Environmental Genomics Data Research Articles

A k-mer-Based Approach for Phylogenetic Classification of Taxa in Environmental Genomic Data.

In the age of genome sequencing, whole-genome data is readily and frequently generated, leading to a wealth of new information that can be used to advance various fields of research. New approaches, such as alignment-free phylogenetic methods that utilize k-mer-based distance scoring, are becoming increasingly popular given their ability to rapidly generate phylogenetic information from whole-genome data. However, these methods have not yet been tested using environmental data, which often tends to be highly fragmented and incomplete. Here, we compare the results of one alignment-free approach (which utilizes the D2 statistic) to traditional multi-gene maximum likelihood trees in 3 algal groups that have high-quality genome data available. In addition, we simulate lower-quality, fragmented genome data using these algae to test method robustness to genome quality and completeness. Finally, we apply the alignment-free approach to environmental metagenome assembled genome data of unclassified Saccharibacteria and Trebouxiophyte algae, and single-cell amplified data from uncultured marine stramenopiles to demonstrate its utility with real datasets. We find that in all instances, the alignment-free method produces phylogenies that are comparable, and often more informative, than those created using the traditional multi-gene approach. The k-mer-based method performs well even when there are significant missing data that include marker genes traditionally used for tree reconstruction. Our results demonstrate the value of alignment-free approaches for classifying novel, often cryptic or rare, species, that may not be culturable or are difficult to access using single-cell methods, but fill important gaps in the tree of life.

A k-mer-based approach for phylogenetic classification of taxa in environmental genomic data

A k-mer-based approach for phylogenetic classification of taxa in environmental genomic data

KAUST Metagenomic Analysis Platform (KMAP), enabling access to massive analytics of re-annotated metagenomic data

Exponential rise of metagenomics sequencing is delivering massive functional environmental genomics data. However, this also generates a procedural bottleneck for on-going re-analysis as reference databases grow and methods improve, and analyses need be updated for consistency, which require access to increasingly demanding bioinformatic and computational resources. Here, we present the KAUST Metagenomic Analysis Platform (KMAP), a new integrated open web-based tool for the comprehensive exploration of shotgun metagenomic data. We illustrate the capacities KMAP provides through the re-assembly of ~ 27,000 public metagenomic samples captured in ~ 450 studies sampled across ~ 77 diverse habitats. A small subset of these metagenomic assemblies is used in this pilot study grouped into 36 new habitat-specific gene catalogs, all based on full-length (complete) genes. Extensive taxonomic and gene annotations are stored in Gene Information Tables (GITs), a simple tractable data integration format useful for analysis through command line or for database management. KMAP pilot study provides the exploration and comparison of microbial GITs across different habitats with over 275 million genes. KMAP access to data and analyses is available at https://www.cbrc.kaust.edu.sa/aamg/kmap.start.

Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery.

Microbes are the oldest and most widespread, phylogenetically and metabolically diverse life forms on Earth. However, they have been discovered only 334 years ago, and their diversity started to become seriously investigated even later. For these reasons, microbial studies that unveil novel microbial lineages and processes affecting or involving microbes deeply (and repeatedly) transform knowledge in biology. Considering the quantitative prevalence of taxonomically and functionally unassigned sequences in environmental genomics data sets, and that of uncultured microbes on the planet, we propose that unraveling the microbial dark matter should be identified as a central priority for biologists. Based on former empirical findings of microbial studies, we sketch a logic of discovery with the potential to further highlight the microbial unknowns.

Coordinating Environmental Genomics and Geochemistry Reveals Metabolic Transitions in a Hot Spring Ecosystem

We have constructed a conceptual model of biogeochemical cycles and metabolic and microbial community shifts within a hot spring ecosystem via coordinated analysis of the “Bison Pool” (BP) Environmental Genome and a complementary contextual geochemical dataset of ∼75 geochemical parameters. 2,321 16S rRNA clones and 470 megabases of environmental sequence data were produced from biofilms at five sites along the outflow of BP, an alkaline hot spring in Sentinel Meadow (Lower Geyser Basin) of Yellowstone National Park. This channel acts as a >22 m gradient of decreasing temperature, increasing dissolved oxygen, and changing availability of biologically important chemical species, such as those containing nitrogen and sulfur. Microbial life at BP transitions from a 92°C chemotrophic streamer biofilm community in the BP source pool to a 56°C phototrophic mat community. We improved automated annotation of the BP environmental genomes using BLAST-based Markov clustering. We have also assigned environmental genome sequences to individual microbial community members by complementing traditional homology-based assignment with nucleotide word-usage algorithms, allowing more than 70% of all reads to be assigned to source organisms. This assignment yields high genome coverage in dominant community members, facilitating reconstruction of nearly complete metabolic profiles and in-depth analysis of the relation between geochemical and metabolic changes along the outflow. We show that changes in environmental conditions and energy availability are associated with dramatic shifts in microbial communities and metabolic function. We have also identified an organism constituting a novel phylum in a metabolic “transition” community, located physically between the chemotroph- and phototroph-dominated sites. The complementary analysis of biogeochemical and environmental genomic data from BP has allowed us to build ecosystem-based conceptual models for this hot spring, reconstructing whole metabolic networks in order to illuminate community roles in shaping and responding to geochemical variability.

Animal performance and stress: responses and tolerance limits at different levels of biological organisation

Recent advances in molecular biology and the use of DNA microarrays for gene expression profiling are providing new insights into the animal stress response, particularly the effects of stress on gene regulation. However, interpretation of the complex transcriptional changes that occur during stress still poses many challenges because the relationship between changes at the transcriptional level and other levels of biological organisation is not well understood. To confront these challenges, a conceptual model linking physiological and transcriptional responses to stress would be helpful. Here, we provide the basis for one such model by synthesising data from organismal, endocrine, cellular, molecular, and genomic studies. We show using available examples from ectothermic vertebrates that reduced oxygen levels and oxidative stress are common to many stress conditions and that the responses to different types of stress, such as environmental, handling and confinement stress, often converge at the challenge of dealing with oxygen imbalance and oxidative stress. As a result, a common set of stress responses exists that is largely independent of the type of stressor applied. These common responses include the repair of DNA and protein damage, cell cycle arrest or apoptosis, changes in cellular metabolism that reflect the transition from a state of cellular growth to one of cellular repair, the release of stress hormones, changes in mitochondrial densities and properties, changes in oxygen transport capacities and changes in cardio-respiratory function. Changes at the transcriptional level recapitulate these common responses, with many stress-responsive genes functioning in cell cycle control, regulation of transcription, protein turnover, metabolism, and cellular repair. These common transcriptional responses to stress appear coordinated by only a limited number of stress-inducible and redox-sensitive transcription factors and signal transduction pathways, such as the immediate early genes c-fos and c-jun, the transcription factors NFkappaB and HIF-1alpha, and the JNK and p38 kinase signalling pathways. As an example of environmental stress responses, we present temperature response curves at organismal, cellular and molecular levels. Acclimation and physiological adjustments that can shift the threshold temperatures for the onset of these responses are discussed and include, for example, adjustments of the oxygen delivery system, the heat shock response, cellular repair system, and transcriptome. Ultimately, however, an organism's ability to cope with environmental change is largely determined by its ability to maintain aerobic scope and to prevent loss in performance. These systemic constraints can determine an organism's long-term survival well before cellular and molecular functions are disturbed. The conceptual model we propose here discusses some of the crosslinks between responses at different levels of biological organisation and the central role of oxygen balance and oxidative stress in eliciting these responses with the aim to help the interpretation of environmental genomic data in the context of organismal function and performance.

Quantitative assessment of protein function prediction from metagenomics shotgun sequences

To assess the potential of protein function prediction in environmental genomics data, we analyzed shotgun sequences from four diverse and complex habitats. Using homology searches as well as customized gene neighborhood methods that incorporate intergenic and evolutionary distances, we inferred specific functions for 76% of the 1.4 million predicted ORFs in these samples (83% when nonspecific functions are considered). Surprisingly, these fractions are only slightly smaller than the corresponding ones in completely sequenced genomes (83% and 86%, respectively, by using the same methodology) and considerably higher than previously thought. For as many as 75,448 ORFs (5% of the total), only neighborhood methods can assign functions, illustrated here by a previously undescribed gene associated with the well characterized heme biosynthesis operon and a potential transcription factor that might regulate a coupling between fatty acid biosynthesis and degradation. Our results further suggest that, although functions can be inferred for most proteins on earth, many functions remain to be discovered in numerous small, rare protein families.

Biology's next revolution

The interpretation of recent environmental genomics data exposes the far-reaching influence of horizontal gene transfer, and is changing our basic concepts of organism, species and evolution itself.

New avenues in protein function prediction

New avenues in protein function prediction