The last step assumes that your ~/public_html/ directory is accessible from the internet. bedToBigBed bedExample.txt myBigBed.bb The steps areĮxplained in more detail in the following sections on this page: (swap macOSX for linux for an Apple environment). The following UNIX commands create one on a Linux machine Note that the bedToBigBed utility uses a substantial amount of memory:Īpproximately 25% more RAM than the uncompressed BED input file. See this wiki page for help in selecting the graphing track data format that is most
See Example #3 below for an example of how to build an additional Hosting section of the Track Hub Help documentation.Īdditional indices can be created for the items in a bigBed file to support item search in track If you do not haveĪccess to a web-accessible server and need hosting space for your bigBed files, please see the Server (http, https, or ftp), not on the UCSC server, and only the portion that is needed for theĬurrently displayed chromosomal position is locally cached as a "sparse file". The bigBed file remains on your local web-accessible Regular BED when working with large data sets. Because of this, bigBed has considerably faster display performance than Those portions of the files needed to display a particular region are transferred to the Genomeīrowser server. The main advantage of the bigBed files is that only The resulting bigBedįiles are in an indexed binary format. BigBed files are createdįrom BED type files using the program bedToBigBed.
Once the code is finished, check *BooBED_All.txt for predicted TFBS branch-of-origin.The bigBed format stores annotation items that can be either a simple or a linked collection ofĮxons, much as BED files do. boo -s data/CTCF/hg19_multiz_Broad_ -r data/CTCF/chip_genCoord_Broad_ -t data/tree_file.nh -x hg19 -k 100 -w -100:100 -a GGGGCKC -q CTCF_kmer.txt boo -m f -s data/CTCF/hg19_multiz_Broad_ -r data/CTCF/chip_genCoord_Broad_ -t data/tree_file.nh -x hg19 -k 100 -w -100:100 -a GGGGCKC -q CTCF_kmer.txt Output files will be written into the same folder as multiple sequence alignment file. Then create a link pointing to those files or copy files to that folder. Once multiple sequence alignment and kmer list are generated, create a new folder (e.g. The meaning of parameter is same as main program BOO. boo_pwm -m w -s data/hg19/CTCF/hg19_multiz_Broad_ -r data/hg19/CTCF/chip_genCoord_Broad_ -w -100:100 -k 100 -t data/tree_file.nh -x hg19 -a GGGGCKC So please include both kmer and its reverse complement.) User can also provide a customer kmer list generated by any other methods (*BOO will not automatically generate reverse complement of kmer. Along with BOO program, we also provide another tools called BOO_pwm to generate PWM motif profile from ChIP-seq peak region and output a kmer list. MafsInRegion chip_genCoord_Broad_ hg19_multiz_Broad_ Genome_UCSC/Human/hg19/multiz46way/*.mafįor a known TF, in order to reduce the calculation time, we use a list of re-computed kmer list to search for TFBS (see example kmer file here). Use mafsInRegion to extract multiple sequence alignment within ChIP-seq peak. Genome_UCSC/Human/hg19/multiz46way/) and uncompress them. Other required files can be generated using the following scripts/tool.ĭownload human multiz46way alignments file from UCSC Genome Browser, put those files in folder (e.g.
Example ChIP-seq peak file can be download here. A threshold will be used to filter peaks with score below user specifiec cutoff(-k). , in which is TF name, is peak rank and is an interger score indicating the peak height.
A ChIP-seq peak file in four column bed format is required to run BOO.
Executable binary programs were also provided.
The fourth column indicates the predicted branch-of-origin of TFBSĭownload the package, uncompressed the file using tar -zxvf boo_0.1.0.tar.gz and follow the instruction in README.txt to install the package. Predicted branch-of-orgin of TFBS in bed file format.