Command line tool for collecting and generating RNA-RBP interaction data in various formats.
pip install rnpfind
Requirements:
rnpfind can be used as a command line tool as follows:
rnpfind <transcript>
where transcript is a gene name such as "PTEN", or an hg38 coordinate range
(such as 11:65497688-65506516)
Note: If a transcript is specified through genomic coordinates, the transcript is assumed to be on the forward strand (the "+" strand). On the other hand, a transcript specified by name will have its strand positioning correctly identified.
The tool takes a transcript as input, and computes the binding sites of various RBPs on the transcript. The information is collected from data sources including RBPDB, ATTRACT, and POSTAR.
The tool produces as output the binding data in a folder (use --out-dir to
specify). A few output formats are supported (use --out-format to specify):
bed format: a widely used
format
for displaying intervals. A bed file created in this way could be visualized
on a genome browser, for example. Note the --trackhub option avaiable to
generate a trackhub structure (useful for hosting a large number of indexed
bed files (bigBed files) and allowing users to view on genome browsers like
the UCSC Genome Browser.
csv format: an NxN table (where N=number of RBPs) showing binding
correlations of RBPs on the particular transcript analyzed. This could be
useful for inferring molecular mechanisms on certain regions of the
transcriptome.
For more options, run rnpfind --help
You can import rnpfind to your Python code as follows:
from rnpfind import rnpfind
# Collect data on Malat1
rnpfind("malat1")
The data is written to disk like in the command line call.
Check help(rnpfind) for keyword arg options.
Perhaps not so usefully, you can find the genome version rnpfind is working
with programatically:
from rnpfind import GENOME_VERSION
print(GENOME_VERSION)
In principle, RNA-RBP interactions can be backed by two forms of evidence: experimental and computational.
The experimental binding sites are collected on large databases such as POSTAR. The computational binding sites are generated by scanning RNA-binding-motifs of various RBPs (collected from RBPDB and ATTRACT) across a transcript to look for hits.
As a result, the tool requires around 6.4GB to function. The data is
downloaded automatically on the first run of the tool, or can be downloaded
manually using rnpfind-download.
If the above memory footprint is too much for you to handle, consider using the web tool avaiable at https://rnpfind.com
Any suggestions / PR requests are welcome!