Regions
Genomic regions are the central element of the region-set focus approach and are represented in practice by BED files that the user uploads to the server.
Data requirements
BED-files should conform to the BED-standard, meaning that they should contain all required BED fields with tab-separated entries:
chromchromStartchromEnd
BED-files that are accepted by HiCognition may contain column names, so the following is acceptable:
chrom start end
chr1 1234 5678
. . .
. . .
. . .
In addition, BED-files can contain comment lines (containing a leading #) as well as track and browser lines. So the following would be an acceptable file:
# This is my favorite file
track name=pairedReads description="Clone Paired Reads" useScore=1
chr22 1000 5000 cloneA 960 + 1000 5000 0 2 567,488, 0,3512
chr22 2000 6000 cloneB 900 - 2000 6000 0 2 433,399, 0,3601
A requirement that you are responsible for yourself is that the BED-file conforms to the selected genome assembly (see the genome assemblies section for more information). HiCognition won’t crash when preprocessing using a BED-file with chromosomes, not in your selected genome assembly but will emit blank data for those regions. But we cannot check whether positions within the bounds of a given assembly are derived from that assembly!
Always check that your BED-file contains coordinates that fit the selected genome assembly!
Types of genomic regions
HiCognition supports two different types of genome regions that will be preprocessed and displayed differently. For a description of how the visualizations adapt to the different types of genomic regions, see the widget section.
Point regions
HiCognition treats point regions as points in the genome. This means that internally, the start and end field in your uploaded BED-file will be collapsed into a single position that represents the center of that region. This region type is suitable when the features you look at likely have no internal structure through the span of your regions. Examples here are Chip-seq peaks of transcription factors, transcriptional start sites of genes, or boundaries of topologically associating domains.
When working with point regions, HiCognition will create views with different window sizes around these point regions to allow exploration of multiple length scales.
Windowsize
Point
Small -----|-----
|
| Point
| --------------|--------------
|
| Point
| --------------------|--------------------
|
v Point
Large ----------------------------|----------------------------
To reduce computational load, the binsizes at which features are aggregated get smaller when windowsizes get larger. The particular setting can be changed in the configuration files of HiCognition (see the configuration section for more details).
Interval regions
HiCognition treats interval regions as regions that have a meaningful start, end and with that size in the genome. This region type is suitable when the features that you look at likely have internal structure through the span of your regions. Treating regions as interval regions is also useful when they have different sizes, and you want to look at features without regard for the region’s size. This is because all preprocessing steps for interval regions change the coordinate system from genomic coordinates to relative coordinates with relation to the size of the regions.
The binsizes that are used to aggregate features are thus defined as a percentage of the interval size. Practically, this means that if you have a particular region that is 1 Mbp in size and another region that is 500 kbp in size, the effective binsize (at 1 %) for the former will be 10 kbp and for the latter 5 kbp. This also means that we interpolate genomic features to fit the target binsize. This is convenient to exclude size effects but can introduce artifacts. So always have that in mind when exploring interval features!
Interval regions will cause genomic features to be scaled to a common size. This can introduce artifacts!
Interval regions are displayed with a little expansion left and right to be able to compare them to a potential baseline:
Left Expansion Region Right Expansion
---------------|---------------------------------|---------------
See the configuration section for how to set the extent of the expansion.
Description of genomic regions
To allow efficient filtering and searching as well as an easier display of genomic regions, we defined a formal description of genomic region sets that is exposed to the user both when adding new regions and when viewing genomic regions. The structure of the description fields of a genomic region set can be seen in the following graph:
In this graph, all nodes representing a value that the user can select are red. If the user’s selection can lead to different selections lower in the graph, these options are displayed as black nodes. The first children of Region are fields that need to be defined for all region sets and have no influence on the later selection hierarchy:
- Assembly | The genome assembly this region set belongs to
- Perturbation | The perturbation condition (or none) that was used in the experiment that led to this dataset
- CellCycleStage | The cell cycle stage the cells were in when the data was collected
- SizeType | Which sizetype (see above) the genomic regions should be treated as
Then, the ValueType needs to be selected. This describes what kind of genomic regions this dataset is. This can be one of the following:
- Peak | A peak from an experiment that produces continuous genomic data e.g., ChiP-Seq
- GenomeAnnotation | A genomic region set that is obtained by integrating multiple data sources and human intervention (e.g., genes, transcriptional start sites, etc.)
- Derived | If regions have been derived from a single other dataset (e.g., TADs derived from a Hi-C experiment)
Selection of a specific ValueType leads to different required metadata downstream. E.g., if the ValueType is Peak than the possible selection of methods is different than when the ValueType is Derived.
Adding genomic regions
Adding genomic regions can be done by uploading a BED-file and filling out the required metadata field. This can be done for single files or with multiple files using the bulk addition option.
Single addition
To add a single genomic region set, open the side drawer and click on the add region button:
Then, a dialog will pop up where you can select a region file, specify its name and select a genome assembly.
When you added the BED-file, options will appear that encapsulate the hierarchical structure describe above
Once you are happy with your selection, you can hit submit, and your dataset will be uploaded!
Bulk addition
If you want to add multiple genomic regions, you can use the bulk addition option:
Bulk addition is organized as a stepper, where you add the same information as in the single addition at the different positions of the stepper.
Managing genomic regions
Managing genomic regions is done using a central table component that can be opened in the data management sidebar:
Viewing
When you click on the Show Regions button, a table dialogue opens that shows you all available genomic region sets:
This top-row of this view allows you to select a specific genome assembly and contains a search field that allows for quick filtering of interesting datasets. For more fine-grained control over the filtering process, you can expand the filter box, which displays the description fields mentioned above.
Per default, only a subset of fields are displayed, but you change this in the fields box:
All interfaces in HiCognition that need dataset selection are using this table component.
Editing
You can edit the description fields and name of a dataset by selecting it in the table component and then hitting the Edit button. This will display the edit dialogue:
Here you can edit most description fields, and once you are finished, you can hit Submit Dataset to save your changes.
The only parameter you cannot edit is the SizeType field, which would invalidate prior preprocessing on the dataset.
Deleting
You can delete datasets by selecting them in the table component and then hitting the Delete button. Note that you can only delete a dataset when you are its owner, not when it has been shared (see the dataset sharing section for more information) with you.
Deleting a dataset will cause all preprocessing data to be lost. Be careful!