Galaxy | Accessible Workflow | Sorghum variant calling

Sorghum variant calling

Annotation:

Step	Annotation
Step 1: Input dataset R1 select at runtime
Step 2: Input dataset R2 select at runtime
Step 3: Input dataset reference genome select at runtime
Step 4: Input dataset annotation select at runtime
Step 5: FastQC Short read data from your current history Output dataset 'output' from step 1 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: 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 7: Bowtie2 Is this single or paired library Paired-end FASTA/Q file #1 Output dataset 'output' from step 1 FASTA/Q file #2 Output dataset 'output' from step 2 Write unaligned reads (in fastq format) to separate file(s) False Write aligned reads (in fastq format) to separate file(s) False Do you want to set paired-end options? No 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 3 Set read groups information? Do not set Select analysis mode 2: Full parameter list Do you want to tweak input options? No Do you want to tweak alignment options? No Do you want to tweak scoring options? No Do you want to use -a or -k options No, do not set Do you want to tweak effort options? No Do you want to tweak Other Options? No Do you want to tweak SAM/BAM Options? No Save the bowtie2 mapping statistics to the history False Job Resource Parameters Use default job resource parameters
Step 8: SnpEff build: Name for the database Sorghumbicolor Input annotations are in GFF GFF dataset to build database from Output dataset 'output' from step 4 Choose the source for the reference genome History Genome in FASTA format Output dataset 'output' from step 3 Select genetic code for this sequence Standard
Step 9: SortSam Select SAM/BAM dataset or dataset collection Output dataset 'output' from step 7 Sort order Coordinate Select validation stringency Lenient
Step 10: FreeBayes Choose the source for the reference genome History Run in batch mode? Run individually BAM dataset Output dataset 'outFile' from step 9 Use the following dataset as the reference sequence Output dataset 'output' from step 3 Limit variant calling to a set of regions? Do not limit Read coverage Use defaults Choose parameter selection level 1. Simple diploid calling Job Resource Parameters Use default job resource parameters
Step 11: VCFfilter: VCF dataset to filter Output dataset 'output_vcf' from step 10 more filters more filters 1 Select the filter type Genotype filter (-g) Specify filtering value DP > 40 Filter entire records, not just alleles False Tag vcf records as positively filtered with this tag, print all records False Tag vcf records as negatively filtered with this tag, print all records False Append the existing filter tag, don't just replace it False Apply --tag-pass on a per-allele basis, adds or sets the corresponding INFO field tag False Inverts the filter, e.g. grep -v False Use logical OR instead of AND to combine filters False Specify a region on which to target the filtering Empty.
Step 12: VcfAllelicPrimitives: Select VCF dataset Output dataset 'out_file1' from step 11 Retain MNPs as separate events False Tag records which are split apart of a complex allele with this flag. Split primitives Do not manipulate records in which either the ALT or REF is longer than (bp) 200 Maintain site and allele-level annotations when decomposing True Maintain genotype-level annotations when decomposing True
Step 13: SnpEff eff: Sequence changes (SNPs, MNPs, InDels) Output dataset 'out_file1' from step 12 Input format VCF Output format VCF (only if input is VCF) Genome source Custom snpEff database in your history SnpEff4.3 Genome Data Output dataset 'snpeff_output' from step 8 Select genetic code for this sequence Standard Upstream / Downstream length 5000 bases Set size for splice sites (donor and acceptor) in bases 2 bases spliceRegion Settings Use Defaults Annotation options Nothing selected. Use custom interval file for annotation select at runtime Only use the transcripts in this file select at runtime Filter output Nothing selected. 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
Step 14: ANNOVAR Annotate VCF Variants Output dataset 'snpeff_output' from step 13 Gene Annotations refGene gencodeV19 Annotation Regions genomicSuperDups phastConsElements46way Annotation Databases 1000g2012apr_all avsift snp137NonFlagged esp6500si_all snp137 cosmic67 Output data type Tabular
Step 15: Group Select data Output dataset 'output' from step 14 Group by column 7 Ignore case while grouping? False Ignore lines beginning with these characters Nothing selected. Operations Operation 1 Type Count On column 7 Round result to nearest integer? NO Replace non numeric data Not available.
Step 16: Sort Sort Dataset Output dataset 'out_file1' from step 15 on column 2 with flavor Numerical sort everything in Descending order Column selections Number of header lines to skip 0

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ken.hsu

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