Probabilistic error correction for RNA sequencing

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Summary

Using human RNA-Seq data, it is shown that SEECER greatly improves on previous methods in terms of quality of read alignment to the genome and assembly accuracy, and is the first to successfully address read error correction problems in Rna-seq data.

Type
article
Published
2013-04-03
Cited by
113
References
56
Access
Open access

Keywords

Biology, Computational biology, RNA-Seq, Transcriptome, DNA sequencing

References

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