Comparative analysis of environmental sequences: potential and challenges

Classification of COVID-19 and Other Pathogenic Sequences: A Dinucleotide Frequency and Machine Learning Approach

The world is grappling with the COVID-19 pandemic caused by the 2019 novel SARS-CoV-2. To better understand this novel virus and its relationship with other pathogens, new methods for analyzing the genome are required. In this study, intrinsic dinucleotide genomic signatures were analyzed for whole genome sequence data of eight pathogenic species, including SARS-CoV-2. The genome sequences were transformed into dinucleotide relative frequencies and classified using the extreme gradient boosting (XGBoost) model. The classification models were trained to a) distinguish between the sequences of all eight species and b) distinguish between sequences of SARS-CoV-2 that originate from different geographic regions. Our method attained 100% in all performance metrics and for all tasks in the eight-species classification problem. Moreover, the models achieved 67% balanced accuracy for the task of classifying the SARS-CoV-2 sequences into the six continental regions and achieved 86% balanced accuracy for the task of classifying SARS-CoV-2 samples as either originating from Asia or not. Analysis of the dinucleotide genomic profiles of the eight species revealed a similarity between the SARS-CoV-2 and MERS-CoV viral sequences. Further analysis of SARS-CoV-2 viral sequences from the six continents revealed that samples from Oceania had the highest frequency of TT dinucleotides as well as the lowest CG frequency compared to the other continents. The dinucleotide signatures of AC, AG,CA, CT, GA, GT, TC, and TG were well conserved across most genomes, while the frequencies of other dinucleotide signatures varied considerably. Altogether, the results from this study demonstrate the utility of dinucleotide relative frequencies for discriminating and identifying similar species.

An environment-sensitive synthetic microbial ecosystem

Microbial ecosystems have been widely used in industrial production, but the inter-relationships of organisms within them haven't been completely clarified due to complex composition and structure of natural microbial ecosystems. So it is challenging for ecologists to get deep insights on how ecosystems function and interplay with surrounding environments. But the recent progresses in synthetic biology show that construction of artificial ecosystems where relationships of species are comparatively clear could help us further uncover the meadow of those tiny societies. By using two quorum-sensing signal transduction circuits, this research designed, simulated and constructed a synthetic ecosystem where various population dynamics formed by changing environmental factors. Coherent experimental data and mathematical simulation in our study show that different antibiotics levels and initial cell densities can result in correlated population dynamics such as extinction, obligatory mutualism, facultative mutualism and commensalism. This synthetic ecosystem provides valuable information for addressing questions in ecology and may act as a chassis for construction of more complex microbial ecosystems.

Shotgun metagenomics of biological stains using ultra-deep DNA sequencing

A detailed molecular analysis of blood or other biological stains at a crime scene is often hampered by the low quantity and quality of the extractable DNA. However, the determination of the origin and composition of a stain is in most cases a prerequisite for the final elucidation of a criminal case. Standard methodologies, e.g. amplification of DNA followed by microsatellite typing or mitochondrial DNA sequencing, are often not sensitive enough to result in sufficient and conclusive data. We have applied ultra-deep DNA sequencing using the 454 pyrosequencing technology on a whole genome amplified (WGA) environmental biological stain, which was analysed unsuccessfully with standard methodologies following WGA. With the combination of WGA and 454 pyrosequencing, however, we were able to generate 7242 single sequences with an average length of 195bp. A total of 1,441,971bp DNA sequences were generated and compared with public DNA sequence databases. Using RepeatMasker and basic logical alignment search tool (BLAST) searches against known microbial and mammalian genomes it was possible to determine the metagenomic composition of the stain, i.e. 4.2% bacterial DNA, 0.3% viral DNA, 2.7% fungal DNA, 10.3% mammalian repetitive DNA, 0.9% porcine DNA, 0.13% human DNA and 81.5% DNA of unknown origin. Our data demonstrate that 454 pyrosequencing has the potential to become a powerful tool not only in basic research but also in the metagenomic analysis of biological trace materials for forensic genetics.

An ORFome assembly approach to metagenomics sequences analysis

Metagenomics is an emerging methodology for the direct genomic analysis of a mixed community of uncultured microorganisms. The current analyses of metagenomics data largely rely on the computational tools originally designed for microbial genomics projects. The challenge of assembling metagenomic sequences arises mainly from the short reads and the high species complexity of the community. Alternatively, individual (short) reads will be searched directly against databases of known genes (or proteins) to identify homologous sequences. The latter approach may have low sensitivity and specificity in identifying homologous sequences, which may further bias the subsequent diversity analysis. In this paper, we present a novel approach to metagenomic data analysis, called Metagenomic ORFome Assembly (MetaORFA). The whole computational framework consists of three steps. Each read from a metagenomics project will first be annotated with putative open reading frames (ORFs) that likely encode proteins. Next, the predicted ORFs are assembled into a collection of peptides using an EULER assembly method. Finally, the assembled peptides (i.e. ORFome) are used for database searching of homologs and subsequent diversity analysis. We applied MetaORFA approach to several metagenomics datasets with low coverage short reads. The results show that MetaORFA can produce long peptides even when the sequence coverage of reads is extremely low. Hence, the ORFome assembly significantly increases the sensitivity of homology searching, and may potentially improve the diversity analysis of the metagenomic data. This improvement is especially useful for metagenomic projects when the genome assembly does not work because of the low sequence coverage.

Evolution and taxonomic distribution of nonribosomal peptide and polyketide synthases

The majority of nonribosomal peptide synthases and type I polyketide synthases are multimodular megasynthases of oligopeptide and polyketide secondary metabolites, respectively. Owing to their multimodular architecture, they synthesize their metabolites in assembly line logic. The ongoing genomic revolution together with the application of computational tools has provided the opportunity to mine the various genomes for these enzymes and identify those organisms that produce many oligopeptide and polyketide metabolites. In addition, scientists have started to comprehend the molecular mechanisms of megasynthase evolution, by duplication, recombination, point mutation and module skipping. This knowledge and computational analyses have been implemented towards predicting the specificity of these megasynthases and the structure of their end products. It is an exciting field, both for gaining deeper insight into their basic molecular mechanisms and exploiting them biotechnologically.

The next meta-challenge for Bioinformatics

The direct sequencing of uncultivable organisms present in complex biological and environmental samples has opportunities to discover new life forms and metabolic processes. This transformational field, known as metagenomics, is generating massive amounts of molecular information that can overwhelm the performance of conventional analysis and visualization algorithms. Here, I briefly highlight some of the emerging challenges this new discipline presents to the computational biology community and point some of the opportunities to develop applications that can translate metagenomic information into biomedical, agricultural, environmental, and industrial applications.

Binning sequences using very sparse labels within a metagenome

BACKGROUND

In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels) to assign other reads based on their compositional similarity.

RESULTS

The proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM), and called Seeded GSOM (S-GSOM). We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments sampled from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds. The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, k-mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all k-mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the >/= 10 reads datasets and comparable in the > or = 8 kb benchmark tests.

CONCLUSION

In the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of the methods tested. Most importantly, the proposed method does not require knowledge from known genomes and uses only very few labels (one per species is sufficient in most cases), which are extracted from the metagenome itself. These advantages make it a very attractive binning method. S-GSOM outperformed the binning methods that depend on already-sequenced genomes, and compares well to the current most advanced binning method, PhyloPythia.

Advances in environmental genomics: towards an integrated view of micro-organisms and ecosystems

Microbial genome sequencing has, for the first time, made accessible all the components needed for both the elaboration and the functioning of a cell. Associated with other global methods such as protein and mRNA profiling, genomics has considerably extended our knowledge of physiological processes and their diversity not only in human, animal and plant pathogens but also in environmental isolates. At a higher level of complexity, the so-called meta approaches have recently shown great promise in investigating microbial communities, including uncultured micro-organisms. Combined with classical methods of physico-chemistry and microbiology, these endeavours should provide us with an integrated view of how micro-organisms adapt to particular ecological niches and participate in the dynamics of ecosystems.

A statistical toolbox for metagenomics: assessing functional diversity in microbial communities

Background

The 99% of bacteria in the environment that are recalcitrant to culturing have spurred the development of metagenomics, a culture-independent approach to sample and characterize microbial genomes. Massive datasets of metagenomic sequences have been accumulated, but analysis of these sequences has focused primarily on the descriptive comparison of the relative abundance of proteins that belong to specific functional categories. More robust statistical methods are needed to make inferences from metagenomic data. In this study, we developed and applied a suite of tools to describe and compare the richness, membership, and structure of microbial communities using peptide fragment sequences extracted from metagenomic sequence data.

Results

Application of these tools to acid mine drainage, soil, and whale fall metagenomic sequence collections revealed groups of peptide fragments with a relatively high abundance and no known function. When combined with analysis of 16S rRNA gene fragments from the same communities these tools enabled us to demonstrate that although there was no overlap in the types of 16S rRNA gene sequence observed, there was a core collection of operational protein families that was shared among the three environments.

Conclusion

The results of comparisons between the three habitats were surprising considering the relatively low overlap of membership and the distinctively different characteristics of the three habitats. These tools will facilitate the use of metagenomics to pursue statistically sound genome-based ecological analyses.

Microbial communities in the human small intestine: coupling diversity to metagenomics

The gastrointestinal tract is the main site where the conversion and absorption of food components takes place. The host-derived physiological processes and the residing microorganisms, especially in the small intestine, contribute to this nutrient supply. To circumvent sampling problems of the small intestine, several model systems have been developed to study microbial diversity and functionality in the small intestine. In addition, metagenomics offers novel possibilities to gain insight into the genetic potential and functional properties of these microbial communities. Here, an overview is presented of the most recent insights into the diversity and functionality of the microorganisms in the human gastrointestinal tract, with a focus on the small intestine.

Metagenomics and biological ontology

Metagenomics is an emerging microbial systems science that is based on the large-scale analysis of the DNA of microbial communities in their natural environments. Studies of metagenomes are revealing the vast scope of biodiversity in a wide range of environments, as well as new functional capacities of individual cells and communities, and the complex evolutionary relationships between them. Our examination of this science focuses on the ontological implications of these studies of metagenomes and metaorganisms, and what they mean for common sense and philosophical understandings of multicellularity, individuality and organism. We show how metagenomics requires us to think in different ways about what human beings are and what their relation to the microbial world is. Metagenomics could also transform the way in which evolutionary processes are understood, with the most basic relationship between cells from both similar and different organisms being far more cooperative and less antagonistic than is widely assumed. In addition to raising fundamental questions about biological ontology, metagenomics generates possibilities for powerful technologies addressed to issues of climate, health and conservation. We conclude with reflections about process-oriented versus entity-oriented analysis in light of current trends towards systems approaches.

Prediction of effective genome size in metagenomic samples

A novel computational approach shows a link between genome size and habitat from analysis of environmental metagenomic DNA reads.

We introduce a novel computational approach to predict effective genome size (EGS; a measure that includes multiple plasmid copies, inserted sequences, and associated phages and viruses) from short sequencing reads of environmental genomics (or metagenomics) projects. We observe considerable EGS differences between environments and link this with ecologic complexity as well as species composition (for instance, the presence of eukaryotes). For example, we estimate EGS in a complex, organism-dense farm soil sample at about 6.3 megabases (Mb) whereas that of the bacteria therein is only 4.7 Mb; for bacteria in a nutrient-poor, organism-sparse ocean surface water sample, EGS is as low as 1.6 Mb. The method also permits evaluation of completion status and assembly bias in single-genome sequencing projects.

The positive role of the ecological community in the genomic revolution

The exponential increase of genomic and metagenomic data, fueled in part by recent advancements in sequencing technology, are greatly expanding our understanding of the phylogenetic diversity and metabolic capacity present in the environment. Two of the central challenges that bioinformaticians and ecologists alike must face are the design of bioinformatic resources that facilitate the analysis of genomic and metagenomic data in a comparative context and the efficient capture and organization of the plethora of descriptive information required to usefully describe these data sets. In this commentary, we review three initiatives presented in the "new frontiers" session of the second SCOPE meeting on Microbial Environmental Genomics (MicroEnGen-II, Shanghai, June 12-15, 2006). These are (1) the Integrated Microbial Genomes Resources (IMG), (2) the Genomic Standards Consortium (GSC), and (3) the Natural Environment Research Council (NERC) Environmental Bioinformatics Centre (NEBC). These integrative bioinformatics and data management initiatives underscore the increasingly important role ecologists have to play in the genomic (metagenomic) revolution.

Two‐Component Systems in Microbial Communities: Approaches and Resources for Generating and Analyzing Metagenomic Data Sets

Two-component signal transduction represents the main mechanism by which bacterial cells interact with their environment. The functional diversity of two-component systems and their relative importance in the different taxonomic groups and ecotypes of bacteria has become evident with the availability of several hundred genomic sequences. The vast majority of bacteria, including many high rank taxonomic units, while being components of complex microbial communities remain uncultured (i.e., have not been isolated or grown in the laboratory). Environmental genomic data from such communities are becoming available, and in addition to its profound impact on microbial ecology it will propel molecular biological disciplines beyond the traditional model organisms. This chapter describes the general approaches used in generating environmental genomic data and how that data can be used to advance the study of two component-systems and signal transduction in general.