Project description:Purpose: ATAC-seq was performed on preterm lambs that were ventilated by invasive mechanical ventilation or noninvasive respiratory support utilizing a mask and compared to gestation-age-matched preterm lambs that were not ventilated and naturally delivered term lambs. Methods: Lung chromatin access profiles were generated for: 1) Unventilated preterm lamb, 2) preterm lambs delivered at gd131 and intubated and mechanically ventilated for 3 days, 3) preterm lambs delivered at gd131 and not intubated and resuscitated by placing a face mask over the nose and mouth and controlling O2 delivery via a computer-controlled electronic blower device, 4) unventilated naturally delivered full-term lambs. Results: Using an optimized data analysis workflow, we mapped between 76 and 96 million sequence reads per sample to the sheep genome Conclusions: Our study represents the first detailed analysis of ventilated preterm lung chromatin access, with biologic replicates, generated by ATAC-seq

2021-06-30 | GSE163807 | GEO

Data Access Committee: Whole Genome Sequencing of 18 sequential samples from 3 CLL patients

Project description:Data Access Committee EGAC00001000221

| EGAC00001000221 | EGA

shallow coverage whole genome sequencing

Project description:Data Access Committee EGAC00001001224

| EGAC00001001224 | EGA

Estimating genome-wide significance for whole-genome sequencing studies.

Project description:Although a standard genome-wide significance level has been accepted for the testing of association between common genetic variants and disease, the era of whole-genome sequencing (WGS) requires a new threshold. The allele frequency spectrum of sequence-identified variants is very different from common variants, and the identified rare genetic variation is usually jointly analyzed in a series of genomic windows or regions. In nearby or overlapping windows, these test statistics will be correlated, and the degree of correlation is likely to depend on the choice of window size, overlap, and the test statistic. Furthermore, multiple analyses may be performed using different windows or test statistics. Here we propose an empirical approach for estimating genome-wide significance thresholds for data arising from WGS studies, and we demonstrate that the empirical threshold can be efficiently estimated by extrapolating from calculations performed on a small genomic region. Because analysis of WGS may need to be repeated with different choices of test statistics or windows, this prediction approach makes it computationally feasible to estimate genome-wide significance thresholds for different analysis choices. Based on UK10K whole-genome sequence data, we derive genome-wide significance thresholds ranging between 2.5 × 10(-8) and 8 × 10(-8) for our analytic choices in window-based testing, and thresholds of 0.6 × 10(-8) -1.5 × 10(-8) for a combined analytic strategy of testing common variants using single-SNP tests together with rare variants analyzed with our sliding-window test strategy.

| S-EPMC4489336 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data