DNApi: A De Novo Adapter Prediction Algorithm for Small RNA Sequencing Data

With the rapid accumulation of publicly available small RNA sequencing datasets, third-party meta-analysis across many datasets is becoming increasingly powerful. Although removing the 3 A adapter is an essential step for small RNA sequencing analysis, the adapter sequence information is not always available in the metadata. The information can be also erroneous even when it is available. In this study, we developed DNApi, a lightweight Python software package that predicts the 3' adapter sequence de novo and provides the user with cleansed small RNA sequences ready for down stream analysis. Tested on 539 publicly available small RNA libraries accompanied with 3' adapter sequences in their metadata, DNApi shows near-perfect accuracy (98.5%) with fast runtime (similar to 2.85 seconds per library) and efficient memory usage (similar to 43 MB on average). In addition to 3' adapter prediction, it is also important to classify whether the input small RNA libraries were already processed, i.e. the 3' adapters were removed. DNApi perfectly judged that given another batch of datasets, 192 publicly available processed libraries were ready-to-map small RNA sequence. DNApi is compatible with Python 2 and 3, and is available at https://github.com/jnktsj/DNApi. The 731 small RNA libraries used for DNApi evaluation were from human tissues and were carefully and manually collected. This study also provides readers with the curated datasets that can be integrated into their studies.