6,266 research outputs found
Software Support for I-ATAC
No full text<p>This is the software support for I-ATAC.</p>
<p>The author has not developed these but sharing open access applications.</p>
HINT-ATAC
No full textTransposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of
open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to
transcription factor binding, is poorly understood for ATAC-seq. We propose the first footprinting method
considering ATAC-seq protocol artefact. HINT-ATAC uses a position dependency model to learn the cleavage
preferences of the transposase. We observe strand-specific cleavage patterns around transcription factor
binding sites, which are determined by local nucleosome architecture. By incorporating all these biases,
HINT-ATAC is able to significantly outperform competing methods in the prediction of transcription factor
binding sites with footprints.</p
ATAC-seq-specific normalization is required to for robust prediction across ATAC-seq protocols.
No full text(A) Max RP20M value per autosomal chromosome before and after filtering extended blacklist regions. (B) An example tandem repeat region with variable signal found on Chr17. (C) The TRIM37 locus on Chr17 exhibits extreme, biologically relevant signal in the breast cancer cell line MCF-7 (red track). (D) Test AUPR in GM12878 for different alignment and normalization strategies (Methods) for bulk ATAC-seq or scATAC-seq (pseudobulk of 5k GM12878 cells). PCR duplicates and Tn5 cut sites that mapped to the extended blacklist (Methods) were removed prior to normalization. Test data is bulk ATAC-seq unless described as "scATAC". "95", "99", and "100" correspond to minmax normalization to the 95th, 99th or 100th-percentile highest ATAC-seq signal. "100" therefore corresponds to standard minmax normalization to the absolute max; this strategy was not robust to outlying ATAC-seq signal and therefore performs poorly when applied to different ATAC-seq alignment strategies or scATAC-seq. The far-right column represents performance on scATACseq data using (1) standard min-max normalization and (2) without applying the extended blacklist; this strategy has the worst performance generalizability to scATAC-seq. (TIF)</p
ATAC-seq dataset overview.
No full textOverview of all ATAC-seq datasets generated and used, including datatype, origin, medium condition, and GEO accession number. (DOCX)</p
ATAC-seq protocol.
No full textWe use ATAC-seq to examine chromatin accessibility for four different tissues in Drosophila melanogaster: adult female brain, ovaries, and both wing and eye-antennal imaginal discs from males. Each tissue is assayed in eight different inbred strain genetic backgrounds, seven associated with a reference quality genome assembly. We develop a method for the quantile normalization of ATAC-seq fragments and test for differences in coverage among genotypes, tissues, and their interaction at 44099 peaks throughout the euchromatic genome. For the strains with reference quality genome assemblies, we correct ATAC-seq profiles for read mis-mapping due to nearby polymorphic structural variants (SVs). Comparing coverage among genotypes without accounting for SVs results in a highly elevated rate (55%) of identifying false positive differences in chromatin state between genotypes. After SV correction, we identify 1050, 30383, and 4508 regions whose peak heights are polymorphic among genotypes, among tissues, or exhibit genotype-by-tissue interactions, respectively. Finally, we identify 3988 candidate causative variants that explain at least 80% of the variance in chromatin state at nearby ATAC-seq peaks.</div
ATAC-seq analysis.
No full text(A) Volcano plot to show differentially accessible peaks, nominally significant peaks (p = 0.05) and shown in pink. (B) Annotation of an example peaks shows increased counts at transcription start sites. (C) Distribution of all peaks in the consensus peak set across genomic features. (D) Example track of ATAC-seq peaks, ethanol treated samples are shaded in blue.</p
Current status of numerical flow prediction for separated nozzle flows
No full textThe European ‘Flow Separation Control Device’ group (FSCD) organized in collaboration with the French ‘Aérodynamiques des tuyères et Arrière-Corps’ group (ATAC) a CFD workshop with test cases on different nozzle flow topics. One of these test cases (1A) was managed by the German Aerospace Center (DLR) and Astrium ST. The objective was to compute the flow inside a strongly over-expanded truncated ideal contour nozzle with respect to the prediction of location and shape of the flow separation, the oblique shock and the Mach disc. Experimental data were provided by DLR.
An introduction to the test facility and the experimental setup is given. The numerical results are evaluated and compared to test data. A concluding synthesis illustrates the current status of nozzle flow computation
ATAC-seq peak genomic distribution.
No full textA. Bar plot showing the genomic distribution of accessible peaks identified in ATAC-seq data in Rh1>GFPKASH during aging. Promoter annotation was based on -/+ 2 kb around transcription start sites. B. DNA binding motif for Clk and Cyc derived from CIS-BP database. (PDF)</p
ENCODE-DCC/atac-seq-pipeline: v2.2.0
No full textUpgrade Caper to >=2.2.0 for new HPC features.
scripts/uninstall_conda_env.sh
$ scripts/install_conda_env.sh
Caper's new HPC feature
Unified command caper hpc for all HPC backend types (slurm, sge, pbs and lsf).
Read Caper's README carefully and make a backup of your Caper's conf file (~/.caper/default.conf) and run caper init YOUR_BACKEND.
caper hpc submit atac.wdl -i INPUT.json --leader-job-name GOOD_NAME_FOR_LEADER [--conda | --singularity]: submits a workflow to HPC's job engine.
caper hpc list: shows all Caper leader jobs
caper hpc abort JOB_ID: terminate a Caper leader job including child job
ATAC-seq on dissected, frozen, embryo halves.
No full text(A) Stage 5, hand sorted Drosophila embryos were flash frozen over dry ice in a buffer containing 5% glycerol and manually sliced in half with a scalpel. Twenty anterior and posterior halves were collected, homogenized, and the nuclei were isolated. ATAC-seq was then performed as described in [34] with three times Tn5 transposase. (B) Scatter plot of normalized ATAC-seq signal over 1kb adjacent windows that tile the Drosophila genome in posterior (x) and anterior (y) samples shows high degree of correlation between the anterior and posterior halves. The Spearman correlation coefficient (denoted by rS) is 0.81. The Pearson correlation coefficient (denoted by rp) is 0.94. X and Y are log transformed. Light blue circles denote point density.</p
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