Test datasets and walkthroughs

Files

Dataset licence: CC0 1.0. Tables and GFF3 in this pack use 1-based inclusive coordinates; bedGraph uses zero-based half-open coordinates. Reference names match across files. No assembly accession applies to these invented chromosomes.

1. Upload and plot numeric data

1. Open a fresh app tab. In Select Data, use one chromosome set and open Create from data files.
2. Upload chromosomes.tsv as chromosomes and numeric.tsv as annotations. Confirm two chromosomes and 200 annotations.
3. In Visualise Data, select Numeric, enable the heat map, and select a bar or scatter plot. Click Create chromoMap.
4. The numeric dataset contains 200 synthetic loci. The four anchor loci retain values geneA=2, geneB=4, geneC=6 and geneD=8. Across all records, the sum is 2046 and the mean is 10.23. These input statistics differ from unweighted averages of window summaries.
5. Search for “resolution”, open the Map resolution section, choose Fixed size, and set Window size to 100 bp. Create the map again. Multiple loci contribute to each window. With Map spanning annotations enabled and Aggregation set to avg, chr1:1–100 contains 13 loci, sum 91 and mean 7. Inspect the tooltip and exported window data. Full 100-bp expectations are provided in expected-results.json.
6. Search “margin”, then “top margin”. Confirm unrelated sibling controls are absent and Open closes the search window.
7. Export PNG, SVG, and window data. Check labels, colors, and values. Export HTML and its two JavaScript dependencies into one folder; open the HTML, then import it into the app. Confirm data and settings survive.

2. Categorical and comparative maps

1. Replace numeric annotations with categorical.tsv, select Categorical, and create the plot. Enable the built-in legend through Plot Properties → Per chromosome set → select the set → Legend → Show legend, then create the plot again. Verify kinase and TF entries and their colors. See legend configuration for the distinct built-in and custom workflows.
2. For a separate comparison, set the chromosome-set count to two. Load the chromosome table into both sets; use numeric.tsv for the first and numeric-second.tsv for the second.
3. Select the first set in Visualise Data and choose Numeric, then select the second set and choose Numeric. Confirm that switching sets retains each selection. Under chromoLinks, import links.tsv, enable Show links, and create the plot. Check geneA→geneA and geneB→geneB across sets.
4. Reorder the sets and create again. Check endpoints, data, and legends remain associated correctly. For a mixed-data test, replace the second set’s annotations with categorical.tsv, choose Categorical only for the second set, and retain Numeric for the first. The geneA/geneB links should still connect matching named loci. Enable each built-in legend separately and verify a numeric color scale for set 1 and category entries for set 2.

3. Gene structure and regional data

1. Under Select Data → Create gene-maps from annotations, select reference.gff3 and click Read genes.
2. Choose geneA, zero flank, and gene structure/transcripts. Run the analysis and render. geneA spans 101–310 on chr1; its exons are 101–150 and 251–310.
3. Repeat with geneD on chr2 and inspect the negative strand.
4. For geneA, choose structure plus regional data and upload signal.bedgraph. Its first two intervals correspond to 101–110 (value 2) and 301–310 (value 4) in the 1-based view.
5. For genome-wide reduction, use the larger reduction demonstration below with fixed 10,000-bp windows. Record the chosen summary operation, resulting values, and any discrepancy for investigation.

4. BAM coverage

1. Choose geneA from reference.gff3 and select the alignment/depth workflow.
2. Upload alignments.bam and alignments.bam.bai. Choose depth; run and render.
3. Covered bases are chr1:101–115. Depth is 1 at 101–105, 2 at 106–110, and 1 at 111–115.
4. Use a fine enough resolution to inspect these changes. A coarse summary is not expected to show individual-base depth.
5. Repeat without the BAI and confirm an explanatory error. Restore it and confirm the workflow recovers.

Mean over covered bases is 20/15; mean including all 1000 chr1 bases is 20/1000. These are different quantities. Record the app's actual denominator and summary settings.

5. Error recovery and persistence

1. After a valid numeric upload, try each invalid TSV as the annotation table.
2. Confirm that the status bar reports the current error without opening a dialog automatically. Select View Logs to inspect the timestamped error and previous activity. Verify that previously accepted input data remain intact and a new plot can still be created. Clear the log history after recording the result.
3. In View/edit, make a coordinate blank and click Apply changes. Confirm rejection; correct it and apply successfully.
4. Save a valid project, refresh, reload it, and compare the plot. Repeat in at least Chrome and Firefox; record Safari and Edge results if available.
5. Test without a local AI model. Basic upload, plotting, search, and export should work without downloading one.

Record your results

Copy this template for each workflow. Do not mark an untested step as passed.

Genome-scale reduction demonstration

Download reduction demo (~2.5 MB) · Instructions · Expected results

This invented three-chromosome genome contains 6,000 genes, 60,000 numeric intervals per sample, and two BAM samples with seeded variation in read counts, start positions and lengths. It demonstrates reduction without a large real-genome download.

1. Upload reduction-reference.gff3 in the large-scale omics section. Select chromosomes, fixed 10,000-bp windows, and gene density with Count. Expect 300 windows with variable gene counts and localized density peaks (fixed random seed 20261101).
2. For numeric summaries, use the same reference and both reduction-sample-*.bedgraph files with Average. Expect 300 windows, each summarizing 200 intervals per sample. Both samples have regional peaks and independent noise; their window means differ. Every window has an expected value in the JSON checkpoints.
3. For sequencing depth, use both reduction-sample-*.bam files and both matching .bam.bai files. Per-window nucleotide-depth means, sums and covered-base counts are listed in the JSON checkpoints. The workflow excludes zero-depth bases.
4. Inspect the window export against reduction-expected.json. This is a demonstration of numerical reduction, not a large-input speed benchmark.

The plain GFF3 needs no index. All inputs use the same invented reference. A real human-genome example can be supplied separately.