How does it work¶

TransFIC takes as input the Functional Impact Score of a somatic mutation provided by one of the aforementioned tools. It then compares that score to the distribution of scores of germline SNVs observed in genes with similar functional annotations (for instance genes with the same molecular function as provided by the Gene Ontologies). The score is thus transformed using the Z-score formula. The result is that mutations in genes that are less tolerant to germline SNVs are amplified, while the scores of mutations on relatively tolerant genes are decreased.

Performance¶

We have implemented the TransFIC of three well-known tools (e.g. SIFT, Polyphen2, MutationAssessor, ...). We compared the performance of these three TransFIC to the original scores provided by the tools on eight different datasets containing a set of positive mutations enriched in cancer drivers and a set of negative SNVs either enriched in cancer passengers or composed of polymorphisms. We found that the TransFIC based on Gene Ontologies Molecular Functions annotation of the three tools classifies better these eight datasets than the original scores. For instance, the TransFIC of SIFT scores showed between ~1.3 and ~5 fold increase in the Matthews Correlation Coefficient (MCC) with respect to original SIFT scores in the classification of the eight datasets. For PPH2 scores, the TransFIC scores improved the MCCs between ~1.1 and ~7 times. Finally, TransFIC MA scores showed MCC between ~1.1 and ~2.5 times greater than original MA scores.

How to cite TransFIC¶

Please, cite this paper if you use TransFIC:

Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation (2012)
Gonzalez-Perez A, Deu-Pons J and Lopez-Bigas N. Genome Medicine. 4:89 doi:10.1186/gm390s
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Don’t forget to mention also the original tools:

• SIFT: PMID:19561590
• Polyphen2: PMID:20354512
• MutationAssessor: PMID:21727090
• Ensembl-variation: PMID:20459805