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A new approach to bias correction in RNA-Seq.
Bioinformatics. 2012 Jan 28;
Authors: Jones DC, Ruzzo WL, Peng X, Katze MG
Abstract
MOTIVATION: Quantification of sequence abundance in RNA-Seq experiments is often conflated by protocol-specific sequence bias. The exact sources of the bias are unknown, but may be influenced by PCR amplification, or differing primer affinities and mixtures, for example. The result is decreased accuracy in many applications, such as de novo gene annotation and transcript quantification. RESULTS: We present a new method to measure and correct for these influences using a simple graphical model. Our model does not rely on existing gene annotations, and model selection is performed automatically making it applicable with few assumptions. We evaluate our method on several data sets, and by multiple criteria, demonstrating that it effectively decreases bias and increases uniformity. Additionally, we provide theoretical and empirical results showing that the method is unlikely to have any effect on unbiased data, suggesting it can be applied with little risk of spurious adjustment. AVAILABILITY: The method is implemented in the seqbias R/Bioconductor package, available freely under the LGPL license from http://bioconductor.org. CONTACT: [email protected].
PMID: 22285831 [PubMed - as supplied by publisher]
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A new approach to bias correction in RNA-Seq.
Bioinformatics. 2012 Jan 28;
Authors: Jones DC, Ruzzo WL, Peng X, Katze MG
Abstract
MOTIVATION: Quantification of sequence abundance in RNA-Seq experiments is often conflated by protocol-specific sequence bias. The exact sources of the bias are unknown, but may be influenced by PCR amplification, or differing primer affinities and mixtures, for example. The result is decreased accuracy in many applications, such as de novo gene annotation and transcript quantification. RESULTS: We present a new method to measure and correct for these influences using a simple graphical model. Our model does not rely on existing gene annotations, and model selection is performed automatically making it applicable with few assumptions. We evaluate our method on several data sets, and by multiple criteria, demonstrating that it effectively decreases bias and increases uniformity. Additionally, we provide theoretical and empirical results showing that the method is unlikely to have any effect on unbiased data, suggesting it can be applied with little risk of spurious adjustment. AVAILABILITY: The method is implemented in the seqbias R/Bioconductor package, available freely under the LGPL license from http://bioconductor.org. CONTACT: [email protected].
PMID: 22285831 [PubMed - as supplied by publisher]
More...