The small RNA Expression Atlas (SEA) is a a repository of small RNA (sRNA) expression datasets.
These expression datasets are systematically annotated with metadata.
For instance, biological metadata includes standardized information about the organism, cell line, cell type, tissue type, potential diseases and more.
Additional annotations include experimental details about instrument models and library strategies.
All the data was analysed with the Oasis pipelines to achieve a comparison of small RNA expression across many studies.
SEA can be searched for sRNAs that originate from miRBase, Ensembl as well as from the repository of novel predicted miRNAs from Oasis.
A search can be performed with individual search terms as well as combinations thereof.
The central feature of SEA is the powerful ontology-based search which allows the user to easily find datasets that are relevant to their research.
(Please note: The screenshots you see in this manual have been edited in order to save screen space.
When you use SEA you will likely see slightly different visual results)
SEArching with SEA
All searches for datasets or sRNAs start by using the search bar on the start page.
When you start typing you should see search suggestions popping up.
Figure 1 shows what it looks like when you start to enter "skin" into the search bar.
As you can see, the search suggestions come from different categories.
Overall SEA supports the following search categories:
Please note that SEA only allows searches based on suggested terms.
All terms that are suggested to you (while typing) are guaranteed to be in the database.
If you are looking for a specific disease/tissue/organism/etc. and no matching terms are suggested,
then there is no dataset with your criteria in the database.
If you now select skin from the suggestions list, you will see that tissue:skin it has been added to the search bar (figure 2).
Combining several search terms
When using several search terms, a dataset will be found if the following rules are satisfied:
For each search category (sRNA ID, disease, tissue, cell type, ...) all search criteria have to be met.
If a category has several search terms (e.g. the user searches for several different diseases), at least one search term of that category has to match
For example, let us assume we search for samples from tissue or muscle in human Psoriasis patients (see Figure 3).
Once we hit the enter key in the search bar, we will go to the search results page (Figure 4).
At the top you will see your search query again.
You can see that SEA searched for skin or muscle tissue.
Given that Psoriasis is a disease of the skin, it is not surprising that we do not find any datasets that contain muscle tissue.
Searching with ontologies
Each dataset in the SEA database is annotated with terms that come from ontologies.
In simple words, an ontology is a list of relationships between words.
For example, if we take the words human and mammal, we can say that a human is a mammal.
And not only humans are mammals, but mice, dogs, dolphins and pigs are mammals too.
But it does not end there.
All mammals are also vertebrates.
And all vertebrates are chordates.
Ontologies are not only restricted to organisms.
Many more ontologies have been defined by independent organisations.
When you use the search in SEA, all datasets will be found that match the search term but also all subterms as they are defined in the ontologies.
For example, if you search for neurodegenerative disease, you will get search results from Alzheimer's and Huntington's disease.
If you search for murinae you will get datasets from mice as well as from rats.
This way you can be as broad or as specific with your search as you wish.
Working with the SEArch results
When you work with SEA, you will most likely be in one of the following situations:
You are interested in a specific sRNA and you want to obtain expression datasets that contain this sRNA.
You are interested in a specific tissue/disease/cell-line and you want to obtain sRNAs that are relevant in this tissue/disease/cell-line.
You know which sRNA and which tissue/disease/cell-line you want to do research on. Now you simply want datasets that feature this sRNA in this particular tissue/disease/cell-line.
(Your starting point might also involve more than one sRNA and/or tissue. Feel free to add more search terms in this case.)
Keep in mind that SEA keeps track of your search/filtering criteria as you go through the results.
If you select a specific sRNA, a specific dataset and/or a specific tissue, all subsequent diagrams and tables will be based only on datasets that fulfill your criteria.
The following examples will use the microRNA hsa-miR-3179 and the tissue skin. Please chose your starting point:
You have searched for a specific sRNA molecule.
The search results should look like figure 5:
Dataset search results
You have searched with specific dataset criteria.
Your search results should look like figure 6:
SmallRNAs in a Dataset
If you selected a specific dataset you should see something similar to figure 7:
Expression of sRNA molecule within one dataset
If you choose to investigate the expression of a single sRNA molecule in a single dataset you get something similar to figure 8:
Small RNA identifiers
SEA uses standard small RNA identifiers for the search.
The user should keep in mind that different types of small RNAs have different conventions when it comes to identifiers.
For instance, microRNA IDs usually start with the species code that they are derived from.
For example, a human microRNA usually starts with hsa-.
The situation is similar to Piwi-interacting RNAs (piRNA).
But instead of a dash the identifiers use an underscore: hsa_.
Small nucleolar RNAs (snoRNAs) IDs tend to start with SNO and
ribsomal RNA (rRNA) IDs usually start with a small r.