Biology subjects

Antoniotti, M.

Publications and source records attributed to Antoniotti, M..

2 recordsLinked to original sources

bioRxiv2018

MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data

The characterization of the metabolic deregulations that distinguish cancer phenotypes, and which might be effectively targeted by ad-hoc strategies, is a key open challenge. To this end, we here introduce MaREA (Metabolic Reaction Enrichment Analysis), a computational pipeline that processes cross-sectional RNAseq data to identify the metabolic reactions that are significantly up-/ down-regulated in different sample subgroups. MaREA relies on the definition of a Reaction Activity Score, computed as a function of the expression level of genes encoding for reaction enzymes, which can also be used as an effective metrics to cluster samples into distinct metabolic subgroups. MaREA finally allows to visualize the results in a graphical form directly on metabolic maps. We apply MaREA to distinct cancer datasets and we show that it can produce useful information and new experimental hypotheses on metabolic deregulation of cancer cells, also allowing to stratify patients in metabolic clusters with significantly different survival expectancy.

bioinformatics↗

bioRxiv2017

A Computational Framework To Infer The Order Of Accumulating Mutations In Individual Tumors

BackgroundA large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. ResultsWe introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. ConclusionsWe show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.

bioinformatics↗