What it does
Performs integrated analyses of a set of peaks (e.g. ChIP data) against a list of "canonical genes" for a specific organism and genome build, identifying the nearest peaks to each canonical gene (and vice versa).
RnaChipIntegrator can be obtained from http://fls-bioinformatics-core.github.com/RnaChipIntegrator/
Input
The peak data must be in a tabular file with at least 3 columns of data for each peak (one peak per line):
| Column | Name | Description |
|---|---|---|
| 1 | chr | Chromosome name |
| 2 | start | Start position of the peak |
| 3 | end | End position of the peak |
Outputs
The key outputs from the tool are two lists compromising the nearest peaks for each gene, and the nearest gene for each peak (one dataset for each list).
There are two formats for reporting: "compact" and "full":
- Compact output reports all the hits for each peak or gene on a single line of output;
- Full output reports each peak/gene pair on a separate line (i.e. a multi-line output format).
In "full" output mode, additional options are available:
- The output files can be "padded" with extra (empty) lines to ensure that there are always the same number of lines for each peak or gene, if fewer than the requested number of hits are found.
- "Summary" datasets can also be requested, which include just the nearest peak reported for each gene (and vice versa).
In either mode these data will also be output in a single MS Excel file, which contains one sheet per result set.
Using "compact" output with the number of hits limited to more than 4 peak/gene pairs (or with no limit at all) can result in a large number of columns in the output files, which in some versions of Galaxy will not be properly displayed. However the data files themselves should be okay.
More information
It is recommended that you refer to the RnaChipIntegrator documentation for information on the contents of each output file:
Credits
This Galaxy tool has been developed within the Bioinformatics Core Facility at the University of Manchester. It runs the RnaChipIntegrator package which has also been developed by this group, and is documented at https://pypi.python.org/pypi/RnaChipIntegrator/
Please kindly acknowledge the Bioinformatics Core Facility if you use this tool.