Month: March 2018

Lancet

[] [Communications Biology]

Genome-wide somatic variant calling using localized colored de Bruijn graphs

“Lancet has better accuracy, especially for indel detection, than widely used somatic callers, such as MuTect, MuTect2, LoFreq, Strelka, and Strelka2.”

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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. “

PheRSs

[PMID: 29590070] [Science]

Phenotype risk scores identify patients with unrecognized Mendelian disease patterns

“We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases.”

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.”