sars-cov-2-ont-artic-variant-calling/COVID-19-ARTIC-ONT
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Run on GalaxyCOVID-19: variation analysis on ARTIC ONT data
This workflow for ONT-sequenced ARTIC data is modeled after the alignment/variant-calling steps of the ARTIC pipeline. It performs, essentially, the same steps as that pipeline’s minion command, i.e. read mapping with minimap2 and variant calling with medaka. Like the Illumina ARTIC workflow it uses ivar for primer trimming. Since ONT-sequenced reads have a much higher error rate than Illumina-sequenced reads and are therefor plagued more by false-positive variant calls, this workflow does make no attempt to handle amplicons affected by potential primer-binding site mutations.
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| ARTIC primer BED | ARTIC primer BED | n/a |
|
| Maximum read length | Maximum read length | Discard reads that are longer than this length |
|
| Minimum read length | Minimum read length | Discard reads that are shorter than this length |
|
| NC_045512.2 FASTA sequence of SARS-CoV-2 | NC_045512.2 FASTA sequence of SARS-CoV-2 | Fasta sequence for Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome |
|
| ONT-sequenced reads | ONT-sequenced reads | ONT reads from ARTIC assay with fastqsanger encoding |
|
Steps
| ID | Name | Description |
|---|---|---|
| 5 | fastp | toolshed.g2.bx.psu.edu/repos/iuc/fastp/fastp/0.20.1+galaxy0 |
| 6 | Map with minimap2 | toolshed.g2.bx.psu.edu/repos/iuc/minimap2/minimap2/2.17+galaxy2 |
| 7 | Samtools view | toolshed.g2.bx.psu.edu/repos/iuc/samtools_view/samtools_view/1.9+galaxy2 |
| 8 | ivar trim | toolshed.g2.bx.psu.edu/repos/iuc/ivar_trim/ivar_trim/1.2.2+galaxy1 |
| 9 | Samtools stats | toolshed.g2.bx.psu.edu/repos/devteam/samtools_stats/samtools_stats/2.0.2+galaxy2 |
| 10 | BamLeftAlign | toolshed.g2.bx.psu.edu/repos/devteam/freebayes/bamleftalign/1.3.1 |
| 11 | medaka consensus tool | toolshed.g2.bx.psu.edu/repos/iuc/medaka_consensus/medaka_consensus/1.0.3+galaxy2 |
| 12 | QualiMap BamQC | toolshed.g2.bx.psu.edu/repos/iuc/qualimap_bamqc/qualimap_bamqc/2.2.2d+galaxy3 |
| 13 | medaka variant tool | toolshed.g2.bx.psu.edu/repos/iuc/medaka_variant/medaka_variant/1.0.3+galaxy3 |
| 14 | Flatten Collection | __FLATTEN__ |
| 15 | Lofreq filter | toolshed.g2.bx.psu.edu/repos/iuc/lofreq_filter/lofreq_filter/2.1.5+galaxy0 |
| 16 | MultiQC | toolshed.g2.bx.psu.edu/repos/iuc/multiqc/multiqc/1.8+galaxy1 |
| 17 | Replace | toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.3 |
| 18 | SnpEff eff covid19 version | toolshed.g2.bx.psu.edu/repos/iuc/snpeff_sars_cov_2/snpeff_sars_cov_2/4.5covid19 |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| length_filtered_reads | length_filtered_reads | n/a |
|
| fastp_report | fastp_report | n/a |
|
| minimap2_mapped_reads | minimap2_mapped_reads | n/a |
|
| filtered_mapped_reads | filtered_mapped_reads | n/a |
|
| primer_trimmed_filtered_mapped_reads | primer_trimmed_filtered_mapped_reads | n/a |
|
| mapped_reads_stats | mapped_reads_stats | n/a |
|
| realigned_primer_trimmed_filtered_mapped_reads | realigned_primer_trimmed_filtered_mapped_reads | n/a |
|
| medaka_consensus_data | medaka_consensus_data | n/a |
|
| bamqc_raw_output | bamqc_raw_output | n/a |
|
| bamqc_html_output | bamqc_html_output | n/a |
|
| medaka_variant_calls | medaka_variant_calls | n/a |
|
| bamqc_raw_output_flattened | bamqc_raw_output_flattened | n/a |
|
| soft_filtered_variants | soft_filtered_variants | n/a |
|
| preprocessing_and_mapping_reports | preprocessing_and_mapping_reports | n/a |
|
| soft_filtered_variants_fixed | soft_filtered_variants_fixed | n/a |
|
| annotated_variants | annotated_variants | n/a |
|
| annotated_variants_stats | annotated_variants_stats | n/a |
|
Version History
v0.3.1 (latest) Created 21st Dec 2021 at 03:01 by WorkflowHub Bot
Updated to v0.3.1
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v0.1 (earliest) Created 12th Mar 2021 at 13:41 by WorkflowHub Bot
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Views: 9092 Downloads: 1562 Runs: 8
Created: 12th Mar 2021 at 13:41
Last updated: 21st Dec 2021 at 03:01
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The COVID-19 pandemic is the first global health crisis to occur in the age of big genomic data. Although data generation capacity is well established and sufficiently standardized, analytical capacity is not. To establish analytical capacity it is necessary to pull together global computational resources and deliver the best open source tools and analysis workflows within a ready to use, universally accessible resource. Such a resource should not be controlled by a single research group, ...
