Galaxy | Australia | Published Workflow | COVID-19: variation analysis on WGS SE data

COVID-19: variation analysis on WGS SE data

Step	Annotation
Step 1: Input dataset collection input select at runtime
Step 2: Input dataset NC_045512.2 FASTA sequence of SARS-CoV-2 select at runtime
Step 3: fastp Single-end or paired reads Single-end Input 1 Output dataset 'output' from step 1 Adapter Trimming Options: Disable adapter trimming False Adapter sequence for input 1 Empty. Global trimming options: Trim front for input 1 Not available. Trim tail for input 1 Not available. Overrepresented Sequence Analysis: Enable overrepresented analysis False Overrepresentation sampling Not available. Filter Options: Quality filtering options: Disable quality filtering False Qualified quality phred Not available. Unqualified percent limit Not available. N base limit Not available. Length filtering options: Disable length filtering False Length required Not available. Maximum length Not available. Low complexity filtering options: Enable low complexity filter False Complexity threshold Not available. Read Modification Options: PolyG tail trimming Automatic trimming for Illumina NextSeq/NovaSeq data PolyG minimum length Not available. PolyX tail trimming Disable polyX trimming UMI processing: Enable unique molecular identifer False UMI location Empty. UMI length Not available. UMI prefix Empty. Per read cutting by quality options: Cut by quality in front (5') False Cut by quality in tail (3') False Cutting window size Not available. Cutting mean quality Not available. Base correction by overlap analysis options: Enable base correction False Output Options: Output HTML report True Output JSON report True
Step 4: Bowtie2 Is this single or paired library Single-end FASTA/Q file Output dataset 'out1' from step 3 Write unaligned reads (in fastq format) to separate file(s) False Write aligned reads (in fastq format) to separate file(s) False Will you select a reference genome from your history or use a built-in index? Use a genome from the history and build index Select reference genome Output dataset 'output' from step 2 Set read groups information? Automatically assign ID using name of history item(s) Select analysis mode 1: Default setting only Do you want to use presets? Very sensitive end-to-end (--very-sensitive) Do you want to tweak SAM/BAM Options? No Save the bowtie2 mapping statistics to the history True
Step 5: MarkDuplicates Select SAM/BAM dataset or dataset collection Output dataset 'output' from step 4 Comments If true do not write duplicates to the output file instead of writing them with appropriate flags set True Assume the input file is already sorted True The scoring strategy for choosing the non-duplicate among candidates SUM_OF_BASE_QUALITIES Regular expression that can be used in unusual situations to parse non-standard read names in the incoming SAM/BAM dataset Empty. The maximum offset between two duplicte clusters in order to consider them optical duplicates 100 Barcode Tag Empty. Select validation stringency Lenient
Step 6: MultiQC Results Results 1 Which tool was used generate logs? fastp Output of fastp Output dataset 'report_json' from step 3 Results 2 Which tool was used generate logs? Bowtie 2 Output of Bowtie 2 Output dataset 'mapping_stats' from step 4 Results 3 Which tool was used generate logs? Picard Picard outputs Picard output 1 Type of Picard output? Markdups Picard output Output dataset 'metrics_file' from step 5 Report title Empty. Custom comment Empty. Use only flat plots (non-interactive images) False Output the multiQC log file? False
Step 7: Realign reads Reads to realign Output dataset 'outFile' from step 5 Choose the source for the reference genome History Reference Output dataset 'output' from step 2 Advanced options: Keep MC, MD, NM, and A flags? False How to handle base qualities of 2? Keep unchanged None 2
Step 8: Insert indel qualities Reads Output dataset 'realigned' from step 7 Indel calculation approach Dindel Choose the source for the reference genome History Reference Output dataset 'output' from step 2
Step 9: Call variants Input reads in BAM format Output dataset 'output' from step 8 Choose the source for the reference genome History Reference Output dataset 'output' from step 2 Call variants across Whole reference Types of variants to call SNVs and indels Variant calling parameters Configure settings Coverage: Minimal coverage 5 Coverage cap 1000000 Paired reads: Use reads from anomalously mapped pairs False Base-calling quality: Minimum baseQ 30 Minimum baseQ for alternate bases 30 Base quality to use for alternate bases Use original base qualities Base alignment quality: Consider base/indel alignment qualities during variant calling? Yes, and prefer existing alignment qualities encoded in input Use the following alignment quality scores Base and indel alignment qualities (BAQ and IDAQ) If BAQ needs to be computed, calculate extended BAQ? True Mapping quality: Minimum mapping quality 20 Consider mapping quality during variant calling? Yes, incorporate MAPQ into joint quality score Maximum mapping quality 255 Source quality: Compute source quality and consider it during variant calling No, don't incorporate source quality into joint quality score Joint quality: Minimum joinedQ 0 Minimum joinedQ for alternate bases 0 Overwrite joinedQs of alternate bases with this value 0 Variant filter parameters Custom filter settings/combinations Significance threshold for calls 0.0005 Bonferroni correction factor for multiple testing 0 Apply default coverage and strand-bias filter? False
Step 10: Lofreq filter List of variants to filter Output dataset 'variants' from step 9 Types of variants to keep SNVs and Indels Quality-based filter options: Filter SNVs based on call quality? No, don't apply call quality filter Filter indels based on call quality? No, don't apply call quality filter Coverage-based filter options: Minimum coverage 0 Maximum coverage 0 Allele frequency filter options: Minimum allele frequency 0.0 Maximum allele frequency 0.0 Strand bias filter options: Filter variants based on supporting strand bias? Yes, filter on multiple testing corrected strand-bias p-value (lofreq default) Multiple-testing corrected p-value threshold 0.001 Multiple testing correction method False-discovery rate Use compound strand-bias filter? True Apply to indels? False Action to be taken for variants that do not pass the filters defined above Keep variants, but indicate failed filters in output FILTER column
Step 11: SnpEff eff: Sequence changes (SNPs, MNPs, InDels) Output dataset 'outvcf' from step 10 Input format VCF Select an annotated Coronavirus genome NC_045512.2: COVID19 Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1 Output format VCF (only if input is VCF) Create CSV report, useful for downstream analysis (-csvStats) False Upstream / Downstream length No upstream / downstream intervals (0 bases) Annotation options Use 'EFF' field compatible with older versions (instead of 'ANN') Use Classic Effect names and amino acid variant annotations (NON_SYNONYMOUS_CODING vs missense_variant and G180R vs p.Gly180Arg/c.538G>C) Use custom interval file for annotation select at runtime Only use the transcripts in this file select at runtime Filter output Do not show DOWNSTREAM changes Do not show INTERGENIC changes Do not show UPSTREAM changes Do not show 5_PRIME_UTR or 3_PRIME_UTR changes Filter out specific Effects No Chromosomal position Use default (based on input type) Text to prepend to chromosome name Empty. Produce Summary Stats True Suppress reporting usage statistics to server True

About this Workflow

Author

nekrut

Related Workflows

All published workflows
Published workflows by nekrut

Rating

Community
(0 ratings, 0.0 average)

Tags

Community: