Step 4 — Expression Feeding

Joins the motif-hits table with your own expression data (RNA-seq, microarray, qPCR) so you can ask “do my motif-bearing genes actually respond?”.

Inputs

  • hits.csv from Step 2.

  • Expression CSV — first column = gene ID, remaining columns = samples / conditions (counts, TPM, log2FC — anything numeric).

Gene-ID Mapping Methods

Expression tables and annotation GFF3s rarely use the same ID space. Cis-GS offers three matching strategies:

  1. Method 1 — Column swap. Pick the expression column that already matches your annotation IDs (e.g. LOC112706767).

  2. Method 2 — Mapping CSV. Supply a two-column lookup (annotation_id, expression_id).

  3. Method 3 — GFF3 Dbxref expansion. Cis-GS parses every Dbxref= and locus_tag= from the GFF3 and tries each synonym against the expression IDs automatically.

Outputs

  • expression_matched.csv — hits joined with their expression values.

  • Per-motif direction-of-effect plot (boxplot of expression of motif-bearing vs motif-free genes).