Galaxy: tabular patterns

Choose by the shape of the input and the result you need. These patterns treat table columns as values, even when a column contains coordinates or sequence text. For coordinate-aware operations use galaxy-interval-patterns, and for FASTA records use galaxy-sequence-patterns. Open the linked operation page for tool parameters and examples. iwc-tabular-operations-survey records the IWC evidence behind the choices.

Rows and columns in one table

• tabular-filter-by-column-value — use Filter1 for a Python predicate over column positions (c1, c2, …), such as a status comparison. Set its header handling for the input.
• tabular-filter-by-regex — use tp_grep_tool to keep or drop whole lines by regex, including comment lines. It does not distinguish columns or preserve a header independently of the match. The page covers Grep1 when header preservation is needed.
• tabular-cut-and-reorder-columns — use Cut1 to select existing columns and put them in output order. It does not calculate values.
• tabular-compute-new-column — use column_maker for a row-wise expression that inserts, replaces, or appends a column.

Combine or summarize tables

• tabular-join-on-key — align two tables by key with tp_easyjoin_tool. For many same-shaped key/value files, the page covers tp_multijoin_tool. Choose the missing-value fill to match downstream meaning.
• tabular-group-and-aggregate-with-datamash — reduce rows by one or more keys with datamash_ops, or reduce the whole file without keys. Check whether input order already groups the keys.
• tabular-sql-query — use query_tabular when the operation needs SQL semantics such as a window function, anti-join, named multi-table join, or a combined projection, computation, and filter. Use the simpler operations above for a single predicate, cut, or computed column.

Text-processing recipes

• tabular-prepend-header — add a fixed first line with awk, accounting for any existing header.
• tabular-synthesize-bed-from-3col — turn chromosome, start, and end columns into six-column BED, including the coordinate conversion and BED datatype.
• tabular-split-taxonomy-string — expand a semicolon-delimited lineage into rank columns, accounting for missing ranks.
• tabular-relabel-by-row-counter — generate labels from row order when that order is the intended identity source.

Between tables and collections

• tabular-to-collection-by-row — split a manifest or table by row/key into collection elements so the next tool can run over them. Choose a stable, unique identifier column.
• tabular-concatenate-collection-to-table — stack rows from a collection of tables into one table. Decide whether to retain element identifiers as row provenance and keep only one header.
• tabular-pivot-collection-to-wide — align a collection of two-column (id, value) tables by id into a wide table, with one value column per element. Choose what missing cells mean. For two ordinary tables, use tabular-join-on-key instead.

See also

• iwc-tabular-operations-survey — tabular-operation survey and evidence trail.
• galaxy-sequence-patterns — companion MOC for sequence-record operations; FASTA↔tabular is the dominant sequence seam.
• galaxy-interval-patterns — companion MOC for coordinate-feature operations.
• galaxy-collection-patterns — companion MOC for collection operations.