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This Week in Genome Biology: Oct 4, 2017

Rice University and University of Texas researchers present a computational approach for translating noisy or incomplete genotyping profiles produced by single-cell sequencing into inferred tumor phylogenies. The team applied its statistical inference method — known as SiFit — to tease out tumor trees and tumor genetic heterogeneity from synthetic data and from single-cell exome sequences generated for hundreds of tumor and non-tumor cells from two individuals with colorectal cancer. "Although the current study focused on cancer, SiFit can potentially also be applied to single-cell mutation profiles from a wide variety of fields, including immunology, neurobiology, microbiology, and tissue mosaicism," the authors note.

A Royal Children's Hospital- and University of Melbourne-led team describes Splatter Bioconductor, a computational framework for simulating single-cell RNA sequence datasets in a reproducible manner. "Splatter can easily estimate parameters for each model from real data, generate synthetic datasets, and quickly create a series of diagnostic plots comparing different simulations and datasets," the researchers explain. They went on to use the framework to evaluate half a dozen single-cell RNA-seq models, including their own simulation known as Splat, alongside real datasets.

Finally, researchers from the Max Delbrük Center for Molecular Medicine, the University of Amsterdam, and elsewhere report on information gleaned from RNA sequence data on heart samples from 97 individuals with dilated cardiomyopathy and more than 100 unaffected controls. With the left ventricle myocardium transcriptomes, the team looked at everything from gene expression and splicing differences in individuals with the inherited heart condition to expression quantitative trait loci patterns particular to the dilated cardiomyopathy hearts. GenomeWeb has more on the study.