https://github.com/ulelab/cv_coverage

https://github.com/ulelab/cv_coverage

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/ulelab/cv_coverage
• Owner: ulelab
• License: gpl-3.0
• Created: 2021-07-01T07:47:33.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2023-09-28T12:09:21.000Z (over 2 years ago)
• Last Synced: 2025-09-05T04:11:42.410Z (10 months ago)
• Language: Python
• Size: 84 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

          
## cv_coverage

[![DOI](https://zenodo.org/badge/381951979.svg)](https://zenodo.org/badge/latestdoi/381951979)

This code compares the coverages of a k-mer group around crosslink events (landmarks) across multiple CLIP datasets. The comparison is conducted within user-specified genomic regions. 

Author: aram.amalietti@gmail.com

**Dependencies** (these are the versions the script was developed with, pandas >= 1 introduced breaking changes, please use these versions):

```

python=3.7  

pandas=0.24.2  

numpy=1.19.2  

pybedtools=0.8.1  

matplotlib=3.3.2

seaborn=0.11.0

```

**Usage**:  

  ```

  python3               

  ```

  - **`xl_in`** *list of BED files containing landmarks around which to display the motifs, given as* `path_to_file1,path_to_file2,path_to_file3 ...`. *It can be a single file or a list of files, the plots will be adjusted accordingly;*  

  - **`motifs`** *group of motifs to be displayed around the input landmarks, for example `AAAA,CCCC,GGGG ...`;*  

  - **`regions`** *group of regions to be considered for analysis. Available regions are `genome` (intron, CDS, UTR3, UTR5, ncRNA, intergenic), `whole_gene` (intron, CDS, UTR3, UTR5), `intergenic`, `intron`, `ncRNA`, `other_exon` (UTR5, CDS), `UTR3` and `UTR5`. Single or multiple regions can be analysed and the plots will adjust accordingly. For example, `intron,ncRNA`;*  

  - **`kmer_len`** *the length of analysed motifs (in bases);*  

  - **`fasta`** *is the path to the genome in fasta format;*  

  - **`fai`** *is the path to the genome index file;*  

  - **`regions_file`** *is a segmentation file in GTF format, usually obtained with iCount segment but can also come from other sources;*  

  - **`smoothing`** *is the size of the smoothing window, usually 12;*  

  - **`percentile`** *defines the percentile for thresholding genomic landmarks by score, if percentile is set to None no thresholding is used and all landmarks will be used for the analisys;*  

  - **`window`** *flanking distance around the landmarks that is analysed and displayed;*  

  - **`use_scores`** *If True, each landmark is weighted by its score, else all landmarks will have equal weigths of 1;*  

  - **`n_cores`** *is the number of threads used in the process (4 is the usual value);*   

  - **`chunk_size`** *is the number of rows per thread (10000 is the usual value);*  

  - **`cap`** *is the max value for any landmarks score. It is only used if use_scores is set to True (20 is the recommended value);*

 **Common issues**

 The script needs writing permission in the staging directory to make `results` directory and environment variable `TMPDIR` for temporary files. If you get `KeyError: 'TMPDIR'` a solution would be to type `export TMPDIR=` in terminal where you want to run the script.

 

 **Outputs**

 - A pdf file with graphs showing % k-mer group coverage around landmarks for each analyzed CLIP dataset;

 - A tsv file with % coverage values (raw values used for plotting);

 - A text file that saves the number of analyzed landmarks in each sample;

 - A tsv file with saved run parameters.