The output file is a peak file containing the super enhancers if you use " -o auto " the peak file named ' superEnhancers. Find super enhancers like you normally would, but add the option " -superSlope " - the idea is to include ALL potential peaks as 'super enhancers' so that we can plot them together. Open the resulting peak file in Excel. The 6th column "Normalized Tag Count" contains the super enhancer score for each region. Simply ploting this column as a line plot will give you a sense of what your plot will look like.
To get an official "Young-lab style" plot you'll have to do some Excel algebra to normalize score by the total. The tag directory you use for super enhancer calculation is probably the most important step.
In theory, any data could be used. Mediator, p, Brd4, etc. This type of analysis is useful for transcription factors, and aims to identify the precise location of DNA-protein contact. Peak finding for broad regions of enrichment found in ChIP-Seq experiments for various histone marks. This analysis finds variable-width peaks. Find Super Enhancers in your data see below. De novo transcript identification from strand specific GRO-Seq.
This attempts to identify transcripts from nascent RNA sequencing reads. More info in the TSS section. Adjusted parameters for DNase-Seq peak finding. DNA methylation analysis - documentation coming soon If " -o " is not specified, the peak file will be written to stdout.
The top portion of the peak file will contain parameters and various analysis information. This output differs somewhat for GRO-Seq analysis, and is explained in more detail later.
Some of the values are self explanatory. This provides an estimate of how well the ChIP worked. Below the header information are the peaks, listed in each row. Columns contain information about each peak: Column 1: PeakID - a unique name for each peak very important that peaks have unique names Identification of Putative Peaks If findPeaks is run in " factor " mode, a fixed peak size is selected based on estimates from the autocorrelation analysis performed during the makeTagDirectory command.
This type of analysis maximizes sensitivity for identifying locations where the factor makes a single contact with the DNA. It then scans the entire genome looking for fixed width clusters with the highest density of tags. As clusters are found, the regions immediately adjacent are excluded to ensure there are no "piggyback peaks" feed off the signal of large peaks. This continues until all tags have been assigned to clusters. After all clusters have been found, a tag threshold is established to correct for the fact that we may expect to see clusters simply by random chance.
Previously, to estimate the expected number of peaks for each tag threshold, HOMER would randomly assign tag positions and repeat the peak finding procedure. HOMER now assumes the local density of tags follows a Poisson distribution, and uses this to estimate the expected peak numbers given the input parameters much more quickly.
Using the expected distribution of peaks, HOMER calculates the expected number of false positives in the data set for each tag threshold, setting the threshold that beats the desired False Discovery Rate specified by the user default: 0. HOMER also uses the reads themselves to estimate the size of the genome i. If this estimate is lower than the default, it will use that value to avoid using too large of a number on smaller genomes For example, if you used findPeaks on drosophila data without specifying "-gsize ".
It is important to note that this false discovery rate controls for the random distribution of tags along the genome, and not any other sources of experimental variation. The initial step of peak finding is to find non-random clusters of tags, but in many cases these clusters may not be representative of true transcription factor binding events. To increase the overall quality of peaks identified by HOMER, 3 separate filtering steps can be applied to the initial, putative peaks identified:.
Additionally, you can use other cleaver experiments as a control, such as a ChIP-Seq experiment for the same factor in another cell or in a knockout. Our experience with peak finding is that often putative peaks are identified in regions of genomic duplication, or in regions where the reference genome likely differs from that of the genome being sequenced. Also, it may be advantageous to remove putative peaks that a spread out over larger regions as it may be difficult to pin-point the important regulatory regions within them.
Be default, HOMER requires the tag density at peaks to be 4-fold greater than in the surrounding 10 kb region. As with input filtering, the comparison must also pass a poisson p-value threshold of 0. A step-by-step pathway that leads to literacy. Tools for navigating social skills, empathy, and confidence. Lessons, and activities personalized to age, interests, and skill level. Research-backed, kid-tested, parent-approved. Builds skills kids and parents are proud of. Fueled by activities kids actually want to play.
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Model CHP. With most uses of annotatePeaks. An example of the output can been seen below:. Otherwise, you can run the script " changeNewLine. If errors occur, it is likely that the file is not in the correct format, or the first column is not actually populated with unique identifiers. The first determines the distance to the nearest TSS and assigns the peak to that gene.
It uses these positions to determine the closest TSS, reporting the distance negative values mean upstream of the TSS, positive values mean downstream , and various annotation information linked to locus including alternative identifiers unigene, entrez gene, ensembl, gene symbol etc. Genomic Annotation To annotate the location of a given peak in terms of important genomic features, annotatePeaks. Two types of output are provided. A second round of "Detailed Annotation" also includes more detailed annotation, also considering repeat elements and CpG islands.
Since some annotation overlap, a priority is assign based on the following in case of ties it's random [i. RefSeq doesn't do it for everyone.
Even more important, as RNA-Seq methods develop, the locations of exons etc.
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