MetaTransformer: deep metagenomic sequencing read classification using self-attention models.

ABSTRACT: Deep learning has emerged as a paradigm that revolutionizes numerous domains of scientific research. Transformers have been utilized in language modeling outperforming previous approaches. Therefore, the utilization of deep learning as a tool for analyzing the genomic sequences is promising, yielding convincing results in fields such as motif identification and variant calling. DeepMicrobes, a machine learning-based classifier, has recently been introduced for taxonomic prediction at species and genus level. However, it relies on complex models based on bidirectional long short-term memory cells resulting in slow runtimes and excessive memory requirements, hampering its effective usability. We present MetaTransformer, a self-attention-based deep learning metagenomic analysis tool. Our tran

SUBMITTER: Wichmann A 

PROVIDER: S-EPMC10495543 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature