GeNemo를 사용 하 여 Epigenomic 데이터의 패턴 기반 검색

The overall goal of this video is to show researchers in the field of epigenetic study how to use the program GeNemo to obtain regions of similarity among different epigenetic marks and different cell types. Our goal in creating this program was to enable researchers to compare their data cell functions to their vast known public databases, such as Encode and Mousington. Informational search with typical research via keywords.

At brink your cells is three K4 primization or other specific curves. While these messes are useful, GeNemo can also search for add ins the actual data, rather than similarities in the data description. Suppose you have a novel DNA-fighting protein with no or little a priori knowledge about what kind of epigenetic modifications or cell counts they're expecting to relate to.

GeNemo can still help to narrow down the potential targets. GeNemo can read files in a variety of formats, including math and Greek, and will use different pattern-matching methods It will also solve the off put regions based on their similarity score, and provides data exporting options. And now, we are going to show how to perform a GeNemo search.

To use GeNemo, you will need your own data file and BED, or big wig format, which can be converted from experimental raw data with common bioinformatics tools, such as the UCSC genome browser. First, go to GeNemo.org. On the main page, you will see a search box with specification options.

Choose which species to compare your data with. For this tutorial, we will use homosapiens as the reference species. Then, choose your own file to upload to GeNemo.

This file can either be hosted online or on your local drive. Copy and paste the URL into this box if your data is online. Or click the button below to upload a local data file.

For this tutorial, we will use a sample file. Sometimes, searches may take a long time to complete. If you do not want to wait, you can provide your email address in this box.

And you will receive an email with a link to the results when the search is finished. If you're providing your own data in BED format, there is another URL to upload a big wig file just for display purposes. You can also specify a search range in this box.

In order to specify a search range, you must specify the chromosome number, as well as the base pair range. The following two inputs would be read the same.Chromosome1:1-1000000. Or by replacing the punctuation marks with spaces, which is the BED file format.

Chromosome1 1 1000000. Now, click the data selection button to select which track types to search for. You may click the check boxes next to each data sample to search against it, but there is also a filter function.

The filter function is helpful for selecting many tracks at the same time. Here, you can select what category to filter from, as well as use the buttons on the bottom of the tab. The first three buttons are pretty self-explanatory.

Filter causes all tracks not belonging to the select categories to become unchecked. Sort of like an and gate. On the other hand, exclude causes the selected categories to become unchecked.

Like a not gate. When you are ready, click the search button to start the search. If you are searching many tracks and, or have not specified a smaller search range, the search may take a while.

In the results box, you will see various sections of the genome where your data file and search species are similar in one or more of the track sequences you specified. You can click download BED file to download a file containing a list of matching regions. Click the show button in each box to visualize the matching section.

On the display section, you will see your own data files track, as well as the matching track or tracks. You can move upstream or downstream by sliding the bar on the top with the base pair labels. As you can see in this example, the mountain here is a similarity between the two tracks.

After watching this video, you should have a good idea of how to obtain regions of similarity among different epigenetic markers using GeNemo. The results of GeNemo search will include epigenetic markers transcription factors finding, DNA methylation, chromogen accessibility, or other types of signal across various tissue types that have similar patterns at certain genomic locations to your data of interest. From there, you may be able to note if this DNA-binding protein may interact with some transcriptional factor, some regulatory elements, or have something to do with chromatin structure, et cetera.

You may further note what types of tissues have the highest similarity to your data set, which may apply the developmental function of your protein of interest. By doing such kind of broad search, GeNemo can return the tentative regions, makers and, or cell types in a relatively low amount of time. From these results, it would also be possible to narrow down the potential areas of interest to derive future experiments, such as choosing the appropriate cell line for the protein in question, testing interactions with specific transcriptional factors, or epigenetic modification via Co IP, 30C, 4C, 5C or HiC.

We hope this tutorial has been helpful, and are constantly working to improve GeNemo. For this end, we welcome any feedback.