolutionary dynamics, by using the high-resolution data it produces. That is why the demand for analytical tools to manage this kind of data is increasing. Here we propose a pipeline capable of producing multi-sample copy-number variation (CNV) analysis on large-scale single-cell DNA sequencing data and investigate spatial and temporal tumor heterogeneity.
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