Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis. (arXiv:2111.08001v1 [q-bio.GN])

Advances in next-generation metagenome sequencing have the potential to
revolutionize the point-of-care diagnosis of novel pathogen infections, which
could help prevent potential widespread transmission of diseases. Given the
high volume of metagenome sequences, there is a need for scalable frameworks to
analyze and segment metagenome sequences from clinical samples, which can be
highly imbalanced. There is an increased need for learning robust
representations from metagenome reads since pathogens within a family can have
highly similar genome structures (some more than 90%) and hence enable the
segmentation and identification of novel pathogen sequences with limited
labeled data. In this work, we propose Metagenome2Vec – a contextualized
representation that captures the global structural properties inherent in
metagenome data and local contextualized properties through self-supervised
representation learning. We show that the learned representations can help
detect six (6) related pathogens from clinical samples with less than 100
labeled sequences. Extensive experiments on simulated and clinical metagenome
data show that the proposed representation encodes compositional properties
that can generalize beyond annotations to segment novel pathogens in an
unsupervised setting.



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