Document Detail

Prediction of Rare Single-Nucleotide Causative Mutations for Muscular Diseases in Pooled Next-Generation Sequencing Experiments.
MedLine Citation:
PMID:  25029289     Owner:  NLM     Status:  Publisher    
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.
Maria Brigida Ferraro; Marco Savarese; Giuseppina Di Fruscio; Vincenzo Nigro; Mario Rosario Guarracino
Related Documents :
25108449 - Models and implementation for relationship problems with dropout.
10198719 - A future prediction type artificial heart system.
23661079 - Evaluation of linkage disequilibrium in wheat with an l1-regularized sparse markov netw...
23473749 - An updated overview of animal models in neuropsychiatry.
23407359 - A dynamic bayesian markov model for phasing and characterizing haplotypes in next-gener...
23862829 - Articulatory overlap as a function of voicing in french and german consonant clusters.
23366079 - A universal hybrid decision tree classifier design for human activity classification.
16779559 - Density, climate and varying return points: an analysis of long-term population fluctua...
20128589 - Modeling and optimization of phenylalanine ammonia lyase stabilization in recombinant e...
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-7-16
Journal Detail:
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:
Created Date:  2014-7-16     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9433358     Medline TA:  J Comput Biol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Development of an Experimental Model of Proliferative Retinopathy by Intravitreal Injection of VEGF1...
Next Document:  The Relationships Between Muscle Force Steadiness and Visual Steadiness in Young and Old Adults.