{"id":174,"date":"2017-05-27T15:19:11","date_gmt":"2017-05-27T15:19:11","guid":{"rendered":"http:\/\/labs.icahn.mssm.edu\/functionalneurogenomics-lab\/?page_id=174"},"modified":"2025-07-22T18:47:55","modified_gmt":"2025-07-22T18:47:55","slug":"resources","status":"publish","type":"page","link":"https:\/\/labs.icahn.mssm.edu\/roussos-lab\/resources\/","title":{"rendered":"Resources"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.9.0″][et_pb_row _builder_version=”4.9.0″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_toggle title=”Transcriptomics, epigenomics and genetic dataset generated or co-generated in our lab ” open=”on” icon_color=”#00aeef” admin_label=”Toggle” _builder_version=”4.9.0″ title_text_color=”#00a6e5″ body_text_color=”#555555″ border_radii=”on|6px|6px|6px|6px” border_color_all=”#00aeef” saved_tabs=”all”]<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Resource<\/strong><\/td>\nDescription<\/strong><\/td>\nManuscript<\/strong><\/td>\nResource<\/strong><\/td>\n<\/tr>\n
PsychAD snRNA-seq
PsychAD genotypes<\/b><\/td>\n		snRNA-seq from DLPFC of 1,494 donors across various neurodegenerative and neuropsychiatric diseases.<\/td>\nPubmed (resource)<\/a>
MedRxiv (capstone)<\/a><\/td>\n		PsychAD web<\/a>
ADKP<\/a>
CELLxGENE<\/a><\/td>\n<\/tr>\n
PD snRNA-seq<\/b><\/td>\nsnRNA-seq from 100 Parkinson’s disease cases and controls x 5 brain regions (= 444 samples after QC).<\/td>\nPubmed<\/a><\/td>\nAMP-PD portal<\/a>
CELLxGENE<\/a><\/td>\n<\/tr>\n
Brain Development
3D genome (II)<\/strong><\/td>\n		Multi-omics dataset (snRNA-seq + snATAC-seq) of 101,924 nuclei from 4 brain regions across 5 developmental time-points from early postmortem to adult.<\/td>\nPubmed<\/a><\/td>\nNIMH NDA<\/a>
CELLxGENE<\/a><\/td>\n<\/tr>\n
CMC ATAC-seq (FANS)<\/strong><\/td>\nFANS-sorted (NeuN+ \/ NeuN-) ATAC-seq data from anterior cingulate cortex and dorsolateral prefrontal cortex of 469 donors of SCZ, BD and neurotypical controls (=1,393 samples).<\/td>\nPubmed<\/a>
MedRxiv<\/a><\/td>\n		NIMH Data Archive<\/a>
Synapse<\/a><\/td>\n<\/tr>\n
AD Microglia (FANS)<\/strong>;
Release #2<\/strong><\/td>\n		FANS-sorted RNA-seq accompanied by SNParrays from over 189 adult Alzheimer’s disease cases and controls.<\/td>\nResearchSq<\/a><\/td>\nSynapse<\/a><\/td>\n<\/tr>\n
AD Microglia (scRNA-seq)<\/b><\/td>\nscRNA-seq on FANS-sorted myeloid cells from 137 postmortem brains (number of cells: 543,012).<\/td>\nMedRxiv<\/a><\/td>\nSynapse<\/a><\/td>\n<\/tr>\n
Brain Development
3D genome
<\/b><\/td>\n		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.<\/td>\nPubmed<\/a><\/td>\nSynapse<\/a><\/td>\n<\/tr>\n
BED GWAS<\/b><\/td>\nBED GWAS summary statistics from the MVP data.<\/td>\nPubmed<\/a><\/td>\ndbGaP<\/a><\/td>\n<\/tr>\n
MS_snRNAseq\u00a0<\/b><\/td>\nsingle-nucleus RNA-seq data from 6 multiple sclerosis cases and 6 controls.<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n
Developing cerebral cortex<\/b><\/td>\nMulti-omics dataset (snRNA-seq + snATAC-seq) of 45,549 cortical nuclei across 6 broad developmental time-points from fetus to adult.<\/td>\nPubmed<\/a><\/td>\nUCSC<\/a>
SCP<\/a>
CELLxGENE<\/a>
GEO<\/a><\/td>\n<\/tr>\n
BOCA 3<\/b><\/td>\nFANS-sorted RNA-seq and ATAC-seq (neurons and non-neurons) across 25 distinct brain regions of 6 adult individuals.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a>
GEO<\/a><\/td>\n<\/tr>\n
LMNA<\/b><\/td>\nATAC-seq in wild-type mice and knockdown LMNA mice.<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n
AD Microglia (FANS)
Release #1<\/b><\/td>\n		FANS-sorted RNA-seq, ATAC-seq accompanied by SNParrays from over 100 adult Alzheimer’s disease cases and controls.<\/td>\nPubmed<\/a><\/td>\nSynapse<\/a><\/td>\n<\/tr>\n
PHG FANS (3 cell types)
ATAC-seq, RNA-seq<\/strong><\/td>\n		FANS-sorted ATAC-seq and RNA-seq on neurons, oligodendrocytes and microglia & astrocytes from 42 donors with AD and controls.<\/td>\nPubmed<\/a><\/td>\nSynapse<\/a><\/td>\n<\/tr>\n
AD NeuN+\/-<\/strong><\/td>\nFANS-sorted ATAC-seq (neurons and non-neurons) across 2 brain regions of over 400 adult Alzheimer’s disease cases and controls.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a>
UCSC<\/a>
Synapse<\/a><\/td>\n<\/tr>\n
MDD NeuN+\/-<\/strong><\/td>\nFANS-sorted ATAC-seq (neurons and non-neurons) from orbitofrontal cortex of over 30 adult MDD cases and controls.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a>
UCSC<\/a>
GEO<\/a><\/td>\n<\/tr>\n
iPSC-Neuron KCl stimulation<\/strong><\/td>\nATAC-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).<\/td>\nPubmed<\/a><\/td>\nWeb<\/a>
UCSC<\/a>
GEO<\/a><\/td>\n<\/tr>\n
COVID IL10RB<\/strong><\/td>\nRNA-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.<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n
COVID brain transcriptome<\/strong><\/td>\nsnRNAseq of human brain tissue from patients with coronavirus disease and controls.<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n
Multi-omics AD supercontrols <\/strong><\/td>\nFANS-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.<\/td>\nPubmed<\/a><\/td>\nSynapse<\/a><\/td>\n<\/tr>\n
Epidiff<\/strong><\/td>\nFANS-sorted (neurons) and bulk ChIP-seq data from PFC brain region of over 500 Schizophrenia cases and controls<\/td>\nPubmed<\/a><\/td>\nUCSC<\/a>
Synapse<\/a><\/td>\n<\/tr>\n
iPSC-Microglia<\/strong><\/td>\nATAC-seq in 3 hiPSC-derived microglia lines.<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n
Striatal striosome profiling<\/strong><\/td>\nATAC seq from EGFP+ and EGFP- cells from postnatal day 3 striatum from GENSAT Nr4a1-EGFP mice<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n
SCZ organoids<\/strong><\/td>\nATAC-seq assay in 5 human developmental cortical interneurons (MGE organoid) and 2 glutamatergic neurons (CO organoid) derived from healthy control vs schizophrenia iPSCs.<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n
BOCA 2<\/strong><\/td>\nFANS-sorted ATAC-seq (neurons and non-neurons) across 14 distinct brain regions of 5 adult individuals.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a>
UCSC<\/a>
GEO<\/a><\/td>\n<\/tr>\n
Epimap\u00a0<\/strong><\/td>\nFANS-sorted and bulk ChIP-seq (neurons and non-neurons) across 2 distinct brain regions of Schizophrenia cases and controls<\/td>\nPubmed<\/a><\/td>\nSynapse<\/a><\/td>\n<\/tr>\n
BOCA<\/strong><\/td>\nFANS-sorted ATAC-seq (neurons and non-neurons) across 14 distinct brain regions of 5 adult individuals.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a>
UCSC<\/a>
GEO<\/a><\/td>\n<\/tr>\n
Prefrontal cortex profiling<\/strong><\/td>\nFANS-sorted ATAC-seq (neurons and non-neurons) in frontopolar prefrontal cortex of 8 adult individuals.<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n
Neat1 -\/- mice profiling<\/strong><\/td>\nCerebral 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).<\/td>\nPubmed<\/a><\/td>\nGEO<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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[\/et_pb_toggle][et_pb_toggle title=”Computational tools, servers, and databases” open=”on” icon_color=”#00aeef” _builder_version=”4.9.0″ title_text_color=”#00a6e5″ body_text_color=”#555555″ border_radii=”on|6px|6px|6px|6px” border_color_all=”#00aeef” saved_tabs=”all”]<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Visit our Github for the most updated list of tools!<\/a><\/td>\n<\/tr>\n
Resource<\/strong><\/td>\nDescription<\/strong><\/td>\nManuscript<\/strong><\/td>\nWeb \/ Github<\/strong><\/td>\n<\/tr>\n
zenith<\/strong><\/td>\nZenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream <\/em>(also mentioned in the list of software in this table)\u00a0by considering the correlation between gene expression traits.<\/td>\nIn preparation<\/td>\nGithub<\/a><\/td>\n<\/tr>\n
remaCor<\/strong><\/td>\nMethod for random effects meta-analysis for correlated test statistics.<\/td>\nIn preparation<\/td>\nGithub<\/a><\/td>\n<\/tr>\n
crumblr
<\/strong><\/td>\n		crumblr package enables analysis of count ratio data using precision-weighted linear (mixed) models, PCA and clustering.<\/td>\nIn preparation<\/td>\nGithub<\/a><\/td>\n<\/tr>\n
dreamlet<\/b><\/td>\nDreamlet 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.<\/td>\nBioRxiv<\/a><\/td>\nGithub<\/a><\/td>\n<\/tr>\n
decorate<\/b><\/td>\nDecorate (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.<\/td>\nPubmed<\/a><\/td>\nGithub<\/a><\/td>\n<\/tr>\n
dream<\/b><\/td>\nMethod for differential expression analysis employing repeated measures designs.<\/td>\nPubmed<\/a><\/td>\nBioconductor<\/a>
Vignette<\/a><\/td>\n<\/tr>\n
mmQTL<\/b><\/td>\nmmQTL is a statistical package applying meta-analysis to detect multiple QTL signals integrating signals among conditions, with control for population structure and relatedness.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a>
Github<\/a>
Zenodo<\/a><\/td>\n<\/tr>\n
EpiXcan<\/strong><\/td>\nEpiXcan 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.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a><\/td>\n<\/tr>\n
GWAS2Genes<\/strong><\/td>\nGWAS2Genes 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.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a><\/td>\n<\/tr>\n
moloc<\/strong><\/td>\nmoloc (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.<\/td>\nPubmed<\/a><\/td>\nWeb<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Resource Description Manuscript Resource PsychAD snRNA-seqPsychAD genotypes snRNA-seq from DLPFC of 1,494 donors across various neurodegenerative and neuropsychiatric diseases. Pubmed (resource)MedRxiv (capstone) PsychAD webADKPCELLxGENE PD snRNA-seq snRNA-seq from 100 Parkinson’s disease cases and controls x 5 brain regions (= 444 samples after QC). Pubmed AMP-PD portalCELLxGENE Brain Development3D genome (II) Multi-omics dataset (snRNA-seq + snATAC-seq) […]<\/p>\n","protected":false},"author":223,"featured_media":0,"parent":0,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"

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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. Please visit the database webpage by clicking on the above logo.\r\n<\/strong><\/p>\r\nCitation:<\/strong>\r\n\r\nLarge-scale Identification of Common Variants Affecting Gene Expression in Traits and Diseases. Mads Engel Hauberg, Wen Zhang,<\/strong> Claudia Giambartolomei<\/strong>, Oscar Franz\u00e9n, David L Morris, Timothy J Vyse, Arno Ruusalepp, the CommonMind Consortium, Pamela Sklar, Eric E. Schadt, Johan L.M. Bj\u00f6rkegren, Panos Roussos<\/strong>. Am J Hum Genet<\/em>. 2017. DOI: 10.1016\/j.ajhg.2017.04.016<\/a>. PMID: 28552197\r\n\r\n<\/div>\r\n\u00a0\r\n\r\n

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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.<\/strong><\/p>\r\nCitation:<\/strong>\r\n\r\nA Bayesian Framework for Multiple Trait Colocalization From Summary Association Statistics. C. Giambartolomei<\/strong>, J. Z. Liu, W. Zhang<\/strong>, M.<\/strong> Hauberg, <\/strong>H. Shi, J. Boocock, J. Pickrell, A. Jaffe, the CommonMind Consortium, B. Pasaniuc, P. Roussos<\/strong>. Bioinformatics<\/em>. 2018 Mar 19. DOI: 10.1093\/bioinformatics\/bty147<\/a>. PMID: 29579179\r\n\r\n<\/div>\r\n

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BOCA (Brain Open Chromatin Atlas) provides the maps of neuronal and non-neuronal chromatin accessibility across 14 distinct brain regions of 5 adult individuals.\r\n<\/strong><\/p>\r\nCitation:<\/strong>\r\n\r\nAn atlas of chromatin accessibility in the adult human brain. Fullard JF, Hauberg ME, Bendl J<\/strong>, Egervari G, Cirnaru MD, Reach SM<\/strong>, Motl J, Ehrlich ME, Hurd YL, Roussos P<\/strong>. Genome Research.2018. DOI: 10.1101\/gr.232488.117<\/a>. PMID: 29945882\r\n\r\n<\/div>\r\n\u00a0\r\n\r\n

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