RSEM
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Application data |
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| Biological application domain(s) | RNA-Seq alignment, RNA-Seq quantification |
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| Maintained? | Maybe |
| Programming language(s) | C++ |
Summary: We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNASeq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels.
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