ANC Workshop: Peggy Series and Sam Heron
Tue 28 Feb 2017, 11:00 - 12:00
IF 4.31/4.33

If you have a question about this talk, please contact: Gareth Beedham (gbeedham)

Samuel Heron


"SARGASSO: A tool for the in silico Separation of Mixed Species RNA-Seq"


Knowledge of the cell-autonomous and non-autonomous mechanisms operating within biological systems is essential to reveal the underlying molecular processes at work and is particularly important in functional studies of neurological diseases and cancers. These studies commonly measure gene expression levels in different cell types, but are often confounded by invasive sample processing. Indeed physical separation techniques for cell mixtures have been shown to trigger stress and apoptosis related genes, obscuring the identification of genes of interest and introducing bias.

Here I present a novel approach which alleviates this issue for the study of gene expression using RNA-seq. Sargasso is a standalone Python tool that conducts in silico sequence separation between closely related species using quality criteria from genome mapping. Reads are separated with a high degree of precision and with minimal data loss, adjustable parameters allow for user controlled emphasis over precision and recall. Once separated, the sets can then be examined using established methods for RNA-seq analysis.



Yuanhua Huang


"Transcriptome-wide splicing quantification in single cells"


Single cell RNA-seq (scRNA-seq) has revolutionised our understanding of transcriptome variability, with profound implications both fundamental and translational. While scRNA-seq provides a comprehensive measurement of stochasticity in transcription, the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here we present BRIE (Bayesian Regression for Isoform Estimation), a Bayesian hierarchical model which resolves these problems by learning an informative prior distribution from multiple single cells. BRIE combines the mixture modelling approach for isoform quantification with a regression approach to learn sequence features which are predictive of splicing events. We validate BRIE on several scRNA-seq data sets, showing that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE therefore expands the scope of scRNA-seq experiments to probe the stochasticity of RNA-processing.


Full paper: