Tag: RNA-Seq

trendsceek & SpatialDE

[PMID: 29553578] [Nature Methods]

Identification of spatial expression trends in single-cell gene expression data

“a method based on marked point processes that identifies genes with statistically significant spatial expression trends.”

[PMID: 29553579] [Nature Methods]

SpatialDE: identification of spatially variable genes

“a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA-sequencing data. “

Advertisements

Childhood Cancer Genomes

[PMID: 29489754] [Nature]

The landscape of genomic alterations across childhood cancers

“a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer.”

[PMID: 29489755] [Nature]

Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours

“a pan-cancer study of somatic alterations, including single nucleotide variants, small insertions or deletions, structural variations, copy number alterations, gene fusions and internal tandem duplications in 1,699 paediatric leukaemias and solid tumours across six histotypes, with whole-genome, whole-exome and transcriptome sequencing data processed under a uniform analytical framework. We report 142 driver genes in paediatric cancers, of which only 45% match those found in adult pan-cancer studies; copy number alterations and structural variants constituted the majority (62%) of events.”

Dearly Drivers of Breast Cancer Mets

[PMID: 29480819] [Journal of Clinical Investigation]

Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer

“matched primary and metastatic breast cancers from 16 individuals and performed RNA-seq and DNA whole-exome sequencing on the primary tumor, 67 matched metastases (2–7 per patient), and a matched normal tissue comparator for each patient.” “predicted metastatic drivers by integrating known protein-protein networks with gene expression and DNA-seq data and the clonal evolution of metastasis within each patient.” “most genetic drivers were DNA copy number changes, the TP53 mutation was a recurrent founding mutation regardless of subtype, and that multiclonal seeding of metastases was frequent and occurred in multiple subtypes. Genetic drivers unique to metastasis were identified as somatic mutations in the estrogen and androgen receptor genes… most metastatic drivers are established in the primary tumor, despite the substantial heterogeneity seen in the metastases.” Work from Charles Perou.

Conquer

[PMID: 29481549] [Nature Methods]

Bias, robustness and scalability in single-cell differential expression analysis

“conquer, a repository of consistently processed, analysis-ready public scRNA-seq data sets that is aimed at simplifying method evaluation and reanalysis of published results.” “Currently, conquer contains 36 data sets: 31 generated with full-length protocols and 5 with 3′-end sequencing (UMI) protocols.”

PennDiff

[PMID: 29474557] [Bioinformatics]

PennDiff: Detecting Differential Alternative Splicing and Transcription by RNA Sequencing

“Differential alternative splicing and transcription (DAST), which describe different usage of transcript isoforms across different conditions, can complement differential expression in characterizing gene regulation. However, the analysis of DAST is challenging because only a small fraction of RNA-seq reads is informative for isoforms. Several methods have been developed to detect exon-based and gene-based DAST, but they suffer from power loss for genes with many isoforms.” “PennDiff, a novel statistical method that makes use of information on gene structures and pre-estimated isoform relative abundances, to detect DAST from RNA-seq data. PennDiff has several advantages. First, grouping exons avoids multiple testing for “exons” originated from the same isoform(s). Second, it utilizes all available reads in exon-inclusion level estimation, which is different from methods that only use junction reads. Third, collapsing isoforms sharing the same alternative exons reduces the impact of isoform expression estimation uncertainty. PennDiff is able to detect DAST at both exon and gene levels, thus offering more flexibility than existing methods. “

LeafCutter

[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.