Cross-kingdom metagenomic profiling of Lake Hillier reveals pigment-rich polyextremophiles and wide-ranging metabolic adaptations

Ronkowski

et al. 2023

Preprint

Motivation: Computational analysis of large-scale metagenomics sequencing datasets has proved to be both incredibly valuable for extracting isolate-level taxonomic and functional insights from complex microbial communities. However, thanks to an ever-expanding ecosystem of metagenomics-specific algorithms and file formats, designing studies, implementing seamless and scalable end-to-end workflows, and exploring the massive amounts of output data have become studies unto themselves. Furthermore, there is little inter-communication between output data of different analytic purposes, such as short-read classification and metagenome-assembled genomes (MAG) reconstruction. One-click pipelines have helped to organize these tools into targeted workflows, but they suffer from general compatibility and maintainability issues. Results: To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed a module-based metagenomics analysis system written in Snakemake, a popular workflow management system, along with a standardized module and working directory architecture. Each module can be run independently or conjointly with a series of others to produce the target data format (ex. short-read preprocessing alone, or short-read preprocessing followed by de novo assembly), and outputs aggregated summary statistics reports and semi-guided Jupyter notebook-based visualizations, The module system is a bioinformatics-optimized scaffold designed to be rapidly iterated upon by the research community at large. Availability: The module template as well as the modules described below can be found at https://github.com/MetaSUB-CAMP.

Section: Introductionmentioning

confidence: 99%

CAMP: A modular metagenomics analysis system for integrated multi-step data exploration

Mak

Ronkowski

et al. 2023

Preprint

“…Large-scale genomics projects such as the Earth Microbiome Project (EMP) ([ 1 ]) and the Human Microbiome Project (HMP) [ 2 ] have pioneered the comparative study of microbial species across targeted ecosystems. In addition, the Tara Oceans Study [ 3 ], the Metagenomic and Metadesign of Subway and Urban Biomes (MetaSUB) Consortium [ 4 , 5 ], and the Extreme Microbiome Project (XMP) [ 6 , 7 , 8 ] have all broadly expanded the collection of diverse environmental microbial samples, revealing biodiversity and genetic differences between environments at a global scale.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Enables Geospatial Microbial Provenance

Bhattacharya

Ryon

et al. 2022

Genes

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset—the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85–89% to 90–94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).

“…Large-scale genomics projects such as the Earth Microbiome Project (EMP) (Gilbert et al, 2014) and the Human Microbiome Project (HMP) (Turnbaugh et al, 2007) have pioneered the comparative study of microbial species across targeted ecosystems. In addition, the Tara Oceans Study (Pesant et al, 2015), the Metagenomic and Metadesign of Subway and Urban Biomes (MetaSUB) Consortium (Danko et al, 2021), and the Extreme Microbiome Project (XMP) (Tighe et al, 2017, Sierra et al, 2022, Tighe et al, 2015) have all broadly expanded the collection of diverse environmental microbial samples, revealing biodiversity and genetic differences between environments at a global scale.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Enables Geospatial Microbial Provenance

Bhattacharya

Ryon

et al. 2022

Preprint

Self Cite

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun sequenced samples from the MetaSUB Consortium dataset: the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. We then used these results to develop Cassandra, a random-forest-based classifier that identifies indicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics.