GenPlay, Einstein Genome Analyzer - User contributions [en]

FAQ

2011-04-15T19:22:47Z

129.98.70.155:

==== How can I make sure I am using the last version of GenPlay ? ====
To be sure that you are not actually using an old version of GenPlay you need to delete the Java temporary files from the Java control panel. On Windows, the Java control panel is accessible from the Windows Control Panel > Java icon.

==== Where can I find the executable of GenPlay? ====
GenPlay can be launched from the [[Web Start]] page of this website.

The jar files of the current and past versions of GenPlay are available in the [[Older Versions]] section of this website.

==== Do I need a license to use GenPlay? ====
GenPlay is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

==== Where can I download the source code of GenPlay? ====
The source code of GenPlay is available from the SVN repository at https://genplay.einstein.yu.edu/svn/GenPlay
You can check out the latest source code with a SVN client with the following command:
svn co https://genplay.einstein.yu.edu/svn/GenPlay

==== During zooming, the item I want to zoom in on keep sliding off the edge of the display. How should I use the zoom functionality to avoid this problem? ====
During the conception of the software we decided to handle this problem as follow:
* A double click on a feature centers the screen on the selected feature.
* A mouse wheel up or down zoom in or out on the feature that is at the center of the screen. An unintended click on the mouse wheel starts the scroll mode which can be undesirable.

==== When does GenPlay use multiple CPUs, and is it possible to add more threaded operations? ====
GenPlay uses more multiple CPUs for the operations only if the computer has more than two cores. When a threaded operation starts, the program checks how many cores are available, and dispatch the threads on each core but one which is kept for the operating system and the display of the GUI. Obviously, if the computer has only one core the operations are started on the same core has the GUI.

==== Does the software use the GPU, and is it possible to do so? ====
The software uses the Java 2D technology for the display of the GUI which takes advantage of the GPU. A set of command line flags can be used with this technology, some of which can improve the performance and push the software to fully benefit from hardware acceleration. Here is the java webpage describing the different flags that can be set: http://download.oracle.com/javase/6/docs/technotes/guides/2d/flags.html

GenPlay:General disclaimer

2011-03-07T21:47:39Z

129.98.70.155:

GenPlay, Einstein Genome Analyzer
Copyright (C) 2009, 2011 Albert Einstein College of Medicine

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.

The program "GenPlay" written by
[mailto:julien.lajugie@einstein.yu.edu?Subject=Email%20sent%20from%genplay.einstein.yu.edu Julien Lajugie] is available at http://genplay.einstein.yu.edu

GenPlay:General disclaimer

2011-03-07T21:46:23Z

129.98.70.155: Created page with "GenPlay, Einstein Genome Analyzer Copyright (C) 2009, 2011 Albert Einstein College of Medicine This program is free software: you can redistribute it and/or modify it under the ..."

GenPlay, Einstein Genome Analyzer
Copyright (C) 2009, 2011 Albert Einstein College of Medicine

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.

The program "GenPlay" written by Julien Lajugie
julien.lajugie@einstein.yu.edu is available at http://genplay.einstein.yu.edu

Tutorials

2011-01-31T16:10:35Z

129.98.70.155: /* ChIP-Seq Analysis */

The following tutorials aim to give you some of the basic concept on the track manipulation techniques.

== ChIP-Seq Analysis ==
'''Goal:''' The objective is first to isolate the peaks from the data generated from a ChIP-Seq experiment. Then, we want to generate a list of genes that have a peak in their promoter and associate for each promoter the score of the peak summit.

'''Note:''' The following tutorial is based on the hg19 genome assembly which is the default genome assembly of GenPlay. If you previously changed the genome assembly used by GenPlay in the configuration menu you would need to restore the hg19 assembly. Please refer to the documentation section of this website for more information on how to change the reference assembly.

'''Note:''' The final result of this tutorial is available as a project that can be loaded from the [http://129.98.70.162/wiki/index.php/Web_Start#Demo Web Start] page of this website.

=== Load the File ===
The first thing to do is to download the [http://www.genplay.net/tutorials/ChIP-Seq/ChIP-Seq_tutorial_input.bed ChIP-Seq input file], the [http://www.genplay.net/tutorials/ChIP-Seq/ChIP-Seq_tutorial_control.bed ChIP-Seq control file] and the [http://www.genplay.net/tutorials/ChIP-Seq/RefSeq_hg19.bed RefSeq gene annotation file] (if the browser open the files in a new tab or window, just select the Save As option of the File menu of your browser to retrieve the files.

After that, you can start GenPlay from the Web Start link that is located on top of this page. The 1 GB link is enough for this tutorial, but generally you should allocate as much memory as you can afford. For this experiment we're going to work only on the first chromosome so the loading time is shorter and the amount of memory needed is smaller.

To obtain the narrowest peak possible, it is generally advisable to correct for the strand bias that is caused by the fact that the cross-linked DNA fragments are sequenced from the end while the actual binding site might be anywhere within the immuno-precipitated fragments. (reviewed in Wilbanks EG et al. Evaluation of algorithm performance in ChIP-seq peak detection. PLoS One. 2010 Jul 8;5(7):e11471.)

To measure the strand bias, we need to load the 3' and 5' reads separately.

To achieve that you will need to right click on the track handler of the 1st row, in order to open the menu that will allow you to load tracks (figure 1). Select the Load Fixed Window Track option.
[[image:tutorial1_empty_track_menu.png|center|frame|Figure 1: Load Menu]]

After selecting the input file, an option window is going to prompt you to enter information on how to load the data. You can keep the default name for the track or change it if you prefer. You then need to choose a window size and a method of score caculation. The size of the window that you should choose depend on the number of reads that are available. The smaller the windows the higher the resolution. For this example, we will choose a window of 100 bp. The option for the score calculation are discussed in the Documentation. For the type of files used in this example, you should choose sum as the method for the score calculation. You can keep the default data precision. But we need to select a strand. Let's start with the 5' strand. You will also need to select the 1st chromosome. To do so, click on the "Modify Selection" button on the bottom right corner of the screen and then uncheck all the chromosomes but the first one. The figure 2 shows how the screen should like before you click on the OK button.
[[image:tutorial1_Load_FWT_menu.png|center|frame|Figure 2: Load Fixed Window Track Menu]]

The operation needs to be repeated for the 3' strand. Once the two tracks are loaded you can modify the Y axis by right clicking on the track handlers and selecting the "Set Y Axis" option. Set the maximum to 100. You can also change the color and the appearance of the peaks. Now that the two tracks are loaded we can graphically determine how much the strands need to be shifted. Select a peak, zoom on it with the mouse wheel and check how far the summits of the same peak on the 5' and the 3' strands are. Verify that this value is the same on other peaks. When you're sure about the value, divide it by two and note this result. This value should be very close to the average size of the insert of the sequenced library.
In the example, we notice that the summits are about 300 bp apart (figure 3) so the shifting value is 150 bp (meaning that the 5' is shifted 150 bp forward and the backward strand is shifted 150 bp backward).
[[image:tutorial1_strand_shifting.png|center|frame|Figure 3: Find Strand Shifting]]

We need to load the file again but this time we're going to load both strands with the appropriate strand shifting. This time the loading screen should look like on the figure 4.

For comparison purpose, you can also load both strand without any shifting. This should clearly show that the shifted peaks are generally narrower than the unshifted peaks.
[[image:tutorial1_Load_FWT_menu2.png|center|frame|Figure 4: Load Fixed Window Track Menu, both strands]]
<br style="clear: both" />

=== Load Control ===
It's now time to load a control track. This track will to help us to remove the peaks (enrichment region) that we believe to be artifacts. These artifacts can be caused either by preferential sequencing of specific fragment by the instruments or by differences between the genome sequenced and the genome assembly used to align the reads, since any repeat in the sequenced genome that is present as a none
repeated region in the genome assembly will result in a peak.

Identifying these outliers can be quite difficult. One useful method is to compare the IP libraries with a control (input) library for the same sample. It's what we want to do in this tutorial.

To load the control file, right click on the handler of an empty track and select "Load Fixed Window Track". Then select the input file that you downloaded earlier. On the next window set the parameters as shown on the figure 5.
[[image:tutorial1_load_control1.png|center|frame|Figure 5: Load Control Parameters]]

=== Normalize Input and Control ===
In order to be comparable, the input and the control tracks need first to be normalized.

The normalize operation of GenPlay divides each score by the some of all the scores of the track and multiply the result by a large constant specified by the user. The only purpose of the constant is to make the score more readable.

Let's start by normalizing the input. You need first to right click on the track handler. After that, select the normalize option of the operation sub-menu as shown on figure 6. You can keep the default constant, just click OK (figure 7).

Once it's done repeat the operation with the control track.

[[image:tutorial1_normalize1.png|center|frame|Figure 6: Normalize Menu]]
[[image:tutorial1_normalize2.png|center|frame|Figure 7: Normalize Dialog Box]]

=== Add Constant to Control ===
One of the specificities of GenPlay is that while computing an operation, the windows with a score of zero are not taken into account.

This is helpful in most cases because we don't know if a zero window is caused by a region that is not sequenced in the genome assembly or if it's a regular region where no read mapped.

In our case though, since we want to compare the input track to the control track, we want to be able to do this comparison even when a window of the control has a null score. Since the comparison will be a subtraction we can add a really small constant (so no window has a score of zero) without impacting significantly the result of the subtraction.

We chose to add a constant of 0.01 to all the windows of the control track. In order to do that, you first need to right click on the handler of the control track (figure 8). Then, choose the "Addition (Constant)" option of the operation sub-menu. Enter 0.01 in the next windows as shown on figure 9 a click OK.

[[image:tutorial1_add_constant1.png|center|frame|Figure 8: Addition Constant Menu]]
[[image:tutorial1_add_constant2.png|center|frame|Figure 9: Constant Dialog Box]]

=== Compare Input to Control ===
We can now compare the input to the control track in order to filter out the artifactual peaks. As we already decided, we want to subtract the control scores from the input scores.

To do so, you need to right right click on the handler of the input track and select the operation sub-menu. Then click on "Two Tracks Operation" as shown on figure 10.

In the first dialog window you need to choose the second track for the operation. Select the control track (figure 11).

The operation to choose is subtraction (figure 12) and you can select the default option (32-Bit) for the result data precision. You also need to choose in which empty track you want the result to be displayed. The figure 13 shows the result of the operation.

[[image:tutorial1_subtract1.png|center|frame|Figure 10: Two Tracks Operation Menu]]
[[image:tutorial1_subtract2.png|center|frame|Figure 11: Dialog to Choose the Second Track]][[image:tutorial1_subtract3.png|center|frame|Figure 12: Dialog to Choose the Operation Type]]
[[image:tutorial1_subtract4.png|center|frame|Figure 13: Result of the Subtraction]]

=== Filter Negative Values ===
Since we're just interesting in the enriched regions we can try to remove all the negative values in order to make the track easier to comprehend.

To filter the track you need to select the "Filter" option of the operation sub-menu (figure 14). On the left panel of the filter window select the threshold option. Set the parameters of the right panel as shown on figure 15. The figure 16 shows the result of the filter operation.

[[image:tutorial1_filter1.png|center|frame|Figure 14: Filter Operation Menu]]
[[image:tutorial1_filter2.png|center|frame|Figure 15: Filter Operation Window]]
[[image:tutorial1_filter3.png|center|frame|Figure 16: Filter Operation Result (the negative values of the track 5 have been removed)]]

We now need to remove the background noise and to keep only the islands (the peaks).

=== Isolate Peaks ===
This goal of this step is to remove the background noise from the track so just the peaks remain.

To do so, right click on the track handler, choose the "Operation" sub-menu and click on the "Find Peaks" option (figure 17).
[[image:Tutorial1 find peaks1.png|center|frame|Figure 17: Find Peaks Operation]]

After the find peaks dialog opens, choose the "Island Finder" option on the right panel and set the parameters as shown on the figure 18.
[[image:Tutorial1 find peaks2.png|center|frame|Figure 18: Find Peaks Menu]]

The island finder is described in the documentation section of this website.
You'll notice that the selected output is "Peak Summits". This means that for each island, the only selected window will be the one with greatest score on the island.

The result should be similar to what is shown on figure 19.
[[image:Tutorial1 find peaks3.png|center|frame|Figure 19: Find Peaks Result (the track 6 contains only the summit of the peaks)]]

=== Extract Gene Promoters ===
First, we need to load the gene track. Right click on an empty track handler and select "Load Gene Track". Select the RefSeq file that we've already downloaded when prompted.

When it's done, right click on the track handler of the gene track and select "Extract Intervals" in the Operation sub-menu (Figure 20).
[[image:Tutorial1 extract promoters1.png|center|frame|Figure 20: Extract Intervals Menu]]

A dialog box will pop-up. We decide to define a promoter as a region that starts 100bp before a gene start position and ends 50bp after. In order to do so, fill in the parameters as shown in figure 21.
[[image:Tutorial1 extract promoters2.png|center|frame|Figure 21: Extract Intervals Dialog]]

You'll finally be asked to select the result track position in the track list. The result track represents only the promoters of the genes of the input track (figure 22).
[[image:Tutorial1 extract promoters3.png|center|frame|Figure 22: Gene Promoters (the track 8 contains only the gene promoters)]]

=== Score Promoters ===
Now that we have a track with the peaks and a track with the promoters we can score the promoters using the score of the peaks and export the result as a bed file.

To score the promoters, right click on the handler of the track with the promoters and select the "Score Exons" option of the "Operation" sub-menu (figure 23).
[[image:Tutorial1 score exons1.png|center|frame|Figure 23: Score Exons Menu]]

You'll be prompted to choose the track containing the scores. Select the track with the peaks extracted.

Then select maximum for the method of calculation and select a track where the result should appear (figure 15). Note that the color of the promoters represents the scores associated to the promoters (as described in the gene track section of the documentation).
[[image:Tutorial1 score exons2.png|center|frame|Figure 24: Score Exons Result]]

The last thing we need to do is to export the result of our analysis. Right click on the newly created track handler. Select "Save As". Choose where you want to save the track and make sure that the file type is set to Bed file. Once it's done, you can open the file that you created with a text editor such as notepad. You'll notice that the result file contains the position (field 1 to 3) of the promoters, the name of the genes (field 4), the strand of the gene (field 6) as well as the scores of the promoters (field 5). For more details about the result file you can refer to the File Type section of the documentation.