| Prediction of Rare Single-Nucleotide Causative Mutations for Muscular Diseases in Pooled Next-Generation Sequencing Experiments. | |
MedLine Citation:
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PMID: 25029289 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Abstract Next-generation sequencing (NGS) is a new approach for biomedical research, useful for the diagnosis of genetic diseases in extremely heterogeneous conditions. In this work, we describe how data generated by high-throughput NGS experiments can be analyzed to find single nucleotide polymorphisms (SNPs) in DNA samples of patients affected by neuromuscular disorders. In particular, we consider untagged pooled NGS data, where DNA samples of different individuals are combined in a single experiment, still providing information with an uncertainty limited to only two patients. At the moment, only few publications address the problem of SNPs detection in pooled experiments, and existing tools are often inaccurate. We propose a computational procedure consisting of two parts. In the first, data are filtered by means of decision rules. The second phase is based on a supervised classification technique. In the present work, we compare different de facto standard supervised and unsupervised procedures to identify and classify variants potentially related to muscular diseases, and we discuss results in terms of statistical and biological validation. |
Authors:
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Maria Brigida Ferraro; Marco Savarese; Giuseppina Di Fruscio; Vincenzo Nigro; Mario Rosario Guarracino |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2014-7-16 |
Journal Detail:
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Title: Journal of computational biology : a journal of computational molecular cell biology Volume: - ISSN: 1557-8666 ISO Abbreviation: J. Comput. Biol. Publication Date: 2014 Jul |
Date Detail:
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Created Date: 2014-7-16 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9433358 Medline TA: J Comput Biol Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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