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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Published on: April 4, 2018

Computational prediction of human deep intronic variation.

Pedro Barbosa^1,2, Rosina Savisaar³, Maria Carmo-Fonseca²

¹LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016,, Lisboa, Portugal.

|October 25, 2023

Summary

Identifying functional genetic variants in deep intronic regions is challenging. This study evaluates computational tools, finding performance varies by region and mechanism, with interpretable tools showing lower predictive power.

Keywords:

abnormal splicing introns machine learning model interpretability variant prediction

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Whole-genome sequencing enables detection of genetic variations in deep intronic regions.
Distinguishing functionally relevant variants from non-effect variants computationally is challenging due to limited benchmarks.

Purpose of the Study:

To provide an overview of computational methods for analyzing deep intronic variation.
To evaluate the performance of these tools across different intronic regions and molecular mechanisms.

Main Methods:

Leveraged diverse datasets to evaluate tool performance.
Compared SpliceAI with newer methods extending its implementation.
Devised a quantitative assessment for tool interpretability.

Main Results:

Tool performance varied significantly depending on the intronic region and variant mechanism.
Cryptic splice site variants were better predicted than those affecting regulatory elements.
Interpretable tools showed decreased predictive power compared to black-box methods.

Conclusions:

Findings offer practical recommendations for computational tool usage in deep intronic regions.
Provides a reference framework for informed decision-making in variant analysis.