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      Improving diagnosis of inherited peripheral neuropathies through gene panel analysis

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          Abstract

          Background

          Inherited peripheral neuropathies (IPN) are the most common inherited neurological condition. It represents a highly heterogeneous group, both clinically and genetically.

          Targeted disease specific gene panel massively parallel sequencing (MPS) seems to be a useful tool in diagnosis of disorders with high genetic heterogeneity.

          Methods

          In our study, we have designed, validated and updated our own custom gene panel of all known genes associated with IPN. One hundred and ninety-eight patients have been tested so far. Only patients in whom mutations in more common causes or relevant genes have already been excluded were enrolled. Five consecutive panel designs were prepared according to recent literature search, the last one covering ninety-three genes. Each patient was tested only once. All data were evaluated with at least two different pipelines.

          Results

          In summary, causative mutation has been found in fifty-one patients (26 %). The results were inconclusive in thirty-one (16 %) patients. No variants of likely significance to IPN were found in one hundred and sixteen (58 %) patients.

          Conclusion

          MPS gene panel enables testing of all known IPN causes at once with high coverage and at an affordable cost making it truly a method of choice also in IPN. Gene panel testing results in several interesting results and findings.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13023-016-0500-5) contains supplementary material, which is available to authorized users.

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          Most cited references21

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

          (2013)
          Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net. Contact: hengli@broadinstitute.org.
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            Manipulation of FASTQ data with Galaxy

            Summary: Here, we describe a tool suite that functions on all of the commonly known FASTQ format variants and provides a pipeline for manipulating next generation sequencing data taken from a sequencing machine all the way through the quality filtering steps. Availability and Implementation: This open-source toolset was implemented in Python and has been integrated into the online data analysis platform Galaxy (public web access: http://usegalaxy.org; download: http://getgalaxy.org). Two short movies that highlight the functionality of tools described in this manuscript as well as results from testing components of this tool suite against a set of previously published files are available at http://usegalaxy.org/u/dan/p/fastq Contact: james.taylor@emory.edu; anton@bx.psu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              Improving SNP discovery by base alignment quality.

              Heng Li (2011)
              I propose a new application of profile Hidden Markov Models in the area of SNP discovery from resequencing data, to greatly reduce false SNP calls caused by misalignments around insertions and deletions (indels). The central concept is per-Base Alignment Quality, which accurately measures the probability of a read base being wrongly aligned. The effectiveness of BAQ has been positively confirmed on large datasets by the 1000 Genomes Project analysis subgroup. http://samtools.sourceforge.net hengli@broadinstitute.org.
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                Author and article information

                Contributors
                petra.lassuthova@gmail.com
                dana.brozkova@seznam.cz
                marcela.krutova@seznam.cz
                jana.neupauerova@hotmail.com
                jana.haberlova@gmail.com
                radim.mazanec@email.cz
                pdrimal@seznam.cz
                pseeman@yahoo.com
                Journal
                Orphanet J Rare Dis
                Orphanet J Rare Dis
                Orphanet Journal of Rare Diseases
                BioMed Central (London )
                1750-1172
                22 August 2016
                22 August 2016
                2016
                : 11
                : 1
                : 118
                Affiliations
                [1 ]Department of Paediatric Neurology, DNA Laboratory, 2nd Faculty of Medicine, Charles University in Prague and University Hospital Motol, Prague, Czech Republic
                [2 ]Department of Paediatric Neurology, 2nd Faculty of Medicine, Charles University in Prague and University Hospital Motol, Prague, Czech Republic
                [3 ]Department of Neurology, 2nd Faculty of Medicine, Charles University in Prague and University Hospital Motol, Prague, Czech Republic
                Article
                500
                10.1186/s13023-016-0500-5
                4994270
                27549087
                4c673d26-fc0a-46cc-994a-0fc0881938cf
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 28 April 2016
                : 9 August 2016
                Funding
                Funded by: IGA MH CR
                Award ID: NT 14348
                Award Recipient :
                Funded by: Supported by MH CZ DRO
                Award ID: University Hospital Motol, Prague,Czech Republic 00064203
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Infectious disease & Microbiology
                inherited peripheral neuropathies,charcot-marie-tooth,targeted gene panel testing,mutation,phenotype

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