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Overview
A genomic variant is represented by a locus (chromosome + position), the reference sequence allele and list of alternates.
Is common, because of the VCF specification, that the reference and alternate fields contain extra bases not needed for the Variant representation. It is completely valid to specify a variation like chr1:100:AC:AT, which is absolutely the same variant that chr1:101:C:T.
The number of possible combinations to represent the same genomic variant is non-unique, so it is mandatory to normalize the representation of the variant in order to determine when two representations are the same or different variants. A failure to recognize this will frequently result in inaccurate analyses.
Steps
The variant normalization focuses on different aspects of the variant representation to make a full normalization.
Chromosome naming
Due to there is not any standard for the chromosome naming, is common to see different names for the same chromosome, depending on the used tools, by adding a prefix to the name. For example, we can see chr[1-22,X,Y] for the One Thousand Genomes Project. It is known that this is a chromosome, it is no needed to add any prefix for each variant. The list of known chromosome prefixes are: chrom, chrm, chr and ch.
Reference/Alternate Trimming and left alignment
Reference and alternate trimming consists on removing the trailing (right trimming) and leading (left trimming) bases that are identical in both alleles.
Left aligning a variant means shifting the start position of that variant to the left while keeping the same alleles length till it is no longer possible to do so.
Right and Left trimming
chr1 100 CTC CCCchr1 101 T CIndels and empty alleles
Variants in OpenCB does not require any "context base", i.e. allows empty alleles for reference or alternate. Insertions and deletions are represented with an empty alleles for the reference or alternate.
Deletion of one base T at position 101 chr1 100 AT A chr1 101 TC C
chr1 101 T -Insertion of one C at position 201 (between 200 and 201) chr1 200 G GC chr1 201 A CA
chr1 201 - CAmbiguous trimming and left alignment
It may happen that, in case of deletion or insertion in a region of repeated nucleotides, the trimming operation can be done in multiple ways, and determining the position of the INDEL is ambiguous. In this example we can find that there are four possible ways for normalize the variant:
chr1 100 CTCTCA CTCAchr1 100 CT -
chr1 101 TC -
chr1 102 CT -
chr1 103 TC -We guarantee the left alignment by performing first the right trimming. This variant will be normalized as:
chr1 100 CT -Left alignment
In order to make a correct left alignment, we need the whole reference genome. OpenCB does not support this operation, but may be supported in the future.
Genotype encoding
There are, basically, two different ways of representing the genotype alleles, with or without the allele sequence. In the second way, instead of using the allele itself, is used the allele code. A 0 value represents the reference allele of the Variant, and any other value is a 1-based index into the alternate alleles. A dot value will represent a missing value.
Using an encoded version will allow to determine easily when a genotype is reference, homozygous or heterozygous.
- Sort unphased genotype alleles
- Codify alleles
chr1 100 A T A/A T/A A/T T|A T/.chr1 100 A T 0/0 0/1 0/1 1|0 ./1Multi-allelic split
- Split multiallelic variants
- Reorder genotypes
chr1 100 A GT:AD T,C 0/0:40,1,0 0/1:19,20,1 2/1:0:20,22chr1 100 A GT:AD T,C 0/0:40,1,0 0/1:19,20,1 2/1:0,20,22
chr1 100 A GT:AD C,T 0/0:40,0,1 0/2:19,1,20 1/2:0,22,20alternate alleles. Genotypes are represented by the two alleles in the sample at the locus.
Different variant calling tools may use subtly different representations for the same biological sequence variant. If variants called from a sample are to be annotated or those from multiple samples are to be merged it is important that variant calls are normalised to ensure consistent representation; see this vt article or GiaB article for info. In some cases normalisation may also be useful to identify and remove spurious duplicates called within a call set from a single sample.
OpenCGA performs variant normalisation by default when genotypes are loaded into the database. The procedures implemented by OpenCGA v2.0 are described in this document. The approach is similar but not identical to other tools that perform variant normalisation such as bcftools, vt, GATK and vcflib. This means that the representation of variants normalised by OpenCGA may differ from those from other tools.
Normalisation Procedure in OpenCGA v2.0
The normalisation procedure implemented by OpenCGA has been designed to resolve ambiguous representations commonly found in VCF data. The OpenCGA variant data model is not constrained by the VCF specification. This allows OpenCGA to represent some genotypes that are difficult for VCF to represent. Normalisation assumes correct VCF input according to the VCF specification, e.g. variant positions are 1-based.
The primary aim of OpenCGA normalisation is to standardise variant representation for storage and annotation within the OpenCGA database. A side effect of the ability to export VCF from OpenCGA is that the database of can be used as a VCF normalisation and merging tool. If used in this way users must be mindful of limitations of VCF in the correct representation of some variants.
Regardless of normalisation the original call as specified in the input VCF is stored by OpenCGA allowing the original record to be recapitulated if required.
Each step of the OpenCGA normalisation procedure as described below is performed sequentially on each record of the input VCF file.
1. Rename chromosomes
Due to the lack of standard for the chromosome naming it is common to see different labels for the same chromosome depending on the variant calling workflow. OpenCGA strips chromosome prefixes (chrom, chrm, chr and ch). For example, chr1 and chromX are normalised to 1 and X respectively. Also, for mitochondria, the label M is normalised to MT.
2. Encode genotypes
VCF allows two different ways of representing the genotype alleles; with or without explicit allele sequence. OpenCGA normalises to the latter, i.e. an allele code is used instead of the allele itself: A 0 value represents the reference allele, and any other value is a 1-based index into the alternate alleles. A pseudo-VCF example of mapping from explicit to coded genotype alleles is shown in the following table:
| Input | Result | |
| Encoding 1 | #CHR POS REF ALT S1 S2 S3 S4 S5 | #CHR POS REF ALT S1 S2 S3 S4 S5 |
3. Split Multi-allelic records
Multi-allelic VCF records are produced in two main scenarios:
- Single-sample: one sample (or individual) is multi-allelic for one specific position, ie. both chromosomes are mutated at the same position with a different allele.
- Multi-sample: as a consequence of merging VCF from different samples, ie. different samples with different alleles come together in the same VCF record
Consider this multi-sample VCF input record at chromosome 1 position 100. It lists four samples with their genotypes being; homozygous reference [AA/AA], heterozygous SNP [AA/AT], heterozygous insertion [AT/AAC] and heterozygous deletion [AA/A]:
#CHROM POS REF ALT FORMAT SAMPLE1 SAMPLE2 SAMPLE3 SAMPLE4 |
OpenCGA splits such multi-allelic record to create one output record for each alternate allele. Note that the multi-allelic nature of each record is maintained and allele-based fields are reordered. This is shown in the pseudo-VCF below;
#CHROM POS REF ALT FORMAT SAMPLE1 SAMPLE2 SAMPLE3 SAMPLE4 |
Each output record from the split is then subjected independently to the remaining steps of the normalisation procedure. Normalisation in OpenCGA can therefore be considered "per allele", not "per position".
4. Left align alleles vs. reference
In the left alignment step the start position of the variant is shifted as far to the left as possible with respect to the reference sequence, "left aligned", as possible. The following example is adapted from the Centre for Statistical Genetics. Consider the following deletion record; its alignment against the reference shows that it falls within a short tandem repeat:
#CHROM POS REF ALT | POS: 12345678901234 |
Several other records could be proposed that would result in the exact same sequence change, for example;
#CHROM POS REF ALT | POS: 12345678901234 |
1 7 ACA A | ALT: A-- |
1 5 ACA A | ALT: A-- |
1 3 GCA A | ALT: G-- |
OpenCGA normalises to the leftmost representation, i.e. the one with the smallest POS value (in this case POS=3).
For optimal left alignment especially in the case of insertions and deletions the flanking sequence of the reference genome is required. The reference genome can be specified with the OpenCGA parameter referenceGenome. Basic normalisation will still be performed if the parameter is omitted but it may be suboptimal.
5. Allele Trimming
Allele trimming consists on removing the leading (left trimming) and trailing (right trimming) bases that are identical in both reference and alternate alleles. Left trimming requires the variant position to be updated, for right trimming the variant position is unchanged.
Simple trimming
The following table shows a basic example of left and right trimming in pseudo-VCF notation.
| Input | Result | |
| Left trim | #CHROM POS REF ALT | #CHROM POS REF ALT |
Right | #CHROM POS REF ALT | #CHROM POS REF ALT |
Trimming InDels
Unlike VCF, variants in OpenCB do not require any "context base". Trimming can therefore result in empty strings for the reference or alternate alleles. The following table shows two valid representations of a deletion of 'T' at position 101 and the insertion of 'T' between positions 100 and 101. The table also shows how OpenCGA normalisation results in a unique variant for both deletion and insertion.
| Input | Result | |
| Deletion | #CHROM POS REF ALT | #CHROM POS REF ALT |
| Insertion | #CHROM POS REF ALT | #CHROM POS REF ALT |
Trim rightmost first
For deletion or insertion in a region of repeated nucleotides the trimming operation can be done in multiple ways. For this input there are four possible ways to normalise the variant. OpenCGA ensures leftmost alignment by performing first the right trimming first
| Input | Possible normalisations | OpenCGA result |
#CHR POS REF ALT | #CHR POS REF ALT | #CHR POS REF ALT |
Example
OpenCGA represents variants internally as JSON objects, not pseudo-VCF records! Example JSON representation of the four variants resulting from normalisation of the single VCF record in the second table is shown on the Variant Normalization Example page. This example uses several of the procedures described above.
Decomposition of alleles
The OpenCGA normalisation process does not, other than for left alignment and trimming, edit individual alleles. For instance, decomposition of multi-nucleotide alleles into single-nucleotide primitives is not currently performed.
Identification of duplicate variants
A result of normalisation can be the identification of duplicated records (i.e. alleles) in a single file/sample. OpenCGA supports two deduplication policies: "Discard all" or "Max qual". The former discards both duplicates whilst the latter retains the duplicate with the highest quality score. In both cases a warning is logged.
Skip normalization
In certain scenarios the normalisation process could be undesired. This process can be skipped in OpenCGA with the option normalizationSkip. Use of this option is strongly discouraged.
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