Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: biorxiv.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (14.7%) to scientific vocabulary
Repository
Single-cell Iso Prep
Basic Info
- Host: GitHub
- Owner: cbg-ethz
- License: mit
- Language: Python
- Default Branch: master
- Size: 150 KB
Statistics
- Stars: 15
- Watchers: 5
- Forks: 1
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
scIsoPrep
A Snakemake pipeline for analyzing multiplexed single-cell PacBio concatenated long-reads, used on ovarian cancer data in our recent publication.
scIsoPrep offers the possibility to unconcatenate, trim, demultiplex large single-cell Pacbio multisample datasets using IsoSeq3. It can also collapse transcripts using cDNA_Cupcake and classify them using SQANTI3. scIsoPrep first collapses transcripts and filter them per cell, and then repeat this step on all cells together in order to create a common isoforms catalog, using reads attached to isoforms passing all filters in individual cells. This software is intended to be used on HPC.
Contents
Requirements
- Python 3.X
- Conda
Installation
Clone repository
First, download scIsoPrep from github and change to the directory:
bash
git clone https://github.com/cbg-ethz/scisoprep
cd scisoprep
Create conda environment
First, create a new conda environment and install all dependencies by running the following from your base conda environment:
bash
./install_scisoprep.sh
Type yes when asked to, this should take 15min.
Usage
Before each usage, you should source the scisoprep environment:
bash
conda activate scIsoPrep
The scIsoPrep wrapper script run_scisoprep.py can be run with the following shell command:
bash
./run_scIsoPrep
It should run for less than a day on HPC, and the output file AllInfo should be found in the results folder.
Before running the pipeline
config file
- input directory
Before running the pipeline, the
config/config.yamlfile needs to be adapted to contain the path to input bam files. It is provided in the first section (specific) of the config file. - resource information
In addition to the input path, further resource information must be provided in the section
specific. This information is primarily specifying the genomic reference used for the reads mapping and the transcriptomic reference required for isoform classification. An exampleconfig.yamlfile ready for adaptation, as well as a brief description of the relevant config blocks, is provided in the directoryconfig/.
- input directory
Before running the pipeline, the
reference files
- A genome fasta file (http://genome.ucsc.edu/cgi-bin/hgGateway?db=hg38)
- A GENCODE gene annotation gtf file (https://www.gencodegenes.org/human/)
sample map
- Provide a sample map file, i.e. a tab delimited text file listing all samples that should be analysed, and how many bam files are associated to it (see example below). ID will be used to name files and identify the sample throughout the pipeline.
- Sample map example:
sample files SampleA 2 SampleB 4 SampleC 2
input data
- This pipeline take as input either concatenated or unconcatenated reads PacBio CCS bam files. I you use concatenated reads input, files should be named
SampleA_1.bam,SampleA_2.bam,SampleB_1.bam, etc. (sample name should correspond to the sample map). If you use unconcatenated reads as input, files should be namedSampleA_1.subreads.bam, etc.
- This pipeline take as input either concatenated or unconcatenated reads PacBio CCS bam files. I you use concatenated reads input, files should be named
Owner
- Name: Computational Biology Group (CBG)
- Login: cbg-ethz
- Kind: organization
- Location: Basel, Switzerland
- Website: https://www.bsse.ethz.ch/cbg
- Twitter: cbg_ethz
- Repositories: 91
- Profile: https://github.com/cbg-ethz
Beerenwinkel Lab at ETH Zurich
GitHub Events
Total
- Watch event: 5
Last Year
- Watch event: 5
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| ArthurDondi | a****i@g****m | 29 |
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0