GeneSplicer: a new computational method for splice site prediction

Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition

Identification of splice sites is imperative for prediction of gene structure. Machine learning-based approaches (MLAs) have been reported to be more successful than the rule-based methods for identification of splice sites. However, the strings of alphabets should be transformed into numeric features through sequence encoding before using them as input in MLAs. In this study, we evaluated the performances of 8 different sequence encoding schemes i.e., Bayes kernel, density and sparse (DS), distribution of tri-nucleotide and 1st order Markov model (DM), frequency difference distance measure (FDDM), paired-nucleotide frequency difference between true and false sites (FDTF), 1st order Markov model (MM1), combination of both 1st and 2nd order Markov model (MM1 + MM2) and 2nd order Markov model (MM2) in respect of predicting donor and acceptor splice sites using 5 supervised learning methods (ANN, Bagging, Boosting, RF and SVM). The encoding schemes and machine learning methods were first evaluated in 4 species i.e., A. thaliana, C. elegans, D. melanogaster and H. sapiens, and then performances were validated with another four species i.e., Ciona intestinalis, Dictyostelium discoideum, Phaeodactylum tricornutum and Trypanosoma brucei. In terms of ROC (receiver-operating-characteristics) and PR (precision-recall) curves, FDTF encoding approach achieved higher accuracy followed by either MM2 or FDDM. Further, SVM was found to achieve higher accuracy (in terms of ROC and PR curves) followed by RF across encoding schemes and species. In terms of prediction accuracy across species, the SVM-FDTF combination was optimum than other combinations of classifiers and encoding schemes. Further, splice site prediction accuracies were observed higher for the species with low intron density. To our limited knowledge, this is the first attempt as far as comprehensive evaluation of sequence encoding schemes for prediction of splice sites is concerned. We have also developed an R-package EncDNA (https://cran.r-project.org/web/packages/EncDNA/index.html) for encoding of splice site motifs with different encoding schemes, which is expected to supplement the existing nucleotide sequence encoding approaches. This study is believed to be useful for the computational biologists for predicting different functional elements on the genomic DNA.

A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples

Background

Splice sites prediction has been a long-standing problem in bioinformatics. Although many computational approaches developed for splice site prediction have achieved satisfactory accuracy, further improvement in predictive accuracy is significant, for it is contributing to predict gene structure more accurately. Determining a proper window size before prediction is necessary. Overly long window size may introduce some irrelevant features, which would reduce predictive accuracy, while the use of short window size with maximum information may performs better in terms of predictive accuracy and time cost. Furthermore, the number of false splice sites following the GT–AG rule far exceeds that of true splice sites, accurate and rapid prediction of splice sites using imbalanced large samples has always been a challenge. Therefore, based on the short window size and imbalanced large samples, we developed a new computational method named chi-square decision table (χ²-DT) for donor splice site prediction.

Results

Using a short window size of 11 bp, χ²-DT extracts the improved positional features and compositional features based on chi-square test, then introduces features one by one based on information gain, and constructs a balanced decision table aimed at implementing imbalanced pattern classification. With a 2000:271,132 (true sites:false sites) training set, χ²-DT achieves the highest independent test accuracy (93.34%) when compared with three classifiers (random forest, artificial neural network, and relaxed variable kernel density estimator) and takes a short computation time (89 s). χ²-DT also exhibits good independent test accuracy (92.40%), when validated with BG-570 mutated sequences with frameshift errors (nucleotide insertions and deletions). Moreover, χ²-DT is compared with the long-window size-based methods and the short-window size-based methods, and is found to perform better than all of them in terms of predictive accuracy.

Conclusions

Based on short window size and imbalanced large samples, the proposed method not only achieves higher predictive accuracy than some existing methods, but also has high computational speed and good robustness against nucleotide insertions and deletions.

Reviewers

This article was reviewed by Ryan McGinty, Ph.D. and Dirk Walther.

Electronic supplementary material

The online version of this article (10.1186/s13062-019-0236-y) contains supplementary material, which is available to authorized users.

De novo Mutations From Whole Exome Sequencing in Neurodevelopmental and Psychiatric Disorders: From Discovery to Application

Neurodevelopmental and psychiatric disorders are a highly disabling and heterogeneous group of developmental and mental disorders, resulting from complex interactions of genetic and environmental risk factors. The nature of multifactorial traits and the presence of comorbidity and polygenicity in these disorders present challenges in both disease risk identification and clinical diagnoses. The genetic component has been firmly established, but the identification of all the causative variants remains elusive. The development of next-generation sequencing, especially whole exome sequencing (WES), has greatly enriched our knowledge of the precise genetic alterations of human diseases, including brain-related disorders. In particular, the extensive usage of WES in research studies has uncovered the important contribution of de novo mutations (DNMs) to these disorders. Trio and quad familial WES are a particularly useful approach to discover DNMs. Here, we review the major WES studies in neurodevelopmental and psychiatric disorders and summarize how genes hit by discovered DNMs are shared among different disorders. Next, we discuss different integrative approaches utilized to interrogate DNMs and to identify biological pathways that may disrupt brain development and shed light on our understanding of the genetic architecture underlying these disorders. Lastly, we discuss the current state of the transition from WES research to its routine clinical application. This review will assist researchers and clinicians in the interpretation of variants obtained from WES studies, and highlights the need to develop consensus analytical protocols and validated lists of genes appropriate for clinical laboratory analysis, in order to reach the growing demands.

Relevance of Titin Missense and Non-Frameshifting Insertions/Deletions Variants in Dilated Cardiomyopathy

Recent advancements in next generation sequencing (NGS) technology have led to the identification of the giant sarcomere gene, titin (TTN), as a major human disease gene. Truncating variants of TTN (TTNtv) especially in the A-band region account for 20% of dilated cardiomyopathy (DCM) cases. Much attention has been focused on assessment and interpretation of TTNtv in human disease; however, missense and non-frameshifting insertions/deletions (NFS-INDELs) are difficult to assess and interpret in clinical diagnostic workflow. Targeted sequencing covering all exons of TTN was performed on a cohort of 530 primary DCM patients from three cardiogenetic centres across Europe. Using stringent bioinformatic filtering, twenty-nine and two rare TTN missense and NFS-INDELs variants predicted deleterious were identified in 6.98% and 0.38% of DCM patients, respectively. However, when compared with those identified in the largest available reference population database, no significant enrichment of such variants was identified in DCM patients. Moreover, DCM patients and reference individuals had comparable frequencies of splice-region missense variants with predicted splicing alteration. DCM patients and reference populations had comparable frequencies of rare predicted deleterious TTN missense variants including splice-region missense variants suggesting that these variants are not independently causative for DCM. Hence, these variants should be classified as likely benign in the clinical diagnostic workflow, although a modifier effect cannot be excluded at this stage.

Usefulness and Limitations of Comprehensive Characterization of mRNA Splicing Profiles in the Definition of the Clinical Relevance of BRCA1/2 Variants of Uncertain Significance

Highly penetrant variants of BRCA1/2 genes are involved in hereditary predisposition to breast and ovarian cancer. The detection of pathogenic BRCA variants has a considerable clinical impact, allowing appropriate cancer-risk management. However, a major drawback is represented by the identification of variants of uncertain significance (VUS). Many VUS potentially affect mRNA splicing, making transcript analysis an essential step for the definition of their pathogenicity. Here, we characterize the impact on splicing of ten BRCA1/2 variants. Aberrant splicing patterns were demonstrated for eight variants whose alternative transcripts were fully characterized. Different events were observed, including exon skipping, intron retention, and usage of de novo and cryptic splice sites. Transcripts with premature stop codons or in-frame loss of functionally important residues were generated. Partial/complete splicing effect and quantitative contribution of different isoforms were assessed, leading to variant classification according to Evidence-based Network for the Interpretation of Mutant Alleles (ENIGMA) consortium guidelines. Two variants could be classified as pathogenic and two as likely benign, while due to a partial splicing effect, six variants remained of uncertain significance. The association with an undefined tumor risk justifies caution in recommending aggressive risk-reduction treatments, but prevents the possibility of receiving personalized therapies with potential beneficial effect. This indicates the need for applying additional approaches for the analysis of variants resistant to classification by gene transcript analyses.

Expanding the genetic and clinical spectrum of the NONO-associated X-linked intellectual disability syndrome

The NONO gene encodes a nuclear protein involved in RNA metabolism. Hemizygous loss-of-function NONO variants have been associated with syndromic intellectual disability and with left ventricular noncompaction (LVNC). A two-year-old boy presented to the University of Utah's Penelope Undiagnosed Disease Program with developmental delay, nonfamilial features, relative macrocephaly, and dilated cardiomyopathy with LVNC and Ebstein anomaly. Brain MRI showed a thick corpus callosum, mild Chiari I malformation, and a flattened pituitary. Exome sequencing identified a novel intronic deletion (c.154+5_154+6delGT) in the NONO gene. Splicing studies demonstrated intron 4 read-through and the use of an alternative donor causing the frameshift p.Asn52Serfs*6. Family segregation analysis showed that the variant occurred de novo in the boy's unaffected mother. MRI and endocrine findings suggest that hypopituitarism may contribute to growth failure, abnormal thyroid hormone levels, cryptorchidism, or delayed puberty in patients with NONO-associated disease. Also, including this case LVNC has been observed in five out of eight patients, and this report also confirms an association between loss of NONO and Ebstein anomaly. In some cases, unrelated individuals share the same pathogenic NONO variants but do not all have clinically significant LVNC, suggesting that additional modifiers may contribute to cardiac phenotypes.

Targeted next generation sequencing reveals genetic defects underlying inherited retinal disease in Iranian families

Purpose

Inherited retinal diseases (IRDs) are clinically and genetically heterogeneous showing progressive retinal cell death which results in vision loss. IRDs include a wide spectrum of disorders, such as retinitis pigmentosa (RP), Leber congenital amaurosis (LCA), cone-rod dystrophy (CRD), and Stargardt disease (STGD1).

Methods

In this study, we performed targeted next-generation sequencing based on molecular inversion probes (MIPs) that allowed the sequence analysis of 108 IRD-associated genes in 50 Iranian IRD probands.

Results

The sequencing and variant filtering led to the identification of putative pathogenic variants in 36 out of 50 (72%) probands. Among 36 unique variants, we identified 20 novel variants in 15 genes. Four out of 36 probands carry compound heterozygous variants, and 32 probands carry homozygous variants.

Conclusions

Employing a cost-effective targeted next-generation sequencing procedure, we identified the genetic causes of different retinal disorders in the majority of Iranian families in this study.

Molecular genetic analysis using targeted NGS analysis of 677 individuals with retinal dystrophy

Inherited retinal diseases (IRDs) are a common cause of visual impairment. IRD covers a set of genetically highly heterogeneous disorders with more than 150 genes associated with one or more clinical forms of IRD. Molecular genetic diagnosis has become increasingly important especially due to expanding number of gene therapy strategies under development. Next generation sequencing (NGS) of gene panels has proven a valuable diagnostic tool in IRD. We present the molecular findings of 677 individuals, residing in Denmark, with IRD and report 806 variants of which 187 are novel. We found that deletions and duplications spanning one or more exons can explain 3% of the cases, and thus copy number variation (CNV) analysis is important in molecular genetic diagnostics of IRD. Seven percent of the individuals have variants classified as pathogenic or likely-pathogenic in more than one gene. Possible Danish founder variants in EYS and RP1 are reported. A significant number of variants were classified as variants with unknown significance; reporting of these will hopefully contribute to the elucidation of the actual clinical consequence making the classification less troublesome in the future. In conclusion, this study underlines the relevance of performing targeted sequencing of IRD including CNV analysis as well as the importance of interaction with clinical diagnoses.

Understanding human DNA variants affecting pre-mRNA splicing in the NGS era

Pre-mRNA splicing, an essential step in eukaryotic gene expression, relies on recognition of short sequences on the primary transcript intron ends and takes place along transcription by RNA polymerase II. Exonic and intronic auxiliary elements may modify the strength of exon definition and intron recognition. Splicing DNA variants (SV) have been associated with human genetic diseases at canonical intron sites, as well as exonic substitutions putatively classified as nonsense, missense or synonymous variants. Their effects on mRNA may be modulated by cryptic splice sites associated to the SV allele, comprehending exon skipping or shortening, and partial or complete intron retention. As splicing mRNA outputs result from combinatorial effects of both intrinsic and extrinsic factors, in vitro functional assays supported by computational analyses are recommended to assist SV pathogenicity assessment for human Mendelian inheritance diseases. The increasing use of next-generating sequencing (NGS) targeting full genomic gene sequence has raised awareness of the relevance of deep intronic SV in genetic diseases and inclusion of pseudo-exons into mRNA. Finally, we take advantage of recent advances in sequencing and computational technologies to analyze alternative splicing in cancer. We explore the Catalog of Somatic Mutations in Cancer (COSMIC) to describe the proportion of splice-site mutations in cis and trans regulatory elements. Genomic data from large cohorts of different cancer types are increasingly available, in addition to repositories of normal and somatic genetic variations. These are likely to bring new insights to understanding the genetic control of alternative splicing by mapping splicing quantitative trait loci in tumors.

Identification and Analysis of Genes Associated with Inherited Retinal Diseases

Inherited retinal diseases (IRDs) display a very high degree of clinical and genetic heterogeneity, which poses challenges in finding the underlying defects in known IRD-associated genes and in identifying novel IRD-associated genes. Knowledge on the molecular and clinical aspects of IRDs has increased tremendously in the last decade. Here, we outline the state-of-the-art techniques to find the causative genetic variants, with special attention for next-generation sequencing which can combine molecular diagnostics and retinal disease gene identification. An important aspect is the functional assessment of rare variants with RNA and protein effects which can only be predicted in silico. We therefore describe the in vitro assessment of putative splice defects in human embryonic kidney cells. In addition, we outline the use of stem cell technology to generate photoreceptor precursor cells from patients' somatic cells which can subsequently be used for RNA and protein studies. Finally, we outline the in silico methods to interpret the causality of variants associated with inherited retinal disease and the registry of these variants.

RNA splicing analysis in genomic medicine

High-throughput next-generation sequencing technologies have led to a rapid increase in the number of sequence variants identified in clinical practice via diagnostic genetic tests. Current bioinformatic analysis pipelines fail to take adequate account of the possible splicing effects of such variants, particularly where variants fall outwith canonical splice site sequences, and consequently the pathogenicity of such variants may often be missed. The regulation of splicing is highly complex and as a result, in silico prediction tools lack sufficient sensitivity and specificity for reliable use. Variants of all kinds can be linked to aberrant splicing in disease and the need for correct identification and diagnosis grows ever more crucial as novel splice-switching antisense oligonucleotide therapies start to enter clinical usage. RT-PCR provides a useful targeted assay of the splicing effects of identified variants, while minigene assays, massive parallel reporter assays and animal models can also be used for more detailed study of a particular splicing system, given enough time and resources. However, RNA-sequencing (RNA-seq) has the potential to be used as a rapid diagnostic tool in genomic medicine. By utilising data science approaches and machine learning, it may prove possible to finally understand and interpret the 'splicing code' and apply this knowledge in human disease diagnostics.

Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach

Background

Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both possible and widely available. RNA-seq provides unprecedented resolution to identify gene structures and resolve the diversity of splicing variants. However, currently available ab initio aligners are vulnerable to spurious alignments due to random sequence matches and sample-reference genome discordance. As a consequence, a significant set of false positive exon junction predictions would be introduced, which will further confuse downstream analyses of splice variant discovery and abundance estimation.

Results

In this work, we present a deep learning based splice junction sequence classifier, named DeepSplice, which employs convolutional neural networks to classify candidate splice junctions. We show (I) DeepSplice outperforms state-of-the-art methods for splice site classification when applied to the popular benchmark dataset HS3D, (II) DeepSplice shows high accuracy for splice junction classification with GENCODE annotation, and (III) the application of DeepSplice to classify putative splice junctions generated by Rail-RNA alignment of 21,504 human RNA-seq data significantly reduces 43 million candidates into around 3 million highly confident novel splice junctions.

Conclusions

A model inferred from the sequences of annotated exon junctions that can then classify splice junctions derived from primary RNA-seq data has been implemented. The performance of the model was evaluated and compared through comprehensive benchmarking and testing, indicating a reliable performance and gross usability for classifying novel splice junctions derived from RNA-seq alignment.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5350-1) contains supplementary material, which is available to authorized users.

Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.

Targeted Molecular Analysis in Adrenocortical Carcinomas: A Strategy Toward Improved Personalized Prognostication

CONTEXT

Adrenocortical carcinoma (ACC) has a heterogeneous prognosis, and current medical therapies have limited efficacy in its advanced stages. Genome-wide multiomics studies identified molecular patterns associated with clinical outcome.

OBJECTIVE

Here, we aimed at identifying a molecular signature useful for both personalized prognostic stratification and druggable targets, using methods applicable in clinical routine.

DESIGN

In total, 117 tumor samples from 107 patients with ACC were analyzed. Targeted next-generation sequencing of 160 genes and pyrosequencing of 4 genes were applied to formalin-fixed, paraffin-embedded (FFPE) specimens to detect point mutations, copy number alterations, and promoter region methylation. Molecular results were combined with clinical/histopathological parameters (tumor stage, age, symptoms, resection status, and Ki-67) to predict progression-free survival (PFS).

RESULTS

In addition to known driver mutations, we detected recurrent alterations in genes not previously associated with ACC (e.g., NOTCH1, CIC, KDM6A, BRCA1, BRCA2). Best prediction of PFS was obtained integrating molecular results (more than one somatic mutation, alterations in Wnt/β-catenin and p53 pathways, high methylation pattern) and clinical/histopathological parameters into a combined score (P < 0.0001, χ2 = 68.6). Accuracy of prediction for early disease progress was 83.3% (area under the receiver operating characteristic curve: 0.872, 95% confidence interval 0.80 to 0.94). Furthermore, 17 potentially targetable alterations were found in 64 patients (e.g., in CDK4, NOTCH1, NF1, MDM2, and EGFR and in DNA repair system).

CONCLUSIONS

This study demonstrates that molecular profiling of FFPE tumor samples improves prognostication of ACC beyond clinical/histopathological parameters and identifies new potential drug targets. These findings pave the way to precision medicine in this rare disease.

Characterization of three ciliopathy pedigrees expands the phenotype associated with biallelic C2CD3 variants

Whole exome sequencing (WES) is utilized in diagnostic odyssey cases to identify the underlying genetic cause associated with complex phenotypes. Recent publications suggest that WES reveals the genetic cause in ~25% of these cases and is most successful when applied to children with neurological disease. The residual 75% of cases remain genetically elusive until more information becomes available in the literature or functional studies are pursued. WES performed on three families with presumed ciliopathy diagnoses, including orofaciodigital (OFD) syndrome, fetal encephalocele, or Joubert-related disorder, identified compound heterozygous variants in C2CD3. Biallelic variants in C2CD3 have previously been associated with ciliopathies, including OFD syndrome type 14 (OFD14; MIM: 615948). As three of the six identified variants were predicted to affect splicing, exon-skipping analysis using either RNA sequencing or PCR-based methods were completed to determine the pathogenicity of these variants, and showed that each of the splicing variants led to a frameshifted protein product. Using these studies in combination with the 2015 ACMG guidelines, each of the six identified variants were classified as either pathogenic or likely pathogenic, and are therefore likely responsible for our patients' phenotypes. Each of the families had a distinct clinical phenotype and severity of disease, extending from lethal to viable. These findings highlight that there is a broad phenotypic spectrum associated with C2CD3-mediated disease and not all patients present with the typical features of OFD14.

In Silico Analysis of Missense Mutations as a First Step in Functional Studies: Examples from Two Sphingolipidoses

In order to delineate a better approach to functional studies, we have selected 23 missense mutations distributed in different domains of two lysosomal enzymes, to be studied by in silico analysis. In silico analysis of mutations relies on computational modeling to predict their effects. Various computational platforms are currently available to check the probable causality of mutations encountered in patients at the protein and at the RNA levels. In this work we used four different platforms freely available online (Protein Variation Effect Analyzer- PROVEAN, PolyPhen-2, Swiss-model Expert Protein Analysis System—ExPASy, and SNAP2) to check amino acid substitutions and their effect at the protein level. The existence of functional studies, regarding the amino acid substitutions, led to the selection of the distinct protein mutants. Functional data were used to compare the results obtained with different bioinformatics tools. With the advent of next-generation sequencing, it is not feasible to carry out functional tests in all the variants detected. In silico analysis seems to be useful for the delineation of which mutants are worth studying through functional studies. Therefore, prediction of the mutation impact at the protein level, applying computational analysis, confers the means to rapidly provide a prognosis value to genotyping results, making it potentially valuable for patient care as well as research purposes. The present work points to the need to carry out functional studies in mutations that might look neutral. Moreover, it should be noted that single nucleotide polymorphisms (SNPs), occurring in coding and non-coding regions, may lead to RNA alterations and should be systematically verified. Functional studies can gain from a preliminary multi-step approach, such as the one proposed here.

Hereditary breast and ovarian cancer in Andalusian families: a genetic population study

Background

The BRCA1/2 mutation profile varies in Spain according to the geographical area studied. The mutational profile of BRCA1/2 in families at risk for hereditary breast and ovarian cancer has not so far been reported in Andalusia (southern Spain).

Methods

We analysed BRCA1/2 germline mutations in 562 high-risk cases with breast and/or ovarian cancer from Andalusian families from 2010 to 2015.

Results

Among the 562 cases, 120 (21.4%) carried a germline pathogenic mutation in BRCA1/2; 50 in BRCA1 (41.7%) and 70 in BRCA2 (58.3%). We detected 67 distinct mutations (29 in BRCA1 and 38 in BRCA2), of which 3 in BRCA1 (c.845C > A, c.1222_1223delAC, c.2527delA) and 5 in BRCA2 (c.293 T > G, c.5558_5559delGT, c.6034delT, c.6650_6654delAAGAT, c.6652delG) had not been previously described. The most frequent mutations in BRCA1 were c.5078_5080delCTG (10%) and c.5123C > A (10%), and in BRCA2 they were c.9018C > A (14%) and c.5720_5723delCTCT (8%). We identified 5 variants of unknown significance (VUS), all in BRCA2 (c.5836 T > C, c.6323G > T, c.9501 + 3A > T, c.8022_8030delGATAATGGA, c.10186A > C). We detected 76 polymorphisms (31 in BRCA1, 45 in BRCA2) not associated with breast cancer risk.

Conclusions

This is the first study reporting the mutational profile of BRCA1/2 in Andalusia. We identified 21.4% of patients harbouring BRCA1/2 mutations, 58.3% of them in BRCA2. We also characterized the clinical data, mutational profile, VUS and haplotype profile.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4537-9) contains supplementary material, which is available to authorized users.

Splicing mutations in human genetic disorders: examples, detection, and confirmation

Precise pre-mRNA splicing, essential for appropriate protein translation, depends on the presence of consensus "cis" sequences that define exon-intron boundaries and regulatory sequences recognized by splicing machinery. Point mutations at these consensus sequences can cause improper exon and intron recognition and may result in the formation of an aberrant transcript of the mutated gene. The splicing mutation may occur in both introns and exons and disrupt existing splice sites or splicing regulatory sequences (intronic and exonic splicing silencers and enhancers), create new ones, or activate the cryptic ones. Usually such mutations result in errors during the splicing process and may lead to improper intron removal and thus cause alterations of the open reading frame. Recent research has underlined the abundance and importance of splicing mutations in the etiology of inherited diseases. The application of modern techniques allowed to identify synonymous and nonsynonymous variants as well as deep intronic mutations that affected pre-mRNA splicing. The bioinformatic algorithms can be applied as a tool to assess the possible effect of the identified changes. However, it should be underlined that the results of such tests are only predictive, and the exact effect of the specific mutation should be verified in functional studies. This article summarizes the current knowledge about the "splicing mutations" and methods that help to identify such changes in clinical diagnosis.

Human Splice-Site Prediction with Deep Neural Networks

Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site. The length of input is 140 nucleotides, with the consensus sequence (i.e., "GT" and "AG" for the donor and acceptor sites, respectively) in the middle. Each input sequence model is applied to the pretrained DNN model that determines the probability that an input is a splice site. The model consists of convolutional layers and bidirectional long short-term memory network layers. The pretraining and validation were conducted using the data set tested in previously reported methods. The performance evaluation results showed that the proposed method can outperform the previous methods. In addition, the pattern learned by the DNNs was visualized as position frequency matrices (PFMs). Some of PFMs were very similar to the consensus sequence. The trained DNN model and the brief source code for the prediction system are uploaded. Further improvement will be achieved following the further development of DNNs.

Markovian encoding models in human splice site recognition using SVM

Splice site recognition is among the most significant and challenging tasks in bioinformatics due to its key role in gene annotation. Effective prediction of splice site requires nucleotide encoding methods that reveal the characteristics of DNA sequences to provide appropriate features to serve as input of machine learning classifiers. Markovian models are the most influential encoding methods that highly used for pattern recognition in biological data. However, a direct performance comparison of these methods in splice site domain has not been assessed yet. This study compares various Markovian encoding models for splice site prediction utilizing support vector machine, as the most outstanding learning method in the domain, and conducts a new precise evaluation of Markovian approaches that corrects this limitation. Moreover, a novel sequence encoding approach based on third order Markov model (MM3) is proposed. The experimental results show that the proposed method, namely MM3-SVM, performs significantly better than thirteen best known state-of-the-art algorithms, while tested on HS3D dataset considering several performance criteria. Further, it achieved higher prediction accuracy than several well-known tools like NNsplice, MEM, MM1, WMM, and GeneID, using an independent test set of 50 genes. We also developed MMSVM, a web tool to predict splice sites in any human sequence using the proposed approach. The MMSVM web server can be assessed at https://pashaei.shinyapps.io/mmsvm.

ABCA4 midigenes reveal the full splice spectrum of all reported noncanonical splice site variants in Stargardt disease

Stargardt disease is caused by variants in the ABCA4 gene, a significant part of which are noncanonical splice site (NCSS) variants. In case a gene of interest is not expressed in available somatic cells, small genomic fragments carrying potential disease-associated variants are tested for splice abnormalities using in vitro splice assays. We recently discovered that when using small minigenes lacking the proper genomic context, in vitro results do not correlate with splice defects observed in patient cells. We therefore devised a novel strategy in which a bacterial artificial chromosome was employed to generate midigenes, splice vectors of varying lengths (up to 11.7 kb) covering almost the entire ABCA4 gene. These midigenes were used to analyze the effect of all 44 reported and three novel NCSS variants on ABCA4 pre-mRNA splicing. Intriguingly, multi-exon skipping events were observed, as well as exon elongation and intron retention. The analysis of all reported NCSS variants in ABCA4 allowed us to reveal the nature of aberrant splicing events and to classify the severity of these mutations based on the residual fraction of wild-type mRNA. Our strategy to generate large overlapping splice vectors carrying multiple exons, creating a toolbox for robust and high-throughput analysis of splice variants, can be applied to all human genes.

Targeted next-generation sequencing provides novel clues for associated epilepsy and cardiac conduction disorder/SUDEP

Sudden unexpected death in epilepsy is an unpredicted condition in patients with a diagnosis of epilepsy, and autopsy does not conclusively identify cause of death. Although the pathophysiological mechanisms that underlie this entity remain unknown, the fact that epilepsy can affect cardiac function is not surprising. The genetic factors involving ion channels co-expressed in the heart and brain and other candidate genes have been previously described. In the present study, 20 epilepsy patients with personal or family history of heart rhythm disturbance/cardiac arrhythmias/sudden death were sequenced using a custom re-sequencing panel. Twenty-six relatives were genetically analysed to ascertain the family segregation in ten individuals. Four subjects revealed variants with positive genotype-phenotype segregation: four missense variants in the CDKL5, CNTNAP2, GRIN2A and ADGRV1 genes and one copy number variant in KCNQ1. The potential pathogenic role of variants in new candidate genes will need further studies in larger cohorts, and the evaluation of the potential pathogenic role in the cardio-cerebral mechanisms requires in vivo/in vitro studies. In addition to family segregation, evaluation of the potential pathogenic roles of these variants in cardio-cerebral mechanisms by in vivo/in vitro studies should also be performed. The potential pathogenic role of variants in new candidate genes will need further studies in larger cohorts.

Identification of deep intronic variants in 15 haemophilia A patients by next generation sequencing of the whole factor VIII gene

Current screening methods for factor VIII gene (F8) mutations can reveal the causative alteration in the vast majority of haemophilia A patients. Yet, standard diagnostic methods fail in about 2 % of cases. This study aimed at analysing the entire intronic sequences of the F8 gene in 15 haemophilia A patients by next generation sequencing. All patients had a mild to moderate phenotype and no mutation in the coding sequence and splice sites of the F8 gene could be diagnosed so far. Next generation sequencing data revealed 23 deep intronic candidate variants in several F8 introns, including six recurrent variants and three variants that have been described before. One patient additionally showed a deletion of 9.2 kb in intron 1, mediated by Alu-type repeats. Several bioinformatic tools were used to score the variants in comparison to known pathogenic F8 mutations in order to predict their deleteriousness. Pedigree analyses showed a correct segregation pattern for three of the presumptive mutations. In each of the 15 patients analysed, at least one deep intronic variant in the F8 gene was identified and predicted to alter F8 mRNA splicing. Reduced F8 mRNA levels and/or stability would be well compatible with the patients’ mild to moderate haemophilia A phenotypes. The next generation sequencing approach used proved an efficient method to screen the complete F8 gene and could be applied as a one-stop sequencing method for molecular diagnostics of haemophilia A.

Note: The work was carried out at the Department of Human Genetics, University of Würzburg, Biocenter, Am Hubland, 97074 Würzburg, Germany. New

Urine-derived podocytes-lineage cells: A promising tool for precision medicine in Alport Syndrome

Alport Syndrome (ATS) is a rare genetic disorder caused by collagen IV genes mutations, leading to glomerular basement membrane damage up to end-stage renal disease. Podocytes, the main component of the glomerular structure, are the only cells able to produce all the three collagens IV alpha chains associated with ATS and thus, they are key players in ATS pathogenesis. However, podocytes-targeted therapeutic strategies have been hampered by the difficulty of non-invasively isolating them and transcripts-based diagnostic approaches are complicated by the inaccessibility of other COL4 chains-expressing cells. We firstly isolated podocyte-lineage cells from ATS patients' urine samples, in a non-invasive way. RT-PCR analysis revealed COL4A3, COL4A4, and COL4A5 expression. Transcripts analysis on RNA extracted from patient's urine derived podocyte-lineage cells allowed defining the pathogenic role of intronic variants, namely one mutation in COL4A3 (c.3882+5G>A), three mutations in COL4A4 (c.1623+2T>A, c.3699_3706+1del, c.2545+143T>A), and one mutation in COL4A5 (c.3454+2T>C). Therefore, our cellular model represents a novel tool, essential to unequivocally prove the effect of spliceogenic intronic variants on transcripts expressed exclusively at a glomerular level. This process is a key step for providing the patient with a definite molecular diagnosis and with a proper recurrence risk. The established system also opens up the possibility of testing personalized therapeutic approaches on disease-relevant cells.

ABCA4 midigenes reveal the full splice spectrum of all reported noncanonical splice site variants in Stargardt disease

Stargardt disease is caused by variants in the ABCA4 gene, a significant part of which are noncanonical splice site (NCSS) variants. In case a gene of interest is not expressed in available somatic cells, small genomic fragments carrying potential disease-associated variants are tested for splice abnormalities using in vitro splice assays. We recently discovered that when using small minigenes lacking the proper genomic context, in vitro results do not correlate with splice defects observed in patient cells. We therefore devised a novel strategy in which a bacterial artificial chromosome was employed to generate midigenes, splice vectors of varying lengths (up to 11.7 kb) covering almost the entire ABCA4 gene. These midigenes were used to analyze the effect of all 44 reported and three novel NCSS variants on ABCA4 pre-mRNA splicing. Intriguingly, multi-exon skipping events were observed, as well as exon elongation and intron retention. The analysis of all reported NCSS variants in ABCA4 allowed us to reveal the nature of aberrant splicing events and to classify the severity of these mutations based on the residual fraction of wild-type mRNA. Our strategy to generate large overlapping splice vectors carrying multiple exons, creating a toolbox for robust and high-throughput analysis of splice variants, can be applied to all human genes.

18F-DOPA PET/CT and 68Ga-DOTANOC PET/CT scans as diagnostic tools in focal congenital hyperinsulinism: a blinded evaluation

Purpose

Focal congenital hyperinsulinism (CHI) is curable by surgery, which is why identification of the focal lesion is crucial. We aimed to determine the use of 18F–fluoro-dihydroxyphenylalanine (18F-DOPA) PET/CT vs. 68Ga-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic-acid-1-Nal3-octreotide (68Ga-DOTANOC) PET/CT as diagnostic tools in focal CHI.

Methods

PET/CT scans of children with CHI admitted to Odense University Hospital between August 2005 and June 2016 were retrospectively evaluated visually and by their maximal standardized uptake values (SUV_max) by two independent examiners, blinded for clinical, surgical and pathological data. Pancreatic histology was used as the gold standard. For patients without surgery, the genetic profile served as the gold standard.

Results

Fifty-five CHI patients were examined by PET/CT (18F-DOPA n = 53, 68Ga-DOTANOC n = 18). Surgery was performed in 34 patients, no surgery in 21 patients. Fifty-one patients had a classifiable outcome, either by histology (n = 33, 22 focal lesions, 11 non-focal) or by genetics (n = 18, all non-focal). The predictive performance of 18F-DOPA PET/CT to identify focal CHI was identical by visual- and cut-off-based evaluation: sensitivity (95% CI) of 1 (0.85–1); specificity of 0.96 (0.82–0.99). The optimal 18F-DOPA PET SUV_max ratio cut-off was 1.44 and the optimal 68Ga-DOTANOC PET SUV_max cut-off was 6.77 g/ml. The area under the receiver operating curve was 0.98 (0.93–1) for 18F-DOPA PET vs. 0.71 (0.43–0.95) for 68Ga-DOTANOC PET (p < 0.03). In patients subjected to surgery, localization of the focal lesion was correct in 91%, and 100%, by 18F-DOPA PET/CT and 68Ga-DOTANOC PET/CT, respectively.

Conclusion

18F-DOPA PET/CT was excellent in predicting focal CHI and superior compared to 68Ga-DOTANOC PET/CT. Further use of 68GA-DOTANOC PET/CT in predicting focal CHI is discouraged.

Electronic supplementary material

The online version of this article (10.1007/s00259-017-3867-1) contains supplementary material, which is available to authorized users.

Rare, protein-truncating variants in ATM, CHEK2 and PALB2, but not XRCC2, are associated with increased breast cancer risks

BACKGROUND

Breast cancer (BC) is the most common malignancy in women and has a major heritable component. The risks associated with most rare susceptibility variants are not well estimated. To better characterise the contribution of variants in ATM, CHEK2, PALB2 and XRCC2, we sequenced their coding regions in 13 087 BC cases and 5488 controls from East Anglia, UK.

METHODS

Gene coding regions were enriched via PCR, sequenced, variant called and filtered for quality. ORs for BC risk were estimated separately for carriers of truncating variants and of rare missense variants, which were further subdivided by functional domain and pathogenicity as predicted by four in silico algorithms.

RESULTS

Truncating variants in PALB2 (OR=4.69, 95% CI 2.27 to 9.68), ATM (OR=3.26; 95% CI 1.82 to 6.46) and CHEK2 (OR=3.11; 95% CI 2.15 to 4.69), but not XRCC2 (OR=0.94; 95% CI 0.26 to 4.19) were associated with increased BC risk. Truncating variants in ATM and CHEK2 were more strongly associated with risk of oestrogen receptor (ER)-positive than ER-negative disease, while those in PALB2 were associated with similar risks for both subtypes. There was also some evidence that missense variants in ATM, CHEK2 and PALB2 may contribute to BC risk, but larger studies are necessary to quantify the magnitude of this effect.

CONCLUSIONS

Truncating variants in PALB2 are associated with a higher risk of BC than those in ATM or CHEK2. A substantial risk of BC due to truncating XRCC2 variants can be excluded.

Alternative Splicing in Genetic Diseases: Improved Diagnosis and Novel Treatment Options

Alternative splicing is an important mechanism to regulate gene expression and to expand the repertoire of gene products in order to accommodate an increase in complexity of multicellular organisms. It needs to be precisely regulated, which is achieved via RNA structure, splicing factors, transcriptional regulation, and chromatin. Changes in any of these factors can lead to disease. These may include the core spliceosome, splicing enhancer/repressor sequences and their interacting proteins, the speed of transcription by RNA polymerase II, and histone modifications. While the basic principle of splicing is well understood, it is still very difficult to predict splicing outcome, due to the multiple levels of regulation. Current molecular diagnostics mainly uses Sanger sequencing of exons, or next-generation sequencing of gene panels or the whole exome. Functional analysis of potential splicing variants is scarce, and intronic variants are often not considered. This likely results in underestimation of the percentage of splicing variants. Understanding how sequence variants may affect splicing is not only crucial for confirmation of diagnosis and for genetic counseling, but also for the development of novel treatment options. These include small molecules, transsplicing, antisense oligonucleotides, and gene therapy. Here we review the current state of molecular mechanisms of splicing regulation and how deregulation can lead to human disease, diagnostics to detect splicing variants, and novel treatment options based on splicing correction.

iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder

Gene splicing is one of the most significant biological processes in eukaryotic gene expression, such as RNA splicing, which can cause a pre-mRNA to produce one or more mature messenger RNAs containing the coded information with multiple biological functions. Thus, identifying splicing sites in DNA/RNA sequences is significant for both the bio-medical research and the discovery of new drugs. However, it is expensive and time consuming based only on experimental technique, so new computational methods are needed. To identify the splice donor sites and splice acceptor sites accurately and quickly, a deep sparse auto-encoder model with two hidden layers, called iSS-PC, was constructed based on minimum error law, in which we incorporated twelve physical-chemical properties of the dinucleotides within DNA into PseDNC to formulate given sequence samples via a battery of cross-covariance and auto-covariance transformations. In this paper, five-fold cross-validation test results based on the same benchmark data-sets indicated that the new predictor remarkably outperformed the existing prediction methods in this field. Furthermore, it is expected that many other related problems can be also studied by this approach. To implement classification accurately and quickly, an easy-to-use web-server for identifying slicing sites has been established for free access at: http://www.jci-bioinfo.cn/iSS-PC.

Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists

Widespread clinical laboratory implementation of next-generation sequencing-based cancer testing has highlighted the importance and potential benefits of standardizing the interpretation and reporting of molecular results among laboratories. A multidisciplinary working group tasked to assess the current status of next-generation sequencing-based cancer testing and establish standardized consensus classification, annotation, interpretation, and reporting conventions for somatic sequence variants was convened by the Association for Molecular Pathology with liaison representation from the American College of Medical Genetics and Genomics, American Society of Clinical Oncology, and College of American Pathologists. On the basis of the results of professional surveys, literature review, and the Working Group's subject matter expert consensus, a four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Cancer genomics is a rapidly evolving field; therefore, the clinical significance of any variant in therapy, diagnosis, or prognosis should be reevaluated on an ongoing basis. Reporting of genomic variants should follow standard nomenclature, with testing method and limitations clearly described. Clinical recommendations should be concise and correlate with histological and clinical findings.

Whole-transcriptome sequencing in blood provides a diagnosis of spinal muscular atrophy with progressive myoclonic epilepsy

At least 15% of the disease-causing mutations affect mRNA splicing. Many splicing mutations are missed in a clinical setting due to limitations of in silico prediction algorithms or their location in noncoding regions. Whole-transcriptome sequencing is a promising new tool to identify these mutations; however, it will be a challenge to obtain disease-relevant tissue for RNA. Here, we describe an individual with a sporadic atypical spinal muscular atrophy, in whom clinical DNA sequencing reported one pathogenic ASAH1 mutation (c.458A>G;p.Tyr153Cys). Transcriptome sequencing on patient leukocytes identified a highly significant and atypical ASAH1 isoform not explained by c.458A>G(p<10-16 ). Subsequent Sanger-sequencing identified the splice mutation responsible for the isoform (c.504A>C;p.Lys168Asn) and provided a molecular diagnosis of autosomal-recessive spinal muscular atrophy with progressive myoclonic epilepsy. Our findings demonstrate the utility of RNA sequencing from blood to identify splice-impacting disease mutations for nonhematological conditions, providing a diagnosis for these otherwise unsolved patients.

Comparison of Alternative Splicing Junction Detection Tools Using RNA-Seq Data

BACKGROUND

Alternative splicing (AS) is a posttranscriptional process that produces differ-ent transcripts from the same gene and is important to produce diverse protein products in response to environmental stimuli. AS occurs at specific sites on the mRNA sequence, some of which have been de-fined. Multiple bioinformatics tools have been developed to detect AS from experimental data.

OBJECTIVES

The goal of this review is to help researchers use specific tools to aid their research and to develop new AS detection tools based on these previously established tools.

METHOD

We selected 15 AS detection tools that were recently published; we classified and delineated them on several aspects. Also, a performance comparison of these tools with the same starting input was conducted.

RESULT

We reviewed the following categorized features of the tools: Publication information, working principles, generic and distinct workflows, running platform, input data requirement, sequencing depth dependency, reads mapped to multiple locations, isoform annotation basis, precise detected AS types, and performance benchmarks.

CONCLUSION

Through comparisons of these tools, we provide a panorama of the advantages and short-comings of each tool and their scopes of application.

Improving genetic diagnosis in Mendelian disease with transcriptome sequencing

Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches.

Clinical application of ACMG-AMP guidelines in HNF1A and GCK variants in a cohort of MODY families

Maturity-onset diabetes of the young (MODY) is a form of monogenic diabetes with autosomal dominant inheritance. GCK -MODY and HNF1A -MODY are the prevalent subtypes. Currently, there is growing concern regarding the correct interpretation of molecular genetic findings. The American College of Medical Genetics and Genomics (ACMG) updated guidelines to interpret and classify molecular variants. This study aimed to determine the prevalence of MODY ( GCK / HNF1A ) in a large cohort of Brazilian families, to report variants related to phenotype, and to classify them according to ACMG guidelines. One hundred and nine probands were investigated, 45% with clinical suspicion of GCK -MODY and 55% with suspicion of HNF1A -MODY. Twenty-five different variants were identified in GCK gene (30 probands-61% of positivity), and 7 variants in HNF1A (10 probands-17% of positivity). Fourteen of them were novel (12- GCK /2- HNF1A ). ACMG guidelines were able to classify a large portion of variants as pathogenic (36%- GCK /86%- HNF1A ) and likely pathogenic (44%- GCK /14%- HNF1A ), with 16% (5/32) as uncertain significance. This allows us to determine the pathogenicity classification more efficiently, and also reinforces the suspected associations with the phenotype among novel variants.

High-confidence coding and noncoding transcriptome maps

The advent of high-throughput RNA sequencing (RNA-seq) has led to the discovery of unprecedentedly immense transcriptomes encoded by eukaryotic genomes. However, the transcriptome maps are still incomplete partly because they were mostly reconstructed based on RNA-seq reads that lack their orientations (known as unstranded reads) and certain boundary information. Methods to expand the usability of unstranded RNA-seq data by predetermining the orientation of the reads and precisely determining the boundaries of assembled transcripts could significantly benefit the quality of the resulting transcriptome maps. Here, we present a high-performing transcriptome assembly pipeline, called CAFE, that significantly improves the original assemblies, respectively assembled with stranded and/or unstranded RNA-seq data, by orienting unstranded reads using the maximum likelihood estimation and by integrating information about transcription start sites and cleavage and polyadenylation sites. Applying large-scale transcriptomic data comprising 230 billion RNA-seq reads from the ENCODE, Human BodyMap 2.0, The Cancer Genome Atlas, and GTEx projects, CAFE enabled us to predict the directions of about 220 billion unstranded reads, which led to the construction of more accurate transcriptome maps, comparable to the manually curated map, and a comprehensive lncRNA catalog that includes thousands of novel lncRNAs. Our pipeline should not only help to build comprehensive, precise transcriptome maps from complex genomes but also to expand the universe of noncoding genomes.

Genotypic spectrum and phenotype correlations of ABCA4-associated disease in patients of south Asian descent

Variants in the ABCA4 gene are the most common cause of juvenile-onset blindness affecting close to 1 in 10 000 people worldwide. Disease severity varies largely according to genotype, which can be specific to ethnic and racial groups. Here we investigate the spectrum of ABCA4 variation and its phenotypic expression in 38 patients of South Asian descent, notably from India, Pakistan, Bangladesh and Sri Lanka. Sequencing of all exons and flanking intronic sequences of ABCA4 revealed disease-causing variants in all patients: 3 in 2.6%, 2 in 81.6% and 1 in 15.8%. Altogether, 36 distinct variants were identified, including 9 previously not described. The most frequent variant c.5882G>A, p.(G1961E) was found in half the patients, the highest ever reported in a single study cohort. The South Asian founder variant c.859-9T>C was identified along with other founder variants ascribed to Danish, Chinese, Mexican and African patients. Patients carrying c.5882G>A, p.(G1961E) exhibited a consistently confined disease phenotype, normal quantitative autofluorescence (qAF) levels and preserved full-field ERG (ffERG) while c.859-9T>C resulted in widespread disease, significantly elevated qAF and reduced to non-detectable ffERG. South Asian patients present with a relatively unique ABCA4 profile comprised of various ethnic founder variants resulting in two or three major retinal phenotypes.

Nonketotic hyperglycinemia: Functional assessment of missense variants in GLDC to understand phenotypes of the disease

The rapid analysis of genomic data is providing effective mutational confirmation in patients with clinical and biochemical hallmarks of a specific disease. This is the case for nonketotic hyperglycinemia (NKH), a Mendelian disorder causing seizures in neonates and early-infants, primarily due to mutations in the GLDC gene. However, understanding the impact of missense variants identified in this gene is a major challenge for the application of genomics into clinical practice. Herein, a comprehensive functional and structural analysis of 19 GLDC missense variants identified in a cohort of 26 NKH patients was performed. Mutant cDNA constructs were expressed in COS7 cells followed by enzymatic assays and Western blot analysis of the GCS P-protein to assess the residual activity and mutant protein stability. Structural analysis, based on molecular modeling of the 3D structure of GCS P-protein, was also performed. We identify hypomorphic variants that produce attenuated phenotypes with improved prognosis of the disease. Structural analysis allows us to interpret the effects of mutations on protein stability and catalytic activity, providing molecular evidence for clinical outcome and disease severity. Moreover, we identify an important number of mutants whose loss-of-functionality is associated with instability and, thus, are potential targets for rescue using folding therapeutic approaches.

Characterization of two novel intronic OPA1 mutations resulting in aberrant pre-mRNA splicing

BACKGROUND

We report two novel splice region mutations in OPA1 in two unrelated families presenting with autosomal-dominant optic atrophy type 1 (ADOA1) (ADOA or Kjer type optic atrophy). Mutations in OPA1 encoding a mitochondrial inner membrane protein are a major cause of ADOA.

METHODS

We analyzed two unrelated families including four affected individuals clinically suspicious of ADOA. Standard ocular examinations were performed in affected individuals of both families. All coding exons, as well as exon-intron boundaries of the OPA1 gene were sequenced. In addition, multiplex ligation-dependent probe amplification (MLPA) was performed to uncover copy number variations in OPA1. mRNA processing was monitored using RT-PCR and subsequent cDNA analysis.

RESULTS

We report two novel splice region mutations in OPA1 in two unrelated individuals and their affected relatives, which were previously not described in the literature. In one family the heterozygous insertion and deletion c.[611-37_611-38insACTGGAGAATGTAAAGGGCTTT;611-6_611-16delCATATTTATCT] was found in all investigated family members leading to the activation of an intronic cryptic splice site. In the second family sequencing of OPA1 disclosed a de novo heterozygous deletion c.2012+4_2012+7delAGTA resulting in exon 18 and 19 skipping, which was not detected in healthy family members.

CONCLUSION

We identified two novel intronic mutations in OPA1 affecting the correct OPA1 pre-mRNA splicing, which was confirmed by OPA1 cDNA analysis. This study shows the importance of transcript analysis to determine the consequences of unclear intronic mutations in OPA1 in proximity to the intron-exon boundaries.

Intragenic FMR1 disease-causing variants: a significant mutational mechanism leading to Fragile-X syndrome

Fragile-X syndrome (FXS) is a frequent genetic form of intellectual disability (ID). The main recurrent mutagenic mechanism causing FXS is the expansion of a CGG repeat sequence in the 5'-UTR of the FMR1 gene, therefore, routinely tested in ID patients. We report here three FMR1 intragenic pathogenic variants not affecting this sequence, identified using high-throughput sequencing (HTS): a previously reported hemizygous deletion encompassing the last exon of FMR1, too small to be detected by array-CGH and inducing decreased expression of a truncated form of FMRP protein, in three brothers with ID (family 1) and two splice variants in boys with sporadic ID: a de novo variant c.990+1G>A (family 2) and a maternally inherited c.420-8A>G variant (family 3). After clinical reevaluation, the five patients presented features consistent with FXS (mean Hagerman's scores=15). We conducted a systematic review of all rare non-synonymous variants previously reported in FMR1 in ID patients and showed that six of them are convincing pathogenic variants. This study suggests that intragenic FMR1 variants, although much less frequent than CGG expansions, are a significant mutational mechanism leading to FXS and demonstrates the interest of HTS approaches to detect them in ID patients with a negative standard work-up.

In Silico Functional Meta-Analysis of 5,962 ABCA4 Variants in 3,928 Retinal Dystrophy Cases

Variants in the ABCA4 gene are associated with a spectrum of inherited retinal diseases (IRDs), most prominently with autosomal recessive (ar) Stargardt disease (STGD1) and ar cone-rod dystrophy. The clinical outcome to a large degree depends on the severity of the variants. To provide an accurate prognosis and to select patients for novel treatments, functional significance assessment of nontruncating ABCA4 variants is important. We collected all published ABCA4 variants from 3,928 retinal dystrophy cases in a Leiden Open Variation Database, and compared their frequency in 3,270 Caucasian IRD cases with 33,370 non-Finnish European control individuals. Next to the presence of 270 protein-truncating variants, 191 nontruncating variants were significantly enriched in the patient cohort. Furthermore, 30 variants were deemed benign. Assessing the homozygous occurrence of frequent variants in IRD cases based on the allele frequencies in control individuals confirmed the mild nature of the p.[Gly863Ala, Gly863del] variant and identified three additional mild variants (p.(Ala1038Val), c.5714+5G>A, and p.(Arg2030Gln)). The p.(Gly1961Glu) variant was predicted to act as a mild variant in most cases. Based on these data, in silico analyses, and American College of Medical Genetics and Genomics guidelines, we provide pathogenicity classifications on a five-tier scale from benign to pathogenic for all variants in the ABCA4-LOVD database.