Metadata-Version: 2.1
Name: covviz
Version: 1.1.2
Summary: Multi-sample coverage browser
Home-page: https://github.com/brwnj/covviz
Author: Joe Brown
Author-email: brwnjm@gmail.com
License: UNKNOWN
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 from the majority of samples. Significance is determined
        using z-scores for all samples at all points using median absolute deviation,
        but in order 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.
        
        # The Python Package
        
        `covviz` is installable via `pip install -U covviz` and analyzes a bed3+
        output format.
        
        ## Usage
        
        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   test_sample1   test_sample2   test_sample3
        ```
        
        Then CLI usage is:
        
        ```
        covviz $bed
        ```
        
        ### 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
        ```
        
        # The Nextflow Workflow
        
        If you're starting with alignment indexes, this workflow aims to simply the
        process of obtaining coverage and generating the coverage browser.
        
        We use [indexcov](https://github.com/brentp/goleft/tree/master/indexcov)
        to quickly estimate the coverage across samples then find regions of large,
        coverage-based anomalies.
        
        The output of `indexcov` is then directly input into `covviz`.
        
        ## Usage
        
        Install `nextflow`:
        
        ```
        curl -s https://get.nextflow.io | bash
        ```
        
        Full nextflow installation instructions are available at:
        https://www.nextflow.io/
        
        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
        + `--gff`
            + file path to gff matching genome build of `--indexes`
        
        ### Workflow Options
        
        + `--outdir`
            + output directory for results
            + default: "./results"
        + `--sexchroms`
            + sex chromosomes as they are in `--indexes`
            + default: "X,Y"
        + `--exclude`
            + regular expression of chromosomes to skip
            + default: "^GL|^hs|^chrEBV$|M$|MT$|^NC|_random$|Un_|^HLA\\-|_alt$|hap\\d+$"
        + `--zthreshold`
            + a sample must greater than this many standard deviations in order to be found significant
            + default: 3.5
        + `--distancethreshold`
            + consecutive significant points must span this distance in order to pass this filter
            + default: 150000
        + `--slop`
            + leading and trailing segments added to significant regions to make them more visible
            + default: 500000
        + `--ped`
            + custom metadata that will be merged with the .ped output of indexcov
            + default: false
        + `--samplecol`
            + the column header for sample IDs in your custom ped file
            + default: "sample_id"
        
        
        # Report
        
        ## 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.
        
        ![significant_regions](data/img/significant_regions.png)
        
        When plotting fewer samples than `--min-samples`, the gray area plot
        will not be displayed. Instead, all sample plot traces will be shown.
        
        ![min_samples](data/img/min_samples.png)
        
        ## Proportions covered
        
        ![proportional_coverage](data/img/proportional_coverage.png)
        
        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.
        
        # License
        
        covviz is free and unrestricted for non-commercial use. For commercial use,
        please contact [bpedersen@base2genomics.com].
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
