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

• Installation
• Usage
• Example data

Requirements

• Python 3.X
• Conda

Installation

Clone repository

First, download scIsoPrep from github and change to the directory:

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:

./install_scisoprep.sh

Type yes when asked to, this should take 15min.

Usage

Before each usage, you should source the scisoprep environment:

conda activate scIsoPrep

The scIsoPrep wrapper script run_scisoprep.py can be run with the following shell command:

./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.yaml file 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 example config.yaml file ready for adaptation, as well as a brief description of the relevant config blocks, is provided in the directory config/.

• 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 named SampleA_1.subreads.bam, etc.