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Farrell, F.

Publications and source records attributed to Farrell, F..

3 recordsLinked to original sources

Novel microbial syntrophies identified by longitudinal metagenomics

Identifying species interactions in a microbial community and how this relates to community function is a key challenge. Towards addressing this challenge, we present here an extensive genome-resolved, longitudinal dataset and associated metadata. We collected weekly samples of microbial communities and recorded operating conditions from industrial methane producing anaerobic digestion reactors for a year. This allowed us to recover 2240 dereplicated metagenome assembled genomes (dMAGs), together with their coverage dynamics and functional annotations from which functional traits were inferred. Of these dMAGs, 1910 were novel species, with 22 representing novel orders and classes. Methanogenic communities are expected to be strongly structured by syntrophic and other associations between the methanogens and syntrophs that produce their substrates. We identified 450 potential syntrophic dMAGs by searching for pairs of methanogenic and non-methanogenic dMAGs that had highly correlated time-series. Genomes of potential syntrophs were enriched for oxidoreductases and sugar transport genes and there was a strong taxonomic signal in their associations with methanogens. Of particular note, we found that Bathyarchaeiea associated specifically with methanogens from the Thermoplasmata, and Thermococci classes. Same syntrophic associations were only rarely observed across multiple reactors, suggesting that syntrophies might be facultative, with particular strains within a species forming syntrophic associations only sometimes and not necessarily always with the same methanogenic partner. The presented results show that longitudinal metagenomics is a highly valuable approach for identifying species and their interactions in microbial communities. One Sentence SummaryLongitudinal study of microbial communities identifies novel species and predicts their interactions and role in community function.

microbiology↗

Machine learning based prediction of functional capabilities in metagenomically assembled microbial genomes

The increasing popularity of genome resolved meta genomics - the binning of genomes of potentially uncultured organisms direct from the environmental DNA - has resulted in a deluge of draft genomes. There is a pressing need to develop methods to interpret this data. Here, we used machine learning to predict functional and metabolic traits of microbes from their genomes. We collated an extensive database of 84 phenotypic traits associated with 9407 prokaryotic genomes and trained different machine learning models on this data. We found that a lasso logistic regression based on the frequency of gene orthologs had the best combination of functional prediction performance and interpretability. This model was able to classify 65 phenotypic traits with greater than 90

microbiology↗

MetQy: an R package to query metabolic functions of genes and genomes

SummaryWith the rapid accumulation of sequencing data from genomic and metagenomic studies, there is an acute need for better tools that facilitate their analyses against biological functions. To this end, we developed MetQy, an open-source R package designed for query-based analysis of functional units in [meta]genomes and/or sets of genes using the The Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Furthermore, MetQy contains visualization and analysis tools and facilitates KEGGs flat file manipulation. Thus, MetQy enables better understanding of metabolic capabilities of known genomes or user-specified [meta]genomes by using the available information and can help guide studies in microbial ecology, metabolic engineering and synthetic biology.\n\nAvailability and ImplementationThe MetQy R package is freely available and can be downloaded from our groups website (http://osslab.lifesci.warwick.ac.uk) or GitHub (https://github.com/OSS-Lab/MetQy).\n\nContactO.Soyer@warwick.ac.uk

microbiology↗