Conclusion

Appleby, David J.

doi:10.7228/manchester/9780719075612.003.0008

Cited by 1 publication

(1 citation statement)

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“…We implemented LSH as a Cython extension ( 48 ). In this manner, we operated on C-strings and could directly call the C++ implementation of MurmurHash3 ( 49 ) for hashing. We also added the option to use mixed-precision computations for an additional speedup and lower memory requirements during training and inference.…”

Section: Methodsmentioning

confidence: 99%

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

Wichmann,

Buschong,

Müller

et al. 2023

NAR Genomics and Bioinformatics

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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 transformer-encoder-based models enable efficient parallelization while outperforming DeepMicrobes in terms of species and genus classification abilities. Furthermore, we investigate approaches to reduce memory consumption and boost performance using different embedding schemes. As a result, we are able to achieve 2× to 5× speedup for inference compared to DeepMicrobes while keeping a significantly smaller memory footprint. MetaTransformer can be trained in 9 hours for genus and 16 hours for species prediction. Our results demonstrate performance improvements due to self-attention models and the impact of embedding schemes in deep learning on metagenomic sequencing data.

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Section: Methodsmentioning

confidence: 99%