RESUMO
The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.
Assuntos
Previsões/métodos , Precursores de RNA/genética , Splicing de RNA/genética , Algoritmos , Processamento Alternativo/genética , Transtorno Autístico/genética , Aprendizado Profundo , Éxons/genética , Humanos , Deficiência Intelectual/genética , Íntrons/genética , Redes Neurais de Computação , Precursores de RNA/metabolismo , Sítios de Splice de RNA/genética , Sítios de Splice de RNA/fisiologiaRESUMO
We present a novel technique for the tract-based statistical analysis of diffusion imaging data. In our technique, we represent each white matter (WM) tract as a tract probability map (TPM): a function mapping a point to its probability of belonging to the tract. We start by automatically clustering the tracts identified in the brain via tractography into TPMs using a novel Gaussian process framework. Then, each tract is modeled by the skeleton of its TPM, a medial representation with a tubular or sheet-like geometry. The appropriate geometry for each tract is implicitly inferred from the data instead of being selected a priori, as is done by current tract-specific approaches. The TPM representation makes it possible to average diffusion imaging based features along directions locally perpendicular to the skeleton of each WM tract, increasing the sensitivity and specificity of statistical analyses on the WM. Our framework therefore facilitates the automated analysis of WM tract bundles, and enables the quantification and visualization of tract-based statistical differences between groups. We have demonstrated the applicability of our framework by studying WM differences between 34 schizophrenia patients and 24 healthy controls.