bedqc

A tool to validate and profile genomic intervals in BED format.

Secure processing

Your files are processed by your browser on your local machine using javascript and tools from the biowasm project. No data is transferred from your local machine.

BED validation

Your file is validated against BEDv1 file specifications. File delimiter, newline character, 0-length intervals, and other common issues are reported.

Try an example

We have prepared two examples to discover bedqc features

Analyzing your BED file

Check interval density by chromosome

You can look at the interval density or count by chromosome compared to what would be expected based on the relative length of each chromosome. For example, segmental duplications have a much higher density than expected on chr16 in the human genome.

Find truncated files

Use interval counts plot to diagnose truncated or incomplete files based on the expected chromosomes of a genome build.

More complex analyses

For instance, since most annotations will not overlap gaps in the same genome build, you can use this tool to help determine your file's genome build comparing to gaps from various builds to find which has the fewest intersecting hits.

Normal

Based on the known chromosome length and total interval count, the intervals are spread evenly among the chromosomes.

Potential issue

Unequal distribution among chromosomes could mean a filter has been applied unevenly or possible file truncation.

Normal

Based on the known chromosome length and total interval count, the intervals are spread evenly among the chromosomes.

Potential issue

Unequal distribution among chromosomes could mean a filter has been applied unevenly or possible file truncation.

Intersecting a BED with known intervals can help us determine the genome by way of elimination (using gaps). It can also instruct us whether or not our intervals hit genes, exons, or introns, which can help sanity check intervals by genome build or per your science question. For instance, if you assume your intervals are exonic then one would expect zero hits with Ensembl introns.

Normal

Our intervals, in the case of intersecting with Gaps, do not overlap structures like telomeres and centromeres of the selected genome build, suggesting the selected genome build is not incorrect.

Potential issue

If we intersect our intervals with Gaps and observe many overlaps, our intervals may not be from the selected genome build as we usually anticipate little overlap with centromeres.

Interpreting interval density plots

Normal

Potential issue

Interpreting interval count plots

Normal

Potential issue

Interpreting intersection plots

Normal

Potential issue