PEAT: An intelligent and efficient paired-end sequencing adapter trimming algorithm

Yun Lung Li, Jui Cheng Weng, Chiung Chih Hsiao, Min Te Chou, Chin Wen Tseng, Jui-Hung Hung*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Background: In modern paired-end sequencing protocols short DNA fragments lead to adapter-appended reads. Current paired-end adapter removal approaches trim adapter by scanning the fragment of adapter on the 3' end of the reads, which are not competent in some applications. Results: Here, we propose a fast and highly accurate adapter-trimming algorithm, PEAT, designed specifically for paired-end sequencing. PEAT requires no adaptor sequence, which is convenient for large-scale meta-analyses. We assessed the performance of PEAT with many adapter trimmers in both simulated and real life paired-end sequencing libraries. The importance of adapter trimming was exemplified by the influence of the downstream analyses on RNA-seq, ChIP-seq and MNase-seq. Several useful guidelines of applying adapter trimmers with aligners were suggested. Conclusions: PEAT can be easily included in the routine paired-end sequencing pipeline. The executable binaries and the standalone C++ source code package of PEAT are freely available online.

Original languageEnglish
Article numberS2
JournalBMC Bioinformatics
Issue number1
StatePublished - 21 Jan 2015

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