49 lines
2.9 KiB
Markdown
49 lines
2.9 KiB
Markdown
## Introduction
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This workflow identifies RNA isoforms using either cDNA or direct RNA (dRNA)
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Oxford Nanopore reads.
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### Preprocesing
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cDNA reads are initially preprocessed by [pychopper](https://github.com/epi2me-labs/pychopper)
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for the identification of full-length reads, as well as trimming and orientation correction (This step is omitted for
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direct RNA reads).
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### Transcript assembly
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#### Reference-aided transcript assembly approach
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* Full length reads are mapped to a supplied reference genome using [minimap2](https://github.com/lh3/minimap2)
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* Transcripts are assembled by [stringtie](http://ccb.jhu.edu/software/stringtie)
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in long read mode (with or without a guide reference annotation) to generate the GFF annotation.
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* The annotation generated by the pipeline is compared to the reference annotation.
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using [gffcompare](http://ccb.jhu.edu/software/stringtie/gffcompare.shtml)
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#### de novo-based transcript assembly (experimental!)
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* Sequence clusters are generated using [isONclust2](https://github.com/nanoporetech/isONclust2)
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* If a reference genome is supplied, cluster quality metrics are determined by comparing
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with clusters generated from a minimap2 alignment.
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* A consensus sequence for each cluster is generated using [spoa](https://github.com/rvaser/spoa)
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* Three rounds of polishing using racon and minimap2 to give a final polished CDS for each gene.
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* Full-length reads are then mapped to these polished CDS.
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* Transcripts are assembled by stringtie as for the reference-based approach.
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* __Note__: This approach is currently not supported with direct RNA reads.
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### Fusion gene detection
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Fusion gene detection is performed using [JAFFA](https://github.com/Oshlack/JAFFA), with the JAFFAL extension for use
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with ONT long reads.
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### Differential expression analysis
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* Differential expression is done using the transcripts output by the workflow.
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* A non redundant transcriptome is found using the merge function in [stringtie](http://ccb.jhu.edu/software/stringtie).
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* The reads are then aligned to the transcriptome using minimap2 in a splice-aware manner.
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* [salmon](https://github.com/COMBINE-lab/salmon) is used for transcript quantification.
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* R packages [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) and [stageR](https://bioconductor.org/packages/release/bioc/html/stageR.html) are used for differential expression analysis.
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* [DEXSeq](https://bioconductor.org/packages/release/bioc/html/DEXSeq.html) is then used for differential transcript usage analysis.
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### Workflow inputs
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- Directory containing cDNA/direct RNA reads. Or a directory containing subdirectories each with reads from different samples
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(in fastq/fastq.gz format)
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- Reference genome in fasta format (required for reference-based assembly).
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- Optional reference annotation in GFF2/3 format (required for differential expression analysis `--de_analysis`).
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- For fusion detection, JAFFAL reference files (see Quickstart)
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