TransVar: a trans-level variant annotator for precision genomics

One DNA sequence can code for multiple different mRNAs, and therefore many different proteins. Conversely, a variant identified at the protein or transcript level may have non-unique genomic origins. For example, EGFR:p.L747S, which mediates acquired resistance of non-small cell lung cancer to tyrosine kinase inhibitors¹, can be translated from multiple genomic variants such as chr7:g.55249076_55249077delinsAG and chr7:g.55242470T>C on different isoforms defined on the human reference assembly GRCh37. One-to-many, many-to-one and many-to-many relationships among sequence variants at the genomic level and those at transcript and protein levels introduce frequent inconsistencies in current practice when vital information about the annotation process (e.g., transcript or isoform IDs) is omitted from variant identifiers.

To facilitate standardization and reveal inconsistency in existing variant annotations, we have designed a novel variant annotator, TransVar, to perform three main functions supporting diverse reference genomes and transcript databases (Fig. 1a): (i) “forward annotation”, which annotates all potential effects of a genomic variant on mRNAs and proteins; (ii) “reverse annotation”, which traces an mRNA or protein variant to all potential genomic origins; and (iii) “equivalence annotation”, which, for a given protein variant, searches for alternative protein variants that have identical genomic origin but are represented based on different isoforms.

Figure 1.

Schematic overview of TransVar and comparison of TransVar with other tools. (a) TransVar performs forward (green arrows) and reverse annotation (pink arrows) and considers all possible mRNA transcripts or protein isoforms available in user-specified reference genome and transcript databases (colored boxes representing exons in various transcripts or isoforms of a gene). Given a variant (black triangle) at any of the genomic, mRNA or protein levels, TransVar is able to infer the associated variants at the other two levels. In reverse annotation, TransVar searches all potential transcripts and reports one variant on each transcript. When there are multiple variants on the same transcript, TransVar reports the variant with minimal nucleotide changes (red text) instead of other alternatives (purple text). (b) Comparison of forward annotation consistency among TransVar, VEP, ANNOVAR, snpEff and Oncotator. Plotted are percentages of variants (Y axis) that had matched protein annotations in COSMIC v67 based on 964,132 unique SNSs, 3,715 MNSs, 11,761 INSs, 24,595 DELs and 166 BLSs (X axis). NA: Protein level annotations not available.

We annotated 964,132 unique single-nucleotide substitutions (SNS), 3,715 multi-nucleotide substitutions (MNS), 11,761 insertions (INS), 24,595 deletions (DEL) and 166 block substitutions (BLS) in the Catalogue of Somatic Mutations in Cancer (COSMIC v67) using TransVar, ANNOVAR², VEP³, snpEff⁴, and Oncotator⁵, and asked whether the resulting protein identifiers (gene name, protein coordinates, and reference amino acid (AA)) match those in COSMIC. We observed comparable consistency in SNS and MNS but variable consistency in INS, DEL and BLS from different annotators (Fig. 1b, Supplementary Table 1 and Supplementary Notes). That finding can largely be attributed to a lack of standardization among variant annotations (codon or AA positions of variants) submitted to COSMIC and among conventions implemented in various annotators. Inconsistency in annotations blurred the lines of evidence for variant frequency estimation and led to inaccurate determination of variant function. TransVar revealed hidden inconsistency in these variant annotations by comprehensively outputting alternative annotations in all available transcripts in standard HGVS nomenclature, and thus resulted in greater consistency in this experiment.

TransVar’s novel reverse annotation can be used to ascertain if two protein variants have identical genomic origin, thus reducing inconsistency in annotation data. It can also reveal whether or not a protein variant has non-unique genomic origins and requires caution in genetic and clinical interpretation. We reverse-annotated the protein level variants in COSMIC and found that even under the constraints imposed by the reference base or AA identity, a sizeable fraction (e.g., 11.9% of single-AA substitutions) were associated with multiple genomic variants (Supplementary Table 2), if transcripts were not specified. Among the 537 variants that were cited as clinically actionable at PersonalizedCancerTherapy.org, 78 (14.5%) (e.g., CDKN2A:p.R87P and ERBB2:p.L755_T759del) could be mapped to multiple genomic locations (Supplementary Table 3). The reverse-annotation functionality also enabled systematic genomic characterization of variants directly identified from proteomic or RNA-seq data. For example, we were able to identify in just a few minutes of compute-time the putative genomic origins of 187,464 (97.69%) protein phosphorylation sites (e.g., p.Y308/p.S473 in AKT1 and p.Y1068/p.Y1172 in EGFR) in human proteins⁶.

Our investigation revealed frequent inconsistencies in current databases and tools and highlighted the importance of standardization. With both forward and reverse annotation enabled in TransVar, we can reveal hidden inconsistency and improve the precision of translational and clinical genomics. The source code and detailed instructions of TransVar is available at https://bitbucket.org/wanding/transvar and a web interface is at http://www.transvar.net.

Supplementary Material

tab1

Supplementary Table 1 Comparing the annotation accuracy of different mutation types using TransVar, ANNOVAR, snpEff, VEP and Oncotator

tab2

Supplementary Table 2 Reverse annotation accuracy of 1,080,304 COSMIC single-amino acid substitutions via different transcript databases

tab3

Supplementary Table 3 Clinically actionable cancer mutations with non-unique genomic origins