Dataset: datav4.csvSource: The data was derived from the National Health and Nutrition Examination Survey (NHANES).Processing: The raw NHANES data underwent a cleaning and preparation process using the shuffle.R script included in this repository, resulting in the datav4.csv file.R ScriptsThis repository includes three R scripts which should generally be run in the following order:shuffle.R:Function: This script is responsible for data cleaning, preprocessing, and a_gout_study_data_codeMing of the initial NHANES data to generate the final analytical dataset (datav4.csv).calculate.R:Function: This script performs the main statistical calculations and generates most of the tables presented in the manuscript.Dependency: This script uses the datav4.csv file generated by shuffle.R.plot.R:Function: This script is used to generate the figures and plots included in the study.Dependency: This script likely uses data objects created by calculate.R or the datav4.csv file directly.System RequirementsR Version: The scripts were developed and tested using R version 4.4.1.Required R PackagesThe following R packages need to be installed to run the scripts:dplyrtidyrggplot2VIMYou can install these packages in R using the following command:install.packages(c("dplyr", "tidyr", "ggplot2", "VIM"))

Dataset: datav4.csvSource: The data was derived from the National Health and Nutrition Examination Survey (NHANES).Processing: The raw NHANES data underwent a cleaning and preparation process using the shuffle.R script included in this repository, resulting in the datav4.csv file.R ScriptsThis repository includes three R scripts which should generally be run in the following order:shuffle.R:Function: This script is responsible for data cleaning, preprocessing, and a_gout_study_data_codeMing of the initial NHANES data to generate the final analytical dataset (datav4.csv).calculate.R:Function: This script performs the main statistical calculations and generates most of the tables presented in the manuscript.Dependency: This script uses the datav4.csv file generated by shuffle.R.plot.R:Function: This script is used to generate the figures and plots included in the study.Dependency: This script likely uses data objects created by calculate.R or the datav4.csv file directly.System RequirementsR Version: The scripts were developed and tested using R version 4.4.1.Required R PackagesThe following R packages need to be installed to run the scripts:dplyrtidyrggplot2VIMYou can install these packages in R using the following command:install.packages(c("dplyr", "tidyr", "ggplot2", "VIM"))

Flag-SSX ChIP-seq and Input read coverage data in bdg format, Peak data. The ChIP-seq data are processed as follows: The raw ChIP-seq data (FASTQ format) were first adaptor-trimmed, then mapped to the human reference genome (hg19) using Bowtie2 program (version 2.1.0) with the default setting. After removing duplicated reads, we used the MACS2 (version 2.1) software to identify peaks using the matched DNA input data as the control. Based on the coordinate of called peaks, the overlap rate between two TF binding peaks was analyzed using the Bedtools program. The peaks were ranked by the number of mapped reads within the peak interval and the top 10% of peaks were selected for motif discovery. The summits of the top 10% peaks were extended by 100 bp on either side. Motifs between 5 and 30 bp in length were identified on both strands. We employed the MEME 4.9.1 toolkit to search DNA motifs and enrichment significance for candidate TFs.

Flag-SSX ChIP-seq and Input read coverage data in bdg format, Peak data. The ChIP-seq data are processed as follows: The raw ChIP-seq data (FASTQ format) were first adaptor-trimmed, then mapped to the human reference genome (hg19) using Bowtie2 program (version 2.1.0) with the default setting. After removing duplicated reads, we used the MACS2 (version 2.1) software to identify peaks using the matched DNA input data as the control. Based on the coordinate of called peaks, the overlap rate between two TF binding peaks was analyzed using the Bedtools program. The peaks were ranked by the number of mapped reads within the peak interval and the top 10% of peaks were selected for motif discovery. The summits of the top 10% peaks were extended by 100 bp on either side. Motifs between 5 and 30 bp in length were identified on both strands. We employed the MEME 4.9.1 toolkit to search DNA motifs and enrichment significance for candidate TFs.

Flag-SSX ChIP-seq and Input read coverage data in bdg format, Peak data

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The Chinese rhesus macaque (Macaca mulatta lasiota) is a subspecies of rhesus macaques that mainly resides in western and central China. Due to their anatomical and physiological similarity with human beings, macaques are a common laboratory model. Also, as several macaques species have been sequenced, such as the Indian rhesus macaque and the crab-eating macaque, examination of the Chinese rhesus macaque (CR) genome offers interesting insights into the entire Macaca genus.The DNA sample for data sequencing and analyses was obtained from a five-year old female CR from southwestern China. The genome was sequenced on the IlluminaGAIIx platform, from which 142-Gb of high-quality sequence, representing 47-fold genome coverage for CR. The total size of the assembled CR genome was about 2.84 Gb, providing 47-fold on average. Scaffolds were assigned to the chromosomes according to the synteny displayed with the Indian rhesus macaque and human genome sequences. About 97% of the CR scaffolds could be placed onto chromosomes.