Tag: RNA-Seq


[PMID: 29229983] [Nature Genetics]

Annotation-free quantification of RNA splicing using LeafCutter

“We developed LeafCutter to study sample and population variation in intron splicing. LeafCutter identifies variable splicing events from short-read RNA-seq data and finds events of high complexity. Our approach obviates the need for transcript annotations and circumvents the challenges in estimating relative isoform or exon usage in complex splicing events. LeafCutter can be used both to detect differential splicing between sample groups and to map splicing quantitative trait loci (sQTLs).” Work from Jonathan Pritchard.


MEC by Single-Cell RNA-seq

[PMID: 29225342] [Nature Communications]

Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing

“Here we report the use of single-cell RNA sequencing to determine the gene expression profile of MECs across four developmental stages; nulliparous, mid gestation, lactation and post involution. Our analysis of 23,184 cells identifies 15 clusters, few of which could be fully characterised by a single marker gene. We argue instead that the epithelial cells—especially in the luminal compartment—should rather be conceptualised as being part of a continuous spectrum of differentiation. Furthermore, our data support the existence of a common luminal progenitor cell giving rise to intermediate, restricted alveolar and hormone-sensing progenitors. This luminal progenitor compartment undergoes transcriptional changes in response to a full pregnancy, lactation and involution.”

Subclonal Evolution of Resistant Cancer Phenotypes

[PMID: 29093439] [Nature Communications]

Combating subclonal evolution of resistant cancer phenotypes

“track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment” “These findings highlight cancer’s ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.”

Spatial-omic data in Early Breast Tumor

[PMID: 29093438] [Nature Communications]

Mapping genomic and transcriptomic alterations spatially in epithelial cells adjacent to human breast carcinoma

“To address this we created a unique dataset of epithelial samples ductoscopically obtained from ducts leading to breast carcinomas and matched samples from ducts on the opposite side of the nipple. Here, we demonstrate that perturbations in mRNA abundance, with increasing proximity to tumour, cannot be explained by copy number aberrations. “


[PMID: 28778180] [Genome Biology]

SuperTranscripts: a data driven reference for analysis and visualisation of transcriptomes

“Numerous methods have been developed to analyse RNA sequencing (RNA-seq) data, but most rely on the availability of a reference genome, making them unsuitable for non-model organisms. Here we present superTranscripts, a substitute for a reference genome, where each gene with multiple transcripts is represented by a single sequence. The Lace software is provided to construct superTranscripts from any set of transcripts, including de novo assemblies. “

Genomes of Metastatic Cancer

[PMID: 28783718] [Nature]

Integrative clinical genomics of metastatic cancer

“whole-exome and -transcriptome sequencing of 500 adult patients with metastatic solid tumours of diverse lineage and biopsy site. The most prevalent genes somatically altered in metastatic cancer included TP53CDKN2APTENPIK3CA, and RB1. Putative pathogenic germline variants were present in 12.2% of cases of which 75% were related to defects in DNA repair.”