Towards accurate detection and genotyping of expressed variants from whole transcriptome sequencing data.
BMC Genomics
; 13 Suppl 2: S6, 2012 Apr 12.
Article
em En
| MEDLINE
| ID: mdl-22537301
ABSTRACT
BACKGROUND:
Massively parallel transcriptome sequencing (RNA-Seq) is becoming the method of choice for studying functional effects of genetic variability and establishing causal relationships between genetic variants and disease. However, RNA-Seq poses new technical and computational challenges compared to genome sequencing. In particular, mapping transcriptome reads onto the genome is more challenging than mapping genomic reads due to splicing. Furthermore, detection and genotyping of single nucleotide variants (SNVs) requires statistical models that are robust to variability in read coverage due to unequal transcript expression levels.RESULTS:
In this paper we present a strategy to more reliably map transcriptome reads by taking advantage of the availability of both the genome reference sequence and transcript databases such as CCDS. We also present a novel Bayesian model for SNV discovery and genotyping based on quality scores.CONCLUSIONS:
Experimental results on RNA-Seq data generated from blood cell tissue of three Hapmap individuals show that our methods yield increased accuracy compared to several widely used methods. The open source code implementing our methods, released under the GNU General Public License, is available at http//dna.engr.uconn.edu/software/NGSTools/.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Análise de Sequência de RNA
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Polimorfismo de Nucleotídeo Único
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Técnicas de Genotipagem
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Transcriptoma
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article