map 18 links
Galaxy: collection patterns
Choose a Galaxy collection operation or map-over recipe from the current and required data shapes.
IWC-grounded workflow construction recipes.
Pattern maps are the entry points; operation and recipe pages are the concrete Galaxy authoring moves that Molds can reference and casts can carry.
- Total
- 54
- Maps
- 5
Pattern Maps
/ start here map 5 links
Galaxy: conditionals patterns
Choose a Galaxy when gate, routed output, fallback, or collection cleanup by what must change downstream.
map 8 links
Galaxy: genomic interval patterns
Use this MOC to choose corpus-grounded Galaxy genomic interval operations and recipes on coordinate features.
map 6 links
Galaxy: sequence patterns
Use this MOC to choose corpus-grounded Galaxy operations on sequence records (FASTA) — interconvert, reformat, merge, length, extract/mask by region.
map 14 links
Galaxy: tabular patterns
Use this MOC to choose corpus-grounded Galaxy tabular transformation patterns.
Browse By Topic
/ tag rollupsCollection
/ 11 implementation patterns draft operation
Collection: build list paired with Apply Rules
Use Apply Rules to promote identifier columns into a list:paired collection, with optional cleanup first.
draft operation
Collection: build named bundle
Use BUILD_LIST to assemble named outputs into a collection bundle for publishing or downstream fan-in.
draft operation
Collection: cleanup after map-over failure
Use FILTER_EMPTY or FILTER_FAILED after map-over when bad elements would break downstream collection steps.
draft operation
Collection: flatten after fan-out
Use FLATTEN to collapse nested collection outputs to a flat list once the outer axis no longer matters.
draft operation
Collection: harmonize by sortlist from identifiers
Use SORTLIST with sort_type:file to reorder one collection by another collection's identifiers.
draft operation
Collection: regex relabel via tabular
Derive collection element identifiers in a tabular mapping, then apply them with RELABEL_FROM_FILE.
draft recipe
Collection: relabel via rules and find/replace
Use Apply Rules, identifier extraction, find/replace, and relabeling for structural fan-out cleanup.
draft operation
Collection: split identifier via rules
Use Apply Rules regex columns to split one collection identifier into nested list identifiers.
draft operation
Collection: swap nesting with Apply Rules
Use Apply Rules to regroup a list:list collection by swapping outer and inner identifier columns.
draft operation
Collection: sync collections by identifier
Use collection_element_identifiers with FILTER_FROM_FILE or RELABEL_FROM_FILE to align sibling collections.
draft operation
Collection: unbox singleton
Use __EXTRACT_DATASET__ with which: first when a one-element collection must become a dataset.
Conditional
/ 4 implementation patterns draft recipe
Conditional: gate on non-empty result
Derive a boolean from empty or non-empty data, then use when to skip reporting or export steps.
draft operation
Conditional: route between alternative outputs
Use when-gated alternatives plus pick_value to merge binary or one-of-N routes into one downstream value.
draft operation
Conditional: run optional step
Use a workflow boolean connected as inputs.when to skip an optional Galaxy step or branch.
draft operation
Conditional: transform or pass through
Gate an optional transform, then use pick_value to pass transformed data when present or original data otherwise.
General
/ 4 implementation patterns draft recipe
Cleanup, sync, and publish non-empty results
Clean sparse mapped outputs, keep sibling collections aligned, then gate report publishing on non-empty results.
draft recipe
Fan-in bundle, consume, and flatten
Bundle parallel outputs into a collection consumer, then flatten nested results for pooled downstream processing.
draft recipe
Manifest to mapped collection lifecycle
Use a manifest or table to build a collection, map a tool per row, then relabel or reshape outputs.
draft recipe
Reshape, relabel, and remap by collection axis
Use Apply Rules and deterministic relabeling when domain fan-out creates the wrong map-over axis.
Interval
/ 7 implementation patterns draft recipe
Interval: build a mask by set algebra
Compute regions from regions: concatenate candidate intervals, merge into non-overlapping spans, then subtract the set to keep. The gops_* set-algebra recipe.
draft operation
Interval: compute coverage
Two coverage modes: genome-wide depth as a bedgraph (genomecoveragebed) and reads counted in given regions (coveragebed). Same family, different question.
draft recipe
Interval: consensus regions by multi-intersect
Find features reproducible across replicates: multi-intersect per-replicate sets, threshold by replicate count, then intersect back against the merged call.
draft operation
Interval: filter or annotate by overlap
Keep, drop, or annotate coordinate features by overlap with a second feature set; bedtools intersect (BED) or vcfvcfintersect (VCF), mapped over a collection.
draft operation
Interval: merge overlapping features
Collapse overlapping or book-ended intervals within one set into single spans; bedtools mergebed or the gops_merge Operate-on-Genomic-Intervals tool.
draft operation
Interval: window or flank features
Extend features by a fixed or fractional amount to build neighborhood windows, clamped to chromosome ends; bedtools slopbed with a genome file.
draft recipe
Interval: windowed coverage around features
Quantify signal in fixed neighborhoods around point features: window the features (slop), collapse overlaps (merge), then count reads in each window (coverage).
Parameter
/ 3 implementation patterns draft operation
Parameter: compose runtime text parameter
Use compose_text_param to build connected text expressions from constants plus runtime scalar values.
draft operation
Parameter: derive from file
Read a one-value dataset with param_value_from_file, including count recipes that feed typed parameters.
draft operation
Parameter: map workflow enum to tool parameter
Use map_param_value to translate workflow enum values into downstream tool codes, flags, or snippets.
Sequence
/ 6 implementation patterns draft operation
Sequence: compute record lengths
Emit a (id, length) table from a FASTA so downstream tabular steps can filter, sort, or threshold records by length; fasta_compute_length.
draft operation
Sequence: extract or mask by region
Turn coordinates into sequence: extract FASTA at BED intervals (getfasta), mask regions by BED (maskfasta), or extract transcripts from a GFF (gffread).
draft operation
Sequence: interconvert FASTA and tabular
Move sequence records between FASTA and a (header, sequence) table so tabular tools can edit them; fasta2tab one way, tab2fasta back.
draft operation
Sequence: merge FASTA and filter unique
Concatenate several FASTA files into one and drop duplicate records by sequence identity in a single step; fasta_merge_files_and_filter_unique_sequences.
draft operation
Sequence: reformat FASTA line width
Rewrap FASTA records to a fixed sequence-line width so downstream tools and viewers get canonical 60/70/80-column output; cshl_fasta_formatter.
draft recipe
Sequence: relabel FASTA headers via tabular
Edit FASTA headers you cannot easily regex in place: fasta2tab, rewrite column 1 with find/replace, then tab2fasta back. The high-value sequence recipe.
Tabular
/ 14 implementation patterns draft operation
Tabular: compute a new column
Append, insert, or replace computed columns with column_maker (Add_a_column1), using a shared input-typing policy and explicit failure handling.
draft operation
Tabular: concatenate collection to table
Stack compatible tabular collection elements into one dataset, with one header and a sample column when needed.
draft operation
Tabular: cut and reorder columns
Use Cut1 with a comma-separated cN list to project — and reorder — columns. Listing out of order is the canonical reorder idiom.
draft operation
Tabular: filter rows by column value
Use Filter1 with a Python expression over cN columns to drop rows. Highest-frequency tabular row filter in IWC.
draft operation
Tabular: filter rows by regex
Use tp_grep_tool for whole-line regex row filters on tabular input. Grep1 is the legacy alternative.
draft operation
Tabular: group and aggregate
Group tabular rows by one-based key columns, sort when needed, and choose Datamash operations and header settings from the intended result.
draft operation
Tabular: join on key
Join tabular files by key with explicit matched-row, missing-value, header, and duplicate-key policies.
draft operation
Tabular: pivot collection to wide
Join a collection of keyed tabular datasets into a wide table, choosing missing-cell fill and output headers to fit the data.
draft operation
Tabular: prepend header
Add a fixed first line with tp_awk_tool, choosing whether to keep or replace the input's first row and whether to rewrite its fields.
draft operation
Tabular: relabel by row counter
Use tp_awk_tool and awk NR to replace each input line with a row-order label, with explicit header and numbering choices.
draft operation
Tabular: split into collection by key
Split a tabular file into a list collection by column value, grouping rows that share an identifier.
draft operation
Tabular: split taxonomy string
Expand a prefixed taxonomy lineage into fixed rank columns with tp_awk_tool, preserving row identity and handling absent ranks.
draft operation
Tabular: SQL query
Load tabular datasets into Query Tabular for SQL joins, window calculations, and queries that combine several tabular operations.
draft operation
Tabular: synthesize BED from 3-column input
Convert one-based inclusive chromosome/start/end rows to six-column BED with awk, after checking the source coordinate convention.