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

COVID-19: variation analysis on WGS PE data

Step	Annotation
Step 1: Input dataset collection Paired Collection (fastqsanger) 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 Paired Collection Select paired collection(s) Output dataset 'output' from step 1 Adapter Trimming Options: Disable adapter trimming False Adapter sequence for input 1 Empty. Adapter sequence for input 2 Empty. Global trimming options: Trim front for input 1 Not available. Trim tail for input 1 Not available. Trim front for input 2 Not available. Trim tail for input 2 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: Map with BWA-MEM Will you select a reference genome from your history or use a built-in index? Use a genome from history and build index Use the following dataset as the reference sequence Output dataset 'output' from step 2 Algorithm for constructing the BWT index Auto. Let BWA decide the best algorithm to use Single or Paired-end reads Paired Collection Select a paired collection Output dataset 'output_paired_coll' from step 3 Enter mean, standard deviation, max, and min for insert lengths. Empty. Set read groups information? Do not set Select analysis mode 1.Simple Illumina mode
Step 5: Samtools view SAM/BAM/CRAM data set Output dataset 'bam_output' from step 4 What would you like to look at? A filtered/subsampled selection of reads Configure filters: Filter by regions No Filter by readgroup No Filter by quality 20 Filter by library Empty. Filter by number of CIGAR bases consuming query sequence Not available. Require that these flags are set Read is paired Read is mapped in a proper pair Exclude reads with any of the following flags set Nothing selected. Exclude reads with all of the following flags set Nothing selected. Configure subsampling: Subsample alignment Specify a downsampling factor Downsampling factor 1.0 Seed for random number generator Not available. What would you like to have reported? All reads retained after filtering and subsampling Produce extra dataset with dropped reads? False Read Reformatting Options: Strip read tags from outputs Collapse backward CIGAR operation False Output format BAM (-b) Reference data No, see help (-output-fmt-option no_ref)
Step 6: MarkDuplicates Select SAM/BAM dataset or dataset collection Output dataset 'outputsam' from step 5 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 7: Samtools stats BAM file Output dataset 'outputsam' from step 5 Set coverage distribution No Exclude reads marked as duplicates False Output One single summary file Filter by SAM flags Do not filter Size of GC-depth bins Not available. Maximum insert size Not available. Minimum read length to generate statistics for Not available. Report only the main part of inserts Not available. BWA trim parameter Not available. Use a reference sequence No Filter by regions No Suppress absence of insertions False Remove overlaps of paired-end reads from coverage and base count computations False Only bases with coverage above this value will be included in the target percentage computation Not available.
Step 8: Realign reads Reads to realign Output dataset 'outFile' from step 6 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 9: 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? Samtools Samtools outputs Samtools output 1 Type of Samtools output? stats Samtools stats output Output dataset 'output' from step 7 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 6 Report title Empty. Custom comment Empty. Use only flat plots (non-interactive images) False Output the multiQC log file? False
Step 10: Insert indel qualities Reads Output dataset 'realigned' from step 8 Indel calculation approach Dindel Choose the source for the reference genome History Reference Output dataset 'output' from step 2
Step 11: Call variants Input reads in BAM format Output dataset 'output' from step 10 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 12: Lofreq filter List of variants to filter Output dataset 'variants' from step 11 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 13: SnpEff eff: Sequence changes (SNPs, MNPs, InDels) Output dataset 'outvcf' from step 12 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: