Transcriptomics, epigenomics and genetic dataset generated or co-generated in our lab
Resource Description Manuscript Resource
PD snRNA-seq snRNA-seq from 100 Parkinson’s disease cases and controls x 5 brain regions (= 500 samples) In preparation AMP-PD portal
AD Microglia (FANS);
Release #2
FANS-sorted RNA-seq accompanied by SNParrays from over 189 adult Alzheimer’s disease cases and controls. ResearchSq Synapse
AD Microglia (scRNA-seq) scRNA-seq on FANS-sorted myeloid cells from 137 postmortem brains (number of cells: 543,012) MedRxiv Synapse
Brain Development
3D genome
ATAC-seq, RNA-seq, H3K4me3/H3K27ac/H3K27me3 ChIP-seq, and Hi-C from Fetal (4 individuals) and FANS-sorted adult (6 controls) postmortem brains. RNA-Seq and Hi-C of scrambled and CNTNAP2 knockdown IPSC-derived neurons. Pubmed Synapse
BED GWAS BED GWAS summary statistics from the MVP data. Pubmed dbGaP
MS_snRNAseq  single-nucleus RNA-seq data from 6 multiple sclerosis cases and 6 controls. Pubmed GEO
Developing cerebral cortex Multi-omics dataset (snRNA-seq + snATAC-seq) of 45,549 cortical nuclei across 6 broad developmental time-points from fetus to adult. Pubmed UCSC
BOCA 3 FANS-sorted RNA-seq and ATAC-seq (neurons and non-neurons) across 25 distinct brain regions of 6 adult individuals. BioRxiv Web
LMNA ATAC-seq in wild-type mice and knockdown LMNA mice. Pubmed GEO
AD Microglia (FANS)
Release #1
FANS-sorted RNA-seq, ATAC-seq accompanied by SNParrays from over 100 adult Alzheimer’s disease cases and controls. Pubmed Synapse
PHG FANS (3 cell types)
ATAC-seq, RNA-seq
FANS-sorted ATAC-seq and RNA-seq on neurons, oligodendrocytes and microglia & astrocytes from 42 donors with AD and controls. Pubmed Synapse
AD NeuN+/- FANS-sorted ATAC-seq (neurons and non-neurons) across 2 brain regions of over 400 adult Alzheimer’s disease cases and controls. Pubmed Web
MDD NeuN+/- FANS-sorted ATAC-seq (neurons and non-neurons) from orbitofrontal cortex of over 30 adult MDD cases and controls. BioRxiv Web
iPSC-Neuron KCl stimulation ATAC-seq and RNA-seq in 11 iPSC-derived neurons from 6 children-onset schizophrenia cases and 5 controls. Each iPSC line is profiled in three conditions (unstimulated, 1hr and 6hr post-stimulation by KCl). Pubmed Web
COVID IL10RB RNA-seq of iPSC-derived neural progenitor cells from 3 donors in multiple conditions to validate that up-regulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes. Pubmed GEO
COVID brain transcriptome snRNAseq of human brain tissue from patients with coronavirus disease and controls. Pubmed GEO
Multi-omics AD supercontrols FANS-sorted ATAC-seq, ChIP-seq H3K4me3 & H3K27ac and RNA-seq (neurons and non-neurons) of 5 human brain regions from 10 “control” donors, i.e. without any neuropsychiatric or neurodegenerative diagnosis. Pubmed Synapse
Epidiff FANS-sorted (neurons) and bulk ChIP-seq data from PFC brain region of over 500 Schizophrenia cases and controls Pubmed UCSC
iPSC-Microglia ATAC-seq in 3 hiPSC-derived microglia lines. Pubmed GEO
Striatal striosome profiling ATAC seq from EGFP+ and EGFP- cells from postnatal day 3 striatum from GENSAT Nr4a1-EGFP mice Pubmed GEO
SCZ organoids ATAC-seq assay in 5 human developmental cortical interneurons (MGE organoid) and 2 glutamatergic neurons (CO organoid) derived from healthy control vs schizophrenia iPSCs. Pubmed GEO
BOCA 2 FANS-sorted ATAC-seq (neurons and non-neurons) across 14 distinct brain regions of 5 adult individuals. Pubmed Web
Epimap  FANS-sorted and bulk ChIP-seq (neurons and non-neurons) across 2 distinct brain regions of Schizophrenia cases and controls Pubmed Synapse
BOCA FANS-sorted ATAC-seq (neurons and non-neurons) across 14 distinct brain regions of 5 adult individuals. Pubmed Web
Prefrontal cortex profiling FANS-sorted ATAC-seq (neurons and non-neurons) in frontopolar prefrontal cortex of 8 adult individuals. Pubmed GEO
Neat1 -/- mice profiling Cerebral frontal cortex mRNA profiles of 2-4 months old wild type and Neat1 -/- mice (all females) generated by deep sequencing (N=5 controls; N=4 Neat1 knockout). Pubmed GEO


Computational tools, servers, and databases
Visit our Github for the most updated list of tools!
Resource Description Manuscript Web / Github
zenith Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream (also mentioned in the list of software in this table) by considering the correlation between gene expression traits. In preparation Github
remaCor Method for random effects meta-analysis for correlated test statistics. In preparation Github
crumblr package enables analysis of count ratio data using precision-weighted linear (mixed) models, PCA and clustering. In preparation Github
dreamlet Dreamlet is a method that uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. BioRxiv Github
decorate Decorate (differential epigenetic correlation test) identifies correlated epigenetic features and finds clusters of features that are differentially correlated between two or more subsets of the data. Pubmed Github
dream Method for differential expression analysis employing repeated measures designs. Pubmed Bioconductor
mmQTL mmQTL is a statistical package applying meta-analysis to detect multiple QTL signals integrating signals among conditions, with control for population structure and relatedness. Pubmed Web
EpiXcan EpiXcan is a repository of transcriptome prediction and gene-trait association. It is based on a method, named as EpiXcan, that increases prediction accuracy in transcriptome imputation by integrating epigenetic data to model the prior probability that a SNP affects transcription. Pubmed Web
GWAS2Genes GWAS2Genes is a database of how common genetic variants affect gene expressions. GWAS2Genes links common variants of GWAS loci to which genes are affected and in which direction by using eQTLs and SMR. Pubmed Web
moloc moloc (multiple-trait-coloc) is a Bayesian statistical framework that integrates GWAS summary data with multiple molecular QTL data to identify regulatory effects at GWAS risk loci. Pubmed Web