Mercurial > repos > iuc > bigwig_outlier_bed
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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/bigwig_outlier_bed commit 3cce4c76a60b9353298fdcf759e893b8fcdfaa77
| author | iuc |
|---|---|
| date | Thu, 25 Jul 2024 14:38:34 +0000 |
| parents | ebcd48f183b3 |
| children |
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## bigwig peak bed maker ### July 30 2024 for the VGP This is a Galaxy tool, for building some of the [NIH MARBL T2T assembly polishing](https://github.com/marbl/training) tools as Galaxy workflows. JBrowse2 now includes a plugin for optional colours to distinguish bed features, shown being tested in the screenshots below. ### Find and mark BigWig peaks to a bed file for display In the spirit of DeepTools, but finding contiguous regions where the bigwig value is either above or below a given centile. 0.99 and 0.01 for example. These quantile cut point values are found and applied over each chromosome using some [cunning numpy code](http://gregoryzynda.com/python/numpy/contiguous/interval/2019/11/29/contiguous-regions.html)   Big differences between chromosomes 14,15,21,22 and Y in this "all contigs" view - explanations welcomed:  [pybedtools](https://github.com/jackh726/bigtools) is used for the bigwig interface. Optionally allow multiple bigwigs to be processed into a single bed - the bed features have the bigwig name in the label for viewing. ### Note on quantiles per chromosome rather than quantiles for the whole bigwig It is just not feasible to hold all contigs in the entire decoded bigwig in RAM to estimate quantiles. It may be better to sample across all chromosomes so as not to lose any systematic differences between them - the current method will hide those differences unfortunately. Sampling might be possible. Looking at the actual quantile values across a couple of test bigwigs suggests that there is not much variation between chromosomes but there's now a tabular report to check them for each input bigwig. ### Table reports The optional table output report gives a crude histogram and the top/bottom 10 values to help understand what is likely to be informative. In this example, there are 26700 zero values so using a lower cutoff quantile is likely to have a lot of them, although a large window requirement will decease the overload... Descriptive measures bigwig test contig chr10_PATERNAL n 135711693 mean 12.178164 std 7.997467 min 0.000000 max 365.000000 qtop 364.00 qbot noqlo First/Last 10 value counts Value Count 0.00 26700 1.00 82900 2.00 261400 3.00 676993 4.00 1665500 5.00 3125700 6.00 5078000 7.00 7469000 8.00 10191700 9.00 12544600 355.00 100 356.00 100 357.00 300 358.00 100 360.00 500 361.00 300 362.00 200 363.00 600 364.00 900 365.00 700 Histogram of bigwig values chr10_PATERNAL 18.25 | 127,047,593 | ************************************************************************** chr10_PATERNAL 36.50 | 7,510,000 | **** chr10_PATERNAL 54.75 | 818,900 | chr10_PATERNAL 73.00 | 117,200 | chr10_PATERNAL 91.25 | 51,900 | chr10_PATERNAL 109.50 | 44,200 | chr10_PATERNAL 127.75 | 21,600 | chr10_PATERNAL 146.00 | 17,900 | chr10_PATERNAL 164.25 | 16,400 | chr10_PATERNAL 182.50 | 18,600 | chr10_PATERNAL 200.75 | 5,400 | chr10_PATERNAL 219.00 | 6,600 | chr10_PATERNAL 237.25 | 6,200 | chr10_PATERNAL 255.50 | 3,900 | chr10_PATERNAL 273.75 | 4,500 | chr10_PATERNAL 292.00 | 7,100 | chr10_PATERNAL 310.25 | 3,000 | chr10_PATERNAL 328.50 | 2,700 | chr10_PATERNAL 346.75 | 3,500 | chr10_PATERNAL 365.00 | 4,500 | chr10_PATERNAL ------------ |------------ | chr10_PATERNAL N= | 135,711,693 | chr10_PATERNAL ------------ |------------ |