Resources

meQTL_fig2

Cross-tissue integration of genetic and epigenetic data offers insight into autism spectrum disorder

Peripheral Blood meQTLs (.gzip)
Cord Blood meQTLs (.gzip)

SCZBPDAUT

Transcriptome analysis of cortical tissue reveals shared sets of downregulated genes in autism and schizophrenia

CQN Normalized Gene Expression Estimates: AUT
CQN Normalized Gene Expression Estimates: SCZ & BPD
Phenotype Data: AUT
Phenotype Data: SCZ & BPD
DGEA Test Statistics

 

cover art

Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism

Data

README
Raw Gene Counts
EDA-Seq Normalized Gene Estimates
Phenotype File (updated 10.24.16)
GC Content and Gene Length
DGE results: all

Gene Lists

Neuronal Markers
Oligodendrocyte Markers
Astrocyte Markers
Type 1 Microglial MarkersMarkers
Type 2 Microglial Markers
Ischemia Markers
Synaptic Proteins
Postsynapitc Density (PSD)
SFARI_2009
Rare de novo
ID_2009
FMR1 Interacting Genes

parM2
parM3
parM13
parM16
parM17
asdM12
asdM16
fmrp_1
fmrp_2
ASD_SFARI_2012
ASD_SFARI_2012_CV
NewFMRPtargets_Pinto
HighConfidence_CNV_Pinto
ASD_Pinto
ID_Pinto
Stein_AGP
Stein_I
Stein_San
Stein_SON
Stein_T

Picture1

RNA-Seq optimization with eQTL gold standards

Scripts

Code for eQTL Analysis
(includes code, example data used in the paper, and a README with short explanation)
Feel free to email sellis18@jhmi.edu with any questions.

journal.pone.0068585.t001

FAST

Software

Application for efficiently running several gene based analysis methods simultaneously
Developed with the Bader lab