An expanded phenotype centric benchmark of variant prioritisation tools.
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ABSTRACT:
SUBMITTER: Anderson D
PROVIDER: S-EPMC9313608 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Next generation sequencing is a standard tool used in clinical diagnostics. In Mendelian diseases the challenge is to discover the single etiological variant among thousands of benign or functionally unrelated variants. After calling variants from aligned sequencing reads, variant prioritisation tools are used to examine the conservation or potential functional consequences of variants. We hypothesised that the performance of variant prioritisation tools may vary by disease phenotype. To test this we created benchmark data sets for variants associated with different disease phenotypes. We found that performance of 24 tested tools is highly variable and differs by disease phenotype. The task of identifying a causative variant amongst a large number of benign variants is challenging for all tools, highlighting the need for further development in the field. Based on our observations, we recommend use of five top performers found in this study (FATHMM, M-CAP, MetaLR, MetaSVM and VEST3). In addition we provide tables indicating which analytical approach works best in which disease context. Variant prioritisation tools are best suited to investigate variants associated with well-studied genetic diseases, as these variants are more readily available during algorithm development than variants associated with rare diseases. We anticipate that further development into disease focussed tools will lead to significant improvements.
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Project description:The transforming growth factor-β-activated kinase 1 (TAK1) encoded by mitogen-activated protein kinase kinase kinase 7 (MAP3K7) is widely expressed and participates in multiple molecular and cellular processes, including growth, differentiation, inflammation, and apoptosis. Pathogenic variants in MAP3K7 have recently been associated with two disorders: cardiospondylocarpofacial syndrome (CSCFS) and frontometaphyseal dysplasia 2 (FMD2). To date, all small in-frame deletions and splice variants in MAP3K7 have been associated with CSCFS, whereas missense variants have been reported in both CSCFS and FMD2. Here, we present a patient with a novel heterozygous likely pathogenic variant, c.125_127del, p.(Val42del), in MAP3K7, only the sixth variant associated with CSCFS to be described in the literature. Although this patient has a phenotype that is most consistent with that of CSCFS, including valvular heart disease, short stature, fusions of the spine and bones of the hands and feet, and certain facial dysmorphisms, he interestingly has some features reported previously in FMD2 but not CSCFS. These include flexion contractures of the elbow and widely spaced first and second toes, highlighting new points of overlap between these two syndromes. We additionally point out features in the patient presented here that are rare but recurrent among CSCFS patients previously reported in the literature, as well as a new distinctive cutaneous finding not previously reported.
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Project description:SCN2A-related disorders secondary to altered function in the voltage-gated sodium channel NaV1.2 are rare with clinically heterogeneous expressions that include epilepsy, autism, and multiple severe to profound impairments and other conditions. To advance understanding of the clinical phenotypes and their relation to channel function, 81 patients (36, 44% female, median age 5.4 years) with 69 unique SCN2A variants were systematically phenotyped and their NaV1.2 channel function systematically assessed. Participants were recruited through the FamileSCN2A Foundation. Primary phenotype (epilepsy of neonatal-onset, N=27; infant onset, N=18; and later onset N=24; and autism without seizures, (N=12) was strongly correlated with a non-seizure severity index (p=0.002), which was based on presence of severe impairments in gross motor, fine motor, communication abilities, gastrostomy tube dependence, and diagnosis of cortical visual impairment and scoliosis. Non-seizure severity was greatest in the neonatal-onset group and least in the autism group (p=0.002). Children with the lowest severity indices were still severely impaired, as reflected by an average Vineland adaptive behavior composite score of 49.5 (>3 SD below the test's norm-referenced mean). Epileptic spasms were significantly more common in infant onset (67%) than in neonatal (22%) or later-onset (29%) epilepsy (p=0.007). Primary phenotype also strongly correlated with variant function (p<0.0001); gain of function and mixed function variants predominated in neonatal-onset epilepsy, shifting to moderate loss of function in infant-onset epilepsy, and severe and complete loss of function in later-onset epilepsy and autism groups. Exploratory cluster analysis identified five groups representing (1) primarily later-onset epilepsy with moderate loss of function variants and low severity indices, (2) mostly infant-onset epilepsy with moderate loss of function variants but higher severity indices, (3) late-onset and autism only with the lowest severity indices (mostly 0) and severe/complete loss of function variants. Two exclusively neonatal clusters were distinguished from each other largely on non-seizure severity scores and secondarily on variant function. The relation between primary phenotype and variant function emphasizes the role of developmental factors in the differential clinical expression of SCN2A variants based on their effects on NaV1.2 channel function. The non-seizure severity of SCN2A disorders depends on a combination of the age at seizure onset (primary phenotype) and variant function. As precision therapies for SCN2A-related disorders advance toward clinical trials, knowledge of the relationship between variant function and clinical disease expression will be valuable for identifying appropriate patients for these trials and in selecting efficient clinical outcomes.
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Project description:A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.
| S-EPMC9950798 | biostudies-literature
Project description:The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods.Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper.It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found.
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Project description:BACKGROUND:An increasing number of precision oncology programmes are being launched world-wide. To support this development, we present the Cancer Variant Explorer (CVE), an R package with an interactive Shiny web browser interface. RESULTS:Leveraging Oncotator and the Drug Gene Interaction Database, CVE offers exploration of variants within single or multiple tumour exomes to identify drivers, resistance mechanisms and to assess druggability. We present example applications including the analysis of an individual patient and a cohort-wide study, and provide a first extension of CVE by adding a tumour-specific co-expression network. CONCLUSIONS:The CVE package allows interactive variant prioritisation to expedite the analysis of cancer sequencing studies. Our framework also includes the prioritisation of druggable targets, allows exploratory analysis of tissue specific networks and is extendable for specific applications by virtue of its modular design. We encourage the use of CVE within translational research studies and molecular tumour boards. The CVE package is available via Bioconductor ( http://bioconductor.org/packages/CVE/ ).
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Project description:MotivationThe application of genome-wide chromosome conformation capture (3C) methods to prokaryotes provided insights into the spatial organization of their genomes and identified patterns conserved across the tree of life, such as chromatin compartments and contact domains. Prokaryotic genomes vary in GC content and the density of restriction sites along the chromosome, suggesting that these properties should be considered when planning experiments and choosing appropriate software for data processing. Diverse algorithms are available for the analysis of eukaryotic chromatin contact maps, but their potential application to prokaryotic data has not yet been evaluated.ResultsHere, we present a comparative analysis of domain calling algorithms using available single-microbe experimental data. We evaluated the algorithms' intra-dataset reproducibility, concordance with other tools and sensitivity to coverage and resolution of contact maps. Using RNA-seq as an example, we showed how orthogonal biological data can be utilized to validate the reliability and significance of annotated domains. We also suggest that in silico simulations of contact maps can be used to choose optimal restriction enzymes and estimate theoretical map resolutions before the experiment. Our results provide guidelines for researchers investigating microbes and microbial communities using high-throughput 3C assays such as Hi-C and 3C-seq.Availability and implementationThe code of the analysis is available at https://github.com/magnitov/prokaryotic_cids.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundTransposable elements (TEs) are an important source of genomic variability in eukaryotic genomes. Their activity impacts genome architecture and gene expression and can lead to drastic phenotypic changes. Therefore, identifying TE polymorphisms is key to better understand the link between genotype and phenotype. However, most genotype-to-phenotype analyses have concentrated on single nucleotide polymorphisms as they are easier to reliable detect using short-read data. Many bioinformatic tools have been developed to identify transposon insertions from resequencing data using short reads. Nevertheless, the performance of most of these tools has been tested using simulated insertions, which do not accurately reproduce the complexity of natural insertions.ResultsWe have overcome this limitation by building a dataset of insertions from the comparison of two high-quality rice genomes, followed by extensive manual curation. This dataset contains validated insertions of two very different types of TEs, LTR-retrotransposons and MITEs. Using this dataset, we have benchmarked the sensitivity and precision of 12 commonly used tools, and our results suggest that in general their sensitivity was previously overestimated when using simulated data. Our results also show that, increasing coverage leads to a better sensitivity but with a cost in precision. Moreover, we found important differences in tool performance, with some tools performing better on a specific type of TEs. We have also used two sets of experimentally validated insertions in Drosophila and humans and show that this trend is maintained in genomes of different size and complexity.ConclusionsWe discuss the possible choice of tools depending on the goals of the study and show that the appropriate combination of tools could be an option for most approaches, increasing the sensitivity while maintaining a good precision.
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Project description:Homozygous variants in MAG, encoding myelin-associated glycoprotein (MAG), have been associated with complicated forms of hereditary spastic paraplegia (HSP). MAG is a glycoprotein member of the immunoglobulin superfamily, expressed by myelination cells. In this study, we identified a novel homozygous missense variant in MAG (c.124T>C; p.Cys42Arg) in a Portuguese family with early-onset autosomal recessive cerebellar ataxia with neuropathy and oculomotor apraxia. We used homozygosity mapping and exome sequencing to identify the MAG variant, and cellular studies to confirm its detrimental effect. Our results showed that this variant reduces protein stability and impairs the post-translational processing (N-linked glycosylation) and subcellular localization of MAG, thereby associating a loss of protein function with the phenotype. Therefore, MAG variants should be considered in the diagnosis of hereditary cerebellar ataxia with oculomotor apraxia, in addition to spastic paraplegia.
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Project description:NECTAR (Non-synonymous Enriched Coding muTation ARchive; http://nectarmutation.org) is a database and web application to annotate disease-related and functionally important amino acids in human proteins. A number of tools are available to facilitate the interpretation of DNA variants identified in diagnostic or research sequencing. These typically identify previous reports of DNA variation at a given genomic location, predict its effects on transcript and protein sequence and may predict downstream functional consequences. Previous reports and functional annotations are typically linked by the genomic location of the variant observed. NECTAR collates disease-causing variants and functionally important amino acid residues from a number of sources. Importantly, rather than simply linking annotations by a shared genomic location, NECTAR annotates variants of interest with details of previously reported variation affecting the same codon. This provides a much richer data set for the interpretation of a novel DNA variant. NECTAR also identifies functionally equivalent amino acid residues in evolutionarily related proteins (paralogues) and, where appropriate, transfers annotations between them. As well as accessing these data through a web interface, users can upload batches of variants in variant call format (VCF) for annotation on-the-fly. The database is freely available to download from the ftp site: ftp://ftp.nectarmutation.org.
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