iPHoP: an integrated machine-learning framework to maximize host prediction for metagenome-assembled virus genomes

Roux, Simon; Camargo, Antônio Pedro; Coutinho, Felipe H.; Dabdoub, Shareef M; Dutilh, Bas E.; Nayfach, Stephen; Tritt, Andrew

doi:10.1101/2022.07.28.501908

Cited by 45 publications

(67 citation statements)

References 78 publications

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“… (F) Phanta’s abundance estimates for Bifidobacterium and predicted Bifidobacterium phages in bulk metagenomes from infants in the four-month cohort (who had a range of diets). This analysis was facilitated by one of Phanta’s provided post-processing scripts, along with host genus predictions that were made by iPHoP 65 and are provided in Phanta’s default database.…”

Section: Resultsmentioning

confidence: 99%

“… (E) Distribution of predicted host genera for viral species in various prevalence categories (e.g., category 75-100 represents the top 25% of viruses in terms of prevalence). These results are based on host genus predictions that were made using iPHoP 65 and are provided in Phanta’s default database.…”

Section: Resultsmentioning

confidence: 99%

“…The second collapses viral abundances in each sample by predicted host, based on provided host predictions for viral species in Phanta's default database. Host predictions were made using iPHoP 65 . The third correlates the abundances of bacterial and viral species in each sample.…”

Section: Provided Post-processing Scriptsmentioning

confidence: 99%

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Phanta: Phage-inclusive profiling of human gut metagenomes

Pinto

Chakraborty

Jain

et al. 2022

Preprint

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The human gut microbiome is a diverse ecosystem that encompasses multiple domains of life and plays a vital role in human health. Due to technical limitations, most microbiome studies have focused on gut prokaryotes, overlooking bacteriophages and other gut viruses. The most common method to profile viruses is to assemble shotgun metagenomic reads - often from virus-enriched samples - and identify viral genomes de novo. While valuable, this resource-intensive and reference-independent method has limited sensitivity. To overcome these drawbacks, we developed Phanta, which profiles human gut metagenomes in a virus-inclusive manner directly from short reads utilizing recently published catalogs of gut viral genomes. Phanta incorporates k-mer based classification tools and was developed with virus-specific properties in mind. Specifically, it includes optimizations considering viruses' small genome size, sequence homology with prokaryotes, and interactions with other members of the gut microbial community. Based on simulations, the workflow is fast and accurate with respect to both prokaryotes and viruses, minimizing false positive species identification using a novel genome coverage-based strategy. When applied to metagenomes from healthy adults, Phanta identified ~200 viral species per sample, ~5x more than the standard assembly-based methods. Notably, we observed a 2:1 ratio between gut viruses and bacteria, with higher interindividual variability of the gut virome compared to the gut bacteriome. Phanta performs equally well on bulk vs. virus-enriched metagenomes, making it possible to study prokaryotes and viruses in a single experiment, with a single analysis. Phanta can tandemly profile gut viruses and prokaryotes in existing and novel datasets, and can therefore identify cross-domain interactions with likely relevance to human health. We expect that Phanta will reduce the barrier to virus-inclusive studies of the human gut microbiome, thus making it standard practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Phanta: Phage-inclusive profiling of human gut metagenomes

Pinto

Chakraborty

Jain

et al. 2022

Preprint

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“…This analysis required us to link phages to host bacteria, which we did primarily via CRISPR-Cas spacer targeting. This has been done many times previously [26][27][28][29] and is believed to be generally robust given that the spacers in a CRISPR locus of a host bacterial genome derive directly from phage genomes 28,43 . Supporting these CRISPR spacer-based links are several lines of evidence.…”

Section: Discussionmentioning

confidence: 99%

“…As CRISPR spacers are fragments of phage genomes stored within CRISPR-Cas systems, a common technique used to link phages to their bacterial hosts is via spacer-phage matching [26][27][28][29] . To find CRISPR-Cas systems encoded within SGA bacteria, we began with a previously compiled database that contained 862 genomes from the Saccharibacteria, Gracilibacteria, and Absconditabacteria (SGA) lineages 30 (Supp.…”

Section: Crispr-cas Systems Within Saccharibacteria Gracilibacteria A...mentioning

confidence: 99%

Host translation machinery is not a barrier to phages that infect both CPR and non-CPR bacteria

Liu

Jaffe

Chen

et al. 2022

Preprint

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Within human microbiomes, Gracilibacteria, Absconditabacteria, and Saccharibacteria, members of Candidate Phyla Radiation (CPR), are increasingly correlated with human oral health and disease. We sought phages that are capable of infecting these bacteria, as these may prove effective for phage therapies to control oral microbiome composition. To do this we analyzed spacer inventories from CRISPR-Cas systems that themselves could be used for phage editing. Absconditabacteria and Gracilibacteria recode the typical TGA stop codon to glycine and are infected by phages that share their host's alternate genetic code. Unexpectedly, however, other predicted phages of Gracilibacteria and Absconditabacteria do not use an alternative genetic code. Some of these phages are predicted to infect both alternatively coded CPR bacteria and the standard code host bacteria upon which the CPR bacteria depend. These phages rely on other stop codons, and thus should be capable of producing viable gene products in either bacterial host type. Interestingly, we predict that phages of Saccharibacteria can replicate in Actinobacteria, which are likely the Saccharibacteria hosts. Overall, this broad host range behavior may be advantageous for the production of CPR phage therapy agents, as non-CPR bacteria are easier to cultivate than CPR bacteria. Absconditabacteria and Gracilibacteria phages may have avoided acquisition of in-frame stop codons to increase the diversity of bacteria in which they can replicate.

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ViWrap: A modular pipeline to identify, bin, classify, and predict viral–host relationships for viruses from metagenomes

Zhou

Martin

Kosmopoulos

et al. 2023

iMeta

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Viruses are increasingly being recognized as important components of human and environmental microbiomes. However, viruses in microbiomes remain difficult to study because of the difficulty in culturing them and the lack of sufficient model systems. As a result, computational methods for identifying and analyzing uncultivated viral genomes from metagenomes have attracted significant attention. Such bioinformatics approaches facilitate the screening of viruses from enormous sequencing data sets originating from various environments. Although many tools and databases have been developed for advancing the study of viruses from metagenomes, there is a lack of integrated tools enabling a comprehensive workflow and analysis platform encompassing all the diverse segments of virus studies. Here, we developed ViWrap, a modular pipeline written in Python. ViWrap combines the power of multiple tools into a single platform to enable various steps of virus analyses, including identification, annotation, genome binning, species‐ and genus‐level clustering, assignment of taxonomy, prediction of hosts, characterization of genome quality, comprehensive summaries, and intuitive visualization of results. Overall, ViWrap enables a standardized and reproducible pipeline for both extensive and stringent characterization of viruses from metagenomes, viromes, and microbial genomes. Our approach has flexibility in using various options for diverse applications and scenarios, and its modular structure can be easily amended with additional functions as necessary. ViWrap is designed to be easily and widely used to study viruses in human and environmental systems. ViWrap is publicly available via GitHub (https://github.com/AnantharamanLab/ViWrap). A detailed description of the software, its usage, and interpretation of results can be found on the website.

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iPHoP: an integrated machine-learning framework to maximize host prediction for metagenome-assembled virus genomes

Cited by 45 publications

References 78 publications

Phanta: Phage-inclusive profiling of human gut metagenomes

Phanta: Phage-inclusive profiling of human gut metagenomes

Host translation machinery is not a barrier to phages that infect both CPR and non-CPR bacteria

ViWrap: A modular pipeline to identify, bin, classify, and predict viral–host relationships for viruses from metagenomes

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