Functional and Molecular Characterization of New SPTLC1 Missense Variants in Patients with Hereditary Sensory and Autonomic Neuropathy Type 1 (HSAN1).

Hereditary sensory and autonomic neuropathy type 1 is an autosomal dominant neuropathy caused by the SPTLC1 or SPTLC2 variants. These variants modify the preferred substrate of serine palmitoyl transferase, responsible for the first step of de novo sphingolipids synthesis, leading to accumulation of cytotoxic deoxysphingolipids. Diagnosis of HSAN1 is based on clinical symptoms, mainly progressive loss of distal sensory keep, and genetic analysis. Aim: Identifying new SPTLC1 or SPTLC2 "gain-of-function" variants raises the question as to their pathogenicity. This work focused on characterizing six new SPTLC1 variants using in silico prediction tools, new meta-scores, 3D modeling, and functional testing to establish their pathogenicity. Methods: Variants from six patients with HSAN1 were studied. In silico, CADD and REVEL scores and the 3D modeling software MITZLI were used to characterize the pathogenic effect of the variants. Functional tests based on plasma sphingolipids quantification (total deoxysphinganine, ceramides, and dihydroceramides) were performed by tandem mass spectrometry. Results: In silico predictors did not provide very contrasting results when functional tests discriminated the different variants according to their impact on deoxysphinganine level or canonical sphingolipids synthesis. Two SPTLC1 variants were newly described as pathogenic: SPTLC1 NM_006415.4:c.998A>G and NM_006415.4:c.1015G>A. Discussion: The combination of the different tools provides arguments to establish the pathogenicity of these new variants. When available, functional testing remains the best option to establish the in vivo impact of a variant. Moreover, the comprehension of metabolic dysregulation offers opportunities to develop new therapeutic strategies for these genetic disorders.

Large-scale comparative evaluation of user-friendly tools for predicting variant-induced alterations of splicing regulatory elements.

Discriminating which nucleotide variants cause disease or contribute to phenotypic traits remains a major challenge in human genetics. In theory, any intragenic variant can potentially affect RNA splicing by altering splicing regulatory elements (SREs). However, these alterations are often ignored mainly because pioneer SRE predictors have proved inefficient. Here, we report the first large-scale comparative evaluation of four user-friendly SRE-dedicated algorithms (QUEPASA, HEXplorer, SPANR, and HAL) tested both as standalone tools and in multiple combined ways based on two independent benchmark datasets adding up to >1,300 exonic variants studied at the messenger RNA level and mapping to 89 different disease-causing genes. These methods display good predictive power, based on decision thresholds derived from the receiver operating characteristics curve analyses, with QUEPASA and HAL having the best accuracies either as standalone or in combination. Still, overall there was a tight race between the four predictors, suggesting that all methods may be of use. Additionally, QUEPASA and HEXplorer may be beneficial as well for predicting variant-induced creation of pseudoexons deep within introns. Our study highlights the potential of SRE predictors as filtering tools for identifying disease-causing candidates among the plethora of variants detected by high-throughput DNA sequencing and provides guidance for their use in genomic medicine settings.

VaRank: a simple and powerful tool for ranking genetic variants.

Background. Most genetic disorders are caused by single nucleotide variations (SNVs) or small insertion/deletions (indels). High throughput sequencing has broadened the catalogue of human variation, including common polymorphisms, rare variations or disease causing mutations. However, identifying one variation among hundreds or thousands of others is still a complex task for biologists, geneticists and clinicians. Results. We have developed VaRank, a command-line tool for the ranking of genetic variants detected by high-throughput sequencing. VaRank scores and prioritizes variants annotated either by Alamut Batch or SnpEff. A barcode allows users to quickly view the presence/absence of variants (with homozygote/heterozygote status) in analyzed samples. VaRank supports the commonly used VCF input format for variants analysis thus allowing it to be easily integrated into NGS bioinformatics analysis pipelines. VaRank has been successfully applied to disease-gene identification as well as to molecular diagnostics setup for several hundred patients. Conclusions. VaRank is implemented in Tcl/Tk, a scripting language which is platform-independent but has been tested only on Unix environment. The source code is available under the GNU GPL, and together with sample data and detailed documentation can be downloaded from http://www.lbgi.fr/VaRank/.

A formalized description of the standard human variant nomenclature in Extended Backus-Naur Form.

BACKGROUND: The use of a standard human sequence variant nomenclature is advocated by the Human Genome Variation Society in order to unambiguously describe genetic variants in databases and literature. There is a clear need for tools that allow the mining of data about human sequence variants and their functional consequences from databases and literature. Existing text mining focuses on the recognition of protein variants and their effects. The recognition of variants at the DNA and RNA levels is essential for dissemination of variant data for diagnostic purposes. Development of new tools is hampered by the complexity of the current nomenclature, which requires processing at the character level to recognize the specific syntactic constructs used in variant descriptions. RESULTS: We approached the gene variant nomenclature as a scientific sublanguage and created two formal descriptions of the syntax in Extended Backus-Naur Form: one at the DNA-RNA level and one at the protein level. To ensure compatibility to older versions of the human sequence variant nomenclature, previously recommended variant description formats have been included. The first grammar versions were designed to help build variant description handling in the Alamut mutation interpretation software. The DNA and RNA level descriptions were then updated and used to construct the context-free parser of the Mutalyzer 2 sequence variant nomenclature checker, which has already been used to check more than one million variant descriptions. CONCLUSIONS: The Extended Backus-Naur Form provided an overview of the full complexity of the syntax of the sequence variant nomenclature, which remained hidden in the textual format and the division of the recommendations across the DNA, RNA and protein sections of the Human Genome Variation Society nomenclature website (http://www.hgvs.org/mutnomen/). This insight into the syntax of the nomenclature could be used to design detailed and clear rules for software development. The Mutalyzer 2 parser demonstrated that it facilitated decomposition of complex variant descriptions into their individual parts. The Extended Backus-Naur Form or parts of it can be used or modified by adding rules, allowing the development of specific sequence variant text mining tools and other programs, which can generate or handle sequence variant descriptions.