Strain-GeMS: optimized subspecies identification from microbiome data based on accurate variant modeling

Decoding populations in the ocean microbiome

Understanding the characteristics and structure of populations is fundamental to comprehending ecosystem processes and evolutionary adaptations. While the study of animal and plant populations has spanned a few centuries, microbial populations have been under scientific scrutiny for a considerably shorter period. In the ocean, analyzing the genetic composition of microbial populations and their adaptations to multiple niches can yield important insights into ecosystem function and the microbiome's response to global change. However, microbial populations have remained elusive to the scientific community due to the challenges associated with isolating microorganisms in the laboratory. Today, advancements in large-scale metagenomics and metatranscriptomics facilitate the investigation of populations from many uncultured microbial species directly from their habitats. The knowledge acquired thus far reveals substantial genetic diversity among various microbial species, showcasing distinct patterns of population differentiation and adaptations, and highlighting the significant role of selection in structuring populations. In the coming years, population genomics is expected to significantly increase our understanding of the architecture and functioning of the ocean microbiome, providing insights into its vulnerability or resilience in the face of ongoing global change. Video Abstract.

Ontology-aware neural network: a general framework for pattern mining from microbiome data

With the rapid accumulation of microbiome data around the world, numerous computational bioinformatics methods have been developed for pattern mining from such paramount microbiome data. Current microbiome data mining methods, such as gene and species mining, rely heavily on sequence comparison. Most of these methods, however, have a clear trade-off, particularly, when it comes to big-data analytical efficiency and accuracy. Microbiome entities are usually organized in ontology structures, and pattern mining methods that have considered ontology structures could offer advantages in mining efficiency and accuracy. Here, we have summarized the ontology-aware neural network (ONN) as a novel framework for microbiome data mining. We have discussed the applications of ONN in multiple contexts, including gene mining, species mining and microbial community dynamic pattern mining. We have then highlighted one of the most important characteristics of ONN, namely, novel knowledge discovery, which makes ONN a standout among all microbiome data mining methods. Finally, we have provided several applications to showcase the advantage of ONN over other methods in microbiome data mining. In summary, ONN represents a paradigm shift for pattern mining from microbiome data: from traditional machine learning approach to ontology-aware and model-based approach, which has found its broad application scenarios in microbiome data mining.

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

metaSNV v2: detection of SNVs and subspecies in prokaryotic metagenomes

SUMMARY

Taxonomic analysis of microbial communities is well supported at the level of species and strains. However, species can contain significant phenotypic diversity and strains are rarely widely shared across global populations. Stratifying the diversity between species and strains can identify 'subspecies', which are a useful intermediary. High-throughput identification and profiling of subspecies is not yet supported in the microbiome field. Here, we use an operational definition of subspecies based on single nucleotide variant (SNV) patterns within species to identify and profile subspecies in metagenomes, along with their distinctive SNVs and genes. We incorporate this method into metaSNV v2, which extends existing SNV-calling software to support further SNV interpretation for population genetics. These new features support microbiome analyses to link SNV profiles with host phenotype or environment and niche-specificity. We demonstrate subspecies identification in marine and fecal metagenomes. In the latter, we analyze 70 species in 7524 adult and infant subjects, supporting a common subspecies population structure in the human gut microbiome and illustrating some limits in subspecies calling.

AVAILABILITY AND IMPLEMENTATION

Source code, documentation, tutorials and test data are available at https://github.com/metasnv-tool/metaSNV and https://metasnv.embl.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PStrain: An Iterative Microbial Strains Profiling Algorithm for Shotgun Metagenomic Sequencing Data

MOTIVATION

The microbial community plays an essential role in human diseases and physiological activities. The functions of microbes can differ due to strain-level differences in the genome sequences. Shotgun metagenomic sequencing allows us to profile the strains in microbial communities practically. However, current methods are underdeveloped due to the highly similar sequences among strains. We observe that strains genotypes at the same single nucleotide variant (SNV) locus can be speculated by the genotype frequencies. Also, the variants in different loci covered by the same reads can provide evidence that they reside on the same strain.

RESULTS

These insights inspire us to design PStrain, an optimization method that utilizes genotype frequencies and the reads which cover multiple SNV loci to profile strains iteratively based on SNVs in a set of MetaPhlAn2 marker genes. Compared to the state-of-art methods, PStrain, on average, improved the performance of inferring strains abundances and genotypes by 87.75% and 59.45%, respectively. We have applied the PStrain package to the dataset with two cohorts of colorectal cancer (CRC) and found that the sequences of Bacteroides coprocola strains are significantly different between CRC and control samples, which is the first time to report the potential role of B. coprocola in the gut microbiota of CRC.

AVAILABILITY

https://github.com/wshuai294/PStrain.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Living Trees: High-Quality Reproducible and Reusable Construction of Bacterial Phylogenetic Trees

An ideal bacterial phylogenetic tree accurately retraces evolutionary history and accurately incorporates mutational, recombination and other events on the appropriate branches. Current strain-level bacterial phylogenetic analysis based on large numbers of genomes lacks reliability and resolution, and is hard to be replicated, confirmed and reused, because of the highly divergent nature of microbial genomes. We present SNPs and Recombination Events Tree (SaRTree), a pipeline using six "living trees" modules that addresses problems arising from the high numbers and variable quality of bacterial genome sequences. It provides for reuse of the tree and offers a major step toward global standardization of phylogenetic analysis by generating deposit files including all steps involved in phylogenetic inference. The tree itself is a "living tree" that can be extended by addition of more sequences, or the deposit can be used to vary the programs or parameters used, to assess the effect of such changes. This approach will allow phylogeny papers to meet the traditional responsibility of providing data and analysis that can be repeated and critically evaluated by others. We used the Acinetobacter baumannii global clone I to illustrate use of SaRTree to optimize tree resolution. An Escherichia coli tree was built from 351 sequences selected from 11,162 genome sequences, with the others added back onto well-defined branches, to show how this facility can greatly improve the outcomes from genome sequencing. SaRTree is designed for prokaryote strain-level analysis but could be adapted for other usage.