GTB - an online genome tolerance browser

Abstract

Background: Accurate methods capable of predicting the impact of single nucleotide variants (SNVs) are assuming ever increasing importance. There exists a plethora of in silico algorithms designed to help identify and prioritize SNVs across the human genome for further investigation. However, no tool exists to visualize the predicted tolerance of the genome to mutation, or the similarities between these methods.

Results: We present the Genome Tolerance Browser (GTB, http://gtb.biocompute.org.uk ): an online genome browser for visualizing the predicted tolerance of the genome to mutation. The server summarizes several in silico prediction algorithms and conservation scores: including 13 genome-wide prediction algorithms and conservation scores, 12 non-synonymous prediction algorithms and four cancer-specific algorithms.

Conclusion: The GTB enables users to visualize the similarities and differences between several prediction algorithms and to upload their own data as additional tracks; thereby facilitating the rapid identification of potential regions of interest.

Keywords: Genome browser; Genome tolerance; Mutation; Pathogenicity prediction; Prediction algorithm; SNVs; Variant effect prediction.

Fig. 1

Tolerance profile of HOXA5 shows regions of similarity between sequence-based prediction algorithms: SIFT and PROVEAN. However, subtle differences in tolerance can be observed when comparing these sequence-based algorithms with a structure-based algorithm, PolyPhen-2. Insight into potential regions of interest can be also obtained from genome-wide prediction algorithms such as FATHMM-MKL and CADD

Fig. 2

A similar trend in intolerance can be observed across LDLR using sequence- and structure-based prediction algorithms, i.e. sequence-based methods tend to agree on intolerance given that they both rely on sequence conservation whereas structure-based algorithms utilize the additional structure-based properties made available to them to show a different tolerance profile. Unlike HOXA5, genome-wide prediction algorithms appear to agree on potential peaks of intolerance across the non-coding region of LDLR

Fig. 3

Subtle differences between generic and cancer-specific prediction algorithms can be observed across TP53. For example, cancer-specific transformations of traditional germline prediction algorithms amplify intolerance across the entire region

Fig. 4

Cancer-specific transformations of traditional germline prediction algorithms amplifies the intolerance of BRCA1