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Module I · Chord

1.1 Tables and field meanings1.2 Produce and import comparisons1.3 Build the layers step by step1.4 Direction, curvature and clearance1.5 Filter and summarize links1.6 Full source-file workflow1.7 Complete chord showcase

Module II · Collinear

2.1 Reuse chord input tables2.2 Switch coordinates and add layers2.3 Routes, clearance and the ruler2.4 Compare CDS neighbourhoods2.5 Focus, anchors and segments2.6 Complete collinear showcase

Module III · Plasmid

3.1 DNA, annotations and 13 references3.2 From a circle to annotation layers3.3 Features, primers and restriction3.4 Thirteen plasmid references3.5 Complete plasmid cases

Consult as needed · Data and layers

Prepare tables and coordinatesImport and convert dataImport nucleotide and protein alignmentsProject protein intervals to genomic coordinatesFeatures, stacking and genomic decorationLabels and local placementQuantitative tracksMappings, scales, guides and themesSet values by sequence and strandCommon adjustments and grammarExport, preview and diagnostics

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Validate ggchord input data before plotting

On this page

• Usage
• Arguments
• Value
• See also
• Examples

Performs structured validation of seq_data, ribbon_data and gene_data so that problems can be found, understood and fixed before plotting. The result is a "ggchord_validation" object with a valid flag, errors (severe problems that make the plot misleading), warnings (drawable but noteworthy issues), per-category counts, a data summary, the original row numbers of every problem, and a list of automatically fixable issues.

Usage

Code

validate_ggchord_data(
  seq_data,
  ribbon_data = NULL,
  gene_data = NULL,
  strict = FALSE,
  check_coordinates = TRUE,
  check_duplicates = TRUE,
  check_self_links = TRUE
)

Arguments

seq_data

data.frame/tibble, required. Basic sequence information (columns accver, length).

ribbon_data

data.frame/tibble, optional. Alignment results (columns qaccver, saccver, qstart, qend, sstart, send).

gene_data

data.frame/tibble, optional. Gene annotation data (columns accver, start, end, strand; anno is optional).

strict

Logical. When TRUE, collect issues and then stop if any severe problem is found. When FALSE (default), return the full diagnostic report without stopping.

check_coordinates

Logical, default TRUE. Whether to check that ribbon/gene coordinates stay inside [1, sequence length].

check_duplicates

Logical, default TRUE. Whether to look for fully duplicated, near-duplicated and highly overlapping records. Reciprocal overlap requires the intersection to cover at least 90 percent of each interval, including descending ribbon endpoints. Reversed feature bounds may cross a circular origin and are excluded from linear overlap diagnostics; exact duplicates are still reported.

check_self_links

Logical, default TRUE. Whether to flag alignment rows where qaccver == saccver.

Value

A "ggchord_validation" object (a list) with at least:

valid

Logical: TRUE when there are no severe errors.

errors

data.frame of severe issues (table, category, row, column, message).

warnings

data.frame of non-severe issues (same columns).

summary

Per-category counts (table, category, severity, n).

data_summary

Counts of sequences/ribbons/genes, unknown IDs, out-of-range rows, etc.

invalid_rows

Named list of original row numbers per problem category.

cleanable

data.frame of fixable issues with suggested actions.

print() and summary() methods are provided.

See also

ggchord_data_contract

Examples

Code

library(dplyr)
data(seq_data_example)
seqs <- seq_data_example |>
  select(accver, length)
data(ribbon_data_example)
links <- ribbon_data_example |>
  select(qaccver, saccver, qstart, qend, sstart, send)
data(gene_data_example)
genes <- gene_data_example |>
  transmute(accver, start, end, strand = source_strand)
result <- validate_ggchord_data(seqs, links, genes)
result$valid