ROI

ROIs ("Regions Of Interest") are genomic regions defined by their coordinates in the genome: chromosomes, starting and ending positions (and strands). They can derive from any kind of NGS experiment (transcription factor binding sites from ChIP-Seq, accessible regions form ATAC-Seq, R-loops from DRIP-Seq, Replication origins from EdU-Seq, ...).

Enrichment files

The enrichment files contain information about aligned reads from any kind of NGS experiment. They are required to perform quantitative analyses and to compute reads enrichment. When an enrichment file is imported, ChroKit knows where the file is located (so the link to the file is imported) and uses them to compute the reads enrichment in a specific ROI (see 'Enrichment association').

Gene list

They are lists of genes with any kind of identifiers (symbols, ENTREZ ID, Ref Seq ID, ENSEMBL ID). Once imported in the program, a gene list is defined as a triplet of ROIs: promoters, transcripts and TES (transcription end sites) of all the genes composing the gene list. They can derive form any kind of source. As example, they can be a set of UP-regulated genes from an RNA-Seq experiment or a set of genes belonging to a specific pathway.

Assembly

A genome assembly is the union of three ROIs: all the annotated promoters, transcripts, TES of a specific organism. The libraries to import assembly must be installed on the system, or they can be downloaded from bioConductor inside the program. Docker images already have mouse and human libraries pre-installed. An assembly is required for various operations, such as importing gene lists or annotating ROIs.

Enrichment association

After importing enrichment files, this operation computes the number of reads aligned for each base pair in genomic ranges of a specific ROI. This is required for analyses involving reads enrichment.

What input files do I need to use ChroKit?

Three kind of files can be imported in ChroKit:

1. Coordinate files, in BED or GTF format: those files are tab-delimited text files with information about genomic coordinates, containing the chromosome number, start and end positions of the genomic ranges. Optionally, they can contain also the strand information. These files must have the following format:

   

   
   
   These files can originate from different sources. For example, they can be the result of a peak caller algorithm in a ChIP-Seq, ATAC-Seq, DRIP-Seq, EdU-Seq experiments. They can be downloaded from published datasets in external repositories, such as GEO (https://www.ncbi.nlm.nih.gov/geo/) or ENCODE (https://www.encodeproject.org/). In summary, these files can originate from different sources or can be custom-defined. These files are 1-based; if a coordinate starts with 0, it will be converted to 1 by ChroKit.


2. Lists of genes: those are a list of genes IDs or symbols with a particular biological interest. They can be provided directly pasting the symbols/IDs inside the program or by uploading a text file containing one symbol/ID for each row. They can be genes belonging to the same pathway, a list of genes upregulated following by a certain treatment or with a particular value of expression. Independently on the source, any list of genes can be used as input. Then, ChroKit will create the promoters, transcripts and transcription end sites corresponding to those genes for genomic analyses.


3. Enrichment files in .bam or .bigWig format: these files derive from the alignment of reads in NGS experiments. Bam files are binary files containing some information about reads, such as the position in the genome and their quality, while bigWig files contain information of the signals in the genome (https://genome.ucsc.edu/goldenPath/help/bigWig.html). Bam files are 0-based if directly accessed, but when these files are used by programs the information becomes 1-based. BigWig files are 1-based. Enrichment files can be used to compute the number of reads inside specific genomic regions of interest. The signal computed from bam files can be easyly normalized within ChroKit by library size or user-defined spike-ins, while bigWig files should have been previously normalized before the import.

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I have genomic coordinates in a BED or GTF file, how can I import them as ROIs (regions of interest)?

Select the file from filesystem (for an example, load YAP_peaks.xls from the ChroKit-master/source/appContent/tutorial_files/ directory. NOTE: if you are running ChroKit using Docker, you will find all the computer files in the folder you chose for mounting, should be the /mnt folder):

Preview the file and then confirm the import:

After importing the ROI, new menus and functions will be available in two additional tabs:

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I have a list of genes, how can i import their promoters and transcripts as ROIs for analyses?

Import a genome assembly (here mm9) to obtain the coordinates of annotated genomic elements:

Import the genelist:

After this step, three ROIs (promoters, transcripts and TES of the imported genes) will be loaded.

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I have enrichment files from sequencing (bam or bigwig): how can I import them?

Select the enrichment file(s) to be imported (formats supported: bam or bigWig*. NOTE: if you are running ChroKit using Docker, you will find all the computer files in the folder you chose for mounting, should be the /mnt folder):

Once imported, a file can be renamed or removed:

*bigWig files are not supported in Windows operating systems.

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How can I analyse genomic ranges of a ROI (genomic annotation and genomic features)?

Option 1: With the example data
Load the example data (1), click 'Genomics' (2) and select the single evaluation analysis (3)

Option 2: With your own data
Alternatively, if you have your own ROIs, start from step 2 and select the ROI that you want to analyze in the step 4

Then look and the plots, adjust their graphical options or download them:

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How can I view the pairwise overlaps between two ROIs?

Option 1: With the example data
Load the example data (1), click 'Genomics' (2) and select the pairwise ROI comparison tab (3).

Option 2: With your own data
Alternatively, if you have your own ROIs, start from step 2 and select the ROIs that you want to analyze (step 4, 5):

Then, visualize the results:

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How can I analyse multiple or complex overlaps with a heatmap?

Option 1: With the example data
Load the example data (1), click 'Genomics' (2) and select the 'Position-based heatmap' tab (3).

Option 2: With your own data
Alternatively, if you have your own ROIs, start from step 2 and select the ROIs that you want to analyze (step 4, 5):

Scroll down for further options:

Then, visualize the results and, optionally, extract new ROIs from a particular combination of overlaps:

Scroll down for further output boxes:

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How can I perform a gene ontology analysis?

Option 1: With the example data
Load the example data (1), click 'Genomics' (2) and select the GO analyses tab (3).

Option 2: With your own data
Alternatively, if you have your own ROIs or a custom gene list, start from step 2 and select the ROIs or genelist that you want to analyze (step 4, 5):

Scroll down for further options:

Then, visualize or download the results:

Scroll down for further output:

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How can I subset ROIs and find sequence patterns?

Option 1: With the example data
Load the example data (1), click 'ROI preparation' (2) and follow the steps to subset ROIs based on overlaps.

Option 2: With your own data
Alternatively, if you have your own ROIs, start from step 2 and select the ROIs to generate the subset (step 5,6) (In the example below, you will find Myc peaks overlapping with promoters).

Then, resize the genomic ranges of the ROI created before (i.e. Myc_promoters), for a better visualization in the heatmap:

Extract the sequence patterns from the ROI created in the previous step (Myc_resized). In the example below, you will find the E-box motif.

Build a heatmap to visualize sequence pattern occurrences within the ROI. Click 'Genomics' (16), go to 'Position-based Heatmap' (17) and follow the steps:

Scroll down for further options:

Visualize, in a heatmap, the sequence patterns within the ROI:

For further information about position-based heatmaps, go to How can I analyse multiple or complex overlaps with a heatmap?

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How can I compare the enrichments in the overlapping regions between two ROIs?

Option 1: With the example data
Load the example data (1), click 'ROI preparation' (2) and follow the steps.

Option 2: With your own data
Alternatively, if you have your own ROIs and enrichments, start from step 2 and select the ROI and enrichments that you want to analyze (step 4, 5):

Repeat the steps 4, 5, 6 for the second ROI (Pol2_peaks_treat.xls):

Once the enrichments are associated to ROIs, click 'Genomics' (7) to proceed with the analyses:

Then, visualize or download the results:

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How can I analyse enrichments in ROIs using interactive heatmaps?

Option 1: With the example data
Load the example data (1), click 'ROI preparation' (2) and follow the steps.

Option 2: With your own data
Alternatively, if you have your own ROIs and enrichments, start from step 2 and select the ROI and enrichments that you want to analyze (step 4, 5):

Scroll down for further options:

Then, play interactively with the heatmap by changing the selected area. Visualize or download the results:

Scroll down for further output boxes:

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How can I analyse the correlation between reads enrichment in ROIs?

Option 1: With the example data
Load the example data (1), click 'Genomics' (2) and follow the steps.

Option 2: With your own data
Alternatively, if you have your own ROIs and enrichments, start from step 2 and select the ROI(s) and enrichments that you want to analyze (step 4, 5):

Output plots will appear on the right:

Scroll down for further output boxes. To view correlations, play interactively with the correlation heatmap by clicking the desired cells:

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How to find and extract genomic regions with particular enrichment patterns?

Option 1: With the example data
Load the example data (1), click 'Genomics' (2) and follow the steps.

Option 2: With your own data
Alternatively, if you have your own ROIs and enrichments, start from step 2 and select the ROI and enrichments that you want to analyze (step 4, 5):

Scroll down for further options:

Then, play interactively with the heatmap by clicking clusters for particular enrichment patterns. You can extract ROIs from a specific cluster:

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How can I analyse a genelist using metagenes?

Option 1: With the example data
Load the example data (1), click 'Genomics' (2) and follow the steps.

Option 2: With your own data
Alternatively, if you have your own gene lists and enrichments, start from step 2 and select the gene lists and enrichments that you want to analyze (step 4, 5):

Then, visualize the results and play with graphical options:

Scroll down for further output boxes:

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