Dataset Information

MotorPlex provides accurate variant detection across large muscle genes both in single myopathic patients and in pools of DNA samples.

ABSTRACT: Mutations in ~100 genes cause muscle diseases with complex and often unexplained genotype/phenotype correlations. Next-generation sequencing studies identify a greater-than-expected number of genetic variations in the human genome. This suggests that existing clinical monogenic testing systematically miss very relevant information.We have created a core panel of genes that cause all known forms of nonsyndromic muscle disorders (MotorPlex). It comprises 93 loci, among which are the largest and most complex human genes, such as TTN, RYR1, NEB and DMD. MotorPlex captures at least 99.2% of 2,544 exons with a very accurate and uniform coverage. This quality is highlighted by the discovery of 20-30% more variations in comparison with whole exome sequencing. The coverage homogeneity has also made feasible to apply a cost-effective pooled sequencing strategy while maintaining optimal sensitivity and specificity.We studied 177 unresolved cases of myopathies for which the best candidate genes were previously excluded. We have identified known pathogenic variants in 52 patients and potential causative ones in further 56 patients. We have also discovered 23 patients showing multiple true disease-associated variants suggesting complex inheritance. Moreover, we frequently detected other nonsynonymous variants of unknown significance in the largest muscle genes. Cost-effective combinatorial pools of DNA samples were similarly accurate (97-99%). MotorPlex is a very robust platform that overcomes for power, costs, speed, sensitivity and specificity the gene-by-gene strategy. The applicability of pooling makes this tool affordable for the screening of genetic variability of muscle genes also in a larger population. We consider that our strategy can have much broader applications.

SUBMITTER: Savarese M

PROVIDER: S-EPMC4172906 | biostudies-other | 2014 Sep

REPOSITORIES: biostudies-other

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MotorPlex provides accurate variant detection across large muscle genes both in single myopathic patients and in pools of DNA samples.

Savarese Marco M Di Fruscio Giuseppina G Mutarelli Margherita M Torella Annalaura A Magri Francesca F Santorelli Filippo Maria FM Comi Giacomo Pietro GP Bruno Claudio C Nigro Vincenzo V

Acta neuropathologica communications 20140911

Mutations in ~100 genes cause muscle diseases with complex and often unexplained genotype/phenotype correlations. Next-generation sequencing studies identify a greater-than-expected number of genetic variations in the human genome. This suggests that existing clinical monogenic testing systematically miss very relevant information.We have created a core panel of genes that cause all known forms of nonsyndromic muscle disorders (MotorPlex). It comprises 93 loci, among which are the largest and mo ...[more]

PMID: 25214167

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Project description:BackgroundRNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq can be used for detecting variants, including single nucleotide polymorphisms, small insertions/deletions, and larger variants, such as copy number variants. Notably, joint analysis of variants with cellular transcriptional states may provide insights into the impact of mutations, especially for complex and heterogeneous samples. However, this analysis is often challenging due to a prohibitively high number of variants and cells, which are difficult to summarize and visualize. Further, there is a dearth of methods that assess and summarize the association between detected variants and cellular transcriptional states.ResultsHere, we introduce XCVATR (eXpressed Clusters of Variant Alleles in Transcriptome pRofiles), a method that identifies variants and detects local enrichment of expressed variants within embedding of samples and cells in single-cell and bulk RNA-seq datasets. XCVATR visualizes local "clumps" of small and large-scale variants and searches for patterns of association between each variant and cellular states, as described by the coordinates of cell embedding, which can be computed independently using any type of distance metrics, such as principal component analysis or t-distributed stochastic neighbor embedding. Through simulations and analysis of real datasets, we demonstrate that XCVATR can detect enrichment of expressed variants and provide insight into the transcriptional states of cells and samples. We next sequenced 2 new single cell RNA-seq tumor samples and applied XCVATR. XCVATR revealed subtle differences in CNV impact on tumors.ConclusionsXCVATR is publicly available to download from https://github.com/harmancilab/XCVATR .

Dataset Information

MotorPlex provides accurate variant detection across large muscle genes both in single myopathic patients and in pools of DNA samples.

Publications

MotorPlex provides accurate variant detection across large muscle genes both in single myopathic patients and in pools of DNA samples.

Similar Datasets

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