wf-transcriptomes-v202/docs/intro.md

Introduction

This workflow identifies RNA isoforms using either cDNA or direct RNA (dRNA) Oxford Nanopore reads.

Preprocesing

cDNA reads are initially preprocessed by pychopper for the identification of full-length reads, as well as trimming and orientation correction (This step is omitted for direct RNA reads).

Transcript assembly

Reference-aided transcript assembly approach

• Full length reads are mapped to a supplied reference genome using minimap2
• Transcripts are assembled by stringtie in long read mode (with or without a guide reference annotation) to generate the GFF annotation.
• The annotation generated by the pipeline is compared to the reference annotation. using gffcompare

de novo-based transcript assembly (experimental!)

• Sequence clusters are generated using isONclust2
  • If a reference genome is supplied, cluster quality metrics are determined by comparing
    with clusters generated from a minimap2 alignment.
• A consensus sequence for each cluster is generated using spoa
• Three rounds of polishing using racon and minimap2 to give a final polished CDS for each gene.
• Full-length reads are then mapped to these polished CDS.
• Transcripts are assembled by stringtie as for the reference-based approach.
• Note: This approach is currently not supported with direct RNA reads.

Fusion gene detection

Fusion gene detection is performed using JAFFA, with the JAFFAL extension for use with ONT long reads.

Workflow inputs

• Directory containing cDNA/direct RNA reads. Or a directory containing subdirectories each with reads from different samples (in fastq/fastq.gz format)
• Reference genome in fasta format (required for reference-based assembly).
• Optional reference annotation in GFF2/3 format.
• For fusion detection, JAFFAL reference files (see Quickstart)