A method of predicting changes in human gene splicing induced by genetic variants in context of cis-acting elements

Abstract

Background: Polymorphic variants and mutations disrupting canonical splicing isoforms are among the leading causes of human hereditary disorders. While there is a substantial evidence of aberrant splicing causing Mendelian diseases, the implication of such events in multi-genic disorders is yet to be well understood. We have developed a new tool (SpliceScan II) for predicting the effects of genetic variants on splicing and cis-regulatory elements. The novel Bayesian non-canonical 5'GC splice site (SS) sensor used in our tool allows inference on non-canonical exons.

Results: Our tool performed favorably when compared with the existing methods in the context of genes linked to the Autism Spectrum Disorder (ASD). SpliceScan II was able to predict more aberrant splicing isoforms triggered by the mutations, as documented in DBASS5 and DBASS3 aberrant splicing databases, than other existing methods. Detrimental effects behind some of the polymorphic variations previously associated with Alzheimer's and breast cancer could be explained by changes in predicted splicing patterns.

Conclusions: We have developed SpliceScan II, an effective and sensitive tool for predicting the detrimental effects of genomic variants on splicing leading to Mendelian and complex hereditary disorders. The method could potentially be used to screen resequenced patient DNA to identify de novo mutations and polymorphic variants that could contribute to a genetic disorder.

Figure 1

An example SpliceScan II output predicting an effect of mutation. IVS2+2delC annotated in DBASS5 [7] as causing familial pulmonary arterial hypertension [56], a single nucleotide deletion which disrupts a strong non-canonical 5'GC SS (shown as purple circle) and causes activation of two cryptic alternatively committed canonical 5'SSs located -60 and -108 nucleotides upstream of the original SS. Here we successfully predict an effect of mutation on the original allele shown in (A), where two alternatively used aberrant exonic isoforms activated as shown in (B). SpliceScan II predicted 3'SSs are represented as black-and-white triangles, 5'SSs are black-and-white circles, predicted exons are shown as blue rectangles. The more intense the color a displayed signal, the higher its predicted strength. In (C) we show an example of SpliceScan II textual output listing factors contributing to non-canonical GC exon score assignment shown in (A).

Figure 2

Sensitivity vs. False positive rate trajectories for various tools. The performance of Bayesian and Maximum entropy SS sensors is compared with the performance of tools specifically built to predict the splicing pattern independent of protein coding context features. (A) Trajectories for 5'SS (B) Trajectories for 3'SS.

Figure 3

5'GC SS Bayesian sensor performance compared with two existing 5'GC SS sensors [12,15]based on weight matrices.

Figure 4

Hypothetical situation of scoring putative 3'SS with SpliceScanmethod, where both strong 5'SSs located downstream positively affect the confidence of 3'SS.

Figure 5

An example LOD profiles for various 5'SS ISEs/ISSs signals in vicinity of a weak 5'SS (with discrete score 1 out of 3 possible). Signal AAGGTAA is a core part of a strong canonical 5'SS and therefore is substantially depleted in vicinity of true exonic boundaries as potential competitor. The distinctive bell-shaped LOD profiles for GGGGTGGG and CGGGGGCG are from the well studied poly-G family of ISEs [31], known to form quadruplex structures [57].

Figure 6

Exonic length distribution depends on strength of flanking SSs. We used substantial correlation of exonic sizes and the strength of SSs to explain how certain events change (compromise) the pattern of splicing, where 3' and 5' SSs strengths are in the discrete range from 1 to 5 as measured by the Bayesian SS sensor [23]. (A) Histogram of the internal exon length distribution. (B) The exonic length distribution histograms were interpolated with the mixture of Beta distributions fit with the Expectation Maximization (EM) as discussed in [23].