Galaxy | Australia | Published Workflow | COVID-19: variation analysis of ARTIC ONT data

COVID-19: variation analysis of ARTIC ONT data

Step	Annotation
Step 1: Input dataset ARTIC primer BED select at runtime
Step 2: Input dataset FASTA sequence of SARS-CoV-2 select at runtime
Step 3: Input dataset collection Collection of ONT-sequenced reads select at runtime
Step 4: Input parameter Minimum read length Not available.
Step 5: Input parameter Maximum read length Not available.
Step 6: fastp Single-end or paired reads Single-end Input 1 Output dataset 'output' from step 3 Adapter Trimming Options: Disable adapter trimming True 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 True 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 Disable polyG tail trimming 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 False
Step 7: Map with minimap2 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 Single or Paired-end reads Single Select fastq dataset Output dataset 'out1' from step 6 Select a profile of preset options Oxford Nanopore read to reference mapping. Slightly more sensitive for Oxford Nanopore to reference mapping (-k15). For PacBio reads, HPC minimizers consistently leads to faster performance and more sensitive results in comparison to normal minimizers. For Oxford Nanopore data, normal minimizers are better, though not much. The effectiveness of HPC is determined by the sequencing error mode. (map-ont) Indexing options: Use homopolymer-compressed k-mer ? False k-mer size Not available. minimizer window size Not available. split index for every N input gigabases Not available. Mapping options: retain at most INT secondary alignments Not available. Max fragment length for PE alignment Not available. filter out top FLOAT fraction of repetitive minimizers Not available. force minimap2 to always use k-mers occuring this many times or fewer Not available. stop chain enlongation if there are no minimizers in INT-bp Not available. bandwidth used in chaining and DP-based alignment Not available. minimal number of minimizers on a chain Not available. minimal chaining score (matching bases minus log gap penalty) Not available. Maximum seed skips during chaining Not available. Maximum number of partial chains checked during chaining Not available. skip self and dual mappings (for the all-vs-all mode) False min secondary-to-primary score ratio Not available. Alignment options: Customize spliced alignment mode? No, use profile setting or leave turned off Score for a sequence match Not available. Penalty for a mismatch Not available. Gap open penalties for deletions Not available. Gap open penalties for insertions Not available. Gap extension penalties; a gap of size k cost '-O + -Ek'. If two numbers are specified, the first is the penalty of extending a deletion and the second for extending an insertion Not available. Gap extension penalty for extending an insertion; if left empty uses the value specified for Gap extension penalties above Not available. Z-drop threshold for truncating an alignment Not available. Z-drop threshold for reverse-complementing the query Not available. minimal peak DP alignment score Not available. Filter seeds towards the ends of chains before performing base-level alignment? True Set advanced output options:* Select an output format BAM don't output base quality False write CIGAR with >65535 ops to the CG tag False minibatch size for mapping (in megabyte) Not available. Output cs tag? Nothing selected. Generate CIGAR False write =/X CIGAR operators False use soft clipping for supplementary alignments ? False
Step 8: Samtools view SAM/BAM/CRAM data set Output dataset 'alignment_output' from step 7 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 1 Filter by library Empty. Filter by number of CIGAR bases consuming query sequence Not available. Require that these flags are set Nothing selected. Exclude reads with any of the following flags set Read is unmapped Alignment of the read is not primary 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 9: ivar trim Bam file Output dataset 'outputsam' from step 8 Source of primer information History BED file with primer sequences and positions Output dataset 'output' from step 1 Minimum length of read to retain after trimming 1 Minimum quality threshold for sliding window to pass 0 Width of sliding window 4 Include reads with no primers False
Step 10: Samtools stats BAM file Output dataset 'outputsam' from step 8 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 11: BamLeftAlign Choose the source for the reference genome History BAM dataset to re-align Output dataset 'output_bam' from step 9 Using reference file Output dataset 'output' from step 2 Maximum number of iterations 5
Step 12: medaka consensus tool Select input alignment Output dataset 'output_bam' from step 11 Select model r941_min_high_g351 Set inference batch size 100 Specify regions to analyse via None Set chunk length of samples 800 Set overlap of chunks 400 Disable use of cuDNN model layers? False Verify integrity of output file after inference? False Save features with consensus? False Set read group Empty. Set tag name Empty. Set tag value Not available. Keep alignments when tag is missing? False Select output file(s) Result
Step 13: QualiMap BamQC Mapped reads input dataset Output dataset 'output_bam' from step 11 Reference genome regions to calculate mapping statistics for All (whole genome) Generate per-base coverage output False Skip duplicate reads Reads flagged as duplicates in input Settings affecting specific plots: Number of bins to use in across-reference plots 400 Draw chromosome limits True Plot expected GC-content distribution of the following reference genome Nothing selected. Homopolymer size 3
Step 14: medaka variant tool Are you pooling HDF5 datasets? No Select consensus file(s) Output dataset 'out_result' from step 12 Choose the source for the reference genome Use a genome from history Use the following dataset as the reference sequence Output dataset 'output' from step 2 Set reference names to limit variant calling Empty. Populate VCF info fields? False Output annotated VCF? Output annotated VCF BAM to annotate the VCF Output dataset 'output_bam' from step 11 Minimum base quality 0 Minimum mapping quality 0 Output log file? True
Step 15: Flatten collection Input Collection Output dataset 'raw_data' from step 13 Join collection identifiers using underscore ( _ )
Step 16: Lofreq filter List of variants to filter Output dataset 'out_annotated' from step 14 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 17: MultiQC Results Results 1 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 10 Results 2 Which tool was used generate logs? Qualimap (BamQC or RNASeq output) Output of Qualimap BamQC Output dataset 'output' from step 15 Report title Empty. Custom comment Empty. Use only flat plots (non-interactive images) False Output the multiQC log file? False
Step 18: Replace File to process Output dataset 'outvcf' from step 16 Find pattern #CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO.* Replace with #CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tSAMPLE Find-Pattern is a regular expression True Replace all occurences of the pattern False Case-Insensitive search False Find whole-words False Ignore first line False Find and Replace text in entire line
Step 19: SnpEff eff: Sequence changes (SNPs, MNPs, InDels) Output dataset 'outfile' from step 18 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 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

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nekrut

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