32 lines
1.8 KiB
Markdown
32 lines
1.8 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/nanoporetech/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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### Reference-aided 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 approach (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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### 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. |