Visualize eCLIP/STAMP metagene density to predict RNA binding protein function [Read the Doc] https://metadensity.readthedocs.io/en/latest/
The environment is available at DockerHub
git clone https://github.com/algaebrown/Metadensity.git
cd Metadensity
# build your own environment!
conda env create -n Metadensity --file environment.yaml
conda activate Metadensity
# copy genome coordinate
cd Metadensity
pip install -e .
Metadensity requires several annotations to work. You need to point those files in config/*.ini. see config/hg38.ini as an example.
These information are genome coordinate, species dependent. So you can keep each species with a seperate .ini.
| Description | Link to download | Essential to run | |
|---|---|---|---|
| GENOME_FA | fasta for the entire genome sequence | https://www.ncbi.nlm.nih.gov/genome/guide/human/ | YES |
| GENCODE | gff3 annotation of exon, intron, gene, transcripts etc | https://www.gencodegenes.org/human/ | YES |
| BRANCHPOINT | branchpoint annotation | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4315302/ or annotation folder |
NO |
| BRANCHPOINT_PRED | machine learning predicted branchpoints | https://pubmed.ncbi.nlm.nih.gov/29092009/ or annotation folder |
NO |
| POLYA | annotation of polyA sites and signals from polyASite | https://polyasite.unibas.ch/atlas | NO |
| MIRNA | annotation of microRNA | https://www.mirbase.org/ | NO |
| SNORNA | annotation of snoRNA | http://scottgroup.med.usherbrooke.ca/snoDB/ | NO |
| LNCRNA | annotation of lncRNA | https://lncipedia.org/ | NO |
| TRANSCRIPT | gff3 annotation from GENCODE, containing only "transcript" | use this script to generate from GENCODE and UCSC canonical transcript list or download them *nonpickle here | YES |
| FEATURE | gff3 annotation from GENCODE, containing only "exon", "CDS", "UTR" and created "introns" | use this script to generate from GENCODE and UCSC canonical transcript list; or download them *nonpickle | YES |
| DATADIR | parsed information of above, python .pickle file | https://www.dropbox.com/sh/hoya37n9pmuqd4l/AABBSpcpjFYIUFWMdIRuJtU4a?dl=0 |
if you are using some coordinate that we don't have precomputed "DATADIR", please check out this notebook on how to build one yourself.
python scripts/run_metadensity_vanilla.py -h
Options:
-h, --help show this help message and exit
-i CSV, --csv=CSV .csv file containing all CLIP files
-u UID, --uid=UID unique ID(uid) to the CLIP in the csv you want to run
on
-t TRANSCRIPT_LIST, --transcript_list=TRANSCRIPT_LIST
list of transcript ids to include in the calculation,
if not specify, use peak-containing transcripts
-o OUTDIR, --out=OUTDIR
output path (figures and arrays)
-s, --single end Whether your CLIP is single end. Affects Metatruncate
objects
-c CONFIG, --config=CONFIG
file path to the config file, genome coordinate
specific
--stat=STAT choose [mean,median]
--background_method=BG
how you want to compute IP to INPUT, choose [relative
information,substraction,None]
--normalization whether to average the signal in a transcript
--truncation Use truncation instead of the entire read
This will run the most vanilla functions of Metadensity
Here we provide some test data to run the script.
cd Metadensity
# download the data
wget https://www.dropbox.com/s/cgkeuqr0cjif558/test_data.tar.gz
# uncompress
tar -xvzf test_data.tar.gz
# modify paths is menifest.csv to correspond to your directory
nano test_data/menifest.csv
# run
cd scripts/
python run_metadensity_vanilla.py -i ../test_data/menifest.csv -u SF3B4_test -o ../test_data --config=../config/hg38.ini