The human splicing code reveals new insights into the genetic determinants of disease

By Hui Yuan Xiong, Babak Alipanahi, Leo J. Lee, Hannes Bretschneider, Daniele Merico, R. Yuen, Y. Hua, Serge Gueroussov, H. Najafabadi, Timothy R. Hughes and 7 more. 2014

1,219 citations recorded by the source corpus.

Predicting defects in RNA splicing Most eukaryotic messenger RNAs (mRNAs) are spliced to remove introns. Splicing generates uninterrupted open reading frames that can be translated into proteins. Splicing is often highly regulated, generating alternative spliced forms that code for variant proteins in different tissues. RNA-binding proteins that bind specific sequences in the mRNA regulate splicing. Xiong et al. develop a computational model that predicts splicing regulation for any mRNA sequence (see the Perspective by Guigó and Valcárcel). They use this to analyze more than half a million mRNA splicing sequence variants in the human genome. They are able to identify thousands of known disease-causing mutations, as well as many new disease candidates, including 17 new autism-linked genes. Science, this issue 10.1126/science.1254806; see also p. 124 A model predicts how thousands of disease-linked nucleotide variants affect messenger RNA splicing. [Also see Perspective by Guigó and Valcárcel] INTRODUCTION Advancing whole-genome precision medicine requires understanding how gene expression is altered by genetic variants, especially those that are far outside of protein-coding regions. We developed a computational technique that scores how strongly genetic variants affect RNA splicing, a critical step in gene expression whose disruption contributes to many diseases, including cancers and neurological disorders. A genome-wide analysis reveals tens of thousands of variants that alter splicing and are enriched with a wide range of known diseases. Our results provide insight into the genetic basis of spinal muscular atrophy, hereditary nonpolyposis colorectal cancer, and autism spectrum disorder. RATIONALE We used “deep learning” computer algorithms to derive a computational model that takes as input DNA sequences and applies general rules to predict splicing in human tissues. Given a test variant, which may be up to 300 nucleotides into an intron, our model can be used to compute a score for how much the variant alters splicing. The model is not biased by existing disease annotations or population data and was derived in such a way that it can be used to study diverse diseases and disorders and to determine the consequences of common, rare, and even spontaneous variants. RESULTS Our technique is able to accurately classify disease-causing variants and provides insights into the role of aberrant splicing in disease. We scored more than 650,000 DNA variants and found that disease-causing variants have higher scores than common variants and even those associated with disease in genome-wide association studies (GWAS). Our model predicts substantial and unexpected aberrant splicing due to variants within introns and exons, including those far from the splice site. For example, among intronic variants that are more than 30 nucleotides away from any splice site, known disease variants alter splicing nine times as often as common variants; among missense exonic disease variants, those that least affect protein function are more than five times as likely as other variants to alter splicing. Autism has been associated with disrupted splicing in brain regions, so we used our method to score variants detected using whole-genome sequencing data from individuals with and without autism. Genes with high-scoring variants include many that have previously been linked with autism, as well as new genes with known neurodevelopmental phenotypes. Most of the high-scoring variants are intronic and cannot be detected by exome analysis techniques. When we scored clinical variants in spinal muscular atrophy and colorectal cancer genes, up to 94% of variants found to alter splicing using minigene reporters were correctly classified. CONCLUSION In the context of precision medicine, causal support for variants independent of existing whole-genome variant studies is greatly needed. Our computational model was trained to predict splicing from DNA sequence alone, without using disease annotations or population data. Consequently, its predictions are independent of and complementary to population data, GWAS, expression-based quantitative trait loci (QTL), and functional annotations of the genome. As such, our technique greatly expands the opportunities for understanding the genetic determinants of disease. “Deep learning” reveals the genetic origins of disease. A computational system mimics the biology of RNA splicing by correlating DNA elements with splicing levels in healthy human tissues. The system can scan DNA and identify damaging genetic variants, including those deep within introns. This procedure has led to insights into the genetics of autism, cancers, and spinal muscular atrophy. To facilitate precision medicine and whole-genome annotation, we developed a machine-learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of more than 650,000 intronic and exonic variants revealed widespread patterns of mutation-driven aberrant splicing. Intronic disease mutations that are more than 30 nucleotides from any splice site alter splicing nine times as often as common variants, and missense exonic disease mutations that have the least impact on protein function are five times as likely as others to alter splicing. We detected tens of thousands of disease-causing mutations, including those involved in cancers and spinal muscular atrophy. Examination of intronic and exonic variants found using whole-genome sequencing of individuals with autism revealed misspliced genes with neurodevelopmental phenotypes. Our approach provides evidence for causal variants and should enable new discoveries in precision medicine.

Subfields: Molecular Biology

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Citations in the corpus

• Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (2018, background): “in reconstruction of brain circuits [2]; analysis of mutations in DNA [3]; prediction of structure-activity of potential drug molecules [4], and analyzing the particle accelerator data [5] [6].”
• Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics (2015, neutral): “f ProtVecs as well as visualization of biological sequences. Distributed Representation Distributed representation has become one of the most successful approaches in machine learning in recent years [4, 5, 7, 9]. The main idea in this approach is encoding and storing information about an item withi”
• Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, neutral): “Deep learning was recently applied to medicine and genomics to reconstruct brain circuits 35 and to predict the activity of potential drug molecules 36 , the effects of mutations in non-coding DNA on gene expressions 37,38 , and the sequence specificities of DNA and RNA-binding proteins 39 .”
• Adversarial Attacks and Defences: A Survey (2018, background): “ation along with more advanced areas like analysing potential of drug molecules [37], reconstruction of brain circuits [49], analysing particle accelerator data [71] [39], effects of mutations in DNA [32]. Deep learning network, with their unparalleled accuracy, have brought in major revolution in A”
• Deep Learning for Health Informatics (2017, neutral): “Deep learning approaches have also outperformed SVM in predicting splicing code and understanding how gene expression changes by genetic variants [48], [130].”
• Deep learning for computational biology (2016, positive): “Attesting to their utility, deep neural networks have been successfully applied to predict splicing activity (Leung et al , 2014; Xiong et al , 2015), specificities of DNA-and RNA-binding proteins Box 1 : Artificial Neural Network An artificial neural network, initially inspired by neural networks…”
• Privacy-preserving deep learning (2015, background): “Deep learning has shown promise for analyzing complex biomedical data related to cancer [13, 22, 32] and genetics [15, 56].”
• FIDDLE: An integrative deep learning framework for functional genomic data inference (2016, methodology): “Several studies have adopted ConvNets to DNA sequence analysis to predict protein binding, protein contact map, alternative splicing and accessibility from DNA sequence (Alipanahi et al. 2015; Xiong et al.2015; Kelley, Snoek, and Rinn 2016; Wang et al. 2016).”
• dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs (2020, methodology): “The companion Java program also supports searching attached databases along with dbNSFP, including dbscSNV, SPIDEX, spliceAI, and dbMTS, which helps to identify candidate diseasecausing SNVs affecting splicing and miRNA binding.”
• Convolutional LSTM Networks for Subcellular Localization of Proteins (2015, background): “Deep neural networks have gained popularity for a wide range of classification tasks in image recognition and speech tagging (Dahl et al., 2012; Krizhevsky et al., 2012) and recently also within biology for prediction of exon skipping events (Xiong et al., 2014).”
• Computer vision for high content screening (2016, background): “Examples of biological applications of deep learning include predicting the effects of genetic variants on RNA splicing (Leung et al ., 2014; Xiong et al ., 2015) and drug profiling and discovery (Ma et al ., 2015; Ramsundar et al ., 2015).”