72 lines
2.7 KiB
Markdown
72 lines
2.7 KiB
Markdown
## Quickstart
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The workflow uses [nextflow](https://www.nextflow.io/) to manage compute and
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software resources, as such nextflow will need to be installed before attempting
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to run the workflow.
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The workflow can currently be run using either
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[Docker](https://www.docker.com/products/docker-desktop),
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[Singularity](https://sylabs.io/singularity/) or
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[conda](https://docs.conda.io/en/latest/miniconda.html) to provide isolation of
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the required software. Each method is automated out-of-the-box provided
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either docker, singularity or conda is installed.
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It is not required to clone or download the git repository in order to run the workflow.
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For more information on running EPI2ME Labs workflows [visit out website](https://labs.epi2me.io/wfindex).
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**Workflow options**
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To obtain the workflow, having installed `nextflow`, users can run:
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```
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nextflow run epi2me-labs/wf-isoforms --help
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```
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to see the options for the workflow.
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**Example execution of a workflow for reference-based transcript assembly**
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This example uses a synthetic SIRV dataset, so we need to tell minimap2 about the non-canonical splice junctions with
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--minimap2_opts '-uf --splice-flank=no'
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```
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OUTPUT=~/output;
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nextflow run wf-isoforms/ --fastq test_data/fastq --ref_genome test_data/SIRV_150601a.fasta --ref_annotation test_data/SIRV_isofroms.gtf
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--minimap2_opts '-uf --splice-flank=no' --out_dir outdir -w workspace_dir -profile conda -resume
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```
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```
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# To evaluate the workflow on a larger Drosophila dataset
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./evaluation/run_evaluation_dmel.sh outdir
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```
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**Example workflow for denovo transcript assembly**
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```
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OUTPUT=~/output
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nextflow run . --fastq test_data/fastq --denovo --ref_genome test_data/SIRV_150601a.fasta -profile local --out_dir ${OUTPUT} -w ${OUTPUT}/workspace \
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--sample sample_id -resume
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```
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A full list of options can be seen in nextflow_schema.json. Below are some commonly used ones.
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- Threshold for including isoforms into interactive table `transcript_table_cov_thresh = 50`
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- Run the denovo pipeline `denovo = true` (default false)
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- To run the workflow with direct RNA reads `--direct_rna` (skips the pychopper step).
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Pychopper and minimap2 can take options via `minimap2_opts` and `pychopper_opts`, for example:
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- When using the SIRV synthetic test data
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- `minimap2_opts = '-uf --splice-flank=no'`
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- pychopper needs to know which cDNA synthesis kit used
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- SQK-PCS109: use `pychopper_opts = '-k PCS109'` (default)
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- SQK-PCS110: use `pychopper_opts = '-k PCS110'`
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- pychopper can use one of two available backends for identifying primers in the raw reads
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- nhmmscan `pychopper opts = '-m phmm'`
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- edlib `pychopper opts = '-m edlib'`
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__Note__: edlib is set by default in the config as it's quite a lot faster. However it may be less sensitive than nhmmscan.
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