FungiLT: A Deep Learning Approach for Species-Level Taxonomic Classification of Fungal ITS Sequences

Abstract

With the explosive growth of sequencing data, rapidly and accurately classifying and identifying species has become a critical challenge in amplicon analysis research. The internal transcribed spacer (ITS) region is widely used for fungal species classification and identification. However, most existing ITS databases cover limited fungal species diversity, and current classification methods struggle to efficiently handle such large-scale data. This study integrates multiple publicly available databases to construct an ITS sequence database encompassing 93,975 fungal species, making it a resource with broader species diversity for fungal taxonomy. In this study, a fungal classification model named FungiLT is proposed, integrating Transformer and BiLSTM architectures while incorporating a dual-channel feature fusion mechanism. On a dataset where each fungal species is represented by 100 ITS sequences, it achieves a species-level classification accuracy of 98.77%. Compared to BLAST, QIIME2, and the deep learning model CNN_FunBar, FungiLT demonstrates significant advantages in ITS species classification. This study provides a more efficient and accurate solution for large-scale fungal classification tasks and offers new technical support and insights for species annotation in amplicon analysis research.