FindAdapt: A python package for fast and accurate adapter detection in small RNA sequencing

Hua Chang Chen, Jing Wang, Yu Shyr, Qi Liu

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Adapter trimming is an essential step for analyzing small RNA sequencing data, where reads are generally longer than target RNAs ranging from 18 to 30 bp. Most adapter trimming tools require adapter information as input. However, adapter information is hard to access, specified incorrectly, or not provided with publicly available datasets, hampering their reproducibility and reusability. Manual identification of adapter patterns from raw reads is labor-intensive and error-prone. Moreover, the use of randomized adapters to reduce ligation biases during library preparation makes adapter detection even more challenging. Here, we present FindAdapt, a Python package for fast and accurate detection of adapter patterns without relying on prior information. We demonstrated that FindAdapt was far superior to existing approaches. It identified adapters successfully in 180 simulation datasets with diverse read structures and 3,184 real datasets covering a variety of commercial and customized small RNA library preparation kits. FindAdapt is stand-alone software that can be easily integrated into small RNA sequencing analysis pipelines.

原文English
文章編號e1011786
期刊PLoS computational biology
20
發行號1
DOIs
出版狀態Published - 2024 1月

All Science Journal Classification (ASJC) codes

  • 生態學、進化論、行為學與系統學
  • 建模與模擬
  • 生態學
  • 分子生物學
  • 遺傳學
  • 細胞與分子神經科學
  • 計算機理論與數學

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