Posts Tagged ‘dw’

neuroDEVELOPMENTS: Neuroscience Research to Clinical Relevance

Monday, April 1st, 2019

the Lieber’s neuroDEVELOPMENTS newsletter, which features the PsychENCODE paper

Read Light Up Your Imagination Argus Large Poster by Trend Enterprises: Language Arts:

Saturday, February 16th, 2019

Nature 03142018 Single cell rnaseq of development of human brain Pfc

Sunday, March 18th, 2018

A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex

Suijuan Zhong
, Shu Zhang
Xiaoqun Wang

New GWAS SCZ loci (nature genetics 2018)

Tuesday, March 6th, 2018

Common #schizophrenia alleles are enriched in mutation-intolerant genes & in regions under strong background selection 50 novel SCZ loci & 145 loci in total, from #GWAS – associated w/ 33 candidate causal genes

We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci.

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection
Nature Genetics (2018)

A survey of human brain transcriptome diversity at the single cell level

Wednesday, June 28th, 2017

Brain #transcriptome diversity at the single cell level Has useful gene-exp. profiles of specific neural cell types

has profiles for 185 biomarker genes for 6 cell types

R package: variancePartition

Sunday, November 13th, 2016

new tool in psychencode meeting

Authorship solution

Thursday, July 28th, 2016

A solution to a perennial problem: Co-first “author ordering determined by coin flip” – eg &

∗Equal contribution. Author ordering determined by coin flip over a Google Hangout.

Similarity network fusion for aggregating data types on a genomic scale : Nature Methods : Nature Publishing Group

Tuesday, February 9th, 2016

Similarity #network fusion for aggregating data types Combines mRNA, miRNA & gene fusions to classify cancer subtypes

Single-cell ChIP-seq

Friday, October 23rd, 2015

#Singlecell ChIPseq reveals…subpopulations defined by chromatin state Sparse data: on order of 1K uniq. reads/cell

Lessons from modENCODE

Saturday, July 11th, 2015