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

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.

GAAP: A Genome Assembly + Annotation Pipeline

Genomic analysis begins with de novo assembly of short-read fragments in order to reconstruct full-length base sequences without exploiting a reference genome sequence. Then, in the annotation step, gene locations are identified within the base sequences, and the structures and functions of these genes are determined. Recently, a wide range of powerful tools have been developed and published for whole-genome analysis, enabling even individual researchers in small laboratories to perform whole-genome analyses on their objects of interest. However, these analytical tools are generally complex and use diverse algorithms, parameter setting methods, and input formats; thus, it remains difficult for individual researchers to select, utilize, and combine these tools to obtain their final results. To resolve these issues, we have developed a genome analysis pipeline (GAAP) for semiautomated, iterative, and high-throughput analysis of whole-genome data. This pipeline is designed to perform read correction, de novo genome (transcriptome) assembly, gene prediction, and functional annotation using a range of proven tools and databases. We aim to assist non-IT researchers by describing each stage of analysis in detail and discussing current approaches. We also provide practical advice on how to access and use the bioinformatics tools and databases and how to implement the provided suggestions. Whole-genome analysis of Toxocara canis is used as case study to show intermediate results at each stage, demonstrating the practicality of the proposed method.

Metagenomic Approaches for Public Health Surveillance of Foodborne Infections: Opportunities and Challenges

Foodborne disease surveillance in the United States is at a critical point. Clinical and diagnostic laboratories are using culture-independent diagnostic tests (CIDTs) to identify the pathogen causing foodborne illness from patient specimens. CIDTs are molecular tests that allow doctors to rapidly identify the bacteria causing illness within hours. CIDTs, unlike previous gold standard methods such as bacterial culture, do not produce an isolate that can be subtyped as part of the national molecular subtyping network for foodborne disease surveillance, PulseNet. Without subtype information, cases can no longer be linked using molecular data to identify potentially related cases that are part of an outbreak. In this review, we discuss the public health needs for a molecular subtyping approach directly from patient specimen and highlight different approaches, including amplicon and shotgun metagenomic sequencing.

Effect of Long-Term Farming Practices on Agricultural Soil Microbiome Members Represented by Metagenomically Assembled Genomes (MAGs) and Their Predicted Plant-Beneficial Genes

To follow the hypothesis that agricultural management practices affect structure and function of the soil microbiome regarding soil health and plant-beneficial traits, high-throughput (HT) metagenome analyses were performed on Chernozem soil samples from a long-term field experiment designated LTE-1 carried out at Bernburg-Strenzfeld (Saxony-Anhalt, Germany). Metagenomic DNA was extracted from soil samples representing the following treatments: (i) plough tillage with standard nitrogen fertilization and use of fungicides and growth regulators, (ii) plough tillage with reduced nitrogen fertilization (50%), (iii) cultivator tillage with standard nitrogen fertilization and use of fungicides and growth regulators, and (iv) cultivator tillage with reduced nitrogen fertilization (50%). Bulk soil (BS), as well as root-affected soil (RS), were considered for all treatments in replicates. HT-sequencing of metagenomic DNA yielded approx. 100 Giga bases (Gb) of sequence information. Taxonomic profiling of soil communities revealed the presence of 70 phyla, whereby Proteobacteria, Actinobacteria, Bacteroidetes, Planctomycetes, Acidobacteria, Thaumarchaeota, Firmicutes, Verrucomicrobia and Chloroflexi feature abundances of more than 1%. Functional microbiome profiling uncovered, i.a., numerous potential plant-beneficial, plant-growth-promoting and biocontrol traits predicted to be involved in nutrient provision, phytohormone synthesis, antagonism against pathogens and signal molecule synthesis relevant in microbe-plant interaction. Neither taxonomic nor functional microbiome profiling based on single-read analyses revealed pronounced differences regarding the farming practices applied. Soil metagenome sequences were assembled and taxonomically binned. The ten most reliable and abundant Metagenomically Assembled Genomes (MAGs) were taxonomically classified and metabolically reconstructed. Importance of the phylum Thaumarchaeota for the analyzed microbiome is corroborated by the fact that the four corresponding MAGs were predicted to oxidize ammonia (nitrification), thus contributing to the cycling of nitrogen, and in addition are most probably able to fix carbon dioxide. Moreover, Thaumarchaeota and several bacterial MAGs also possess genes with predicted functions in plant-growth-promotion. Abundances of certain MAGs (species resolution level) responded to the tillage practice, whereas the factors compartment (BS vs. RS) and nitrogen fertilization only marginally shaped MAG abundance profiles. Hence, soil management regimes promoting plant-beneficial microbiome members are very likely advantageous for the respective agrosystem, its health and carbon sequestration and accordingly may enhance plant productivity. Since Chernozem soils are highly fertile, corresponding microbiome data represent a valuable reference resource for agronomy in general.

ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data

Metagenomic analysis of environmental samples provides deep insight into the enzymatic mixture of the corresponding niches, capable of revealing peptide sequences with novel functional properties exploiting the high performance of next-generation sequencing (NGS) technologies. At the same time due to their ever increasing complexity, there is a compelling need for ever larger computational configurations to ensure proper bioinformatic analysis, and fine annotation. With the aiming to address the challenges of such an endeavor, we have developed a novel web-based application named ANASTASIA (automated nucleotide aminoacid sequences translational plAtform for systemic interpretation and analysis). ANASTASIA provides a rich environment of bioinformatic tools, either publicly available or novel, proprietary algorithms, integrated within numerous automated algorithmic workflows, and which enables versatile data processing tasks for (meta)genomic sequence datasets. ANASTASIA was initially developed in the framework of the European FP7 project HotZyme, whose aim was to perform exhaustive analysis of metagenomes derived from thermal springs around the globe and to discover new enzymes of industrial interest. ANASTASIA has evolved to become a stable and extensible environment for diversified, metagenomic, functional analyses for a range of applications overarching industrial biotechnology to biomedicine, within the frames of the ELIXIR-GR project. As a showcase, we report the successful in silico mining of a novel thermostable esterase termed “EstDZ4” from a metagenomic sample collected from a hot spring located in Krisuvik, Iceland.

Genotypic antimicrobial resistance assays for use on E. coli isolates and stool specimens

Antimicrobial resistance (AMR) is an emerging public health problem and methods for surveillance are needed. We designed 85 sequence-specific PCR reactions to detect 79 genes or mutations associated with resistance across 10 major antimicrobial classes, with a focus on E. coli. The 85 qPCR assays demonstrated >99.9% concordance with sequencing. We evaluated the correlation between genotypic resistance markers and phenotypic susceptibility results on 239 E. coli isolates. Both sensitivity and specificity exceeded 90% for ampicillin, ceftriaxone, cefepime, imipenem, ciprofloxacin, azithromycin, gentamicin, amikacin, trimethoprim/sulfamethoxazole, tetracycline, and chloramphenicol phenotypic susceptibility results. We then evaluated the assays on direct stool specimens and observed a sensitivity of 97% ± 5 but, as expected, a lower specificity of 75% ± 31 versus the genotype of the E. coli cultured from stool. Finally, the assays were incorporated into a convenient TaqMan Array Card (TAC) format. These assays may be useful for tracking AMR in E. coli isolates or directly in stool for targeted testing of the fecal antibiotic resistome.

Functioning of the Intestinal Ecosystem: From New Technologies in Microbial Research to Practical Poultry Feeding – A Review

Unlike classical microbiology which focuses on bacteria capable of growing in vitro, metagenomics is a study of genetic information originating from microflora which aims to characterise the microbiome, namely the common genome of bacteria, archaea, fungi, protozoa and viruses living in the host. Metagenomics relies on next-generation sequencing (NGS), a large-scale sequencing technique which allows millions of sequential reactions to be carried out in parallel to decode entire communities of microorganisms. Metagenomic analyses support taxonomic analyses (involving gene fragments encoding ribosomal RNAs 5S and 16S in bacteria) or functional analyses for identifying genes encoding proteins that participate in the regulation of metabolic pathways in the body. New metagenomics technologies expand our knowledge of the phylogenetic structure of microflora in the gastrointestinal tract of poultry, and they support the identification of previously unknown groups of microbiota, mainly those occurring in small numbers. Next-generation sequencing also provides indirect information about the quantitative structure of the genes of gut microorganisms, but microbial activity and changes in the proportions of microbial metabolites that affect the host’s intestinal integrity and metabolism remain insufficiently investigated. Therefore, research studies are undertaken to investigate the proportions of the key microbial metabolites in the intestinal contents of poultry relative to changes in the population size of the most important bacterial groups, including those determined by cheaper techniques.

Improving data workflow systems with cloud services and use of open data for bioinformatics research

Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community.

Recovery and functional validation of hidden soil enzymes in metagenomic libraries

The vast microbial diversity on the planet represents an invaluable source for identifying novel activities with potential industrial and therapeutic application. In this regard, metagenomics has emerged as a group of strategies that have significantly facilitated the analysis of DNA from multiple environments and has expanded the limits of known microbial diversity. However, the functional characterization of enzymes, metabolites, and products encoded by diverse microbial genomes is limited by the inefficient heterologous expression of foreign genes. We have implemented a pipeline that combines NGS and Sanger sequencing as a way to identify fosmids within metagenomic libraries. This strategy facilitated the identification of putative proteins, subcloning of targeted genes and preliminary characterization of selected proteins. Overall, the in silico approach followed by the experimental validation allowed us to efficiently recover the activity of previously hidden enzymes derived from agricultural soil samples. Therefore, the methodology workflow described herein can be applied to recover activities encoded by environmental DNA from multiple sources.

Genome-wide profiling of human papillomavirus DNA integration in liquid-based cytology specimens from a Gabonese female population using HPV capture technology

Human papillomavirus (HPV) is recognised as the cause of precancerous and cancerous cervical lesions. Furthermore, in high-grade lesions, HPV is frequently integrated in the host cell genome and associated with the partial or complete loss of the E1 and E2 genes, which regulate the activity of viral oncoproteins E6 and E7. In this study, using a double-capture system followed by high-throughput sequencing, we determined the HPV integration status present in liquid-based cervical smears in an urban Gabonese population. The main inclusion criteria were based on cytological grade and the detection of the HPV16 genotype using molecular assays. The rate of HPV integration in the host genome varied with cytological grade: 85.7% (6/7), 71.4% (5/7), 66.7% (2/3) 60% (3/5) and 30.8% (4/13) for carcinomas, HSIL, ASCH, LSIL and ASCUS, respectively. For high cytological grades (carcinomas and HSIL), genotypes HPV16 and 18 represented 92.9% of the samples (13/14). The integrated form of HPV16 genotype was mainly found in high-grade lesions in 71.4% of samples regardless of cytological grade. Minority genotypes (HPV33, 51, 58 and 59) were found in LSIL samples, except HPV59, which was identified in one HSIL sample. Among all the HPV genotypes identified after double capture, 10 genotypes (HPV30, 35, 39, 44, 45, 53, 56, 59, 74 and 82) were detected only in episomal form. Our study revealed that the degree of HPV integration varies with cervical cytological grade. The integration event might be a potential clinical prognostic biomarker for the prediction of the progression of neoplastic lesions.

Metagenomic analysis of uncultured microorganisms and their enzymatic attributes

Although second generation biofuel technology is a sustainable route for bioethanol production it is not currently a robust technology because of certain hindrances viz., unavailability of potential enzyme resources, low efficiency of enzymes and restricted availability of potent enzymes that work under harsh conditions in industrial processes. Therefore, bioprospecting of extremophilic microorganisms using metagenomics is a promising alternative to discover novel microbes and enzymes with efficient tolerance to unfavourable conditions and thus could revolutionize the energy sector. Metagenomics a recent field in "omics" technology enables the genomic study of uncultured microorganisms with the goal of better understanding microbial dynamics. Metagenomics in conjunction with NextGen Sequencing technology facilitates the sequencing of microbial DNA directly from environmental samples and has expanded, and transformed our knowledge of the microbial world. However, filtering the meaningful information from the millions of genomic sequences offers a serious challenge to bioinformaticians. The current review holds the opinion tool 'know- how' to unravel the secrets of nature while expediting the bio-industrial world. We also discuss the novel biocatalytic agents discovered through metagenomics and how bioengineering plays a pivotal role to enhance their efficiency.

Characterizing Small RNAs in Filamentous Fungi Using the Rice Blast Fungus, Magnaporthe oryzae, as an Example

The goal of this chapter is to provide a framework of sequential steps for small RNA (sRNA) analysis in filamentous fungi. Here, we present protocols for (1) comparative analysis of sRNAs in different conditions, (2) comparisons of sRNA libraries to RNAseq data and (3) identification and analysis of methylguanosine-capped and polyadenylated sRNAs (CPA-sRNAs). This species of small RNA is particularly interesting in Magnaporthe oryzae, as they map to transcription start and end sites of protein-coding genes. While we do not provide specific command lines for scripts, we provide a general framework for steps needed to carry out all three types of analyses, including relevant references, websites and free online tools. Screenshots are provided from our own customized interface using M. oryzae as an example, to assist the reader in visualizing many of the steps.

Measuring metagenome diversity and similarity with Hill numbers

The first step of any metagenome sequencing project is to get the inventory of OTU abundances (operational taxonomic units) and/or metagenomic gene abundances. The former is generated with 16S-rRNA-tagged amplicon sequencing technology, and the latter can be generated from either gene-targeted or whole-sample shotgun metagenomics technologies. With 16S-rRNA data sets, measuring community diversity with diversity indexes such as species richness and Shannon's index has been a de facto standard analysis; nevertheless, similarly comprehensive approaches to metagenomic gene abundances are still largely missing, despite that both OTU and gene abundances are DNA reads. Here, we adapt the Hill numbers, which were reintroduced to macrocommunity ecology recently and are now widely regarded as a most appropriate measure system for ecological diversity, for measuring metagenome alpha-, beta- and gamma-diversities, and similarity. Our proposal includes the following: (a) Metagenomic gene (MG) diversity measures the single-gene-level metagenome diversity; (b) Type-I metagenome functional gene cluster (MFGC) diversity measures the diversity of functional gene clusters but ignoring within-cluster gene abundance information; (c) Type-II MFGC diversity considers within-cluster gene abundances information and integrates gene-cluster-level metagenome diversity and functional gene redundancy information; and (d) Four classes of Hill-numbers-based similarity metrics, including local gene overlap, regional gene overlap, gene homogeneity measure and gene turnover complement, were introduced in terms of MG and MFGC, respectively. We demonstrate the proposal with the gut metagenomes from healthy and IBD (inflammatory bowel disease) cohorts. The Hill numbers offer a unified approach to cohesively and comprehensively measuring the ecological and metagenome diversities of microbiomes.

Clustering huge protein sequence sets in linear time

Metagenomic datasets contain billions of protein sequences that could greatly enhance large-scale functional annotation and structure prediction. Utilizing this enormous resource would require reducing its redundancy by similarity clustering. However, clustering hundreds of millions of sequences is impractical using current algorithms because their runtimes scale as the input set size N times the number of clusters K, which is typically of similar order as N, resulting in runtimes that increase almost quadratically with N. We developed Linclust, the first clustering algorithm whose runtime scales as N, independent of K. It can also cluster datasets several times larger than the available main memory. We cluster 1.6 billion metagenomic sequence fragments in 10 h on a single server to 50% sequence identity, >1000 times faster than has been possible before. Linclust will help to unlock the great wealth contained in metagenomic and genomic sequence databases.

Billions of metagenomic and genomic sequences fill up public datasets, which makes similarity clustering an important and time-critical analysis step. Here, the authors develop Linclust, an algorithm with linear time complexity that can cluster over a billion sequences within hours on a single server.

Using metagenomics to investigate human and environmental resistomes

Antibiotic resistance is a global health concern declared by the WHO as one of the largest threats to modern healthcare. In recent years, metagenomic DNA sequencing has started to be applied as a tool to study antibiotic resistance in different environments, including the human microbiota. However, a multitude of methods exist for metagenomic data analysis, and not all methods are suitable for the investigation of resistance genes, particularly if the desired outcome is an assessment of risks to human health. In this review, we outline the current state of methods for sequence handling, mapping to databases of resistance genes, statistical analysis and metagenomic assembly. In addition, we provide an overview of important considerations related to the analysis of resistance genes, and recommend some of the currently used tools and methods that are best equipped to inform research and clinical practice related to antibiotic resistance.

Applications of next-generation sequencing analysis for the detection of hepatocellular carcinoma-associated hepatitis B virus mutations

BACKGROUND

Next-generation sequencing (NGS) is a powerful and high-throughput method for the detection of viral mutations. This article provides a brief overview about optimization of NGS analysis for hepatocellular carcinoma (HCC)-associated hepatitis B virus (HBV) mutations, and hepatocarcinogenesis of relevant mutations.

MAIN BODY

For the application of NGS analysis in the genome of HBV, four noteworthy steps were discovered in testing. First, a sample-specific reference sequence was the most effective mapping reference for NGS. Second, elongating the end of reference sequence improved mapping performance at the end of the genome. Third, resetting the origin of mapping reference sequence could probed deletion mutations and variants at a certain location with common mutations. Fourth, using a platform-specific cut-off value to distinguish authentic minority variants from technical artifacts was found to be highly effective. One hundred and sixty-seven HBV single nucleotide variants (SNVs) were found to be studied previously through a systematic literature review, and 12 SNVs were determined to be associated with HCC by meta-analysis. From comprehensive research using a HBV genome-wide NGS analysis, 60 NGS-defined HCC-associated SNVs with their pathogenic frequencies were identified, with 19 reported previously. All the 12 HCC-associated SNVs proved by meta-analysis were confirmed by NGS analysis, except for C1766T and T1768A which were mainly expressed in genotypes A and D, but including the subgroup analysis of A1762T. In the 41 novel NGS-defined HCC-associated SNVs, 31.7% (13/41) had cut-off values of SNV frequency lower than 20%. This showed that NGS could be used to detect HCC-associated SNVs with low SNV frequency. Most SNV II (the minor strains in the majority of non-HCC patients) had either low (< 20%) or high (> 80%) SNV frequencies in HCC patients, a characteristic U-shaped distribution pattern. The cut-off values of SNV frequency for HCC-associated SNVs represent their pathogenic frequencies. The pathogenic frequencies of HCC-associated SNV II also showed a U-shaped distribution. Hepatocarcinogenesis induced by HBV mutated proteins through cellular pathways was reviewed.

CONCLUSION

NGS analysis is useful to discover novel HCC-associated HBV SNVs, especially those with low SNV frequency. The hepatocarcinogenetic mechanisms of novel HCC-associated HBV SNVs defined by NGS analysis deserve further investigation.

Probiotic Lactobacillus Paracasei Expressing a Nucleic Acid-Hydrolyzing Minibody (3D8 Scfv) Enhances Probiotic Activities in Mice Intestine as Revealed by Metagenomic Analyses

Probiotics are well known for their beneficial effects for animals, including humans and livestock. Here, we tested the probiotic activity of Lactobacillus paracasei expressing 3D8 scFv, a nucleic acid-hydrolyzing mini-antibody, in mice intestine. A total of 18 fecal samples derived from three different conditions at two different time points were subjected to high-throughput 16S ribosomal RNA (rRNA) metagenomic analyses. Bioinformatic analyses identified an average of 290 operational taxonomic units. After administration of L. paracasei, populations of the probiotics L. paracasei, Lactobacillus reuteri, and Pediococcus acidilactici increased, whereas the population of harmful bacteria such as Helicobacter species decreased. Furthermore, continuous administration of L. paracasei resulted in L. paracasei emerging as the dominant probiotic after competition with other existing probiotics. Expression of 3D8 scFv protein specifically increased the population of P. acidilactici, which is another probiotic. In summary, our results showed that L. paracasei expressing 3D8 scFv protein enhanced probiotic activity in mice intestine with no observable side effects. Thus, the system developed in this study may be a good tool for the expression of recombinant protein using probiotics.

A bioinformatics pipeline integrating predictive metagenomics profiling for the analysis of 16S rDNA/rRNA sequencing data originated from foods

The recent advances in molecular biology, such as the advent of next-generation sequencing (NGS) platforms, have paved the way to new exciting tools which rapidly transform food microbiology. Nowadays, NGS methods such as 16S rDNA/rRNA metagenomics or amplicon sequencing are used for the taxonomic profiling of the food microbial communities. Although 16S rDNA/rRNA NGS-based microbial data are not suited for the investigation of the functional potential of the identified operational taxonomic units as compared to shotgun metagenomics, advances in the bioinformatics discipline allow now the performance of such studies. In this paper, a bioinformatics workflow is described integrating predictive metagenomics profiling with specific application to food microbiology data. Bioinformatics tools pertinent to each sub-module of the pipeline are suggested as well. The published 16S rDNA/rRNA amplicon data originated from an Italian Grana-type cheese, using an NGS platform, was employed to demonstrate the predictive metagenomics profiling approach. The pipeline identified the microbial community and the changes that occurred in the microbial profile during manufacture of the food product studied (taxonomic profiling). The workflow also indicated significant changes in the functional profiling of the community. The tool may help to investigate the functional potential, alterations, and interactions of a microbial community. The proposed workflow may also find an application in the investigation of the ecology of foodborne pathogens encountered in various food products.

Pseudoalignment for metagenomic read assignment

Motivation

Read assignment is an important first step in many metagenomic analysis workflows, providing the basis for identification and quantification of species. However ambiguity among the sequences of many strains makes it difficult to assign reads at the lowest level of taxonomy, and reads are typically assigned to taxonomic levels where they are unambiguous. We explore connections between metagenomic read assignment and the quantification of transcripts from RNA-Seq data in order to develop novel methods for rapid and accurate quantification of metagenomic strains.

Results

We find that the recent idea of pseudoalignment introduced in the RNA-Seq context is highly applicable in the metagenomics setting. When coupled with the Expectation-Maximization (EM) algorithm, reads can be assigned far more accurately and quickly than is currently possible with state of the art software, making it possible and practical for the first time to analyze abundances of individual genomes in metagenomics projects.

Availability and Implementation

Pipeline and analysis code can be downloaded from http://github.com/pachterlab/metakallisto.

Contact

lpachter@math.berkeley.edu.

Opening the black box of spring water microbiology from alpine karst aquifers to support proactive drinking water resource management

Over the past 15 years, pioneering interdisciplinary research has been performed on the microbiology of hydrogeologically well-defined alpine karst springs located in the Northern Calcareous Alps (NCA) of Austria. This article gives an overview on these activities and links them to other relevant research. Results from the NCA springs and comparable sites revealed that spring water harbors abundant natural microbial communities even in aquifers with high water residence times and the absence of immediate surface influence. Apparently, hydrogeology has a strong impact on the concentration and size of the observed microbes, and total cell counts (TCC) were suggested as a useful means for spring type classification. Measurement of microbial activities at the NCA springs revealed extremely low microbial growth rates in the base flow component of the studied spring waters and indicated the importance of biofilm-associated microbial activities in sediments and on rock surfaces. Based on genetic analysis, the autochthonous microbial endokarst community (AMEC) versus transient microbial endokarst community (TMEC) concept was proposed for the NCA springs, and further details within this overview article are given to prompt its future evaluation. In this regard, it is well known that during high-discharge situations, surface-associated microbes and nutrients such as from soil habitats or human settlements-potentially containing fecal-associated pathogens as the most critical water-quality hazard-may be rapidly flushed into vulnerable karst aquifers. In this context, a framework for the comprehensive analysis of microbial pollution has been proposed for the NCA springs to support the sustainable management of drinking water safety in accordance with recent World Health Organization guidelines. Near-real-time online water quality monitoring, microbial source tracking (MST) and MST-guided quantitative microbial-risk assessment (QMRA) are examples of the proposed analytical tools. In this context, this overview article also provides a short introduction to recently emerging methodologies in microbiological diagnostics to support reading for the practitioner. Finally, the article highlights future research and development needs. This article is categorized under: 1Engineering Water > Water, Health, and Sanitation2Science of Water > Water Extremes3Water and Life > Nature of Freshwater Ecosystems.

MoBiDiC Prioritization Algorithm, a Free, Accessible, and Efficient Pipeline for Single-Nucleotide Variant Annotation and Prioritization for Next-Generation Sequencing Routine Molecular Diagnosis

Interpretation of next-generation sequencing constitutes the main limitation of molecular diagnostics. In diagnosing myopathies and muscular dystrophies, another issue is efficiency in predicting the pathogenicity of variants identified in large genes, especially TTN; current in silico prediction tools show limitations in predicting and ranking the numerous variants of such genes. We propose a variant-prioritization tool, the MoBiDiCprioritization algorithm (MPA). MPA is based on curated interpretation of data on previously reported variants, biological assumptions, and splice and missense predictors, and is used to prioritize all types of single-nucleotide variants. MPA was validated by comparing its sensitivity and specificity to those of dbNSFP database prediction tools, using a data set composed of DYSF, DMD, LMNA, NEB, and TTN variants extracted from expert-reviewed and ExAC databases. MPA obtained the best annotation rates for missense and splice variants. As MPA aggregates the results from several predictors, individual predictor errors are counterweighted, improving the sensitivity and specificity of missense and splice variant predictions. We propose a sequential use of MPA, beginning with the selection of variants with higher scores and followed by, in the absence of candidate pathologic variants, consideration of variants with lower scores. We provide scripts and documentation for free academic use and a validated annotation pipeline scaled for panel and exome sequencing to prioritize single-nucleotide variants from a VCF file.

Microbiological profile of chicken carcasses: A comparative analysis using shotgun metagenomic sequencing

In the last few years metagenomic and 16S rRNA sequencing have completly changed the microbiological investigations of food products. In this preliminary study, the microbiological profile of chicken carcasses collected from animals fed with different diets were tested by using shotgun metagenomic sequencing. A total of 15 carcasses have been collected at the slaughetrhouse at the end of the refrigeration tunnel from chickens reared for 35 days and fed with a control diet (n=5), a diet supplemented with 1500 FTU/kg of commercial phytase (n=5) and a diet supplemented with 1500 FTU/kg of commercial phytase and 3g/kg of inositol (n=5). Ten grams of neck and breast skin were obtained from each carcass and submited to total DNA extraction by using the DNeasy Blood & Tissue Kit (Qiagen). Sequencing libraries have been prepared by using the Nextera XT DNA Library Preparation Kit (Illumina) and sequenced in a HiScanSQ (Illumina) at 100 bp in paired ends. A number of sequences ranging between 5 and 9 million was obtained for each sample. Sequence analysis showed that Proteobacteria and Firmicutes represented more than 98% of whole bacterial populations associated to carcass skin in all groups but their abundances were different between groups. Moraxellaceae and other degradative bacteria showed a significantly higher abundance in the control compared to the treated groups. Furthermore, Clostridium perfringens showed a relative frequency of abundance significantly higher in the group fed with phytase and Salmonella enterica in the group fed with phytase plus inositol. The results of this preliminary study showed that metagenome sequencing is suitable to investigate and monitor carcass microbiota in order to detect specific pathogenic and/or degradative populations.

Application of Metagenomic Analyses in Dentistry as a Novel Strategy Enabling Complex Insight into Microbial Diversity of the Oral Cavity

The composition of the oral microbiome in healthy individuals is complex and dynamic, and depends on many factors, such as anatomical location in the oral cavity, diet, oral hygiene habits or host immune responses. It is estimated at present that worldwide about 2 billion people suffer from diseases of the oral cavity, mainly periodontal disease and dental caries. Importantly, the oral microflora involved in local infections may spread and cause systemic, even life-threatening infections. In search for etiological agents of infections in dentistry, traditional approaches are not sufficient, as about 50% of oral bacteria are not cultivable. Instead, metagenomic analyses are particularly useful for studies of the complex oral microbiome - both in healthy individuals, and in patients with oral and dental diseases. In this paper we review the current and future applications of metagenomic studies in evaluation of both the composition of the oral microbiome as well as its potential pathogenic role in infections in dentistry.

aRNApipe: a balanced, efficient and distributed pipeline for processing RNA-seq data in high-performance computing environments

Summary

The wide range of RNA-seq applications and their high-computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project-oriented pipeline for processing of RNA-seq data in high-performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high-performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing and sequence variant calling. aRNApipe is project-oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis.

Availability and documentation

https://github.com/HudsonAlpha/aRNAPipe ; DOI: 10.5281/zenodo.202950.

Contact

rmyers@hudsonalpha.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

PALADIN: protein alignment for functional profiling whole metagenome shotgun data

Motivation

Whole metagenome shotgun sequencing is a powerful approach for assaying the functional potential of microbial communities. We currently lack tools that efficiently and accurately align DNA reads against protein references, the technique necessary for constructing a functional profile. Here, we present PALADIN-a novel modification of the Burrows-Wheeler Aligner that provides accurate alignment, robust reporting capabilities and orders-of-magnitude improved efficiency by directly mapping in protein space.

Results

We compared the accuracy and efficiency of PALADIN against existing tools that employ nucleotide or protein alignment algorithms. Using simulated reads, PALADIN consistently outperformed the popular DNA read mappers BWA and NovoAlign in detected proteins, percentage of reads mapped and ontological similarity. We also compared PALADIN against four existing protein alignment tools: BLASTX, RAPSearch2, DIAMOND and Lambda, using empirically obtained reads. PALADIN yielded results seven times faster than the best performing alternative, DIAMOND and nearly 8000 times faster than BLASTX. PALADIN's accuracy was comparable to all tested solutions.

Availability and Implementation

PALADIN was implemented in C, and its source code and documentation are available at https://github.com/twestbrookunh/paladin.

Contact

anthonyw@wildcats.unh.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Retrospective Review of Microbiological Methods Applied in Studies Following the Deepwater Horizon Oil Spill

The Deepwater Horizon (DWH) oil spill in the Gulf of Mexico in 2010 resulted in serious damage to local marine and coastal environments. In addition to the physical removal and chemical dispersion of spilled oil, biodegradation by indigenous microorganisms was regarded as the most effective way for cleaning up residual oil. Different microbiological methods were applied to investigate the changes and responses of bacterial communities after the DWH oil spills. By summarizing and analyzing these microbiological methods, giving recommendations and proposing some methods that have not been used, this review aims to provide constructive guidelines for microbiological studies after environmental disasters, especially those involving organic pollutants.

A Reference Viral Database (RVDB) To Enhance Bioinformatics Analysis of High-Throughput Sequencing for Novel Virus Detection

To facilitate bioinformatics analysis of high-throughput sequencing (HTS) data for the detection of both known and novel viruses, we have developed a new reference viral database (RVDB) that provides a broad representation of different virus species from eukaryotes by including all viral, virus-like, and virus-related sequences (excluding bacteriophages), regardless of their size. In particular, RVDB contains endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Sequences were clustered to reduce redundancy while retaining high viral sequence diversity. A particularly useful feature of RVDB is the reduction of cellular sequences, which can enhance the run efficiency of large transcriptomic and genomic data analysis and increase the specificity of virus detection.

Detection of distantly related viruses by high-throughput sequencing (HTS) is bioinformatically challenging because of the lack of a public database containing all viral sequences, without abundant nonviral sequences, which can extend runtime and obscure viral hits. Our reference viral database (RVDB) includes all viral, virus-related, and virus-like nucleotide sequences (excluding bacterial viruses), regardless of length, and with overall reduced cellular sequences. Semantic selection criteria (SEM-I) were used to select viral sequences from GenBank, resulting in a first-generation viral database (VDB). This database was manually and computationally reviewed, resulting in refined, semantic selection criteria (SEM-R), which were applied to a new download of updated GenBank sequences to create a second-generation VDB. Viral entries in the latter were clustered at 98% by CD-HIT-EST to reduce redundancy while retaining high viral sequence diversity. The viral identity of the clustered representative sequences (creps) was confirmed by BLAST searches in NCBI databases and HMMER searches in PFAM and DFAM databases. The resulting RVDB contained a broad representation of viral families, sequence diversity, and a reduced cellular content; it includes full-length and partial sequences and endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Testing of RVDBv10.2, with an in-house HTS transcriptomic data set indicated a significantly faster run for virus detection than interrogating the entirety of the NCBI nonredundant nucleotide database, which contains all viral sequences but also nonviral sequences. RVDB is publically available for facilitating HTS analysis, particularly for novel virus detection. It is meant to be updated on a regular basis to include new viral sequences added to GenBank.

IMPORTANCE To facilitate bioinformatics analysis of high-throughput sequencing (HTS) data for the detection of both known and novel viruses, we have developed a new reference viral database (RVDB) that provides a broad representation of different virus species from eukaryotes by including all viral, virus-like, and virus-related sequences (excluding bacteriophages), regardless of their size. In particular, RVDB contains endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Sequences were clustered to reduce redundancy while retaining high viral sequence diversity. A particularly useful feature of RVDB is the reduction of cellular sequences, which can enhance the run efficiency of large transcriptomic and genomic data analysis and increase the specificity of virus detection.

Closed-reference metatranscriptomics enables in planta profiling of putative virulence activities in the grapevine trunk disease complex

Grapevines, like other perennial crops, are affected by so-called 'trunk diseases', which damage the trunk and other woody tissues. Mature grapevines typically contract more than one trunk disease and often multiple grapevine trunk pathogens (GTPs) are recovered from infected tissues. The co-existence of different GTP species in complex and dynamic microbial communities complicates the study of the molecular mechanisms underlying disease development, especially under vineyard conditions. The objective of this study was to develop and optimize a community-level transcriptomics (i.e. metatranscriptomics) approach that could monitor simultaneously the virulence activities of multiple GTPs in planta. The availability of annotated genomes for the most relevant co-infecting GTPs in diseased grapevine wood provided the unprecedented opportunity to generate a multi-species reference for the mapping and quantification of DNA and RNA sequencing reads. We first evaluated popular sequence read mappers using permutations of multiple simulated datasets. Alignment parameters of the selected mapper were optimized to increase the specificity and sensitivity for its application to metagenomics and metatranscriptomics analyses. Initial testing on grapevine wood experimentally inoculated with individual GTPs confirmed the validity of the method. Using naturally infected field samples expressing a variety of trunk disease symptoms, we show that our approach provides quantitative assessments of species composition, as well as genome-wide transcriptional profiling of potential virulence factors, namely cell wall degradation, secondary metabolism and nutrient uptake for all co-infecting GTPs.

Challenges and Approaches in Microbiome Research: From Fundamental to Applied

We face major agricultural challenges that remain a threat for global food security. Soil microbes harbor enormous potentials to provide sustainable and economically favorable solutions that could introduce novel approaches to improve agricultural practices and, hence, crop productivity. In this review we give an overview regarding the current state-of-the-art of microbiome research by discussing new technologies and approaches. We also provide insights into fundamental microbiome research that aim to provide a deeper understanding of the dynamics within microbial communities, as well as their interactions with different plant hosts and the environment. We aim to connect all these approaches with potential applications and reflect how we can use microbial communities in modern agricultural systems to realize a more customized and sustainable use of valuable resources (e.g., soil).

Viruses in Soil Ecosystems: An Unknown Quantity Within an Unexplored Territory

Viral abundance in soils can range from below detection limits in hot deserts to over 1 billion per gram in wetlands. Abundance appears to be strongly influenced by water availability and temperature, but a lack of informational standards creates difficulties for cross-study analysis. Soil viral diversity is severely underestimated and undersampled, although current measures of viral richness are higher for soils than for aquatic ecosystems. Both morphometric and metagenomic analyses have raised questions about the prevalence of nontailed, ssDNA viruses in soils. Soil is complex and critically important to terrestrial biodiversity and human civilization, but impacts of viral activities on soil ecosystem services are poorly understood. While information from aquatic systems and medical microbiology suggests the potential for viral influences on nutrient cycles, food web interactions, gene transfer, and other key processes in soils, very few empirical data are available. To understand the soil virome, much work remains.

Relative abundance of deformed wing virus, Varroa destructor virus 1, and their recombinants in honey bees (Apis mellifera) assessed by kmer analysis of public RNA-Seq data

Deformed wing virus (DWV) is a major pathogen of concern to apiculture, and recent reports have indicated the local predominance and potential virulence of recombinants between DWV and a related virus, Varroa destructor virus 1 (VDV). However, little is known about the frequency and titer of VDV and recombinants relative to DWV generally. In this study, I assessed the relative occurrence and titer of DWV and VDV in public RNA-seq accessions of honey bee using a rapid, kmer-based approach. Three recombinant types were detectable graphically and corroborated by de novo assembly. Recombination breakpoints did not disrupt the capsid-encoding region, consistent with previous reports, and both VDV- and DWV-derived capsids were observed in recombinant backgrounds. High abundance of VDV kmers was largely restricted to recombinant forms. Non-metric multidimensional scaling identified genotypic clusters among DWV isolates, which was corroborated by read mapping and consensus generation. The recently described DWV-C lineage was not detected in the searched accessions. The data further highlight the utility of high-throughput sequencing to monitor viral polymorphisms and statistically test biological predictors of titer, and point to the need for consistent methodologies and sampling schemes.

Cyanobacterial Diversity in Microbial Mats from the Hypersaline Lagoon System of Araruama, Brazil: An In-depth Polyphasic Study

Microbial mats are complex, micro-scale ecosystems that can be found in a wide range of environments. In the top layer of photosynthetic mats from hypersaline environments, a large diversity of cyanobacteria typically predominates. With the aim of strengthening the knowledge on the cyanobacterial diversity present in the coastal lagoon system of Araruama (state of Rio de Janeiro, Brazil), we have characterized three mat samples by means of a polyphasic approach. We have used morphological and molecular data obtained by culture-dependent and -independent methods. Moreover, we have compared different classification methodologies and discussed the outcomes, challenges, and pitfalls of these methods. Overall, we show that Araruama's lagoons harbor a high cyanobacterial diversity. Thirty-six unique morphospecies could be differentiated, which increases by more than 15% the number of morphospecies and genera already reported for the entire Araruama system. Morphology-based data were compared with the 16S rRNA gene phylogeny derived from isolate sequences and environmental sequences obtained by PCR-DGGE and pyrosequencing. Most of the 48 phylotypes could be associated with the observed morphospecies at the order level. More than one third of the sequences demonstrated to be closely affiliated (best BLAST hit results of ≥99%) with cyanobacteria from ecologically similar habitats. Some sequences had no close relatives in the public databases, including one from an isolate, being placed as “loner” sequences within different orders. This hints at hidden cyanobacterial diversity in the mats of the Araruama system, while reinforcing the relevance of using complementary approaches to study cyanobacterial diversity.

Assembling metagenomes, one community at a time

BACKGROUND

Metagenomics allows unprecedented access to uncultured environmental microorganisms. The analysis of metagenomic sequences facilitates gene prediction and annotation, and enables the assembly of draft genomes, including uncultured members of a community. However, while several platforms have been developed for this critical step, there is currently no clear framework for the assembly of metagenomic sequence data.

RESULTS

To assist with selection of an appropriate metagenome assembler we evaluated the capabilities of nine prominent assembly tools on nine publicly-available environmental metagenomes, as well as three simulated datasets. Overall, we found that SPAdes provided the largest contigs and highest N50 values across 6 of the 9 environmental datasets, followed by MEGAHIT and metaSPAdes. MEGAHIT emerged as a computationally inexpensive alternative to SPAdes, assembling the most complex dataset using less than 500 GB of RAM and within 10 hours.

CONCLUSIONS

We found that assembler choice ultimately depends on the scientific question, the available resources and the bioinformatic competence of the researcher. We provide a concise workflow for the selection of the best assembly tool.

The Grapevine and Wine Microbiome: Insights from High-Throughput Amplicon Sequencing

From the time when microbial activity in wine fermentation was first demonstrated, the microbial ecology of the vineyard, grape, and wine has been extensively investigated using culture-based methods. However, the last 2 decades have been characterized by an important change in the approaches used for microbial examination, due to the introduction of DNA-based community fingerprinting methods such as DGGE, SSCP, T-RFLP, and ARISA. These approaches allowed for the exploration of microbial community structures without the need to cultivate, and have been extensively applied to decipher the microbial populations associated with the grapevine as well as the microbial dynamics throughout grape berry ripening and wine fermentation. These techniques are well-established for the rapid more sensitive profiling of microbial communities; however, they often do not provide direct taxonomic information and possess limited ability to detect the presence of rare taxa and taxa with low abundance. Consequently, the past 5 years have seen an upsurge in the application of high-throughput sequencing methods for the in-depth assessment of the grapevine and wine microbiome. Although a relatively new approach in wine sciences, these methods reveal a considerably greater diversity than previously reported, and identified several species that had not yet been reported. The aim of the current review is to highlight the contribution of high-throughput next generation sequencing and metagenomics approaches to vineyard microbial ecology especially unraveling the influence of vineyard management practices on microbial diversity.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (https://wiki.cyverse.org/wiki/x/dgGtAQ). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Multi-omics approaches to disease

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline

Background

The accuracy of metagenomic assembly is usually compromised by high levels of polymorphism due to divergent reads from the same genomic region recognized as different loci when sequenced and assembled together. A viral quasispecies is a group of abundant and diversified genetically related viruses found in a single carrier. Current mainstream assembly methods, such as Velvet and SOAPdenovo, were not originally intended for the assembly of such metagenomics data, and therefore demands for new methods to provide accurate and informative assembly results for metagenomic data.

Results

In this study, we present a hybrid method for assembling highly polymorphic data combining the partial de novo-reference assembly (PDR) strategy and the BLAST-based assembly pipeline (BBAP). The PDR strategy generates in situ reference sequences through de novo assembly of a randomly extracted partial data set which is subsequently used for the reference assembly for the full data set. BBAP employs a greedy algorithm to assemble polymorphic reads. We used 12 hepatitis B virus quasispecies NGS data sets from a previous study to assess and compare the performance of both PDR and BBAP. Analyses suggest the high polymorphism of a full metagenomic data set leads to fragmentized de novo assembly results, whereas the biased or limited representation of external reference sequences included fewer reads into the assembly with lower assembly accuracy and variation sensitivity. In comparison, the PDR generated in situ reference sequence incorporated more reads into the final PDR assembly of the full metagenomics data set along with greater accuracy and higher variation sensitivity. BBAP assembly results also suggest higher assembly efficiency and accuracy compared to other assembly methods. Additionally, BBAP assembly recovered HBV structural variants that were not observed amongst assembly results of other methods. Together, PDR/BBAP assembly results were significantly better than other compared methods.

Conclusions

Both PDR and BBAP independently increased the assembly efficiency and accuracy of highly polymorphic data, and assembly performances were further improved when used together. BBAP also provides nucleotide frequency information. Together, PDR and BBAP provide powerful tools for metagenomic data studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1630-z) contains supplementary material, which is available to authorized users.

The metagenomics worldwide research

Metagenomics is the technique, or set of techniques, whose main objective is to determine the microbial population that can be found in a determined environment, studied in the context of its community. For this, it uses the techniques of massive sequencing, or next generation sequencing, due to the difficulties presented by traditional techniques when trying to transfer all the microorganisms present in a given environment to the laboratory. Metagenomics is a newly created technique, which was born at the beginning of the twenty-first century, and since then the interest of the world scientific community in fields as diverse as medicine, biotechnology, agriculture or genetics has not left to grow. In this article, the authors make a historical review of the metagenomics, analyze and evaluate the different massive sequencing platforms used for metagenomic assays, review the current literature on this subject and advance future problems with which researchers who decide to go deeper in this field could find. In this way, the prior knowledge of the researcher will facilitate the approach of his research.

Divergent functional isoforms drive niche specialisation for nutrient acquisition and use in rumen microbiome

Many microbes in complex competitive environments share genes for acquiring and utilising nutrients, questioning whether niche specialisation exists and if so, how it is maintained. We investigated the genomic signatures of niche specialisation in the rumen microbiome, a highly competitive, anaerobic environment, with limited nutrient availability determined by the biomass consumed by the host. We generated individual metagenomic libraries from 14 cows fed an ad libitum diet of grass silage and calculated functional isoform diversity for each microbial gene identified. The animal replicates were used to calculate confidence intervals to test for differences in diversity of functional isoforms between microbes that may drive niche specialisation. We identified 153 genes with significant differences in functional isoform diversity between the two most abundant bacterial genera in the rumen (Prevotella and Clostridium). We found Prevotella possesses a more diverse range of isoforms capable of degrading hemicellulose, whereas Clostridium for cellulose. Furthermore, significant differences were observed in key metabolic processes indicating that isoform diversity plays an important role in maintaining their niche specialisation. The methods presented represent a novel approach for untangling complex interactions between microorganisms in natural environments and have resulted in an expanded catalogue of gene targets central to rumen cellulosic biomass degradation.

Finding Genes in Genome Sequence

Gene finding is the process of identifying genome sequence regions representing stretches of DNA that encode biologically active products, such as proteins or functional noncoding RNAs. As this is usually the first step in the analysis of any novel genomic sequence or resequenced sample of well-known organisms, it is a very important issue, as all downstream analyses depend on the results. This chapter describes the biological basis for gene finding, and the programs and computational approaches that are available for the automated identification of protein-coding genes. For bacterial, archaeal, and eukaryotic genomes, as well as for multi-species sequence data originating from environmental community studies, the state of the art in automated gene finding is described.

European healthcare systems readiness to shift from ‘one-size fits all’ to personalized medicine

Personalized medicine (PM) is no longer an abstract healthcare approach. It has become a reality over the last years and is already successfully applied in the various medical fields. Although there are success stories of implementing PM, there are still many more opportunities to further implement and make full use of the potential of PM. We assessed the system readiness of healthcare systems in Europe to shift from the predominant ‘one size fits all’ healthcare approach to PM. We conclude that European healthcare systems are only partially ready for PM. Key challenges such as integration of big data, health literacy, reimbursement and regulatory issues need to be overcome in order to strengthen the implementation and uptake of PM.

Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics

One of main steps in a study of microbial communities is resolving their composition, diversity and function. In the past, these issues were mostly addressed by the use of amplicon sequencing of a target gene because of reasonable price and easier computational postprocessing of the bioinformatic data. With the advancement of sequencing techniques, the main focus shifted to the whole metagenome shotgun sequencing, which allows much more detailed analysis of the metagenomic data, including reconstruction of novel microbial genomes and to gain knowledge about genetic potential and metabolic capacities of whole environments. On the other hand, the output of whole metagenomic shotgun sequencing is mixture of short DNA fragments belonging to various genomes, therefore this approach requires more sophisticated computational algorithms for clustering of related sequences, commonly referred to as sequence binning. There are currently two types of binning methods: taxonomy dependent and taxonomy independent. The first type classifies the DNA fragments by performing a standard homology inference against a reference database, while the latter performs the reference-free binning by applying clustering techniques on features extracted from the sequences. In this review, we describe the strategies within the second approach. Although these strategies do not require prior knowledge, they have higher demands on the length of sequences. Besides their basic principle, an overview of particular methods and tools is provided. Furthermore, the review covers the utilization of the methods in context with the length of sequences and discusses the needs for metagenomic data preprocessing in form of initial assembly prior to binning.

High Throughput Sequencing: An Overview of Sequencing Chemistry

In the present century sequencing is to the DNA science, what gel electrophoresis was to it in the last century. From 1977 to 2016 three generation of the sequencing technologies of various types have been developed. Second and third generation sequencing technologies referred commonly to as next generation sequencing technology, has evolved significantly with increase in sequencing speed, decrease in sequencing cost, since its inception in 2004. GS FLX by 454 Life Sciences/Roche diagnostics, Genome Analyzer, HiSeq, MiSeq and NextSeq by Illumina, Inc., SOLiD by ABI, Ion Torrent by Life Technologies are various type of the sequencing platforms available for second generation sequencing. The platforms available for the third generation sequencing are Helicos™ Genetic Analysis System by SeqLL, LLC, SMRT Sequencing by Pacific Biosciences, Nanopore sequencing by Oxford Nanopore's, Complete Genomics by Beijing Genomics Institute and GnuBIO by BioRad, to name few. The present article is an overview of the principle and the sequencing chemistry of these high throughput sequencing technologies along with brief comparison of various types of sequencing platforms available.

A Cell-Cell Communication-Based Screening System for Novel Microbes with Target Enzyme Activities

The development of synthetic biological devices has increased rapidly in recent years and the practical benefits of such biological devices are becoming increasingly clear. Here, we further improved the design of a previously reported high-throughput genetic enzyme screening system by investigating device-compatible biological components and phenol-mediated cell-cell communication, both of which increased the efficiency and practicality of the screening device without requiring the use of flow cytometry analysis. A sensor cell was designed to detect novel microbes with target enzyme activities on solid media by forming clear, circular colonies with fluorescence around the unknown microbes producing target enzymes. This mechanism of detection was enabled by the combination of pre-effector phenolic substrate treatment in the presence of target enzyme-producing microbes and control of the growth and fluorescence of remote sensor cells via phenol-mediated cell-cell communication. The sensor cells were applied to screen soil bacteria with phosphatase activity using phenyl phosphate as phenolic substrates. The sensor cells facilitated successful visualization of phosphatase activity in unknown microbes, which were identified by 16S rRNA analysis. Enzyme activity assays confirmed that the proposed screening technique was able to find 23 positive clones out of 33 selected colonies. Since many natural enzymatic reactions produce phenolic compounds from phenol-derived substrates, we anticipate that the proposed technique may have broad applications in the assessment and screening of novel microbes with target enzymes of interest. This method also can provide insights into the identification of novel enzymes for which screening assays are not yet available.