Galaxy | Australia | Published Workflow | COVID-19: Alternate pre processing

COVID-19: Alternate pre processing

Annotation: 1a/6: This is the alternate read preprocessing workflow for use with the shared COVID-19: Raw Data starting history. (Which can be imported via "Shared Data" -> "Histories" in the menu)

Step	Annotation
Step 1: Input dataset collection Illumina Paired end collection select at runtime
Step 2: Input dataset collection ONT single end collection 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 20 Unqualified percent limit 20 N base limit Not available. Length filtering options: Disable length filtering False Length required 50 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: NanoPlot Select multifile mode batch Type of the file(s) to work on fastq files Output dataset 'output' from step 2 Options for filtering or transforming input prior to plotting: Drop reads longer than length specified. Not available. Drop reads shorter than length specified. Not available. Drop outlier reads with extreme long length. False Reduce dataset to N reads by random sampling. Not available. Logarithmic scaling of lengths in plots. True Use qualities as theoretical percent identities. False Use aligned read lengths rather than sequenced length (bam mode). False Drop reads with an average quality lower than specified. Not available. Which read type to extract information about from summary. Nothing selected. Use if you want to split the summary file by barcode. False Options for customizing the plots created: Specify a color for the plots. Nothing selected. Specify the output format of the plots. png Specify the bivariate format of the plots. Nothing selected. Hide the N50 mark in the read length histogram. False Show the N50 mark in the read length histogram. False
Step 5: FastQC Short read data from your current history Output dataset 'output' from step 2 Contaminant list select at runtime Adapter list select at runtime Submodule and Limit specifing file select at runtime Disable grouping of bases for reads >50bp False Lower limit on the length of the sequence to be shown in the report Not available. length of Kmer to look for 7
Step 6: Map with minimap2 Will you select a reference genome from your history or use a built-in index? Use a built-in genome index Using reference genome hg38 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. Single or Paired-end reads Single Select fastq dataset Output dataset 'output' from step 2 Select analysis mode (sets default) 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. Set advanced mapping options: 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. max intron length in thousand (effective with -xsplice; changing -r) Not available. max fragment length (effective with -xsr or in the fragment mode) 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. skip self and dual mappings (for the all-vs-all mode) False min secondary-to-primary score ratio Not available. retain at most INT secondary alignments Not available. Set advanced alignment options: 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 score Not available. minimal peak DP alignment score Not available. how to find GT-AG Nothing selected. 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. write =/X CIGAR operators False use soft clipping for supplementary alignments ? False
Step 7: MultiQC Results Results 1 Which tool was used generate logs? fastp Output of fastp Output dataset 'report_json' from step 3 Report title Empty. Custom comment Empty. Output the multiQC log file? False
Step 8: Map with BWA-MEM Will you select a reference genome from your history or use a built-in index? Use a built-in genome index Using reference genome hg38 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 9: MultiQC Results Results 1 Which tool was used generate logs? FastQC FastQC outputs FastQC output 1 Type of FastQC output? Raw data FastQC output Output dataset 'text_file' from step 5 Report title Empty. Custom comment Empty. Output the multiQC log file? False
Step 10: Filter SAM or BAM, output SAM or BAM SAM or BAM file to filter Output dataset 'alignment_output' from step 6 Header in output Include header Minimum MAPQ quality score Not available. Filter on bitwise flag yes Only output alignments with all of these flag bits set The read is unmapped Skip alignments with any of these flag bits set Nothing selected. Select alignments from Library Empty. Select alignments from Read Group Empty. Output alignments overlapping the regions in the BED file select at runtime Use inverse selection False Select regions (only used when the input is in BAM format) Empty. Select the output format BAM (-b)
Step 11: Filter SAM or BAM, output SAM or BAM SAM or BAM file to filter Output dataset 'bam_output' from step 8 Header in output Include header Minimum MAPQ quality score Not available. Filter on bitwise flag yes Only output alignments with all of these flag bits set The read is unmapped The mate is unmapped Skip alignments with any of these flag bits set Nothing selected. Select alignments from Library Empty. Select alignments from Read Group Empty. Output alignments overlapping the regions in the BED file select at runtime Use inverse selection False Select regions (only used when the input is in BAM format) Empty. Select the output format BAM (-b)
Step 12: MergeSamFiles Select SAM/BAM dataset or dataset collection Output dataset 'output1' from step 10 Merge the sequence dictionaries of the datasets being merged False Assume the input file is already sorted False Comments Select validation stringency Lenient
Step 13: MergeSamFiles Select SAM/BAM dataset or dataset collection Output dataset 'output1' from step 11 Merge the sequence dictionaries of the datasets being merged False Assume the input file is already sorted False Comments Select validation stringency Lenient
Step 14: Samtools fastx BAM or SAM file to convert Output dataset 'outFile' from step 12 Output format FASTQ Default quality if none is given Not available. Output quality in the OQ tag if available False add Illumina Casava 1.8 format entry to header (eg 1:N:0:ATCACG) False outputs unspecific Copy RG/BC/QT tags to output header False copy arbitrary tags to the FASTA header line Empty. Omit or append read numbers no Require that these flags are set Nothing selected. Exclude reads with any of the following flags set Nothing selected. Exclude reads with all of the following flags set Nothing selected. Write index read files No
Step 15: Samtools fastx BAM or SAM file to convert Output dataset 'outFile' from step 13 Output format FASTQ Default quality if none is given Not available. Output quality in the OQ tag if available False add Illumina Casava 1.8 format entry to header (eg 1:N:0:ATCACG) False outputs READ1 READ2 Copy RG/BC/QT tags to output header False copy arbitrary tags to the FASTA header line Empty. Omit or append read numbers no Require that these flags are set Nothing selected. Exclude reads with any of the following flags set Nothing selected. Exclude reads with all of the following flags set Nothing selected. Write index read files No

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COVID-19: Alternate pre processing

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