Search – Common Metabolic Diseases Genome Atlas

Pipeline
DPL857456
active
Peak Alignment Pipeline
Assays: ATAC-seq
Software: Peak alignment
Pipeline
DPL255HPT
active
scRNA-seq clustering for HPAP dataset
Pipeline
DPL066WNI
active
Pseudo-bulk gene expression and differential activity (Elgamal et al.)
Assays: single cell isolation followed by RNA-seq
Pipeline
DPL472KCS
active
Annotation pipeline popularVote (popV for short) from Tabula Sapiens
Assays: single cell isolation followed by RNA-seq
Pipeline
DPL126DIZ
active
Pipeline holder for unknown or undefined pipeline
Pipeline
DPL005RCG
active
A short overview of the main analysis steps for eQTL Catalogue
Assays: RNA-seq, whole genome sequencing assay
Software: RNA-seq quantification: eQTL-Catalogue/rnaseq, Gene expression QC and normalisation: eQTL-Catalogue/qcnorm, Genotype QC and imputation: eQTL-Catalogue/genimpute, Association testing and fine mapping: eQTL-Catalogue/qtlmap
Pipeline
DPL172PKH
active
RNA-seq Pipeline PLoS Genet. 2021-05-13;17(5):e100953
Assays: RNA-seq
Software: STAR, RSEM
Pipeline
DPL610SXT
active
RNA-seq Pipeline (Paired-ended, ENCODE)
Assays: RNA-seq
Software: STAR, bedGraphToBigWig, RSEM
Pipeline
DPL610SXTsingleended
active
RNA-seq Pipeline (Single-ended, ENCODE)
Assays: RNA-seq
Software: STAR, RSEM, bedGraphToBigWig
Pipeline
DPL010CNP
active
Activity by Contact
Assays: ATAC-seq, ChIP-seq, DNase-seq
Software: MACS2, BEDTools, makeCandidateRegions.py, run.neighborhoods.py, predict.py, getVariantOverlap.py