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https://github.com/ncrnalab/ribofy
ORF detection using RiboSeq data
https://github.com/ncrnalab/ribofy
Last synced: 2 months ago
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ORF detection using RiboSeq data
- Host: GitHub
- URL: https://github.com/ncrnalab/ribofy
- Owner: ncrnalab
- License: mit
- Created: 2021-06-24T11:16:06.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-08-19T07:12:04.000Z (about 3 years ago)
- Last Synced: 2024-06-30T15:45:08.093Z (3 months ago)
- Language: Python
- Size: 95.7 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-riboseq - Code - sites | (ORF Calling)
README
# ribofy: ORF detection using RiboSeq data
Ribofy is a fast and simple python-based tool for detection of phased p-sites across open-reading-frames (ORFs)
## Installation
* pip (soon)
```
pip install ribofy
```
* from source
```
git clone https://github.com/ncrnalab/ribofy.git
cd ribofy
python setup.py install
```
## Running ribofy
First, all ORFs are assembled from an annotation file (preferably [gencode](https://www.gencodegenes.org/) GTF) and the corresponding genome fasta (should not take more than 5-10 minutes). This is only required once per genome/annotation:
```
ribofy orfs --gtf --fa
```
The genome fasta-file must be indexed prior to ORF assembly:
```
samtools faidx
```
Currently, ribofy is compatible with STAR, kallisto and salmon mapped reads. Recommended mapping commands:
* STAR
```
STAR --genomeDir --outSAMtype BAM SortedByCoordinate
--readFilesIn --readFilesCommand zcat --outFileNamePrefix .
```
* salmon
```
salmon quant -i --gcBias --validateMappings {additional_params} --writeMappings= -o
```
* kallisto
```
kallisto quant -i --bias -o --single --pseudobam --fr-stranded -l 30 -s 2
```
Note that for kallisto and salmon, genome indexing should be performed with reduced k-mer value to allow mapping of <30nt ribosome-protected fragments.
Before running ribofy, bam-files should be sorted and indexed:
```
samtools sort >
samtools index
```
Then, run ribofy:
```
ribofy detect --orfs --bams --prefix
```
## Under the hood
1) Ribofy infers the p-site offsets for read-lengths between 25 and 35 (although this can be customized) and outputs the \.offset.txt
2) Then, for each ORF, ribofy counts the p-sites and evaluates the statistical enrichment of in-frame p-sites. This outputs the \.phasing.txt
3) Finally, Ribofy collects the individual ORFs into ORF-groups (collapsing overlapping and correlating ORFs), preserving only the highest expressed ORF (based on overall coverage), performs ORF-type specific FDR corrections and outputs the final \.results.txt
## Citation
*in preparation*
## Contact
Thomas Hansen ([email protected])