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The human microbiome, which is linked to various diseases by growing evidence, has a profound impact on human health. Since changes in the composition of the microbiome across time are associated with disease and clinical outcomes, microbiome analysis should be performed in a longitudinal study. However, due to limited sample sizes and differing numbers of timepoints for different subjects, a significant amount of data cannot be utilized, directly affecting the quality of analysis results. Deep generative models have been proposed to address this lack of data issue. Specifically, a generative adversarial network (GAN) has been successfully utilized for data augmentation to improve prediction tasks. Recent studies have also shown improved performance of GAN-based models for missing value imputation in a multivariate time series dataset compared with traditional imputation methods.

This work proposes DeepMicroGen, a bidirectional recurrent neural network-based GAN model, trained on the temporal relationship between the observations, to impute the missing microbiome samples in longitudinal studies. DeepMicroGen outperforms standard baseline imputation methods, showing the lowest mean absolute error for both simulated and real datasets. Finally, the proposed model improved the predicted clinical outcome for allergies, by providing imputation for an incomplete longitudinal dataset used to train the classifier.

DeepMicroGen is publicly available at https://github.com/joungmin-choi/DeepMicroGen.

 

ABSTRACT Bacterial mobile genetic elements (MGEs) encode functional modules that perform both core and accessory functions for the element, the latter of which are often only transiently associated with the element. The presence of these accessory genes, which are often close homologs to primarily immobile genes, incur high rates of false positives and, therefore, limits the usability of these databases for MGE annotation. To overcome this limitation, we analyzed 10,776,849 protein sequences derived from eight MGE databases to compile a comprehensive set of 6,140 manually curated protein families that are linked to the “life cycle” (integration/excision, replication/recombination/repair, transfer, stability/transfer/defense, and phage-specific processes) of plasmids, phages, integrative, transposable, and conjugative elements. We overlay experimental information where available to create a tiered annotation scheme of high-quality annotations and annotations inferred exclusively through bioinformatic evidence. We additionally provide an MGE-class label for each entry (e.g., plasmid or integrative element), and assign to each entry a major and minor category. The resulting database, mobileOG-db (for mobile orthologous groups), comprises over 700,000 deduplicated sequences encompassing five major mobileOG categories and more than 50 minor categories, providing a structured language and interpretable basis for an array of MGE-centered analyses. mobileOG-db can be accessed at mobileogdb.flsi.cloud.vt.edu/, where users can select, refine, and analyze custom subsets of the dynamic mobilome. IMPORTANCE The analysis of bacterial mobile genetic elements (MGEs) in genomic data is a critical step toward profiling the root causes of antibiotic resistance, phenotypic or metabolic diversity, and the evolution of bacterial genera. Existing methods for MGE annotation pose high barriers of biological and computational expertise to properly harness. To bridge this gap, we systematically analyzed 10,776,849 proteins derived from eight databases of MGEs to identify 6,140 MGE protein families that can serve as candidate hallmarks, i.e., proteins that can be used as “signatures” of MGEs to aid annotation. The resulting resource, mobileOG-db, provides a multilevel classification scheme that encompasses plasmid, phage, integrative, and transposable element protein families categorized into five major mobileOG categories and more than 50 minor categories. mobileOG-db thus provides a rich resource for simple and intuitive element annotation that can be integrated seamlessly into existing MGE detection pipelines and colocalization analyses. 

Antibiotic resistance is a continually rising threat to global health. A primary driver of the evolution of new strains of resistant pathogens is the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). However, identifying and quantifying ARGs subject to HGT remains a significant challenge. Here, we introduce HT-ARGfinder (horizontally transferred ARG finder), a pipeline that detects and enumerates horizontally transferred ARGs in metagenomic data while also estimating the directionality of transfer. To demonstrate the pipeline, we applied it to an array of publicly-available wastewater metagenomes, including hospital sewage. We compare the horizontally transferred ARGs detected across various sample types and estimate their directionality of transfer among donors and recipients. This study introduces a comprehensive tool to track mobile ARGs in wastewater and other aquatic environments. 

In the fight to limit the global spread of antibiotic resistance, the assembly of environmental metagenomes has the potential to provide rich contextual information (e.g., taxonomic hosts, carriage on mobile genetic elements) about antibiotic resistance genes (ARG) in the environment. However, computational challenges associated with assembly can impact the accuracy of downstream analyses. This work critically evaluates the impact of assembly leveraging short reads, nanopore MinION long-reads, and a combination of the two (hybrid) on ARG contextualization for ten environmental metagenomes using seven prominent assemblers (IDBA-UD, MEGAHIT, Canu, Flye, Opera-MS, metaSpades and HybridSpades). While short-read and hybrid assemblies produced similar patterns of ARG contextualization, raw or assembled long nanopore reads produced distinct patterns. Based on an in-silico spike-in experiment using real and simulated reads, we show that low to intermediate coverage species are more likely to be incorporated into chimeric contigs across all assemblers and sequencing technologies, while more abundant species produce assemblies with a greater frequency of inversions and insertion/deletions (indels). In sum, our analyses support hybrid assembly as a valuable technique for boosting the reliability and accuracy of assembly-based analyses of ARGs and neighboring genes at environmentally-relevant coverages, provided that sufficient short-read sequencing depth is achieved.