Computational analyses of bacterial strains from shotgun reads

Ventolero, Minerva Fatimae H.; Wang, Saidi; Hu, Haiyan; Li, Xiaoman

doi:10.1093/bib/bbac013

Cited by 7 publications

(8 citation statements)

References 111 publications

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“…We focused on the sets for the evolutionary and phylogenetic studies, not for the completeness of the assembled genomes in metagenomics. We ended up with the six sets except the one from Alneberg et al We included the Alneberg et al set because it was used in several strain studies [7, 8, 28].…”

Section: Methodsmentioning

confidence: 99%

“…Because of this property of ubiquitous existence and uniqueness in each genome, USCGs are widely used to study the evolution and classification of species [2][3][4]. In the past two decades, with the enormous amount of metagenomic data generated, USCGs are also routinely employed for taxonomic profiling of microbial species and completeness evaluation of metagenome-assembled genomes in shotgun metagenomic studies [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…In the past two decades, with the enormous amount of metagenomic data generated, USCGs are also routinely employed for taxonomic profiling of microbial species and completeness evaluation of metagenome-assembled genomes in shotgun metagenomic studies [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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A revisit to universal single-copy genes in bacterial genomes

Wang

Ventolero

et al. 2022

Preprint

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Universal single-copy genes (USCGs) are widely used for species classification and taxonomic profiling. Despite many studies, our understanding of USCGs in bacterial genomes might be out of date, especially how different the USCGs are in different studies, how well a set of USCGs can distinguish two bacterial species, how capable each USCG is in discriminating species, whether USCGs can separate different strains of a bacterial species, and so on. To fill the gaps, we studied USCGs in the most updated sequenced bacterial genomes. We showed that different USCG sets are quite different while coming from highly similar functional categories. We also found that although USCGs occur once in almost all bacterial genomes, each USCG does occur multiple times in certain genomes. We demonstrated that USCGs are reliable markers to distinguish different species and different strains of a bacterial species. The USCG ArgS is shown to be more powerful than other USCGs to tell different species in the same genus and different strains from the same species apart. Our study shed new light on the evolution of USCGs, which will facilitate their applications in phylogenomic and metagenomic studies.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A revisit to universal single-copy genes in bacterial genomes

Wang

Ventolero

et al. 2022

Preprint

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“…StrainFinder is a method that assigns strain genotypes and assesses strain frequencies for subpopulations of microbial communities with maximum likelihood estimates based on allele frequencies [53]. Ventolero et al investigated 20 tools that attempt to infer bacterial strain genomes from shotgun read sequences [54]. They systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools.…”

Section: Related Workmentioning

confidence: 99%

Comparison between ribosomal assembly and machine learning tools for microbial identification of organisms with different characteristics

Chau

Rojas

Jetcheva

et al. 2022

Preprint

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We compare several bioinformatics approaches for the assembly and identification of 16S rRNA sequences from metagenomes: PhyloFlash, MEGAHIT and MetaSPAdes followed by Barrnap and Blast or cmsearch, as well as Mothur, UniCycler, PathRacer. We also evaluated two machine learning approaches: DNABERT and DeLUCS. We used 2 synthetic mock community datasets for our evaluation and evaluate the tools' effectiveness on genomes of varying properties: repetitiveness, GC content, genome size, and coverage. The assembly-based tools and the machine learning approaches showed complementary performance on identifying organisms varying on these four properties. PhyloFlash gave the most True Positives identifying 22/26 organisms, and missing 4 with 1 spurious hit. PhyloFlash made mistakes on 16S reconstruction for species of higher repetitiveness, low or high genome coverage or GC content, as well as communities with similar species. On the other hand, 16S reconstruction by the whole-genome assembly followed by cmsearch, Barrnap and Blast identified some of the organisms phyloFlash missed but had more spurious hits. Cmsearch following MetaSPAdes gave the best results in whole-genome assembly for 16S identification as it identified most of the species that phyloFlash missed. The ML tools, such as DeLUCS, identified most of the species in the mock community datasets that PhyloFlash missed, but missed other species of low or high GC content, extreme repetitiveness, and small genomes. A rRNA-focused assembler like PhyloFlash gave results faster and closer to the truth, as opposed to assembling the metagenome and finding the rRNAs in the assembly (with cmsearch or barrnap) that gave many spurious hits. DeLUCS and phyloFlash showed complementary performance on our data, individually identifying most organisms with few spurious hits and together identifying almost all organisms. Our insights show the tools have various strengths and weaknesses specific to the characteristics of the genomes involved.

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“…Reads are short, containing sequencing errors, and mixed with other reads from different species and strains in the sample. Because of the importance of bacterial strains, many studies have explored identifying known and reconstructing novel bacterial strains from shotgun reads or shotgun metagenomics 18–20 .…”

Section: Introductionmentioning

confidence: 99%

Are known bacterial strains really present?

Ventolero

Wang

et al. 2022

Preprint

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Known strains are widely used to study bacterial genomes in shotgun metagenomic samples and to understand drug resistance to bacterial infection. However, bacterial genomes constantly evolve, with mutations accumulated in the genomes over time. It is thus unclear whether the previously identified known bacterial strains are still helpful or even valid for understanding a current shotgun metagenomic sample. To address this question, here we revisited the known strains of the bacterial species S. aureus and S. epidermis identified in 68 atopic dermatitis shotgun metagenomic samples. We find that even the same tools cannot predict the previously reported known strains on the same samples, and most known strains identified by different tools are different. Moreover, the coverage in the pooled and individual samples supports none of the inferred known strain. Our study thus indicates the limitation of the known-strain-based studies, especially those on rapidly evolving bacterial species. Our study also called for de novo bacterial strain identification directly from shotgun metagenomic reads.

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Computational analyses of bacterial strains from shotgun reads

Cited by 7 publications

References 111 publications

A revisit to universal single-copy genes in bacterial genomes

A revisit to universal single-copy genes in bacterial genomes

Comparison between ribosomal assembly and machine learning tools for microbial identification of organisms with different characteristics

Are known bacterial strains really present?

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