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https://github.com/brentp/bigwig-nim
command-line querying+conversion of bigwigs and a nim wrapper for dpryan's libbigwig
https://github.com/brentp/bigwig-nim
bigbed bigwig genomics high-throughput-sequencing nim nim-lang
Last synced: about 1 month ago
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command-line querying+conversion of bigwigs and a nim wrapper for dpryan's libbigwig
- Host: GitHub
- URL: https://github.com/brentp/bigwig-nim
- Owner: brentp
- License: mit
- Created: 2019-07-31T16:03:04.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-02T00:11:49.000Z (over 4 years ago)
- Last Synced: 2024-10-13T02:21:11.192Z (2 months ago)
- Topics: bigbed, bigwig, genomics, high-throughput-sequencing, nim, nim-lang
- Language: Nim
- Homepage:
- Size: 185 KB
- Stars: 16
- Watchers: 3
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- License: LICENSE
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README
# bigwig for nim
[](https://travis-ci.com/brentp/bigwig-nim)
[](https://brentp.github.io/bigwig-nim/lib.html)
## Command Line
bigwig-nim includes a command-line tool distributed as a static binary [here](https://github.com/brentp/bigwig-nim/releases/latest).
It supports converting bed to bigwig and bigwig to bed and extracting stats (mean, coverage, etc) for regions in a bigwig.
There are other tools to do this, including [kentTools](https://hgwdev.gi.ucsc.edu/~kent/src/) which has a more restrictive license and does not supported (b)gzipped input and [bwtools](https://github.com/CRG-Barcelona/bwtool) which seems to provide similar functionality (but I am not able to build it).
### view
To convert a bed with the value in the 4th column to bigwig, use:
```Shell
bigwig view $bed_in --value-column 4 --chrom-sizes $fai -O bigwig -o $bigwig_out
```
`bigwig` will automatically determine the best data format for each block (fixed span and step or per-base) most of the
CPU time is spent parsing the input bed file.
### stats
To get the mean value for a given region (in this case on chromosome 22)
```Shell
bigwig stats --stat mean $bigwig 22:145000-155000
# or a bed file or regions
bigwig stats --stat mean $bigwig $bed
```
Output is tab-delimited `chrom`, `start`, `stop`, `stat` for each row in the bed (or just once for the region).
The supported stats are `mean`, `min`, `max`, `coverage`, `sum` with a special-case for the stat of `header` which
shows the chromosomes, lengths and mean coverages for each chromosome in the bigwig file.
## Reading
```Nim
var bw: BigWig
bw.open(path, fmRead)
# avoid allocating when possible
var values: seq[float32]
bw.values(values, "chr1", 0, 2222)
for iv in bw.intervals("chr2", 999, 88888): # iterator.
# tuple[start: int, stop: int, value: float32]
# for bigbed
for iv in bw.entries("chr2", 999, 88888): # iterator.
# tuple[start: int, stop: int, value: cstring]
# value contains "SQL" for bigbed entry.
# single value
var m: seq[float32] = bw.stats("chr2", 999, 9999, stat=Stat.mean)
# multiple bins:
var L: seq[float32] = bw.stats("chr2", 999, 9999, stat=Stat.min, nBins=10)
echo bw.header # @[(name: "1", length: 195471971, tid: 0'u32), (name: "10", length: 130694993, tid: 1'u32)]
bw.close
```
## Writing
```Nim
var wtr:BigWig
doAssert wtr.open("tests/writer.bw", fmWrite)
wtr.setHeader(@[(name:"chr1", length: 2000, tid: 0'u32)])
wtr.writeHeader
# add intervals with tuples
wtr.add("chr1", @[(start: 22, stop: 33, value: 0.01'f32), (start: 44, stop: 55, value: 155'f32)])
# or with, for example a span of 15 bases:
wtr.add("chr1", 15, @[(start: 20, value: 0.01'f32), (start: 30, value: 155'f32)])
# or an array of values with a given span and step:
var values = @[0.1'f32, 0.2, 0.3, 0.4]
wtr.add("chr1", 100, values, span=100, step=200) # 100-200 is 0.1, 300-400 is 0.2 ...
wtr.close()
```