Project description:Chemically-induced tongue cancer cells were extracted and isolated from C57BL6 mice. They were then grafted twice into the neck of C57BL6 mice in order to increase their tumorigenic capacities before being isolated and cultured again until passage 9. RNA was extracted from these cells for transcriptomic analysis.
Project description:Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared to existing single-cell methodologies, raising questions for the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome-sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.
Project description:Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared to existing single-cell methodologies, raising questions for the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome-sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.
Project description:Structural variation (SV) plays a fundamental role in genome evolution and can underlie inherited or acquired diseases such as cancer. Long-read sequencing technologies have led to improvements in the characterization of structural variants (SVs), although paired-end sequencing offers better scalability. Here, we present dysgu, which calls SVs or indels using paired-end or long reads. Dysgu detects signals from alignment gaps, discordant and supplementary mappings, and generates consensus contigs, before classifying events using machine learning. Additional SVs are identified by remapping of anomalous sequences. Dysgu outperforms existing state-of-the-art tools using paired-end or long-reads, offering high sensitivity and precision whilst being among the fastest tools to run. We find that combining low coverage paired-end and long-reads is competitive in terms of performance with long-reads at higher coverage values.
Project description:Linked-read sequencing enables greatly improves haplotype assembly over standard paired-end analysis. The detection of mosaic single-nucleotide variants benefits from haplotype assembly when the model is informed by the mapping between constituent reads and linked reads. Samovar evaluates haplotype-discordant reads identified through linked-read sequencing, thus enabling phasing and mosaic variant detection across the entire genome. Samovar trains a random forest model to score candidate sites using a dataset that considers read quality, phasing, and linked-read characteristics. Samovar calls mosaic single-nucleotide variants (SNVs) within a single sample with accuracy comparable with what previously required trios or matched tumor/normal pairs and outperforms single-sample mosaic variant callers at minor allele frequency 5%-50% with at least 30X coverage. Samovar finds somatic variants in both tumor and normal whole-genome sequencing from 13 pediatric cancer cases that can be corroborated with high recall with whole exome sequencing. Samovar is available open-source at https://github.com/cdarby/samovar under the MIT license.