FindAdapt: A python package for fast and accurate adapter detection in small RNA sequencing
- bgtaylor1
- Nov 22, 2024
- 1 min read
Date: | 22 January 2024 |
PMID: | |
Category: | N/A |
Authors: | Hua-Chang Chen, Jing Wang, Yu Shyr, Qi Liu |
Abstract: |
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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.
Acknowledgements:
The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute, or the National Institute of Health.
The Translational and Basic Science Research in Early Lesions (TBEL) Research Consortia is supported and funded by grants from the National Cancer Institute and the National Institutes of Health under the following award numbers:
Project Number: | Awardee Organization |
U54CA274374 | Fred Hutchinson Cancer Center |
U54CA274375 | Houston Methodist Research Institute |
U54CA274370 | Johns Hopkins University |
U54CA274371 | UT MD Anderson Cancer Center |
U54CA274367 | Vanderbilt University Medical Center |
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