Galaxy | Europe | Published Workflow | Pre-processing with Alevin - Part 1

Pre-processing with Alevin - Part 1

Step	Annotation
Step 1: Input dataset Mus_musculus.GRCm38.100.gtf.gz select at runtime
Step 2: Input dataset Read 2 - containing transcript select at runtime
Step 3: Input dataset Mus_musculus.GRCm38.cdna.all.fa select at runtime
Step 4: Input dataset Read 1 - containing cell barcode & UMI select at runtime
Step 5: GTF2GeneList Ensembl GTF file Output dataset 'output' from step 1 Feature type for which to derive annotation gene Field to place first in output table gene_id Suppress header line in output? True Comma-separated list of field names to extract from the GTF (default: use all fields) gene_id,gene_name,mito Append version to transcript identifiers? True Flag mitochondrial features? True Comma-separated list of possible mitochondrial chromosome names (case insensitive) mt,mitochondrion_genome,mito,m,chrM,chrMt Comma-separated list of possible mitochondrial biotypes (case insensitive) mt_trna,mt_rrna,mt_trna_pseudogene Filter a FASTA-format cDNA file to match annotations? False
Step 6: GTF2GeneList Ensembl GTF file Output dataset 'output' from step 1 Feature type for which to derive annotation transcript Field to place first in output table transcript_id Suppress header line in output? True Comma-separated list of field names to extract from the GTF (default: use all fields) transcript_id,gene_id Append version to transcript identifiers? True Flag mitochondrial features? False Filter a FASTA-format cDNA file to match annotations? True FASTA-format cDNA/ transcript file Output dataset 'output' from step 3 Annotation field to match with sequences. transcript_id
Step 7: Alevin Select a reference transcriptome from your history or use a built-in index? Use one from the history Salmon index: Transcripts fasta file Output dataset 'fasta_output' from step 6 Kmer length 31 Perfect Hash False Single or paired-end reads? Paired-end file1 Output dataset 'output' from step 4 file2 Output dataset 'output' from step 2 Relative orientation of reads within a pair Mates are oriented toward each other (I = inward) Specify the strandedness of the reads read comes from the reverse strand (SR) protocol DropSeq Single Cell protocol Transcript to gene map file Output dataset 'feature_annotation' from step 6 Retrieve all output files True Optional commands: Whitelist file select at runtime noDedup False mrna select at runtime rrna select at runtime dumpBfh False dumpFeatures True dumpUmiGraph False dumpMtx True forceCells Not available. expectCells Not available. numCellBootstraps Not available. minScoreFraction Not available. keepCBFraction 1.0 lowRegionMinNumBarcodes Not available. maxNumBarcodes Not available. freqThreshold 3
Step 8: Alevin Select a reference transcriptome from your history or use a built-in index? Use one from the history Salmon index: Transcripts fasta file Output dataset 'fasta_output' from step 6 Kmer length 31 Perfect Hash False Single or paired-end reads? Paired-end file1 Output dataset 'output' from step 4 file2 Output dataset 'output' from step 2 Relative orientation of reads within a pair Mates are oriented toward each other (I = inward) Specify the strandedness of the reads read comes from the reverse strand (SR) protocol DropSeq Single Cell protocol Transcript to gene map file Output dataset 'feature_annotation' from step 6 Retrieve all output files True Optional commands: Whitelist file select at runtime noDedup False mrna select at runtime rrna select at runtime dumpBfh False dumpFeatures True dumpUmiGraph False dumpMtx True forceCells Not available. expectCells Not available. numCellBootstraps Not available. minScoreFraction Not available. keepCBFraction Not available. lowRegionMinNumBarcodes Not available. maxNumBarcodes Not available. freqThreshold Not available.
Step 9: salmonKallistoMtxTo10x .mtx-format matrix Output dataset 'quants_mat.mtx' from step 7 Tab-delimited genes file Output dataset 'quants_mat_cols.txt' from step 7 Tab-delimited barcodes file Output dataset 'quants_mat_rows.txt' from step 7 Prefix to prepend to cell names / barcodes Empty.
Step 10: Droplet barcode rank plot Input MTX-format matrix? False A two-column tab-delimited file, with barcodes in the first column and frequencies in the second Output dataset 'raw_cb_frequency.txt' from step 8 Label to place in plot title Barcode rank plot (raw barcode frequencies) Number of bins used in barcode count frequency distribution 50 Above-baseline multiplier to calculate roryk threshold 1.5
Step 11: Droplet barcode rank plot Input MTX-format matrix? True Matrix-market format matrix file, with cells by column (overrides --barcode-frequencies if supplied) Output dataset 'quants_mat.mtx' from step 8 For use with --mtx-matrix: force interpretation of matrix to assume cells are by row, rather than by column (default) True Label to place in plot title Barcode rank plot (Alevin-processed) Number of bins used in barcode count frequency distribution 50 Above-baseline multiplier to calculate roryk threshold 1.5
Step 12: Join two Datasets Join Output dataset 'genes_out' from step 9 using column 1 with Output dataset 'feature_annotation' from step 5 and column 1 Keep lines of first input that do not join with second input Yes Keep lines of first input that are incomplete Yes Fill empty columns No Keep the header lines No
Step 13: Cut Cut columns c1,c4,c5 Delimited by Tab From Output dataset 'out_file1' from step 12
Step 14: DropletUtils Read10x Expression matrix in sparse matrix format (.mtx) Output dataset 'matrix_out' from step 9 Gene table Output dataset 'out_file1' from step 13 Barcode/cell table Output dataset 'barcodes_out' from step 9 Should metadata file be added? False
Step 15: DropletUtils emptyDrops SingleCellExperiment rdata object Output dataset 'output_rds' from step 14 UMI count lower bound 5 Number of iterations 1000 FDR filter for removal of barcodes with no cells 0.01 Should results be returned for barcodes with totals less than or equal to the value of 'lower'? False Ignore barcodes threshold Not available. Retain barcodes threshold Not available. Should barcodes estimated to have no cells be removed from the output object? True
Step 16: DropletUtils emptyDrops SingleCellExperiment rdata object Output dataset 'output_rds' from step 14 UMI count lower bound 100 Number of iterations 1000 FDR filter for removal of barcodes with no cells 0.01 Should results be returned for barcodes with totals less than or equal to the value of 'lower'? False Ignore barcodes threshold Not available. Retain barcodes threshold Not available. Should barcodes estimated to have no cells be removed from the output object? True
Step 17: SCEasy convert Direction of conversion SingleCellExperiment to AnnData Input object in SingleCellExperiment RDS format Output dataset 'output_rdata' from step 15 Name of the assay to be transferred as main layer counts

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wendi.bacon.training

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