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Produces merged raw Illumina paired-end reads with low false positive rates (FPRs). PEAR combines reads by maximizing the assembly score (AS) of the read overlap via a scoring matrix. It evaluates all possible paired-end read overlaps and does not require the target fragment size as input. This method takes quality scores and sequence matches into account.

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PEAR classification

PEAR specifications

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PEAR distribution


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The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany; Graduate School for Computing in Medicine and Life Sciences, Institut für Neuro- und Bioinformatik, University of Lübeck, Lübeck, Germany; Karlsruhe Institute of Technology, Institute for Theoretical Informatics, Karlsruhe, Germany

Funding source(s)

Supported by a HITS scholarship, the DFG project STA-860/4 and the Graduate School for Computing in Medicine and Life Sciences, University of Lubeck.

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