Next generation sequencing and bioinformatic bottlenecks: the current state of metagenomic data analysis

Microbial composition and function in reclaimed mine sites along a reclamation chronosequence become increasingly similar to undisturbed reference sites

Mine reclamation historically focuses on enhancing plant coverage to improve below and aboveground ecology. However, there is a great need to study the role of soil microorganisms in mine reclamation, particularly long-term studies that track the succession of microbial communities. Here, we investigate the trajectory of microbial communities of mining sites reclaimed between three and 26 years. We used high-throughput amplicon sequencing to characterize the bacterial and fungal communities. We quantified how similar the reclaimed sites were to unmined, undisturbed reference sites and explored the trajectory of microbial communities along the reclamation chronosequence. We also examined the ecological processes that shape the assembly of bacterial communities. Finally, we investigated the functional potential of the microbial communities through metagenomic sequencing. Our results reveal that the reclamation age significantly impacted the community compositions of bacterial and fungal communities. As the reclamation age increases, bacterial and fungal communities become similar to the unmined, undisturbed reference site, suggesting a favorable succession in microbial communities. The bacterial community assembly was also significantly impacted by reclamation age and was primarily driven by stochastic processes, indicating a lesser influence of environmental properties on the bacterial community. Furthermore, our read-based metagenomic analysis showed that the microbial communities' functional potential increasingly became similar to the reference sites. Additionally, we found that the plant richness increased with the reclamation age. Overall, our study shows that both above- and belowground ecological properties of reclaimed mine sites trend towards undisturbed sites with increasing reclamation age. Further, it demonstrates the importance of microbial genomics in tracking the trajectory of ecosystem reclamation.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Metagenomics-Based Microbial Ecological Community Threshold and Indicators of Anthropogenic Disturbances in Estuarine Sediments

Assessing the impacts of cumulative anthropogenic disturbances on estuarine ecosystem health is challenging. Using spatially distributed sediments from the Pearl River Estuary (PRE) in southern China, which are significantly influenced by anthropogenic activities, we demonstrated that metagenomics-based surveillance of benthic microbial communities is a robust approach to assess anthropogenic impacts on estuarine benthic ecosystems. Correlational and threshold analyses between microbial compositions and environmental conditions indicated that anthropogenic disturbances in the PRE sediments drove the taxonomic and functional variations in the benthic microbial communities. An ecological community threshold of anthropogenic disturbances was identified, which delineated the PRE sediments into two groups (H and L) with distinct taxa and functional traits. Group H, located nearshore and subjected to a higher level of anthropogenic disturbances, was enriched with pollutant degraders, putative human pathogens, fecal pollution indicators, and functional traits related to stress tolerance. In contrast, Group L, located offshore and subjected to a lower level of anthropogenic disturbances, was enriched with halotolerant and oligotrophic taxa and functional traits related to growth and resource acquisition. The machine learning random forest model identified a number of taxonomic and functional indicators that could differentiate PRE sediments between Groups H and L. The identified ecological community threshold and microbial indicators highlight the utility of metagenomics-based microbial surveillance in assessing the adverse impacts of anthropogenic disturbances in estuarine sediments, which can assist environmental management to better protect ecosystem health.

Alterations in microbiome of COVID-19 patients and its impact on forensic investigations

The human microbiome is vital for maintaining human health and has garnered substantial attention in recent years, particularly in the context of the coronavirus disease 2019 (COVID-19) outbreak. Studies have underscored significant alterations in the microbiome of COVID-19 patients across various body niches, including the gut, respiratory tract, oral cavity, skin, and vagina. These changes manifest as shifts in microbiota composition, characterized by an increase in opportunistic pathogens and a decrease in beneficial commensal bacteria. Such microbiome transformations may play a pivotal role in influencing the course and severity of COVID-19, potentially contributing to the inflammatory response. This ongoing relationship between COVID-19 and the human microbiome serves as a compelling subject of research, underscoring the necessity for further investigations into the underlying mechanisms and their implications for patient health. Additionally, these alterations in the microbiome may have significant ramifications for forensic investigations, given the microbiome's potential in establishing individual characteristics. Consequently, changes in the microbiome could introduce a level of complexity into forensic determinations. As research progresses, a more profound understanding of the human microbiome within the context of COVID-19 may offer valuable insights into disease prevention, treatment strategies, and its potential applications in forensic science. Consequently, this paper aims to provide an overarching review of microbiome alterations due to COVID-19 and the associated impact on forensic applications, bridging the gap between the altered microbiome of COVID-19 patients and the challenges forensic investigations may encounter when analyzing this microbiome as a forensic biomarker.

Molecular insights and omics-based understanding of plant-microbe interactions under drought stress

The detrimental effects of adverse environmental conditions are always challenging and remain a major concern for plant development and production worldwide. Plants deal with such constraints by physiological, biochemical, and morphological adaptations as well as acquiring mutual support of beneficial microorganisms. As many stress-responsive traits of plants are influenced by microbial activities, plants have developed a sophisticated interaction with microbes to cope with adverse environmental conditions. The production of numerous bioactive metabolites by rhizospheric, endo-, or epiphytic microorganisms can directly or indirectly alter the root system architecture, foliage production, and defense responses. Although plant-microbe interactions have been shown to improve nutrient uptake and stress resilience in plants, the underlying mechanisms are not fully understood. "Multi-omics" application supported by genomics, transcriptomics, and metabolomics has been quite useful to investigate and understand the biochemical, physiological, and molecular aspects of plant-microbe interactions under drought stress conditions. The present review explores various microbe-mediated mechanisms for drought stress resilience in plants. In addition, plant adaptation to drought stress is discussed, and insights into the latest molecular techniques and approaches available to improve drought-stress resilience are provided.

Rumen multi-omics addressing diet-host-microbiome interplay in farm animals: a review

Continuous improvement in the living standards of developing countries, calls for an urgent need of high quality meat and dairy products. The farm animals have a micro-ecosystem in gastro-intestinal tract, comprising of a wide variety of flora and fauna which converts roughages and agricultural byproducts as well as nutrient rich concentrate sources into the useful products such as volatile fatty acids and microbial crude proteins. The microbial diversity changes according to composition of the feed, host species/breed and host's individual genetic makeup. From culture methods to next-generation sequencing technologies, the knowledge has emerged a lot to know-how of microbial world viz. their identification, enzymatic activities and metabolites which are the keys of ruminant's successful existence. The structural composition of ruminal community revealed through metagenomics can be elaborated by metatranscriptomics and metabolomics through deciphering their functional role in metabolism and their responses to the external and internal stimuli. These highly sophisticated analytical tools have made possible to correlate the differences in the feed efficiency, nutrients utilization and methane emissions to their rumen microbiome. The comprehensively understood rumen microbiome will enhance the knowledge in the fields of animal nutrition, biotechnology and climatology through deciphering the significance of each and every domain of residing microbial entity. The present review undertakes the recent investigations regarding rumen multi-omics viz. taxonomic and functional potential of microbial populations, host-diet-microbiome interactions and correlation with metabolic dynamics.

PhaGenus: genus-level classification of bacteriophages using a Transformer model

MOTIVATION

Bacteriophages (phages for short), which prey on and replicate within bacterial cells, have a significant role in modulating microbial communities and hold potential applications in treating antibiotic resistance. The advancement of high-throughput sequencing technology contributes to the discovery of phages tremendously. However, the taxonomic classification of assembled phage contigs still faces several challenges, including high genetic diversity, lack of a stable taxonomy system and limited knowledge of phage annotations. Despite extensive efforts, existing tools have not yet achieved an optimal balance between prediction rate and accuracy.

RESULTS

In this work, we develop a learning-based model named PhaGenus, which conducts genus-level taxonomic classification for phage contigs. PhaGenus utilizes a powerful Transformer model to learn the association between protein clusters and support the classification of up to 508 genera. We tested PhaGenus on four datasets in different scenarios. The experimental results show that PhaGenus outperforms state-of-the-art methods in predicting low-similarity datasets, achieving an improvement of at least 13.7%. Additionally, PhaGenus is highly effective at identifying previously uncharacterized genera that are not represented in reference databases, with an improvement of 8.52%. The analysis of the infants' gut and GOV2.0 dataset demonstrates that PhaGenus can be used to classify more contigs with higher accuracy.

Recent Advances in Metagenomic Approaches, Applications, and Challenge

Advances in metagenomics analysis with the advent of next-generation sequencing have extended our knowledge of microbial communities as compared to conventional techniques providing advanced approach to identify novel and uncultivable microorganisms based on their genetic information derived from a particular environment. Shotgun metagenomics involves investigating the DNA of the entire community without the requirement of PCR amplification. It provides access to study all genes present in the sample. On the other hand, amplicon sequencing targets taxonomically important marker genes, the analysis of which is restricted to previously known DNA sequences. While sequence-based metagenomics is used to analyze DNA sequences directly from the environment without the requirement of library construction and with limited identification of novel genes and products that can be complemented by functional genomics, function-based metagenomics requires fragmentation and cloning of extracted metagenome DNA in a suitable host with subsequent functional screening and sequencing clone for detection of a novel gene. Although advances were made in metagenomics, different challenges arise. This review provides insight into advances in the metagenomic approaches combined with next-generation sequencing, their recent applications highlighting the emerging ones, such as in astrobiology, forensic sciences, and SARS-CoV-2 infection diagnosis, and the challenges associated. This review further discusses the different types of metagenomics and outlines advancements in bioinformatics tools and their significance in the analysis of metagenomic datasets.

Scientific characterization methods for better utilization of cattle dung and urine: a concise review

Cattle are usually raised for food, manure, leather, therapeutic, and draught purposes. Biowastes from cattle, such as dung and urine, harbor a diverse group of crucial compounds, metabolites/chemicals, and microorganisms that may benefit humans for agriculture, nutrition, therapeutics, industrial, and other utility products. Several bioactive compounds have been identified in cattle dung and urine, which possess unique properties and may vary based on agro-climatic zones and feeding practices. Therefore, cattle dung and urine have great significance, and a balanced nutritional diet may be a key to improved quality of these products/by-products. This review primarily focuses on the scientific aspects of biochemical and microbial characterization of cattle biowastes. Various methods including genomics for analyzing cattle dung and gas chromatography-mass spectroscopy for cattle urine have been reviewed. The presented information might open doors for the further characterization of cattle resources for heterogeneous applications in the production of utility items and addressing research gaps. Methods for cattle's dung and urine characterization.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Effective Biocorrosive Control in Oil Industry Facilities: 16S rRNA Gene Metabarcoding for Monitoring Microbial Communities in Produced Water

Microbiologically influenced corrosion (MIC) or biocorrosion is a complex biological and physicochemical process, Strategies for monitoring MIC are frequently based on microbial cultivation methods, while microbiological molecular methods (MMM) are not well-established in the oil industry in Brazil. Thus, there is a high demand for the development of effective protocols for monitoring biocorrosion with MMM. The main aim of our study was to analyze the physico-chemi- cal features of microbial communities occurring in produced water (PW) and in enrichment cultures in oil pipelines of the petroleum industry. In order to obtain strictly comparable results, the same samples were used for both culturing and metabarcoding. PW samples displayed higher phylogenetic diversity of bacteria and archaea whereas PW enrichments cultures showed higher dominance of bacterial MIC-associated genera. All samples had a core community composed of 19 distinct genera, with MIC-associated Desulfovibrio as the dominant genus. We observed significant associations between the PW and cultured PW samples, with a greater number of associations found between the cultured sulfate-reducing bacteria (SRB) samples and the uncultured PW samples. When evaluating the correlation between the physicochemical characteristics of the environment and the microbiota of the uncultivated samples, we suggest that the occurrence of anaerobic digestion metabolism can be characterized by well-defined phases. Therefore, the detection of microorganisms in uncultured PW by metabarcoding, along with physi-cochemical characterization, can be a more efficient method compared to the culturing method, as it is a less laborious and cost-effective method for monitoring MIC microbial agents in oil industry facilities.

Methods to improve the accuracy of next-generation sequencing

Next-generation sequencing (NGS) is present in all fields of life science, which has greatly promoted the development of basic research while being gradually applied in clinical diagnosis. However, the cost and throughput advantages of next-generation sequencing are offset by large tradeoffs with respect to read length and accuracy. Specifically, its high error rate makes it extremely difficult to detect SNPs or low-abundance mutations, limiting its clinical applications, such as pharmacogenomics studies primarily based on SNP and early clinical diagnosis primarily based on low abundance mutations. Currently, Sanger sequencing is still considered to be the gold standard due to its high accuracy, so the results of next-generation sequencing require verification by Sanger sequencing in clinical practice. In order to maintain high quality next-generation sequencing data, a variety of improvements at the levels of template preparation, sequencing strategy and data processing have been developed. This study summarized the general procedures of next-generation sequencing platforms, highlighting the improvements involved in eliminating errors at each step. Furthermore, the challenges and future development of next-generation sequencing in clinical application was discussed.

To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities

High-throughput profiling of microbial functional traits involved in various biogeochemical cycling pathways using shotgun metagenomic sequencing has been routinely applied in microbial ecology and environmental science. Multiple bioinformatics data processing approaches are available, including assembly-based (single-sample assembly and multi-sample assembly) and read-based (merged reads and raw data). However, it remains not clear how these different approaches may differ in data analyses and affect result interpretation. In this study, using two typical shotgun metagenome datasets recovered from geographically distant coastal sediments, the performance of different data processing approaches was comparatively investigated from both technical and biological/ecological perspectives. Microbially mediated biogeochemical cycling pathways, including nitrogen cycling, sulfur cycling and B12 biosynthesis, were analyzed. As a result, multi-sample assembly provided the most amount of usable information for targeted functional traits, at a high cost of computational resources and running time. Single-sample assembly and read-based analysis were comparable in obtaining usable information, but the former was much more time- and resource-consuming. Critically, different approaches introduced much stronger variations in microbial profiles than biological differences. However, community-level differences between the two sampling sites could be consistently observed despite the approaches being used. In choosing an appropriate approach, researchers shall balance the trade-offs between multiple factors, including the scientific question, the amount of usable information, computational resources and time cost. This study is expected to provide valuable technical insights and guidelines for the various approaches used for metagenomic data analysis.

Discovering untapped microbial communities through metagenomics for microplastic remediation: recent advances, challenges, and way forward

Microplastics (MPs) are ubiquitous pollutants persisting almost everywhere in the environment. With the increase in anthropogenic activities, MP accumulation is increasing enormously in aquatic, marine, and terrestrial ecosystems. Owing to the slow degradation of plastics, MPs show an increased biomagnification probability of persistent, bioaccumulative, and toxic substances thereby creating a threat to environmental biota. Thus, remediation of MP-pollutants requires efficient strategies to circumvent the mobilization of contaminants leaching into the water, soil, and ultimately to human beings. Over the years, several microorganisms have been characterized by the potential to degrade different plastic polymers through enzymatic actions. Metagenomics (MGs) is an effective way to discover novel microbial communities and access their functional genetics for the exploration and characterization of plastic-degrading microbial consortia and enzymes. MGs in combination with metatranscriptomics and metabolomics approaches are a powerful tool to identify and select remediation-efficient microbes in situ. Advancement in bioinformatics and sequencing tools allows rapid screening, mining, and prediction of genes that are capable of polymer degradation. This review comprehensively summarizes the growing threat of microplastics around the world and highlights the role of MGs and computational biology in building effective response strategies for MP remediation.

Corrosion-influencing microorganisms in petroliferous regions on a global scale: systematic review, analysis, and scientific synthesis of 16S amplicon metagenomic studies

The objective of the current systematic review was to evaluate the taxonomic composition and relative abundance of bacteria and archaea associated with the microbiologically influenced corrosion (MIC), and the prediction of their metabolic functions in different sample types from oil production and transport structures worldwide. To accomplish this goal, a total of 552 published studies on the diversity of microbial communities using 16S amplicon metagenomics in oil and gas industry facilities indexed in Scopus, Web of Science, PubMed and OnePetro databases were analyzed on 10th May 2021. The selection of articles was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies that performed amplicon metagenomics to obtain the microbial composition of samples from oil fields were included. Studies that evaluated oil refineries, carried out amplicon metagenomics directly from cultures, and those that used DGGE analysis were removed. Data were thoroughly investigated using multivariate statistics by ordination analysis, bivariate statistics by correlation, and microorganisms' shareability and uniqueness analysis. Additionally, the full deposited databases of 16S rDNA sequences were obtained to perform functional prediction. A total of 69 eligible articles was included for data analysis. The results showed that the sulfidogenic, methanogenic, acid-producing, and nitrate-reducing functional groups were the most expressive, all of which can be directly involved in MIC processes. There were significant positive correlations between microorganisms in the injection water (IW), produced water (PW), and solid deposits (SD) samples, and negative correlations in the PW and SD samples. Only the PW and SD samples displayed genera common to all petroliferous regions, Desulfotomaculum and Thermovirga (PW), and Marinobacter (SD). There was an inferred high microbial activity in the oil fields, with the highest abundances of (i) cofactor, (ii) carrier, and (iii) vitamin biosynthesis, associated with survival metabolism. Additionally, there was the presence of secondary metabolic pathways and defense mechanisms in extreme conditions. Competitive or inhibitory relationships and metabolic patterns were influenced by the physicochemical characteristics of the environments (mainly sulfate concentration) and by human interference (application of biocides and nutrients). Our worldwide baseline study of microbial communities associated with environments of the oil and gas industry will greatly facilitate the establishment of standardized approaches to control MIC.

Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases

Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail.

An Overview on the Impact of Microbiota on Malaria Transmission and Severity: Plasmodium-Vector-Host Axis

PURPOSE

Malaria, which is a vector-borne disease caused by Plasmodium sp., continue to become a serious threat, causing more than 600,000 deaths annually, especially in developing countries. Due to the lack of a long-term, and effective vaccine, and an increasing resistance to antimalarials, new strategies are needed for prevention and treatment of malaria. Recently, the impact of microbiota on development and transmission of Plasmodium, and the severity of malaria has only begun to emerge, although its contribution to homeostasis and a wide variety of disorders is well-understood. Further evidence has shown that microbiota of both mosquito and human host play important roles in transmission, progression, and clearance of Plasmodium infection. Furthermore, Plasmodium can cause significant alterations in the host and mosquito gut microbiota, affecting the clinical outcome of malaria.

METHODOLOGY

In this review, we attempt to summarize results from published studies on the influence of the host microbiota on the outcome of Plasmodium infections in both arthropods and mammalian hosts.

CONCLUSION

Modifications of microbiota may be an important potential strategy in blocking Plasmodium transmission in vectors and in the diagnosis, treatment, and prevention of malaria in humans in the future.

Abundance, diversity, and host assignment of total, intracellular, and extracellular antibiotic resistance genes in riverbed sediments

Human health risk assessment for environmental antibiotic resistant microbes requires not only quantifying the abundance of antibiotic resistance genes (ARGs) in environmental matrices, but also understanding their hosts and genetic context. Further, differentiating ARGs in intracellular and extracellular DNA (iDNA and eDNA) fractions may help refine our understanding of ARG transferability. The objectives of this study were to understand the (O1) abundance and diversity of extracellular, intracellular, and total ARGs along a land use gradient and (O2) impact of bioinformatics pipeline on the assignment of putative hosts for the ARGs observed in the different DNA fractions. Sediment samples were collected along a land use gradient in the Raritan River, New Jersey, USA. DNA was extracted to separate eDNA and iDNA and qPCR was performed for select ARGs and the 16S rRNA gene. Shotgun metagenomic sequencing was performed on DNA extracts for the different DNA fractions. ARG hosts were assigned via two different bioinformatic pipelines: network analysis of raw reads versus assembly. Results of the two pipelines were compared to evaluate their performance in terms of number and diversity of linkages and accuracy of in silico matrix spike host assignments. No differences were observed in the 16S rRNA gene normalized sul1 concentrations between the DNA fractions. The overall microbial community structure was more similar for iDNA and total DNA compared to eDNA and generally clustered by sampling site. ARGs associated with mobile genetic elements increased in iDNA for the downstream sites. Regarding host assignment, the raw reads pipeline via network analysis identified 247 ARG hosts as compared to 53 hosts identified by assembly pipeline. Other comparisons between the pipelines were made including ARG assignment to taxa containing waterborne pathogens and practical considerations regarding processing time.

Potential Use of Microbial Community Genomes in Various Dimensions of Agriculture Productivity and Its Management: A Review

Agricultural productivity is highly influenced by its associated microbial community. With advancements in omics technology, metagenomics is known to play a vital role in microbial world studies by unlocking the uncultured microbial populations present in the environment. Metagenomics is a diagnostic tool to target unique signature loci of plant and animal pathogens as well as beneficial microorganisms from samples. Here, we reviewed various aspects of metagenomics from experimental methods to techniques used for sequencing, as well as diversified computational resources, including databases and software tools. Exhaustive focus and study are conducted on the application of metagenomics in agriculture, deciphering various areas, including pathogen and plant disease identification, disease resistance breeding, plant pest control, weed management, abiotic stress management, post-harvest management, discoveries in agriculture, source of novel molecules/compounds, biosurfactants and natural product, identification of biosynthetic molecules, use in genetically modified crops, and antibiotic-resistant genes. Metagenomics-wide association studies study in agriculture on crop productivity rates, intercropping analysis, and agronomic field is analyzed. This article is the first of its comprehensive study and prospects from an agriculture perspective, focusing on a wider range of applications of metagenomics and its association studies.

Virome of Ixodes ricinus, Dermacentor reticulatus, and Haemaphysalis concinna Ticks from Croatia

Tick-borne diseases are a serious threat to both public and veterinary health. In this study, we used high-throughput sequencing to characterize the virome of three tick species implicated in the spread of vector-borne disease throughout Croatia. Ten viruses were identified, including seven potential novel species within the viral families Flaviviridae, Nyamiviridae, Rhabdoviridae, Peribunyaviridae, Phenuiviridae, and Nairoviridae.

Metagenomic community composition and resistome analysis in a full-scale cold climate wastewater treatment plant

BACKGROUND

Wastewater treatment plants are an essential part of maintaining the health and safety of the general public. However, they are also an anthropogenic source of antibiotic resistance genes. In this study, we characterized the resistome, the distribution of classes 1-3 integron-integrase genes (intI1, intI2, and intI3) as mobile genetic element biomarkers, and the bacterial and phage community compositions in the North End Sewage Treatment Plant in Winnipeg, Manitoba. Samples were collected from raw sewage, returned activated sludge, final effluent, and dewatered sludge. A total of 28 bacterial and viral metagenomes were sequenced over two seasons, fall and winter. Integron-integrase genes, the 16S rRNA gene, and the coliform beta-glucuronidase gene were also quantified during this time period.

RESULTS

Bacterial classes observed above 1% relative abundance in all treatments were Actinobacteria (39.24% ± 0.25%), Beta-proteobacteria (23.99% ± 0.16%), Gamma-proteobacteria (11.06% ± 0.09%), and Alpha-proteobacteria (9.18 ± 0.04%). Families within the Caudovirales order: Siphoviridae (48.69% ± 0.10%), Podoviridae (23.99% ± 0.07%), and Myoviridae (19.94% ± 0.09%) were the dominant phage observed throughout the NESTP. The most abundant bacterial genera (in terms of average percent relative abundance) in influent, returned activated sludge, final effluent, and sludge, respectively, includes Mycobacterium (37.4%, 18.3%, 46.1%, and 7.7%), Acidovorax (8.9%, 10.8%, 5.4%, and 1.3%), and Polaromonas (2.5%, 3.3%, 1.4%, and 0.4%). The most abundant class of antibiotic resistance in bacterial samples was tetracycline resistance (17.86% ± 0.03%) followed by peptide antibiotics (14.24% ± 0.03%), and macrolides (10.63% ± 0.02%). Similarly, the phage samples contained a higher prevalence of macrolide (30.12% ± 0.30%), peptide antibiotic (10.78% ± 0.13%), and tetracycline (8.69% ± 0.11%) resistance. In addition, intI1 was the most abundant integron-integrase gene throughout treatment (1.14 × 104 gene copies/mL) followed by intI3 (4.97 × 103 gene copies/mL) while intI2 abundance remained low (6.4 × 101 gene copies/mL).

CONCLUSIONS

Wastewater treatment successfully reduced the abundance of bacteria, DNA phage and antibiotic resistance genes although many antibiotic resistance genes remained in effluent and biosolids. The presence of integron-integrase genes throughout treatment and in effluent suggests that antibiotic resistance genes could be actively disseminating resistance between both environmental and pathogenic bacteria.

Gut Microbiota Dysbiosis and COVID-19: Possible Links

Microbiota in the gastrointestinal system is a major determinant in health and disease status with its influence on immunity. Bidirectional relationship between gut microbiota and host immune system is well balanced in healthy individuals and a disruption (dysbiosis) can lead to gastrointestinal inflammations and metabolic disorders. Growing evidence support the cross-talk between gastrointestinal microbiota and lung that maintains host homeostasis and reduces the risk of disease development. The Gut-lung axis is possibly involved in the severity of COVID-19 with the association of dysbiosis. Targeted alterations in the gut microbiota could be considered to alleviate the disease severity.

Bioprospecting of cowdung microflora for sustainable agricultural, biotechnological and environmental applications

The review aims at highlighting the manifold applications of cow dung (CD) and CD microflora covering agricultural, biotechnological and environmental applications. The update research on CD microflora and CD in agricultural domain such as biocontrol, growth promotion, organic fertilizer, sulfur oxidation, phosphorus solubilization, zinc mobilization and underlying mechanisms involved in these processes are discussed. The significance of CD applications in tropical agriculture in context to climate change is briefly emphasized. The advances on genomics and proteomics of CD microflora for enhanced yield of enzymes, organic acids, alternative fuels (biomethane and biohydrogen) and other biocommodities, and environmental applications in context to biosorption of heavy metals, biodegradation of xenobiotics, etc. have been given critical attention.

Farming systems influence the compositional, structural, and functional characteristics of the sugarcane-associated microbiome

Sugarcane (Saccharum spp.) has been produced worldwide as a relevant source of food and sustainable energy. However, the constant need to increase crop yield has led to excessive use of synthetic agrochemical inputs such as inorganic fertilizers, herbicides, and pesticides in plant cultures. It is known that these conventional practices can lead to deleterious effects on health and the environment. Organic farming emerges as a sustainable alternative to conventional systems; however, farm management influences in plant-associated microbiomes remain unclear. Here, the aim is to identify the effects of farming systems on the sugarcane microbiota. To address this issue, we sampled the microbiota from soils and plants under organic and conventional farming from two crop fields in Brazil. Then, we evaluated their compositional, structural, and functional traits through amplification and sequencing of phylogenetic markers of bacteria (16S rRNA gene, V3-V4 region) and fungi (Internal Transcribed Spacer - ITS2). The data processing and analyses by the DADA2 pipeline revealed 12,839 bacterial and 3,222 fungal sequence variants. Moreover, differences between analogous niches were detected considering the contrasting farming systems, with samples from the conventional system showing a slightly greater richness and diversity of microorganisms. The composition is also different between the farming systems, with 389 and 401 differentially abundant taxa for bacteria and fungi, respectively, including taxa capable of promoting plant growth. The microbial co-occurrence networks showed structural changes in microbial communities, where organic networks were more cohesive since they had closer taxa and less modularity by niches. Finally, the functional prediction revealed enriched metabolic pathways, including the increased presence of antimicrobial resistance in the conventional farming system. Taken together, our findings reveal functional, structural, and compositional adaptations of the microbial communities associated with sugarcane plants in the field, according to farming management. With this, we point out the need to unravel the mechanisms driving these adaptations.

Discovery and Surveillance of Tick-Borne Pathogens

Within the past 30 yr molecular assays have largely supplanted classical methods for detection of tick-borne agents. Enhancements provided by molecular assays, including speed, throughput, sensitivity, and specificity, have resulted in a rapid increase in the number of newly characterized tick-borne agents. The use of unbiased high throughput sequencing has enabled the prompt identification of new pathogens and the examination of tick microbiomes. These efforts have led to the identification of hundreds of new tick-borne agents in the last decade alone. However, little is currently known about the majority of these agents beyond their phylogenetic classification. Our article outlines the primary methods involved in tick-borne agent discovery and the current status of our understanding of tick-borne agent diversity.

Metagenomics: a path to understanding the gut microbiome

The gut microbiome is a major determinant of host health, yet it is only in the last 2 decades that the advent of next-generation sequencing has enabled it to be studied at a genomic level. Shotgun sequencing is beginning to provide insight into the prokaryotic as well as eukaryotic and viral components of the gut community, revealing not just their taxonomy, but also the functions encoded by their collective metagenome. This revolution in understanding is being driven by continued development of sequencing technologies and in consequence necessitates reciprocal development of computational approaches that can adapt to the evolving nature of sequence datasets. In this review, we provide an overview of current bioinformatic strategies for handling metagenomic sequence data and discuss their strengths and limitations. We then go on to discuss key technological developments that have the potential to once again revolutionise the way we are able to view and hence understand the microbiome.

Prokaryotic and eukaryotic diversity in hydrothermal continental systems

The term extremophile was suggested more than 30 years ago and represents microorganisms that are capable of developing and living under extreme conditions, these conditions being particularly hostile to other types of microorganisms and to humankind. In terrestrial hydrothermal sites, like hot springs, "mud pools", solfataras, and geysers, the dominant extreme conditions are high temperature, low or high pH, and high levels of salinity. The diversity of microorganisms inhabiting these sites is determined by the conditions of the environment. Organisms belonging to the domains Archaea and Bacteria are more represented than the one belonging to Eukarya. Eukarya members tend to be less present because of their lower tolerance to higher temperatures, however, they perform important ecosystem processes when present. Both prokaryotes and eukaryotes have morphological and physical adaptations that allow them to colonize extreme environments. Microbial mats are complex associations of microorganisms that help the colonization of more extreme systems. In this review, a characterization of prokaryotic and eukaryotic organisms that populate terrestrial hydrothermal systems are made.

VB12Path for Accurate Metagenomic Profiling of Microbially Driven Cobalamin Synthesis Pathways

Cobalamin (vitamin B12; VB₁₂) is an indispensable nutrient for all living entities in the Earth’s biosphere and plays a vital role in both natural and host environments. Currently in the metagenomic era, gene families of interest are extracted and analyzed based on functional profiles by searching shotgun metagenomes against public databases. However, critical issues exist in applying public databases for specific processes such as VB₁₂ biosynthesis pathways. We developed a curated functional gene database termed VB₁₂Path for accurate metagenomic profiling of VB₁₂ biosynthesis gene families of microbial communities in complex environments. VB₁₂Path contains a total of 60 VB₁₂ synthesis gene families, 287,731 sequences, and 21,154 homology groups, and it aims to provide accurate functional and taxonomic profiles of VB₁₂ synthesis pathways for shotgun metagenomes and minimize false-positive assignments. VB₁₂Path was applied to characterize cobalamin biosynthesis gene families in human intestines and marine environments. The results demonstrated that ocean and human intestine had dramatically different VB₁₂ synthesis processes and that gene families belonging to salvage and remodeling pathway dominated human intestine but were lowest in the ocean ecosystem. VB₁₂Path is expected to be a useful tool to study cobalamin biosynthesis processes via shotgun metagenome sequencing in both environmental and human microbiome research.

IMPORTANCE Vitamin B12 (VB₁₂) is an indispensable nutrient for all living entities in the world but can only be synthesized by a small subset of prokaryotes. Therefore, this small subset of prokaryotes controls ecosystem stability and host health to some extent. However, critical accuracy and comprehensiveness issues exist in applying public databases to profile VB₁₂ synthetic gene families and taxonomic groups in complex metagenomes. In this study, we developed a curated functional gene database termed VB₁₂Path for accurate metagenomic profiling of VB₁₂ communities in complex environments. VB₁₂Path is expected to serve as a valuable tool to uncover the hidden microbial communities producing this precious nutrient on Earth.

The roles of code in biology

The way in which computer code is perceived and used in biological research has been a source of some controversy and confusion, and has resulted in sub-optimal outcomes related to reproducibility, scalability and productivity. We suggest that the confusion is due in part to a misunderstanding of the function of code when applied to the life sciences. Code has many roles, and in this paper we present a three-dimensional taxonomy to classify those roles and map them specifically to the life sciences. We identify a "sweet spot" in the taxonomy-a convergence where bioinformaticians should concentrate their efforts in order to derive the most value from the time they spend using code. We suggest the use of the "inverse Conway maneuver" to shape a research team so as to allow dedicated software engineers to interface with researchers working in this "sweet spot." We conclude that in order to address current issues in the use of software in life science research such as reproducibility and scalability, the field must reevaluate its relationship with software engineering, and adapt its research structures to overcome current issues in bioinformatics such as reproducibility, scalability and productivity.

Shifts of microbiota during cheese production: impact on production and quality

The high-throughput DNA sequencing (HTS) method is used to identify microbes in cheese and their potential functional properties. The technique can be applied to the microbiota of the cheese processing environment, raw milk, curd, whey, and starter cultures, and be used to improve the quality, safety, and other physicochemical properties of the final product. The HTS method is also utilized to study the microbiota shift of different types of cheeses during processing, as the composition and functional properties of the microbiome provide unique characteristics to different cheeses. Although there are several reviews that focused on microbiota of various types of cheeses, this review focuses on evaluating the microbiota shift of different types of cheese production and highlights key bacteria in each step of the processing as well as microbiota of various types of cheeses. KEY POINTS: • High-throughput sequencing can be applied to identify microbiota in cheese. • Microbiota in cheese is changed during making process and aging. • Starter culture plays an important role to establish microbiota in cheese.

Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota

In this paper we compared taxonomic results obtained by metataxonomics (16S rRNA gene sequencing) and metagenomics (whole shotgun metagenomic sequencing) to investigate their reliability for bacteria profiling, studying the chicken gut as a model system. The experimental conditions included two compartments of gastrointestinal tracts and two sampling times. We compared the relative abundance distributions obtained with the two sequencing strategies and then tested their capability to distinguish the experimental conditions. The results showed that 16S rRNA gene sequencing detects only part of the gut microbiota community revealed by shotgun sequencing. Specifically, when a sufficient number of reads is available, Shotgun sequencing has more power to identify less abundant taxa than 16S sequencing. Finally, we showed that the less abundant genera detected only by shotgun sequencing are biologically meaningful, being able to discriminate between the experimental conditions as much as the more abundant genera detected by both sequencing strategies.

HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes

Metagenomics became a standard strategy to comprehend the functional potential of microbial communities, including the human microbiome. Currently, the number of metagenomes in public repositories is increasing exponentially. The Sequence Read Archive (SRA) and the MG-RAST are the two main repositories for metagenomic data. These databases allow scientists to reanalyze samples and explore new hypotheses. However, mining samples from them can be a limiting factor, since the metadata available in these repositories is often misannotated, misleading, and decentralized, creating an overly complex environment for sample reanalysis. The main goal of the HumanMetagenomeDB is to simplify the identification and use of public human metagenomes of interest. HumanMetagenomeDB version 1.0 contains metadata of 69 822 metagenomes. We standardized 203 attributes, based on standardized ontologies, describing host characteristics (e.g. sex, age and body mass index), diagnosis information (e.g. cancer, Crohn's disease and Parkinson), location (e.g. country, longitude and latitude), sampling site (e.g. gut, lung and skin) and sequencing attributes (e.g. sequencing platform, average length and sequence quality). Further, HumanMetagenomeDB version 1.0 metagenomes encompass 58 countries, 9 main sample sites (i.e. body parts), 58 diagnoses and multiple ages, ranging from just born to 91 years old. The HumanMetagenomeDB is publicly available at https://webapp.ufz.de/hmgdb/.

Raw transcriptomics data to gene specific SSRs: a validated free bioinformatics workflow for biologists

Recent advances in next-generation sequencing technologies have paved the path for a considerable amount of sequencing data at a relatively low cost. This has revolutionized the genomics and transcriptomics studies. However, different challenges are now created in handling such data with available bioinformatics platforms both in assembly and downstream analysis performed in order to infer correct biological meaning. Though there are a handful of commercial software and tools for some of the procedures, cost of such tools has made them prohibitive for most research laboratories. While individual open-source or free software tools are available for most of the bioinformatics applications, those components usually operate standalone and are not combined for a user-friendly workflow. Therefore, beginners in bioinformatics might find analysis procedures starting from raw sequence data too complicated and time-consuming with the associated learning-curve. Here, we outline a procedure for de novo transcriptome assembly and Simple Sequence Repeats (SSR) primer design solely based on tools that are available online for free use. For validation of the developed workflow, we used Illumina HiSeq reads of different tissue samples of Santalum album (sandalwood), generated from a previous transcriptomics project. A portion of the designed primers were tested in the lab with relevant samples and all of them successfully amplified the targeted regions. The presented bioinformatics workflow can accurately assemble quality transcriptomes and develop gene specific SSRs. Beginner biologists and researchers in bioinformatics can easily utilize this workflow for research purposes.

In silico benchmarking of metagenomic tools for coding sequence detection reveals the limits of sensitivity and precision

BACKGROUND

High-throughput sequencing can establish the functional capacity of a microbial community by cataloging the protein-coding sequences (CDS) present in the metagenome of the community. The relative performance of different computational methods for identifying CDS from whole-genome shotgun sequencing is not fully established.

RESULTS

Here we present an automated benchmarking workflow, using synthetic shotgun sequencing reads for which we know the true CDS content of the underlying communities, to determine the relative performance (sensitivity, positive predictive value or PPV, and computational efficiency) of different metagenome analysis tools for extracting the CDS content of a microbial community. Assembly-based methods are limited by coverage depth, with poor sensitivity for CDS at < 5X depth of sequencing, but have excellent PPV. Mapping-based techniques are more sensitive at low coverage depths, but can struggle with PPV. We additionally describe an expectation maximization based iterative algorithmic approach which we show to successfully improve the PPV of a mapping based technique while retaining improved sensitivity and computational efficiency.

CONCLUSION

Our benchmarking approach reveals the trade-offs of assembly versus alignment-based approaches and the relative performance of specific implementations when one wishes to extract the protein coding capacity of microbial communities.

RBUD: A New Functional Potential Analysis Approach for Whole Microbial Genome Shotgun Sequencing

Whole metagenome shotgun sequencing is a powerful approach to detect the functional potential of microbial communities. Currently, the read-based metagenomics profiling for established database (RBED) method is one of the two kinds of conventional methods for species and functional annotations. However, the databases, which are established based on test samples or specific reference genomes or protein sequences, limit the coverage of global microbial diversity. The other assembly-based metagenomics profiling for unestablished database (ABUD) method has a low utilization rate of reads, resulting in a lot of biological information loss. In this study, we proposed a new method, read-based metagenomics profiling for unestablished database (RBUD), based on Metagenome Database of Global Microorganisms (MDGM), to solve the above problems. To evaluate the accuracy and effectiveness of our method, the intestinal bacterial composition and function analyses were performed in both avian colibacillosis chicken cases and type 2 diabetes mellitus patients. Comparing to the existing methods, RBUD is superior in detecting proteins, percentage of reads mapping and ontological similarity of intestinal microbes. The results of RBUD are in better agreement with the classical functional studies on these two diseases. RBUD also has the advantages of fast analysis speed and is not limited by the sample size.

Improvement of Surface-Enhanced Raman Scattering Method for Single Bacterial Cell Analysis

Surface-enhanced Raman scattering (SERS) is a useful tool for label-free analysis of bacteria at the single cell level. However, low reproducibility limits the use of SERS. In this study, for the sake of sensitive and reproducible Raman spectra, we optimized the methods for preparing silver nanoparticles (AgNPs) and depositing AgNPs onto a cell surface. We found that fast dropwise addition of AgNO₃ into the reductant produced smaller and more stable AgNPs, with an average diameter of 45 ± 4 nm. Compared with that observed after simply mixing the bacterial cells with AgNPs, the SERS signal was significantly improved after centrifugation. To optimize the SERS enhancement method, the centrifugal force, method for preparing AgNPs, concentration of AgNPs, ionic strength of the solution used to suspend the cells, and density of the cells were chosen as impact factors and optimized through orthogonal experiments. Finally, the improved method could generate sensitive and reproducible SERS spectra from single Escherichia coli cells, and the SERS signals primarily arose from the cell envelope. We further verified that this optimal method was feasible for the detection of low to 25% incorporation of ¹³C isotopes by the cells and the discrimination of different bacterial species. Our work provides an improved method for generating sensitive and reproducible SERS spectra.

Metagenomic analysis of bacteria in stallion semen

Bacteria colonize stallion semen during collection and processing which may cause disease in inseminated females or negatively affect sperm quality during storage prior to insemination. Antibiotics are added to semen extenders to control the growth of these bacteria but may induce antimicrobial resistance. Research into alternatives to antibiotics for this purpose requires knowledge of which bacteria are present in semen. Not all bacteria in semen, however, can be identified by conventional microbiological techniques. The objectives of the study were to: i) determine which bacteria are present in stallion semen using metagenomics; and ii) investigate individual differences in bacterial content in semen from all stallions on one premises. Bacterial DNA was extracted from ejaculates from seven stallions (one ejaculate per stallion) and bacteria were identified using 16S sequencing. In total, 83 bacterial genera were identified, varying from 25 to 52 among different individuals. There was a negative correlation (r = -0.81212; P <  0.05) between the presence of Treponema spp. and Advenella spp. In conclusion, most of the bacteria present in stallion semen could be identified to genus level by 16S sequencing even when present at a low frequency. This method of identification may help to clarify individual variation in bacterial content and its potential effects on fertility.

MinION Sequencing of colorectal cancer tumour microbiomes-A comparison with amplicon-based and RNA-Sequencing

BACKGROUND

Recent evidence suggests a role for the gut microbiome in the development and progression of many diseases and many studies have been carried out to analyse the microbiome using a variety of methods. In this study, we compare MinION sequencing with meta-transcriptomics and amplicon-based sequencing for microbiome analysis of colorectal tumour tissue samples.

METHODS

DNA and RNA were extracted from 11 colorectal tumour samples. 16S rRNA amplicon sequencing and MinION sequencing was carried out using genomic DNA, and RNA-Sequencing for meta-transcriptomic analysis. Non-human MinION and RNA-Sequencing reads, and 16S rRNA amplicon sequencing reads were taxonomically classified using a database built from available RefSeq bacterial and archaeal genomes and a k-mer based algorithm in Kraken2. Concordance between the three platforms at different taxonomic levels was tested on a per-sample basis using Spearman's rank correlation.

RESULTS

The average number of reads per sample using RNA-Sequencing was greater than 129 times that generated using MinION sequencing. However, the average read length of MinION sequences was more than 13 times that of RNA or 16S rRNA amplicon sequencing. Taxonomic assignment using 16S sequencing was less reliable beyond the genus level, and both RNA-Sequencing and MinION sequencing could detect greater numbers of phyla and genera in the same samples, compared to 16S sequencing. Bacterial species associated with colorectal cancer, Fusobacterium nucleatum, Parvimonas micra, Bacteroides fragilis and Porphyromonas gingivalis, were detectable using MinION, RNA-Sequencing and 16S rRNA amplicon sequencing data.

CONCLUSIONS

Long-read sequences generated using MinION sequencing can compensate for low numbers of reads for bacterial classification. MinION sequencing can discriminate between bacterial strains and plasmids and shows potential as a cost-effective tool for rapid microbiome sequencing in a clinical setting.

TAMA: improved metagenomic sequence classification through meta-analysis

BACKGROUND

Microorganisms are important occupants of many different environments. Identifying the composition of microbes and estimating their abundance promote understanding of interactions of microbes in environmental samples. To understand their environments more deeply, the composition of microorganisms in environmental samples has been studied using metagenomes, which are the collections of genomes of the microorganisms. Although many tools have been developed for taxonomy analysis based on different algorithms, variability of analysis outputs of existing tools from the same input metagenome datasets is the main obstacle for many researchers in this field.

RESULTS

Here, we present a novel meta-analysis tool for metagenome taxonomy analysis, called TAMA, by intelligently integrating outputs from three different taxonomy analysis tools. Using an integrated reference database, TAMA performs taxonomy assignment for input metagenome reads based on a meta-score by integrating scores of taxonomy assignment from different taxonomy classification tools. TAMA outperformed existing tools when evaluated using various benchmark datasets. It was also successfully applied to obtain relative species abundance profiles and difference in composition of microorganisms in two types of cheese metagenome and human gut metagenome.

CONCLUSION

TAMA can be easily installed and used for metagenome read classification and the prediction of relative species abundance from multiple numbers and types of metagenome read samples. TAMA can be used to more accurately uncover the composition of microorganisms in metagenome samples collected from various environments, especially when the use of a single taxonomy analysis tool is unreliable. TAMA is an open source tool, and can be downloaded at https://github.com/jkimlab/TAMA.

Benchmarking of computational error-correction methods for next-generation sequencing data

BACKGROUND

Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown.

RESULTS

In this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods.

CONCLUSIONS

In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.

NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes

MOTIVATION

The nitrogen (N) cycle is a collection of important biogeochemical pathways in the Earth ecosystem and has gained extensive foci in ecology and environmental studies. Currently, shotgun metagenome sequencing has been widely applied to explore gene families responsible for N cycle processes. However, there are problems in applying publically available orthology databases to profile N cycle gene families in shotgun metagenomes, such as inefficient database searching, unspecific orthology groups and low coverage of N cycle genes and/or gene (sub)families.

RESULTS

To solve these issues, this study built a manually curated integrative database (NCycDB) for fast and accurate profiling of N cycle gene (sub)families from shotgun metagenome sequencing data. NCycDB contains a total of 68 gene (sub)families and covers eight N cycle processes with 84 759 and 219 146 representative sequences at 95 and 100% identity cutoffs, respectively. We also identified 1958 homologous orthology groups and included corresponding sequences in the database to avoid false positive assignments due to 'small database' issues. We applied NCycDB to characterize N cycle gene (sub)families in 52 shotgun metagenomes from the Global Ocean Sampling expedition. Further analysis showed that the structure and composition of N cycle gene families were most strongly correlated with latitude and temperature. NCycDB is expected to facilitate N cycle studies via shotgun metagenome sequencing approaches in various environments. The framework developed in this study can be served as a good reference to build similar knowledge-based functional gene databases in various processes and pathways.

AVAILABILITY AND IMPLEMENTATION

NCycDB database files are available at https://github.com/qichao1984/NCyc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

One System for All: Is Mass Spectrometry a Future Alternative for Conventional Antibiotic Susceptibility Testing?

The two main pillars of clinical microbiological diagnostics are the identification of potentially pathogenic microorganisms from patient samples and the testing for antibiotic susceptibility (AST) to allow efficient treatment with active antimicrobial agents. While routine microbial species identification is increasingly performed with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), routine AST still largely relies on conventional and molecular techniques such as broth microdilution or disk and gradient diffusion tests, PCR and automated variants thereof. However, shortly after the introduction of MALDI-TOF MS based routine identification, first attempts to perform AST on the same instruments were reported. Today, a number of different approaches to perform AST with MALDI-TOF MS and other MS techniques have been proposed, some restricted to particular microbial taxa and resistance mechanisms while others being more generic. Further, while some of the methods are in a stage of proof of principles, others are already commercialized. In this review we discuss the different principal approaches of mass spectrometry based AST and evaluate the advantages and disadvantages compared to conventional and molecular techniques. At present, the possibility that MS will soon become a routine tool for AST seems unlikely – still, the same was true for routine microbial identification a mere 15 years ago.

Diversity, Ecology, and Prevalence of Antimicrobials in Nature

Microorganisms possess a variety of survival mechanisms, including the production of antimicrobials that function to kill and/or inhibit the growth of competing microorganisms. Studies of antimicrobial production have largely been driven by the medical community in response to the rise in antibiotic-resistant microorganisms and have involved isolated pure cultures under artificial laboratory conditions neglecting the important ecological roles of these compounds. The search for new natural products has extended to biofilms, soil, oceans, coral reefs, and shallow coastal sediments; however, the marine deep subsurface biosphere may be an untapped repository for novel antimicrobial discovery. Uniquely, prokaryotic survival in energy-limited extreme environments force microbial populations to either adapt their metabolism to outcompete or produce novel antimicrobials that inhibit competition. For example, subsurface sediments could yield novel antimicrobial genes, while at the same time answering important ecological questions about the microbial community.