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In previous years, students from over 100 different schools participated in SCORE! Wrestling. The co-op model allows us to offer wrestling to all CPS students through centralized locations. SCORE! Wrestling uses a co-op model, meaning, instead of hosting one team per school, students can attend any of the 12 Beat the Streets locations, all of which are hosted at Chicago Public Schools. Using a no-cut, inclusive model, students are able to enjoy the benefits of team sports regardless of experience or ability.īeat the Streets Chicago is proud to be the official wrestling organization for CPS SCORE! Since 2016, Beat the Streets Chicago, in cooperation with the CPS Office of Sports Management, has managed the wrestling program, bringing in its own philosophy of "youth wrestling for life champions." Supplementary_files_format_and_content: ! is the official interscholastic athletics league for CPS elementary schools. Supplementary_files_format_and_content: combined_RNA-seq_counts.csv: Raw RNA-seq counts for all experimental samples Supplementary_files_format_and_content: : CHi-C interacting HindIII fragments (bait chromosome, bait start, bait end, other-end chromosome, other-end start, other-end end, CHiCAGO score) Supplementary_files_format_and_content: ATAC.narrowPeak: ATAC-seq narrow peaks (chromosome, start, end, id, score, strand, signalValue, pValue, qValue, peak point-source) Supplementary_files_format_and_content: : Hi-C compartment coordinates (chromosome, start, end) Supplementary_files_format_and_content: merged_: TAD coordinates (chromosome, start, end) Supplementary_files_format_and_content: bait_: HindIII fragments targeted (chromsosome, start, end, locus) and commands from HOMER were used to find compartments A and B (with option -opp). TADs generated were merged across replicates and time by command from HOMER. HOMER command makeTagDirectory (-tbp 1) and analyzeHiC (-res 40000 -balance) were used to generate normalized Hi-C interaction matrices.į command from HOMER were used to find TADs from the normalized interaction matrices generated from Hi-C data. Hi-C reads were mapped to GRCh38 by HiCUP and then converted to HOMER format.

Counts data for exons and introns were generated using DEXSeq and summed to get the counts data for each gene. RNA-seq data were mapped to GRCh38 by STAR. Peaks were then combined across all the time points and counts data were extracted for each time point by Diffbind. Bam files of the three replicates for each time point were merged and MACS2 were applied to call peaks on each merged bam file. Reads less than 30bp were filtered out by SAMtools. Individual ATAC-seq reads were mapped to GRCh38 by Bowtie2. Interactions across time with CHiCAGO score over 5 were picked up and corresponding counts data were extracted from CHiCAGO results This Sample represents 1 pool of 3 individualsĬHiC were mapped to GRCh38 using HICUP, CHiCAGO was applied to each aligned bam file. Primary human CD4+ T-cells were isolated from PBMCs using an EasySep T-cell isolation kit (StemCell) Libraries for RNA-seq were prepared using the NEB Next Ultra Directional RNA-seq reagents and protocol using 100ng of nuclear RNA as Input. Samples were either pooled in equal amounts (same individuals as for Hi-C to create matched samples), or processed individually to give duplicate samples. RNA-seq, Primary CD4+ T-cells, 2hr stimulated, replicate 1įive million CD4+ T-cells were harvested, stored in Qiagen RNAprotect solution and the nuclear RNA isolated using a Qiagen RNeasy kit and quantified. GEO help: Mouse over screen elements for information.