This pipeline was created as part of the GOLD project.

Installation

Dependencies

• python >= 3.9
  • seaborn
  • pandas
  • matplotlib
• seqkit
• lima
• fastqc
• minimap2
• samtools
• bedtools

Install environment from conda

conda env create -n gold -f env.yaml

Usage

Clone the repository

git clone git@github.com:dridk/pacbio_rna_seq.git

Edit config.yaml

• FASTQ The Fastq file path generated by PacBio Sequencing
• BARCODE The Fasta file path describing barcodes used by lima for demultiplexing ( see example in repository )
• PRIMERS The Fasta file describing primers used for PacBio amplicon sequencing ( see example in repository )
• REFERENCE The fasta reference file used by minimap2 for alignement ( e.g: hg19.fa )

Run the pipeline

Put your_file.fastq generated by PacBio in the same folder than config.yaml and run the following command. You can edit how many threads you want to use with --cores option.

snakemake -Fp --cores 10 --configfile config.yaml 

Output

The pipeline will generate one file per barcode and amplicon. For instance HBB.bc1022.bam contains aligned reads from HBB amplicon and bc1022 barcode identifer.

• debarcoding.{barcode}--{barcode}.fastq : Demultiplexed reads
• {amplicon}.{barcode}.fastq : Transcripts reads
• {amplicon}.{barcode}.bam : Aligned transcripts Reads
• {amplicon}.{barcode}.bed : Transcripts structures as a bed file
• {amplicon}.{barcode}.hash.bed : Transcripts structures as a bed file with a unique ID to identify the transcript
• {amplicon}.{barcode}.hash.png : Distribution plot of transcripts
• cluster.{amplicon}.png : Transcripts abundance heatmap

For instance, the following heatmap shows transcript abundances for each barcode. Each transcript is identified by a hash number generated from the transcript structure bed file. This make possible to identify transcripts among differents samples.