vembrane: variant filtering using python expressions

vembrane allows to simultaneously filter variants based on any INFO or FORMAT field, CHROM, POS, ID, REF, ALT, QUAL, FILTER, and the annotation field ANN. When filtering based on ANN, annotation entries are filtered first. If no annotation entry remains, the entire variant is deleted.

vembrane relies on pysam for reading/writing VCF/BCF files.

For a comparison with similar tools have a look at the vembrane benchmarks.

Installation

vembrane is available in bioconda and can either be installed into an existing conda environment with mamba install -c bioconda vembrane or into a new named environment mamba create -n environment_name -c bioconda vembrane. Alternatively, if you are familiar with git and poetry, clone this repository and run poetry install. See docs/develop.md for further details.

vembrane filter

Usage

vembrane takes two positional arguments: The filter expression and the input file; the latter may be omitted to read from stdin instead, making it easy to use vembrane in pipe chains.

usage: vembrane filter [options] expression [input vcf/bcf]

options:
  -h, --help            show this help message and exit
  --output OUTPUT, -o OUTPUT
                        Output file. If not specified, output is written to STDOUT.
  --output-fmt {vcf,bcf,uncompressed-bcf}, -O {vcf,bcf,uncompressed-bcf}
                        Output format.
  --annotation-key FIELDNAME, -k FIELDNAME
                        The INFO key for the annotation field. Defaults to "ANN".
  --aux NAME PATH, -a NAME PATH
                        Path to an auxiliary file containing a set of symbols.
  --keep-unmatched      Keep all annotations of a variant if at least one of them
                        passes the expression (mimics SnpSift behaviour).
  --preserve-order      Ensures that the order of the output matches that of the input.
                        This is only useful if the input contains breakends (BNDs)
                        since the order of all other variants is preserved anyway.

Filter expression

The filter expression can be any valid python expression that evaluates to bool. However, functions and symbols available have been restricted to the following:

• all, any
• abs, len, max, min, round, sum
• enumerate, filter, iter, map, next, range, reversed, sorted, zip
• dict, list, set, tuple
• bool, chr, float, int, ord, str
• Any function or symbol from math
• Any function from statistics
• Regular expressions via re
• custom functions:
  • without_na(values: Iterable[T]) -> Iterable[T] (keep only values that are not NA)
  • replace_na(values: Iterable[T], replacement: T) -> Iterable[T] (replace values that are NA with some other fixed value)
  • genotype related:
    • count_hom, count_het , count_any_ref, count_any_var, count_hom_ref, count_hom_var
    • is_hom, is_het, is_hom_ref , is_hom_var
    • has_ref, has_var

Available fields

The following VCF fields can be accessed in the filter expression:

¹ depends on type specified in VCF header

² for the usual snpeff and vep annotations, custom types have been specified; any unknown ANN field will simply be of type str. If something lacks a custom parser/type, please consider filing an issue in the issue tracker.

³ vembrane does not handle multi-allelic records itself. Instead, such files should be preprocessed by either of the following tools (preferably even before annotation):

• bcftools norm -m-any […]
• gatk LeftAlignAndTrimVariants […] --split-multi-allelics
• vcfmulti2oneallele […]

Examples

• Only keep annotations and variants where gene equals "CDH2" and its impact is "HIGH":

  vembrane filter 'ANN["Gene_Name"] == "CDH2" and ANN["Annotation_Impact"] == "HIGH"' variants.bcf

• Only keep variants with quality at least 30:

  vembrane filter 'QUAL >= 30' variants.vcf

• Only keep annotations and variants where feature (transcript) is ENST00000307301:

  vembrane filter 'ANN["Feature"] == "ENST00000307301"' variants.bcf

• Only keep annotations and variants where protein position is less than 10:

  vembrane filter 'ANN["Protein"].start < 10' variants.bcf

• Only keep variants where mapping quality is exactly 60:

  vembrane filter 'INFO["MQ"] == 60' variants.bcf

• Only keep annotations and variants where consequence contains the word "stream" (matching "upstream" and "downstream"):

  vembrane filter 're.search("(up|down)stream", ANN["Consequence"])' variants.vcf

• Only keep annotations and variants where CLIN_SIG contains "pathogenic", "likely_pathogenic" or "drug_response":

  vembrane filter \
    'any(entry in ANN["CLIN_SIG"]
         for entry in ("pathogenic", "likely_pathogenic", "drug_response"))' \
    variants.vcf

  Using set operations, the same may also be expressed as:

  vembrane filter \
    'not {"pathogenic", "likely_pathogenic", "drug_response"}.isdisjoint(ANN["CLIN_SIG"])' \
    variants.vcf

• Filter on sample specific values:

  • by sample name:

    vembrane filter 'FORMAT["DP"]["specific_sample_name"] > 0' variants.vcf

  • by sample index:

    vembrane filter 'FORMAT["DP"][0] > 0' variants.vcf

  • by sample name based on the index in the list of SAMPLES:

    vembrane filter 'FORMAT["DP"][SAMPLES[0]] > 0' variants.vcf

  • using all or a subset of SAMPLES:

    vembrane filter 'mean(FORMAT["DP"][s] for s in SAMPLES) > 10' variants.vcf

• Filter on genotypes for specific samples (named "kid", "mom", "dad"):

  vembrane filter \
    'is_het("kid") and is_hom_ref("mom") and is_hom_ref("dad") and \
     all(FORMAT["DP"][s] > 10 for s in ["kid", "mom", "dad"])' \
    variants.vcf

• Explicitly access the GT field for the first sample in the file:

  vembrane filter 'FORMAT["GT"][0] == (1, 1)' variants.vcf

Custom ANN types

vembrane parses entries in the annotation field as outlined in docs/ann_types.md.

Missing values in annotations

If a certain annotation field lacks a value, it will be replaced with the special value of NA. Comparing with this value will always result in False, e.g. ANN["MOTIF_POS"] > 0 will always evaluate to False if there was no value in the "MOTIF_POS" field of ANN (otherwise the comparison will be carried out with the usual semantics).

Since you may want to use the regex module to search for matches, NA also acts as an empty str, such that re.search("nothing", NA) returns nothing instead of raising an exception.

Explicitly handling missing/optional values in INFO or FORMAT fields can be done by checking for NA, e.g.: INFO["DP"] is NA.

Handling missing/optional values in fields other than INFO or FORMAT can be done by checking for None, e.g ID is not None.

Sometimes, multi-valued fields may contain missing values; in this case, the without_na function can be convenient, for example: mean(without_na(FORMAT['DP'][s] for s in SAMPLES)) > 2.3. It is also possible to replace NA with some constant value with the replace_na function: mean(replace_na((FORMAT['DP'][s] for s in SAMPLES), 0.0)) > 2.3

Auxiliary files

vembrane supports additional files, such as lists of genes or ids with the --aux NAME path/to/file option. The file should contain one item per line and is parsed as a set. For example vembrane filter --aux genes genes.txt "ANN['SYMBOL'] in AUX['genes']" variants.vcf will keep only records where the annotated symbol is in the set specified in genes.txt.

vembrane table

In addition to the filter subcommand, vembrane (≥ 0.5) also supports writing tabular data with the table subcommand. In this case, an expression which evaluates to tuple is expected, for example:

vembrane table 'CHROM, POS, 10**(-QUAL/10), ANN["CLIN_SIG"]' input.vcf > table.tsv

When handling multi-sample VCFs, you often want to iterate over all samples in a record by looking at a FORMAT field for all of them. However, if you use a standard Python list comprehension (something like [FORMAT['DP'][sample] for sample in SAMPLES]), this would yield a single column with a list containing one entry per sample (something like [25, 32, 22] for three samples with the respective depths).

In order to have a separate column for each sample, you can use the for_each_sample() function in both the main vembrane table expression and the --header expression. It should contain one lambda expression with exactly one argument, which will be substituted by the sample names in the lambda expression.

For example, you could specifiy expressions for the --header and the main VCF record evaluation like this:

vembrane table --header 'CHROM, POS, for_each_sample(lambda sample: f"{sample}_depth")' 'CHROM, POS, for_each_sample(lambda s: FORMAT["DP"][s])' input.vcf > table.tsv

Given a VCF file with samples Sample_1, Sample_2 and Sample_3, the header would expand to be printed as:

CHROM  POS   Sample_1_depth   Sample_2_depth   Sample_3_depth

and the expression to evaluate on each VCF record would become:

(CHROM, POS, FORMAT['DP']['Sample_1'], FORMAT['DP']['Sample_2'], FORMAT['DP']['Sample_3'])

When not supplying a --header expression, the entries of the expanded main expression become the column names in the header. When supplying a header via --header, its for_each_sample() expects an expression which can be evaluated to str and must have the same number of fields as the main expression.

Please note that, as anywhere in vembrane, you can use arbitrary Python expressions in for_each_sample() lambda expressions. So you can for example perform computations on fields or combine multiple fields into one value:

vembrane table 'CHROM, POS, for_each_sample(lambda sample: FORMAT["AD"][sample] / FORMAT["DP"][sample] * QUAL)' input.vcf > table.tsv

Instead of using the for_each_sample (wide format) machinery, it is also possible to generate the data in long format by specifying the --long flag. In this case, the first column will always be called SAMPLE and there's an additional variable of the same name available for the expressions. For example:

vembrane table --long 'CHROM, POS, FORMAT["AD"][SAMPLE] / FORMAT["DP"][SAMPLE] * QUAL' input.vcf > long_table.tsv

vembrane annotate

vembrane is able to annotate vcf files with a given table-like file. In addition to the vcf and annotation file, the user has to provide a configuration file.

Configuration (Example):

## example.yaml
annotation:
    file: "example.tsv" # the table-like annotation file column with header
    columns:
      chrom: "chrom" # column name of the annotation file refering to the chromosome
      start: "chromStart" # column name of the annotation file refering to the chromosome start
      stop: "chromEnd" # column name of the annotation file refering to the chromosome end
    delimiter: "\t" # delimiter of the columns
    values:
    - value: # a new annotation entry in the info field of the vcf
        vcf_name: "genehancer_score" # the name of annotation entry
        number: "1" # number of values for each entry
        description: "Score from genehancer." # description of this entry in the header
        type: "Float" # type of the values
        expression: "DATA['score'][0]" # any python expression to calculate the value(s)
                                       # DATA['score'] refers to the 'score' column of the annotation field
    - value: # a second annotation entry to annotate
        vcf_name: "genehancer_score2"
        number: "1"
        description: "Score from genehancer."
        type: "Float"
        expression: "log(max(DATA['score']) * 2)"

example.tsv (Example):

chrom	chromStart	chromEnd	name	score
chr10	76001	77000	HJSDHKD	463
chr10	120054	130024	HJSJHKD	463
chr10	432627	492679	IDASJLD	327
chr10	540227	872071	SZAGHSD	435
chr10	654480	1000200	HSJKJSD	12

Exemplary invocation: vembrane annotate example.yaml example.bcf > annotated.vcf.

Internally for each vcf record the overlapping regions of the annotation file are determined and stored in DATA. The expression may then access the DATA object and its columns by the columns names to generate a single or multiple values of cardinality number of type type. These values are stored in the new annotation entry under the name vcf_name and with header description description.

Citation

Check the "Cite this repository" entry in the sidebar for citation options.

Also, please read should-I-cite-this-software for background.

Authors

• Marcel Bargull (@mbargull)
• Jan Forster (@jafors)
• Till Hartmann (@tedil)
• Johannes Köster (@johanneskoester)
• Elias Kuthe (@eqt)
• David Lähnemann (@dlaehnemann)
• Felix Mölder (@felixmoelder)
• Christopher Schröder (@christopher-schroeder)