Left mouse click a gene symbol or a microRNA ID to pop up a menu for viewing different annotation resources. User can zoom in the diagram by using the mouse scroll wheel or double-clicking. User can zoom out the diagram by using the mouse scroll wheel or shift double-clicking. Alternatively, use the buttons at the top of the page, or use the control items at the end of the zoom indicator at the top of the ideogram.
User can pan the diagram by clicking and dragging across the Ideogram area. Alternatively, drag the context rectangle on the zoom indicator at the top of the ideogram. To select a region on a chromosome: Move the mouse cursor to a position on the chromosome, press the shift key, the cursor turns to cross hair style, left mouse down, move the cursor to select a region of your interest, release the mouse.
If there are symbols inside the selected region, a menu allows user to copy the symbols to a new window or open the region in the UCSC Genome Browser. A slider at the top of the viewer indicates the viewer portion that is shown on the screen.
User can drag the slider controls to view a horizontal portion of the viewer. Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats 2, commits. Failed to load latest commit information. View code. About Chromosome visualization for the web eweitz.
Releases 52 v1. PhenoGram offers a complete genomic picture. Data that relates gene loci, phenotypes, or other attributes to genome location can be complex, and summarizing such data with visualization methods can be important for better understanding results.
PhenoGram was developed in Ruby, using the RMagick graphics library. The software can be downloaded for use at the command line. The software can also be used via a web-based graphical user interface without a need for downloading the software, and a screen capture of the web interface is shown in Figure 1.
An example file is available at that site for trying out PhenoGram with the web-based graphical user interface. Screen capture of the PhenoGram web-interface.
The researcher will upload an input file, provide a title of the resultant figure, and then choose other options. There are multiple options that can be used to create various plots, and Table 1 shows the complete list of command line arguments, which are also available on the web interface.
A single, tab-delimited input file is required to produce a PhenoGram plot. At a minimum, the input file must contain columns to identify the chromosome, and the base-pair position or base-pair to base-pair region to be plotted.
Other columns such phenotype, annotation, ancestry or group, and position-color provide additional PhenoGram visualization options. Table 2 summarizes the formatting parameters of the input file. To show the utility of PhenoGram, and the ways that multiple options can be combined for different types of plots, we describe here several example uses of this software.
We chose this data because allowed us to represent multiple phenotypes across the genome and highlight other relationships in the data such as pleiotropy or ancestry. A key of phenotypes and corresponding circle colors are displayed across the bottom of the image.
PhenoGram has multiple options for altering the graphical style of the colored circles. An Ideogram of all 22 chromosomes is plotted, along with the X and Y chromosomes.
Lines are plotted on the chromosomes corresponding to the base-pair location of each SNP, and the line connects to colored circles representing the phenotype s associated with that SNP. Depending on the amount of data to be plotted, as well as the proximity of genomic regions, different spacing may need to be used to optimally plot multiple data points. For example, an input file with a great number of phenotypes may produce a plot with circles that are too closely juxtaposed.
Thus, PhenoGram has several options for modifying the spatial presentation of the circles or other annotation on PhenoGram plots. Figure 3 shows the results of using different PhenoGram spacing algorithms that can mitigate the issue of overlapping plotted data. The first spacing method is standard spacing and is the default spacing method used by PhenoGram. The different annotation spacing methods available with PhenoGram. PhenoGram has several options for modifying the spatial presentation of the circles or other annotation on PhenoGram plots: The default of standard spacing , the equal spacing method placing circles or other annotation at equal intervals along the chromosome, and proximity spacing that minimizes circle or annotation overlap while keeping points near their chromosomal locations.
The option to plot a single chromosome was used for this figure. The colors of the plotted circles can be alternately generated based on five different algorithms, shown in Figure 4. Finally, it is possible to provide in the input file a column that designates a group identifier for a subset of phenotypes such that all those of a similar identifier are plotted in a gradient of one color.
The five phenotype color generation methods available in PhenoGram. For a small number of phenotypes, the color list method assigns easily-discernible colors. With a greater number of phenotypes, the standard generator attempts to maximize the color separation between the phenotypes.
A random generator may also be used, as well as a method for web-safe colors. The grouped method makes it possible to plot phenotypes with the same designated identifier in a gradient of similar colors. Similar to grouping data by phenotype, it is possible to overlay a second grouping by ancestry. Shown in Figure 5 , the plot resulting from the incorporation of this data into the input file depicts each ancestry group as a unique shape while still differentiating phenotypes with a color generation method.
Here, the phenotype shapes are displayed without a black outline. GWAS catalog data was also used in this plot in order to show the combination of the diverse phenotype colors and distinct shapes by ancestry across the genome.
PhenoGram currently accepts up to three different ancestry groups, with each subsequent group beyond three appearing as a circle. Figure 5 displays how PhenoGram can help visualize the relationships between genome location, phenotypes, and ancestry. Adding in a shape to indicate a grouping, such as ancestry. Designation of ancestry in the PhenoGram input file will result in all phenotypes of each ancestry identifier being plotted with a unique shape in addition to showing a phenotype color.
The input file can take up to three ancestry or other group identifiers. PhenoGram can also create plots that contain, rather than colored shapes, only colored lines that transverse the chromosomes. In this way, the software is also useful for visualizing genome or single-chromosome SNP coverage from a genotyping array as well as to show locations of sequenced loci or other regions of interest. Further, it is possible in the PhenoGram input file to highlight base-pair regions via the use of integer-coded color options and to annotate positions.
In Figure 6 , a dense region of genotyping of the array on chromosome six is annotated; this region is the major histocompatibility complex MHC region. Plotting lines at base-pair locations using PhenoGram.
Each line represents a base-pair location genotyped on the immunochip genotyping array, an array with variants chosen for previous association with the autoimmune response and the immune system.
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