Dataset Information

A supervised learning approach for taxonomic classification of core-photosystem-II genes and transcripts in the marine environment.

ABSTRACT: BACKGROUND: Cyanobacteria of the genera Synechococcus and Prochlorococcus play a key role in marine photosynthesis, which contributes to the global carbon cycle and to the world oxygen supply. Recently, genes encoding the photosystem II reaction center (psbA and psbD) were found in cyanophage genomes. This phenomenon suggested that the horizontal transfer of these genes may be involved in increasing phage fitness. To date, a very small percentage of marine bacteria and phages has been cultured. Thus, mapping genomic data extracted directly from the environment to its taxonomic origin is necessary for a better understanding of phage-host relationships and dynamics. RESULTS: To achieve an accurate and rapid taxonomic classification, we employed a computational approach combining a multi-class Support Vector Machine (SVM) with a codon usage position specific scoring matrix (cuPSSM). Our method has been applied successfully to classify core-photosystem-II gene fragments, including partial sequences coming directly from the ocean, to seven different taxonomic classes. Applying the method on a large set of DNA and RNA psbA clones from the Mediterranean Sea, we studied the distribution of cyanobacterial psbA genes and transcripts in their natural environment. Using our approach, we were able to simultaneously examine taxonomic and ecological distributions in the marine environment. CONCLUSION: The ability to accurately classify the origin of individual genes and transcripts coming directly from the environment is of great importance in studying marine ecology. The classification method presented in this paper could be applied further to classify other genes amplified from the environment, for which training data is available.

SUBMITTER: Tzahor S

PROVIDER: S-EPMC2696472 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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A supervised learning approach for taxonomic classification of core-photosystem-II genes and transcripts in the marine environment.

Tzahor Shani S Man-Aharonovich Dikla D Kirkup Benjamin C BC Yogev Tali T Berman-Frank Ilana I Polz Martin F MF Polz Martin F MF Béjà Oded O Mandel-Gutfreund Yael Y

BMC genomics 20090516

<h4>Background</h4>Cyanobacteria of the genera Synechococcus and Prochlorococcus play a key role in marine photosynthesis, which contributes to the global carbon cycle and to the world oxygen supply. Recently, genes encoding the photosystem II reaction center (psbA and psbD) were found in cyanophage genomes. This phenomenon suggested that the horizontal transfer of these genes may be involved in increasing phage fitness. To date, a very small percentage of marine bacteria and phages has been cul ...[more]

PMID: 19445709

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Project description:Microbial communities play key roles in ocean ecosystems through regulation of biogeochemical processes such as carbon and nutrient cycling, food web dynamics, and gut microbiomes of invertebrates, fish, reptiles, and mammals. Assessments of marine microbial diversity are therefore critical to understanding spatiotemporal variations in microbial community structure and function in ocean ecosystems. With recent advances in DNA shotgun sequencing for metagenome samples and computational analysis, it is now possible to access the taxonomic and genomic content of ocean microbial communities to study their structural patterns, diversity, and functional potential. However, existing taxonomic classification tools depend upon manually curated phylogenetic trees, which can create inaccuracies in metagenomes from less well-characterized communities, such as from ocean water. Herein, we explore the utility of deep learning tools-DeepMicrobes and a novel Residual Network architecture-that leverage natural language processing and convolutional neural network architectures to map input sequence data (k-mers) to output labels (taxonomic groups) without reliance on a curated taxonomic tree. We trained both models using metagenomic reads simulated from marine microbial genomes in the MarRef database. The performance of both models (accuracy, precision, and percent microbe predicted) was compared with the standard taxonomic classification tool Kraken2 using 10 complex metagenomic data sets simulated from MarRef. Our results demonstrate that time, compute power, and microbial genomic diversity still pose challenges for machine learning (ML). Moreover, our results suggest that high genome coverage and rectification of class imbalance are prerequisites for a well-trained model, and therefore should be a major consideration in future ML work. IMPORTANCE Taxonomic profiling of microbial communities is essential to model microbial interactions and inform habitat conservation. This work develops approaches in constructing training/testing data sets from publicly available marine metagenomes and evaluates the performance of machine learning (ML) approaches in read-based taxonomic classification of marine metagenomes. Predictions from two models are used to test accuracy in metagenomic classification and to guide improvements in ML approaches. Our study provides insights on the methods, results, and challenges of deep learning on marine microbial metagenomic data sets. Future machine learning approaches can be improved by rectifying genome coverage and class imbalance in the training data sets, developing alternative models, and increasing the accessibility of computational resources for model training and refinement.

Project description:Cyanophages (cyanobacterial viruses) are important agents of horizontal gene transfer among marine cyanobacteria, the numerically dominant photosynthetic organisms in the oceans. Some cyanophage genomes carry and express host-like photosynthesis genes, presumably to augment the host photosynthetic machinery during infection. To study the prevalence and evolutionary dynamics of this phenomenon, 33 cultured cyanophages of known family and host range and viral DNA from field samples were screened for the presence of two core photosystem reaction center genes, psbA and psbD. Combining this expanded dataset with published data for nine other cyanophages, we found that 88% of the phage genomes contain psbA, and 50% contain both psbA and psbD. The psbA gene was found in all myoviruses and Prochlorococcus podoviruses, but could not be amplified from Prochlorococcus siphoviruses or Synechococcus podoviruses. Nearly all of the phages that encoded both psbA and psbD had broad host ranges. We speculate that the presence or absence of psbA in a phage genome may be determined by the length of the latent period of infection. Whether it also carries psbD may reflect constraints on coupling of viral- and host-encoded PsbA-PsbD in the photosynthetic reaction center across divergent hosts. Phylogenetic clustering patterns of these genes from cultured phages suggest that whole genes have been transferred from host to phage in a discrete number of events over the course of evolution (four for psbA, and two for psbD), followed by horizontal and vertical transfer between cyanophages. Clustering patterns of psbA and psbD from Synechococcus cells were inconsistent with other molecular phylogenetic markers, suggesting genetic exchanges involving Synechococcus lineages. Signatures of intragenic recombination, detected within the cyanophage gene pool as well as between hosts and phages in both directions, support this hypothesis. The analysis of cyanophage psbA and psbD genes from field populations revealed significant sequence diversity, much of which is represented in our cultured isolates. Collectively, these findings show that photosynthesis genes are common in cyanophages and that significant genetic exchanges occur from host to phage, phage to host, and within the phage gene pool. This generates genetic diversity among the phage, which serves as a reservoir for their hosts, and in turn influences photosystem evolution.

Dataset Information

A supervised learning approach for taxonomic classification of core-photosystem-II genes and transcripts in the marine environment.

Publications

A supervised learning approach for taxonomic classification of core-photosystem-II genes and transcripts in the marine environment.

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