Multi-sample coverage browser
Project description
covviz
Coverage visualization; a many-sample coverage browser.
The aim of covviz
is to highlight regions of significant
(passing the user's z-score threshold) and sustained (beyond user specified
distance) deviation of coverage depth from the majority of samples. Significance is determined
using z-scores for all samples at all points using median absolute deviation.
In order for regions to be highlighted, points must be significant
consecutively throughout a user specified distance.
If you are analyzing a low number of samples, deviation may be irrelevant. In
this case, we can set --min-samples
to be greater than our sample total
to skip Z-threshold calculation and plot coverages for all samples at all
points.
Getting started
From alignments (.bam and/or .cram)
Alignments must be indexed. The input for the covviz
workflow are the indexes
of the alignments. For BAM, that would be .bai, and .crai for CRAM. Indexes
can be generated using samtools on your
sorted alignments:
samtools index mybam.bam
# generates mybam.bam.bai
Installation and usage
Install Nextflow if you don't already have it. The only dependency is Java 8 or later, then you run:
curl -s https://get.nextflow.io | bash
Or via Bioconda using:
conda install -c bioconda nextflow
Full nextflow installation instructions are available at: https://www.nextflow.io/
There is no need to download the covviz code prior to execution or any software dependencies when using a container service like Docker or Singularity.
Docker/Singularity
To simplify prerequisite software installations and software version tracking,
we strongly recommend running covviz
using Docker or Singularity. Docker
installation instructions for your operating system are available at:
https://docs.docker.com/install/
Then, with Docker or Singularity we run:
nextflow run brwnj/covviz -latest -profile docker \
--indexes 'data/indexes/*.crai' \
--fai data/g1k_v37_decoy.fa.fai \
--gff data/Homo_sapiens.GRCh37.82.gff3.gz
Which gives us ./results/covviz_report.html
.
Required arguments
--indexes
- quoted file path with wildcard ('*.crai') to cram or bam indexes
--fai
- file path to .fai reference index
A complete list of arguments can be displayed using:
nextflow run brwnj/covviz -latest --help
Nextflow arguments
In the example above -latest
pulls whatever the latest covviz
code exists on GitHub
prior to execution and -profile docker
sets -with-docker
within Nextflow.
Other notable options are -resume
, which when running a workflow a second will start
where previous runs of the workflow left off; and -work-dir
which sets the location of
all intermediate files generated throughout the workflow.
From coverage intervals (.bed)
The covviz
CLI accepts bed3+ as input. If you've already generated your coverage
files you can start here and not the Nextflow workflow.
If you would prefer to run indexcov
yourself across your .bai or .crai files,
the workflow above simply runs:
fai=data/g1k_v37_decoy.fa.fai
goleft indexcov --directory myproject --fai $fai *.crai
This will generate the expected inputs in their anticipated formats for the covviz
CLI.
Expected file format
To analyze your coverage data it needs to be in bed3+ format and include a header with sample IDs. The first three column headers are agnostic, but for samples test_sample1, test_sample2, and test_sample3, this would look like:
#chrom start end sample1 sample2 sample3
Installation of CLI and usage
To install the covviz
Python package use:
pip install -U covviz
Then CLI usage is:
covviz $bed
A complete list of arguments can be displayed using:
covviz --help
Adding custom metadata (.ped)
There is support for non-indexcov .ped files, though you may have to change the default column IDs pertaining to the column which contains the sample ID and the sex of the sample.
covviz --ped $ped --sample-col sample_col --sex sex_col $bed
Adding annotation tracks
Currently we support GFF, VCF, and BED. GFF tracks are added using --gff
where features are 'gene' and attributes have 'Name='. Feature type and
attribute regex can be configured using --gff-feature
and --gff-attr
.
VCF tracks (v4.1) are added with --vcf
with the entire INFO string
being displayed by default. Specifying --vcf-info
with something like
'CLNDN=' will grab just that field when using ClinVar variants. Including
large INFO strings for all variants can dramatically increase the size
of the covviz report.
Region based annotation tracks can be added using --bed
. The name field
will be used to identify the regions when present.
Annotation tracks, --gff
, --vcf
, and --bed
, may be specified
multiple times.
In all cases, 'chr' will be stripped from the chromosome names.
Interpreting the output
Interactive example
See: https://brwnj.github.io/covviz/
Scaled chromosome coverage
Significant regions will be displayed in color atop a gray region which represents the upper and lower bounds of a given point minus any values deemed significant.
When plotting fewer samples than --min-samples
, the gray area plot
will not be displayed. Instead, all sample plot traces will be shown.
Proportions covered
The metadata table will be displayed below the plots.
Interaction
Clicking on plot traces highlights the line and searches the metadata. Double-clicking de-selects lines, resets the plot, and de-selects samples from the table. Clicking on the gene track launches a search for the gene's respective Gene Card. In cases where genes overlap, multiple windows/tabs will be opened.
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