The effect of sequencing errors on metagenomic gene prediction

Clinical Metagenomic Next-Generation Sequencing for Diagnosis of Central Nervous System Infections: Advances and Challenges

Central nervous system (CNS) infections carry a substantial burden of morbidity and mortality worldwide, and accurate and timely diagnosis is required to optimize management. Metagenomic next-generation sequencing (mNGS) has proven to be a valuable tool in detecting pathogens in patients with suspected CNS infection. By sequencing microbial nucleic acids present in a patient's cerebrospinal fluid, brain tissue, or samples collected outside of the CNS, such as plasma, mNGS can detect a wide range of pathogens, including rare, unexpected, and/or fastidious organisms. Furthermore, its target-agnostic approach allows for the identification of both known and novel pathogens. This is particularly useful in cases where conventional diagnostic methods fail to provide an answer. In addition, mNGS can detect multiple microorganisms simultaneously, which is crucial in cases of mixed infections without a clear predominant pathogen. Overall, clinical mNGS testing can help expedite the diagnostic process for CNS infections, guide appropriate management decisions, and ultimately improve clinical outcomes. However, there are key challenges surrounding its use that need to be considered to fully leverage its clinical impact. For example, only a few specialized laboratories offer clinical mNGS due to the complexity of both the laboratory methods and analysis pipelines. Clinicians interpreting mNGS results must be aware of both false negatives-as mNGS is a direct detection modality and requires a sufficient amount of microbial nucleic acid to be present in the sample tested-and false positives-as mNGS detects environmental microbes and their nucleic acids, despite best practices to minimize contamination. Additionally, current costs and turnaround times limit broader implementation of clinical mNGS. Finally, there is uncertainty regarding the best practices for clinical utilization of mNGS, and further work is needed to define the optimal patient population(s), syndrome(s), and time of testing to implement clinical mNGS.

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.

Analysis of mutational genotyping using correctable decoding sequencing with superior specificity

The ability to accurately identify SNPs or low-abundance mutations is important for early clinical diagnosis of diseases, but the existing high-throughput sequencing platforms are limited in terms of their accuracy. Here, we propose a correctable decoding sequencing strategy that may be used for high-throughput sequencing platforms. This strategy is based on adding a mixture of two types of mononucleotides, natural nucleotide and cyclic reversible termination (CRT), for cyclic sequencing. Using the synthetic characteristic of CRTs, about 75% of the calls are unambiguous for a single sequencing run, and the remaining ambiguous sequence can be accurately deduced by two parallel sequencing runs. We demonstrate the feasibility of this strategy, and its cycle efficiency can reach approximately 99.3%. This strategy is proved to be effective for correcting errors and identifying whether the sequencing information is correct or not. And its conservative theoretical error rate was determined to be 0.0009%, which is lower than that of Sanger sequencing. In addition, we establish that the information of only a single sequencing run can be used to detect samples with known mutation sites. We apply this strategy to accurately identify a mutation site in mitochondrial DNA from human cells.

A Review of Basic Bioinformatic Techniques for Microbial Community Analysis in an Anaerobic Digester

Biogas production involves various types of intricate microbial populations in an anaerobic digester (AD). To understand the anaerobic digestion system better, a broad-based study must be conducted on the microbial population. Deep understanding of the complete metagenomics including microbial structure, functional gene form, similarity/differences, and relationships between metabolic pathways and product formation, could aid in optimization and enhancement of AD processes. With advancements in technologies for metagenomic sequencing, for example, next generation sequencing and high-throughput sequencing, have revolutionized the study of microbial dynamics in anaerobic digestion. This review includes a brief introduction to the basic process of metagenomics research and includes a detailed summary of the various bioinformatics approaches, viz., total investigation of data obtained from microbial communities using bioinformatics methods to expose metagenomics characterization. This includes (1) methods of DNA isolation and sequencing, (2) investigation of anaerobic microbial communities using bioinformatics techniques, (3) application of the analysis of anaerobic microbial community and biogas production, and (4) restriction and prediction of bioinformatics analysis on microbial metagenomics. The review has been concluded, giving a summarized insight into bioinformatic tools and also promoting the future prospects of integrating humungous data with artificial intelligence and neural network software.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Evaluation of the correctable decoding sequencing as a new powerful strategy for DNA sequencing

This article proposed the correctable decoding sequencing technology with conservative theoretical error rate of 0.0009%, and evaluated its robustness by simulation. This technology can provide a powerful new protocol for NGS platforms, enabling accurate identification of rare mutations in medicine.

Next-generation sequencing (NGS) promises to revolutionize precision medicine, but the existing sequencing technologies are limited in accuracy. To overcome this limitation, we propose the correctable decoding sequencing strategy, which is a duplex sequencing protocol with conservative theoretical error rates of 0.0009%. This rate is lower than that for Sanger sequencing. Here, we simulate the sequencing reactions by the self-developed software, and find that this approach has great potential in NGS in terms of sequence decoding, reassembly, error correction, and sequencing accuracy. Besides, this approach can be compatible with most SBS-based sequencing platforms, and also has the ability to compensate for some of the shortcomings of NGS platforms, thereby broadening its application for researchers. Hopefully, it can provide a powerful new protocol that can be used as an alternative to the existing NGS platforms, enabling accurate identification of rare mutations in a variety of applications in biology and medicine.

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.

There is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Australia and western and central Europe, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we compare the temporal ordering of lopinavir resistance mutations in HIV-1 subtypes B versus C. To this end, we combine the Hidden Conjunctive Bayesian Network (H-CBN) model with an approximate inference scheme enabling comparisons of larger networks. We show that the development of resistance to lopinavir differs significantly between subtypes B and C, such that findings based on subtype B sequences can not always be applied to sybtype C. The described methodology is suitable for comparing different subgroups in the context of other evolutionary processes.

Metagenomics: Is it a powerful tool to obtain lipases for application in biocatalysis?

In recent years, metagenomic strategies have been widely used to isolate and identify new enzymes from uncultivable components of microbial communities. Among these enzymes, various lipases have been obtained from metagenomic libraries from different environments and characterized. Although many of these lipases have characteristics that could make them interesting for application in biocatalysis, relatively little work has been done to evaluate their potential to catalyze industrially important reactions. In the present article, we highlight the latest research on lipases obtained through metagenomic tools, focusing on studies of activity and stability and investigations of application in biocatalysis. We also discuss the challenges of metagenomic approaches for the bioprospecting of new lipases.

Genetic repertoires of anaerobic microbiomes driving generation of biogas

BACKGROUND

Biogas production is an attractive technology for a sustainable generation of renewable energy. Although the microbial community is fundamental for such production, the process control is still limited to technological and chemical parameters. Currently, most of the efforts on microbial management system (MiMaS) are focused on process-specific marker species and community dynamics, but a practical implementation is in its infancy. The high number of unknown and uncharacterized microorganisms in general is one of the reasons hindering further advancements.

RESULTS

A Biogas Metagenomics Hybrid Assembly (BioMETHA) database, derived from microbiomes of biogas plants, was generated using a dedicated assembly strategy for different metagenomic datasets. Long reads from nanopore sequencing (MinION) were combined with short, more accurate second-generation sequencing reads (Illumina). The hybrid assembly resulted in 231 genomic bins each representing a taxonomic unit with an average completeness of 47%. Functional annotation identified 13,190 non-redundant genes covering roughly 207 k coding sequences. Mapping rates of metagenomics DNA derived from diverse biogas plants and laboratory reactors increased up to 73%. In addition, an EC (enzyme commission) reference sequence collection (ERSC) was generated whose genes are crucial for biogas-related processes, consisting of 235 unique EC numbers organized in 52 metabolic modules. Mapping rates of metatranscriptomic data to this ERSC revealed coverages of up to 93%. Process parameters and imbalances of laboratory reactors could be reconstructed by evaluating abundance of biogas-specific metabolic modules using metatranscriptomic data derived from various fermenter systems.

CONCLUSION

This newly established metagenomic hybrid assembly in combination with an EC reference sequence collection might help to shed light on the microbial dark matter of biogas plants by contributing to the development of a reference for biogas plant microbiome-specific gene sequences. Considering a biogas microbiome as a complex meta-organism expressing a meta-transcriptome, the approach established here could lay the foundation for a function-based microbial management system.

Recent advances in sequence assembly: principles and applications

The application of advanced sequencing technologies and the rapid growth of various sequence data have led to increasing interest in DNA sequence assembly. However, repeats and polymorphism occur frequently in genomes, and each of these has different impacts on assembly. Further, many new applications for sequencing, such as metagenomics regarding multiple species, have emerged in recent years. These not only give rise to higher complexity but also prevent short-read assembly in an efficient way. This article reviews the theoretical foundations that underlie current mapping-based assembly and de novo-based assembly, and highlights the key issues and feasible solutions that need to be considered. It focuses on how individual processes, such as optimal k-mer determination and error correction in assembly, rely on intelligent strategies or high-performance computation. We also survey primary algorithms/software and offer a discussion on the emerging challenges in assembly.

Tackling critical parameters in metazoan meta-barcoding experiments: a preliminary study based on coxI DNA barcode

Nowadays DNA meta-barcoding is a powerful instrument capable of quickly discovering the biodiversity of an environmental sample by integrating the DNA barcoding approach with High Throughput Sequencing technologies. It mainly consists of the parallel reading of informative genomic fragment/s able to discriminate living entities. Although this approach has been widely studied, it still needs optimization in some necessary steps requested in its advanced accomplishment. A fundamental element concerns the standardization of bioinformatic analyses pipelines. The aim of the present study was to underline a number of critical parameters of laboratory material preparation and taxonomic assignment pipelines in DNA meta-barcoding experiments using the cytochrome oxidase subunit-I (coxI) barcode region, known as a suitable molecular marker for animal species identification. We compared nine taxonomic assignment pipelines, including a custom in-house method, based on Hidden Markov Models. Moreover, we evaluated the potential influence of universal primers amplification bias in qPCR, as well as the correlation between GC content with taxonomic assignment results. The pipelines were tested on a community of known terrestrial invertebrates collected by pitfall traps from a chestnut forest in Italy. Although the present analysis was not exhaustive and needs additional investigation, our results suggest some potential improvements in laboratory material preparation and the introduction of additional parameters in taxonomic assignment pipelines. These include the correct setup of OTU clustering threshold, the calibration of GC content affecting sequencing quality and taxonomic classification, as well as the evaluation of PCR primers amplification bias on the final biodiversity pattern. Thus, careful attention and further validation/optimization of the above-mentioned variables would be required in a DNA meta-barcoding experimental routine.

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data

Background

Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Nevertheless these approaches lack sensitivity and have difficulties coping with rare and subtle motifs.

Results

We developed DiNAMO (for DNA MOtif), a new software based on an exhaustive and efficient algorithm for IUPAC motif discovery. We evaluated DiNAMO on synthetic and real datasets with two different applications, namely ChIP-seq peaks and Systematic Sequencing Error analysis. DiNAMO proves to compare favorably with other existing methods and is robust to noise.

Conclusions

We shown that DiNAMO software can serve as a tool to search for degenerate motifs in an exact manner using IUPAC models. DiNAMO can be used in scanning mode with sliding windows or in fixed position mode, which makes it suitable for numerous potential applications.

Availability

https://github.com/bonsai-team/DiNAMO.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2215-1) contains supplementary material, which is available to authorized users.

Metagenome enrichment approach used for selection of oil-degrading bacteria consortia for drill cutting residue bioremediation

Drill cuttings leave behind thousands of tons of residues without adequate treatment, generating a large environmental liability. Therefore knowledge about the microbial community of drilling residue may be useful for developing bioremediation strategies. In this work, samples of drilling residue were enriched in different culture media in the presence of petroleum, aiming to select potentially oil-degrading bacteria and biosurfactant producers. Total DNA was extracted directly from the drill cutting samples and from two enriched consortia and sequenced using the Ion Torrent platform. Taxonomic analysis revealed the predominance of Proteobacteria in the metagenome from the drill cuttings, while Firmicutes was enriched in consortia samples. Functional analysis using the Biosurfactants and Biodegradation Database (BioSurfDB) revealed a similar pattern among the three samples regarding hydrocarbon degradation and biosurfactants production pathways. However, some statistical differences were observed between samples. Namely, the pathways related to the degradation of fatty acids, chloroalkanes, and chloroalkanes were enriched in consortia samples. The degradation colorimetric assay using dichlorophenolindophenol as an indicator was positive for several hydrocarbon substrates. The consortia were also able to produce biosurfactants, with biosynthesis of iturin, lichnysin, and surfactin among the more abundant pathways. A microcosms assay followed by gas chromatography analysis showed the efficacy of the consortia in degrading alkanes, as we observed a reduction of around 66% and 30% for each consortium in total alkanes. These data suggest the potential use of these consortia in the bioremediation of drilling residue based on autochthonous bioaugmentation.

Elevated temperature drives kelp microbiome dysbiosis, while elevated carbon dioxide induces water microbiome disruption

Global climate change includes rising temperatures and increased pCO2 concentrations in the ocean, with potential deleterious impacts on marine organisms. In this case study we conducted a four-week climate change incubation experiment, and tested the independent and combined effects of increased temperature and partial pressure of carbon dioxide (pCO2), on the microbiomes of a foundation species, the giant kelp Macrocystis pyrifera, and the surrounding water column. The water and kelp microbiome responded differently to each of the climate stressors. In the water microbiome, each condition caused an increase in a distinct microbial order, whereas the kelp microbiome exhibited a reduction in the dominant kelp-associated order, Alteromondales. The water column microbiomes were most disrupted by elevated pCO2, with a 7.3 fold increase in Rhizobiales. The kelp microbiome was most influenced by elevated temperature and elevated temperature in combination with elevated pCO2. Kelp growth was negatively associated with elevated temperature, and the kelp microbiome showed a 5.3 fold increase Flavobacteriales and a 2.2 fold increase alginate degrading enzymes and sulfated polysaccharides. In contrast, kelp growth was positively associated with the combination of high temperature and high pCO2 'future conditions', with a 12.5 fold increase in Planctomycetales and 4.8 fold increase in Rhodobacteriales. Therefore, the water and kelp microbiomes acted as distinct communities, where the kelp was stabilizing the microbiome under changing pCO2 conditions, but lost control at high temperature. Under future conditions, a new equilibrium between the kelp and the microbiome was potentially reached, where the kelp grew rapidly and the commensal microbes responded to an increase in mucus production.

Bioinformatics for NGS-based metagenomics and the application to biogas research

Metagenomics has proven to be one of the most important research fields for microbial ecology during the last decade. Starting from 16S rRNA marker gene analysis for the characterization of community compositions to whole metagenome shotgun sequencing which additionally allows for functional analysis, metagenomics has been applied in a wide spectrum of research areas. The cost reduction paired with the increase in the amount of data due to the advent of next-generation sequencing led to a rapidly growing demand for bioinformatic software in metagenomics. By now, a large number of tools that can be used to analyze metagenomic datasets has been developed. The Bielefeld-Gießen center for microbial bioinformatics as part of the German Network for Bioinformatics Infrastructure bundles and imparts expert knowledge in the analysis of metagenomic datasets, especially in research on microbial communities involved in anaerobic digestion residing in biogas reactors. In this review, we give an overview of the field of metagenomics, introduce into important bioinformatic tools and possible workflows, accompanied by application examples of biogas surveys successfully conducted at the Center for Biotechnology of Bielefeld University.

Large-scale inference of conjunctive Bayesian networks

The continuous time conjunctive Bayesian network (CT-CBN) is a graphical model for analyzing the waiting time process of the accumulation of genetic changes (mutations). CT-CBN models have been successfully used in several biological applications such as HIV drug resistance development and genetic progression of cancer. However, current approaches for parameter estimation and network structure learning of CBNs can only deal with a small number of mutations (<20). Here, we address this limitation by presenting an efficient and accurate approximate inference algorithm using a Monte Carlo expectation-maximization algorithm based on importance sampling. The new method can now be used for a large number of mutations, up to one thousand, an increase by two orders of magnitude. In simulation studies, we present the accuracy as well as the running time efficiency of the new inference method and compare it with a MLE method, expectation-maximization, and discrete time CBN model, i.e. a first-order approximation of the CT-CBN model. We also study the application of the new model on HIV drug resistance datasets for the combination therapy with zidovudine plus lamivudine (AZT + 3TC) as well as under no treatment, both extracted from the Swiss HIV Cohort Study database.

AVAILABILITY AND IMPLEMENTATION

The proposed method is implemented as an R package available at https://github.com/cbg-ethz/MC-CBN CONTACT: niko.beerenwinkel@bsse.ethz.ch

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MetaGaAP: A Novel Pipeline to Estimate Community Composition and Abundance from Non-Model Sequence Data

Next generation sequencing and bioinformatic approaches are increasingly used to quantify microorganisms within populations by analysis of ‘meta-barcode’ data. This approach relies on comparison of amplicon sequences of ‘barcode’ regions from a population with public-domain databases of reference sequences. However, for many organisms relevant ‘barcode’ regions may not have been identified and large databases of reference sequences may not be available. A workflow and software pipeline, ‘MetaGaAP,’ was developed to identify and quantify genotypes through four steps: shotgun sequencing and identification of polymorphisms in a metapopulation to identify custom ‘barcode’ regions of less than 30 polymorphisms within the span of a single ‘read’, amplification and sequencing of the ‘barcode’, generation of a custom database of polymorphisms, and quantitation of the relative abundance of genotypes. The pipeline and workflow were validated in a ‘wild type’ Alphabaculovirus isolate, Helicoverpa armigera single nucleopolyhedrovirus (HaSNPV-AC53) and a tissue-culture derived strain (HaSNPV-AC53-T2). The approach was validated by comparison of polymorphisms in amplicons and shotgun data, and by comparison of predicted dominant and co-dominant genotypes with Sanger sequences. The computational power required to generate and search the database effectively limits the number of polymorphisms that can be included in a barcode to 30 or less. The approach can be used in quantitative analysis of the ecology and pathology of non-model organisms.

MetLab: An In Silico Experimental Design, Simulation and Analysis Tool for Viral Metagenomics Studies

Metagenomics, the sequence characterization of all genomes within a sample, is widely used as a virus discovery tool as well as a tool to study viral diversity of animals. Metagenomics can be considered to have three main steps; sample collection and preparation, sequencing and finally bioinformatics. Bioinformatic analysis of metagenomic datasets is in itself a complex process, involving few standardized methodologies, thereby hampering comparison of metagenomics studies between research groups. In this publication the new bioinformatics framework MetLab is presented, aimed at providing scientists with an integrated tool for experimental design and analysis of viral metagenomes. MetLab provides support in designing the metagenomics experiment by estimating the sequencing depth needed for the complete coverage of a species. This is achieved by applying a methodology to calculate the probability of coverage using an adaptation of Stevens’ theorem. It also provides scientists with several pipelines aimed at simplifying the analysis of viral metagenomes, including; quality control, assembly and taxonomic binning. We also implement a tool for simulating metagenomics datasets from several sequencing platforms. The overall aim is to provide virologists with an easy to use tool for designing, simulating and analyzing viral metagenomes. The results presented here include a benchmark towards other existing software, with emphasis on detection of viruses as well as speed of applications. This is packaged, as comprehensive software, readily available for Linux and OSX users at https://github.com/norling/metlab.

Evaluating techniques for metagenome annotation using simulated sequence data

The advent of next-generation sequencing has allowed huge amounts of DNA sequence data to be produced, advancing the capabilities of microbial ecosystem studies. The current challenge is to identify from which microorganisms and genes the DNA originated. Several tools and databases are available for annotating DNA sequences. The tools, databases and parameters used can have a significant impact on the results: naïve choice of these factors can result in a false representation of community composition and function. We use a simulated metagenome to show how different parameters affect annotation accuracy by evaluating the sequence annotation performances of MEGAN, MG-RAST, One Codex and Megablast. This simulated metagenome allowed the recovery of known organism and function abundances to be quantitatively evaluated, which is not possible for environmental metagenomes. The performance of each program and database varied, e.g. One Codex correctly annotated many sequences at the genus level, whereas MG-RAST RefSeq produced many false positive annotations. This effect decreased as the taxonomic level investigated increased. Selecting more stringent parameters decreases the annotation sensitivity, but increases precision. Ultimately, there is a trade-off between taxonomic resolution and annotation accuracy. These results should be considered when annotating metagenomes and interpreting results from previous studies.

The sequence annotation accuracies of metagenomic analysis tools were evaluated using a simulated metagenome.

RAS testing in metastatic colorectal cancer: advances in Europe

Personalized medicine shows promise for maximizing efficacy and minimizing toxicity of anti-cancer treatment. KRAS exon 2 mutations are predictive of resistance to epidermal growth factor receptor-directed monoclonal antibodies in patients with metastatic colorectal cancer. Recent studies have shown that broader RAS testing (KRAS and NRAS) is needed to select patients for treatment. While Sanger sequencing is still used, approaches based on various methodologies are available. Few CE-approved kits, however, detect the full spectrum of RAS mutations. More recently, "next-generation" sequencing has been developed for research use, including parallel semiconductor sequencing and reversible termination. These techniques have high technical sensitivities for detecting mutations, although the ideal threshold is currently unknown. Finally, liquid biopsy has the potential to become an additional tool to assess tumor-derived DNA. For accurate and timely RAS testing, appropriate sampling and prompt delivery of material is critical. Processes to ensure efficient turnaround from sample request to RAS evaluation must be implemented so that patients receive the most appropriate treatment. Given the variety of methodologies, external quality assurance programs are important to ensure a high standard of RAS testing. Here, we review technical and practical aspects of RAS testing for pathologists working with metastatic colorectal cancer tumor samples. The extension of markers from KRAS to RAS testing is the new paradigm for biomarker testing in colorectal cancer.

REPURPOSING GERMLINE EXOMES OF THE CANCER GENOME ATLAS DEMANDS A CAUTIOUS APPROACH AND SAMPLE-SPECIFIC VARIANT FILTERING

When seeking to reproduce results derived from whole-exome or genome sequencing data that could advance precision medicine, the time and expense required to produce a patient cohort make data repurposing an attractive option. The first step in repurposing is setting some quality baseline for the data so that conclusions are not spurious. This is difficult because there can be variations in quality from center to center, clinic to clinic and even patient to patient. Here, we assessed the quality of the whole-exome germline mutations of TCGA cancer patients using patterns of nucleotide substitution and negative selection against impactful mutations. We estimated the fraction of false positive variant calls for each exome with respect to two gold standard germline exomes, and found large variability in the quality of SNV calls between samples, cancer subtypes, and institutions. We then demonstrated how variant features, such as the average base quality for reads supporting an allele, can be used to identify sample-specific filtering parameters to optimize the removal of false positive calls. We concluded that while these germlines have many potential applications to precision medicine, users should assess the quality of the available exome data prior to use and perform additional filtering steps.

Marine Metagenome as A Resource for Novel Enzymes

More than 99% of identified prokaryotes, including many from the marine environment, cannot be cultured in the laboratory. This lack of capability restricts our knowledge of microbial genetics and community ecology. Metagenomics, the culture-independent cloning of environmental DNAs that are isolated directly from an environmental sample, has already provided a wealth of information about the uncultured microbial world. It has also facilitated the discovery of novel biocatalysts by allowing researchers to probe directly into a huge diversity of enzymes within natural microbial communities. Recent advances in these studies have led to a great interest in recruiting microbial enzymes for the development of environmentally-friendly industry. Although the metagenomics approach has many limitations, it is expected to provide not only scientific insights but also economic benefits, especially in industry. This review highlights the importance of metagenomics in mining microbial lipases, as an example, by using high-throughput techniques. In addition, we discuss challenges in the metagenomics as an important part of bioinformatics analysis in big data.

Hybrid de novo tandem repeat detection using short and long reads

Background

As one of the most studied genome rearrangements, tandem repeats have a considerable impact on genetic backgrounds of inherited diseases. Many methods designed for tandem repeat detection on reference sequences obtain high quality results. However, in the case of a de novo context, where no reference sequence is available, tandem repeat detection remains a difficult problem. The short reads obtained with the second-generation sequencing methods are not long enough to span regions that contain long repeats. This length limitation was tackled by the long reads obtained with the third-generation sequencing platforms such as Pacific Biosciences technologies. Nevertheless, the gain on the read length came with a significant increase of the error rate. The main objective of nowadays studies on long reads is to handle the high error rate up to 16%.

Methods

In this paper we present MixTaR, the first de novo method for tandem repeat detection that combines the high-quality of short reads and the large length of long reads. Our hybrid algorithm uses the set of short reads for tandem repeat pattern detection based on a de Bruijn graph. These patterns are then validated using the long reads, and the tandem repeat sequences are constructed using local greedy assemblies.

Results

MixTaR is tested with both simulated and real reads from complex organisms. For a complete analysis of its robustness to errors, we use short and long reads with different error rates. The results are then analysed in terms of number of tandem repeats detected and the length of their patterns.

Conclusions

Our method shows high precision and sensitivity. With low false positive rates even for highly erroneous reads, MixTaR is able to detect accurate tandem repeats with pattern lengths varying within a significant interval.

Classification of metagenomics data at lower taxonomic level using a robust supervised classifier

As more and more completely sequenced genomes become available, the taxonomic classification of metagenomic data will benefit greatly from supervised classifiers that can be updated instantaneously in response to new genomes. Currently, some supervised classifiers have been developed to assess the organism of metagenomic sequences. We have found that the existing supervised classifiers usually cannot discriminate the training data from different classes accurately when the data contain some outliers. However, the training genomic data (bacterial and archaeal genomes) usually contain a portion of outliers, which come from sequencing errors, phage invasions, and some highly expressed genes, etc. The outliers, treated as noises, prohibit the development of classifiers with better prediction accuracy. To solve the problem, we present a robust supervised classifier, weighted support vector domain description (WSVDD), which can eliminate the interference from some outliers for training genomic data and then generate more accurate data domain descriptions for each taxonomic class. The experimental results demonstrate WSVDD is more robust than other classifiers for simulated Sanger and 454 reads with different outlier rates. In addition, in experiments performed on simulated metagenomes and real gut metagenomes, WSVDD also achieved better prediction accuracy than other classifiers.

Molecular cloning using polymerase chain reaction, an educational guide for cellular engineering

Background

Over the last decades, molecular cloning has transformed biological sciences. Having profoundly impacted various areas such as basic science, clinical, pharmaceutical, and environmental fields, the use of recombinant DNA has successfully started to enter the field of cellular engineering. Here, the polymerase chain reaction (PCR) represents one of the most essential tools. Due to the emergence of novel and efficient PCR reagents, cloning kits, and software, there is a need for a concise and comprehensive protocol that explains all steps of PCR cloning starting from the primer design, performing PCR, sequencing PCR products, analysis of the sequencing data, and finally the assessment of gene expression. It is the aim of this methodology paper to provide a comprehensive protocol with a viable example for applying PCR in gene cloning.

Results

Exemplarily the sequence of the tdTomato fluorescent gene was amplified with PCR primers wherein proper restriction enzyme sites were embedded. Practical criteria for the selection of restriction enzymes and the design of PCR primers are explained. Efficient cloning of PCR products into a plasmid for sequencing and free web-based software for the consecutive analysis of sequencing data is introduced. Finally, confirmation of successful cloning is explained using a fluorescent gene of interest and murine target cells.

Conclusions

Using a practical example, comprehensive PCR-based protocol with important tips was introduced. This methodology paper can serve as a roadmap for researchers who want to quickly exploit the power of PCR-cloning but have their main focus on functional in vitro and in vivo aspects of cellular engineering.

Electronic supplementary material

The online version of this article (doi:10.1186/1754-1611-9-2) contains supplementary material, which is available to authorized users.

Do it yourself guide to genome assembly

Bioinformatics skills required for genome sequencing often represent a significant hurdle for many researchers working in computational biology. This humble effort highlights the significance of genome assembly as a research area, focuses on its need to remain accurate, provides details about the characteristics of the raw data, examines some key metrics, emphasizes some tools and draws attention to a generic tutorial with example data that outlines the whole pipeline for next-generation sequencing. The article concludes by pointing out some major future research problems.

MetaProx: the database of metagenomic proximons

MetaProx is the database of metagenomic proximons: a searchable repository of proximon objects conceived with two specific goals. The first objective is to accelerate research involving metagenomic functional interactions by providing a database of metagenomic operon candidates. Proximons represent a special subset of directons (series of contiguous co-directional genes) where each member gene is in close proximity to its neighbours with respect to intergenic distance. As a result, proximons represent significant operon candidates where some subset of proximons is the set of true metagenomic operons. Proximons are well suited for the inference of metagenomic functional networks because predicted functional linkages do not rely on homology-dependent information that is frequently unavailable in metagenomic scenarios. The second objective is to explore representations for semistructured biological data that can offer an alternative to the traditional relational database approach. In particular, we use a serialized object implementation and advocate a Data as Data policy where the same serialized objects can be used at all levels (database, search tool and saved user file) without conversion or the use of human-readable markups. MetaProx currently includes 4 210 818 proximons consisting of 8 926 993 total member genes.

Database URL:http://metaprox.uwaterloo.ca

Compositional profile of α / β-hydrolase fold proteins in mangrove soil metagenomes: prevalence of epoxide hydrolases and haloalkane dehalogenases in oil-contaminated sites

The occurrence of genes encoding biotechnologically relevant α/β-hydrolases in mangrove soil microbial communities was assessed using data obtained by whole-metagenome sequencing of four mangroves areas, denoted BrMgv01 to BrMgv04, in São Paulo, Brazil. The sequences (215 Mb in total) were filtered based on local amino acid alignments against the Lipase Engineering Database. In total, 5923 unassembled sequences were affiliated with 30 different α/β-hydrolase fold superfamilies. The most abundant predicted proteins encompassed cytosolic hydrolases (abH08; ∼ 23%), microsomal hydrolases (abH09; ∼ 12%) and Moraxella lipase-like proteins (abH04 and abH01; < 5%). Detailed analysis of the genes predicted to encode proteins of the abH08 superfamily revealed a high proportion related to epoxide hydrolases and haloalkane dehalogenases in polluted mangroves BrMgv01-02-03. This suggested selection and putative involvement in local degradation/detoxification of the pollutants. Seven sequences that were annotated as genes for putative epoxide hydrolases and five for putative haloalkane dehalogenases were found in a fosmid library generated from BrMgv02 DNA. The latter enzymes were predicted to belong to Actinobacteria, Deinococcus-Thermus, Planctomycetes and Proteobacteria. Our integrated approach thus identified 12 genes (complete and/or partial) that may encode hitherto undescribed enzymes. The low amino acid identity (< 60%) with already-described genes opens perspectives for both production in an expression host and genetic screening of metagenomes.

High-throughput and sensitive next-generation droplet digital PCR assay for the quantitation of the hepatitis C virus mutation at core amino acid 70

The next-generation droplet digital polymerase chain reaction (ddPCR) assay employs an emulsion-based endpoint to quantitate the amount of target DNA and is more robust than real-time PCR when analyzing sequence variations. However, no studies have applied this technique to quantitate mutations in polymorphic viral genomes. To develop this approach, a ddPCR-based assay was designed to quantitate with high-throughput and sensitivity mutations and their frequencies in codon 70 of the hepatitis C virus (HCV) gene that encodes the Core protein. The assay was linear from 2.5 to 10(5) copies per assay, and the limit of detection of mutants in the presence of a 20,000-fold excess of wild type was 0.005%. The results correlated well with those obtained using the COBAS(®) TaqMan(®) HCV Test, which is a real-time PCR assay for the quantitative detection of HCV RNA in human serum (n=87; range, 2.3-7.7log10IU/mL; Pearson's R(2)=0.9120; p<0.0001). The median frequencies of mutations by ddPCR were 0.262% (n=55; range, 0-37.951%) and 99.687% (n=32; range, 52.191-100%) for the wild-type and mutant sequences, respectively, by direct sequencing. The ddPCR assay should be useful for quantitating mutations in other polymorphic viral genomes.

SIBIS: a Bayesian model for inconsistent protein sequence estimation

MOTIVATION

The prediction of protein coding genes is a major challenge that depends on the quality of genome sequencing, the accuracy of the model used to elucidate the exonic structure of the genes and the complexity of the gene splicing process leading to different protein variants. As a consequence, today's protein databases contain a huge amount of inconsistency, due to both natural variants and sequence prediction errors.

RESULTS

We have developed a new method, called SIBIS, to detect such inconsistencies based on the evolutionary information in multiple sequence alignments. A Bayesian framework, combined with Dirichlet mixture models, is used to estimate the probability of observing specific amino acids and to detect inconsistent or erroneous sequence segments. We evaluated the performance of SIBIS on a reference set of protein sequences with experimentally validated errors and showed that the sensitivity is significantly higher than previous methods, with only a small loss of specificity. We also assessed a large set of human sequences from the UniProt database and found evidence of inconsistency in 48% of the previously uncharacterized sequences. We conclude that the integration of quality control methods like SIBIS in automatic analysis pipelines will be critical for the robust inference of structural, functional and phylogenetic information from these sequences.

AVAILABILITY AND IMPLEMENTATION

Source code, implemented in C on a linux system, and the datasets of protein sequences are freely available for download at http://www.lbgi.fr/∼julie/SIBIS.

miR-146a promotes the initiation and progression of melanoma by activating Notch signaling

Oncogenic mutations in BRAF and NRAS occur in 70% of melanomas. In this study, we identify a microRNA, miR-146a, that is highly upregulated by oncogenic BRAF and NRAS. Expression of miR-146a increases the ability of human melanoma cells to proliferate in culture and form tumors in mice, whereas knockdown of miR-146a has the opposite effects. We show these oncogenic activities are due to miR-146a targeting the NUMB mRNA, a repressor of Notch signaling. Previous studies have shown that pre-miR-146a contains a single nucleotide polymorphism (C>G rs2910164). We find that the ability of pre-miR-146a/G to activate Notch signaling and promote oncogenesis is substantially higher than that of pre-miR-146a/C. Analysis of melanoma cell lines and matched patient samples indicates that during melanoma progression pre-miR-146a/G is enriched relative to pre-miR-146a/C, resulting from a C-to-G somatic mutation in pre-miR-146a/C. Collectively, our results reveal a central role for miR-146a in the initiation and progression of melanoma. DOI: http://dx.doi.org/10.7554/eLife.01460.001.

Stitching gene fragments with a network matching algorithm improves gene assembly for metagenomics

Motivation: One of the difficulties in metagenomic assembly is that homologous genes from evolutionarily closely related species may behave like repeats and confuse assemblers. As a result, small contigs, each representing a short gene fragment, instead of complete genes, may be reported by an assembler. This further complicates annotation of metagenomic datasets, as annotation tools (such as gene predictors or similarity search tools) typically perform poorly on configs encoding short gene fragments.

Results: We present a novel way of using the de Bruijn graph assembly of metagenomes to improve the assembly of genes. A network matching algorithm is proposed for matching the de Bruijn graph of contigs against reference genes, to derive ‘gene paths’ in the graph (sequences of contigs containing gene fragments) that have the highest similarities to known genes, allowing gene fragments contained in multiple contigs to be connected to form more complete (or intact) genes. Tests on simulated and real datasets show that our approach (called GeneStitch) is able to significantly improve the assembly of genes from metagenomic sequences, by connecting contigs with the guidance of homologous genes—information that is orthogonal to the sequencing reads. We note that the improvement of gene assembly can be observed even when only distantly related genes are available as the reference. We further propose to use ‘gene graphs’ to represent the assembly of reads from homologous genes and discuss potential applications of gene graphs to improving functional annotation for metagenomics.

Availability: The tools are available as open source for download at http://omics.informatics.indiana.edu/GeneStitch

Contact:yye@indiana.edu

Gene prediction in metagenomic fragments based on the SVM algorithm

Background

Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms from various environments, such as human body, without isolation and cultivation. Accurately identifying genes from metagenomic fragments is one of the most fundamental issues.

Results

In this article, we present a novel gene prediction method named MetaGUN for metagenomic fragments based on a machine learning approach of SVM. It implements in a three-stage strategy to predict genes. Firstly, it classifies input fragments into phylogenetic groups by a k-mer based sequence binning method. Then, protein-coding sequences are identified for each group independently with SVM classifiers that integrate entropy density profiles (EDP) of codon usage, translation initiation site (TIS) scores and open reading frame (ORF) length as input patterns. Finally, the TISs are adjusted by employing a modified version of MetaTISA. To identify protein-coding sequences, MetaGun builds the universal module and the novel module. The former is based on a set of representative species, while the latter is designed to find potential functionary DNA sequences with conserved domains.

Conclusions

Comparisons on artificial shotgun fragments with multiple current metagenomic gene finders show that MetaGUN predicts better results on both 3' and 5' ends of genes with fragments of various lengths. Especially, it makes the most reliable predictions among these methods. As an application, MetaGUN was used to predict genes for two samples of human gut microbiome. It identifies thousands of additional genes with significant evidences. Further analysis indicates that MetaGUN tends to predict more potential novel genes than other current metagenomic gene finders.

Deep sequencing reveals mutagenic effects of ribavirin during monotherapy of hepatitis C virus genotype 1-infected patients

The preeminent mode of action of the broad-spectrum antiviral nucleoside ribavirin in the therapy of chronic hepatitis C is currently unresolved. Particularly under contest are possible mutagenic effects of ribavirin that may lead to viral extinction by lethal mutagenesis of the hepatitis C virus (HCV) genome. We applied ultradeep sequencing to determine ribavirin-induced sequence changes in the HCV coding region (nucleotides [nt] 330 to 9351) of patients treated with 6-week ribavirin monotherapy (n = 6) in comparison to placebo (n = 6). Baseline HCV RNA levels maximally declined on average by -0.8 or -0.1 log10 IU/ml in ribavirin- versus placebo-treated patients. No general increase in rates of nucleotide substitutions in ribavirin-treated patients was observed. However, more HCV genome positions with high G-to-A and C-to-U transition rates were detected between baseline and treatment week 6 in ribavirin-treated patients in comparison to placebo-treated patients (rate of 0.0041 transitions per base pair versus rate of 0.0022 transitions per base pair; P = 0.049). Similarly, the sensitive detection of low-frequency minority variants by statistical filtering indicated significantly more positions with G-to-A and C-to-U transitions in ribavirin-treated patients than in placebo-treated patients (rate of 0.0331 transitions versus rate of 0.0186 transitions per G/C-containing position at baseline; P = 0.018). In contrast, non-ribavirin-associated A-to-G and U-to-C transitions were not enriched in the ribavirin group (P = 0.152). We conclude that ribavirin exerts a mutagenic effect on the virus in patients with chronic hepatitis C by facilitating G-to-A and C-to-U nucleotide transitions.

ORFcor: identifying and accommodating ORF prediction inconsistencies for phylogenetic analysis

The high-throughput annotation of open reading frames (ORFs) required by modern genome sequencing projects necessitates computational protocols that sometimes annotate orthologous ORFs inconsistently. Such inconsistencies hinder comparative analyses by non-uniformly extending or truncating 5′ and/or 3′ sequence ends, causing ORFs that are in fact identical to artificially diverge. Whereas strategies exist to correct such inconsistencies during whole-genome annotation, equivalent software designed to correct subsets of these data without genome reannotation is lacking. We therefore developed ORFcor, which corrects annotation inconsistencies using consensus start and stop positions derived from sets of closely related orthologs. ORFcor corrects inconsistent ORF annotations in diverse test datasets with specificities and sensitivities approaching 100% when sufficiently related orthologs (e.g., from the same taxonomic family) are available for comparison. The ORFcor package is implemented in Perl, multithreaded to handle large datasets, includes related scripts to facilitate high-throughput phylogenomic analyses, and is freely available at www.currielab.wisc.edu/downloads.html.

Gene capture coupled to high-throughput sequencing as a strategy for targeted metagenome exploration

Next-generation sequencing (NGS) allows faster acquisition of metagenomic data, but complete exploration of complex ecosystems is hindered by the extraordinary diversity of microorganisms. To reduce the environmental complexity, we created an innovative solution hybrid selection (SHS) method that is combined with NGS to characterize large DNA fragments harbouring biomarkers of interest. The quality of enrichment was evaluated after fragments containing the methyl coenzyme M reductase subunit A gene (mcrA), the biomarker of methanogenesis, were captured from a Methanosarcina strain and a metagenomic sample from a meromictic lake. The methanogen diversity was compared with direct metagenome and mcrA-based amplicon pyrosequencing strategies. The SHS approach resulted in the capture of DNA fragments up to 2.5 kb with an enrichment efficiency between 41 and 100%, depending on the sample complexity. Compared with direct metagenome and amplicons sequencing, SHS detected broader mcrA diversity, and it allowed efficient sampling of the rare biosphere and unknown sequences. In contrast to amplicon-based strategies, SHS is less biased and GC independent, and it recovered complete biomarker sequences in addition to conserved regions. Because this method can also isolate the regions flanking the target sequences, it could facilitate operon reconstructions.

Pathogen typing in the genomics era: MLST and the future of molecular epidemiology

Multi-locus sequence typing (MLST) is a high-resolution genetic typing approach to identify species and strains of pathogens impacting human health, agriculture (animals and plants), and biosafety. In this review, we outline the general concepts behind MLST, molecular approaches for obtaining MLST data, analytical approaches for MLST data, and the contributions MLST studies have made in a wide variety of areas. We then look at the future of MLST and their relative strengths and weaknesses with respect to whole genome sequence typing approaches that are moving into the research arena at an ever-increasing pace. Throughout the paper, we provide exemplar references of these various aspects of MLST. The literature is simply too vast to make this review comprehensive, nevertheless, we have attempted to include enough references in a variety of key areas to introduce the reader to the broad applications and complications of MLST data.

MetaGeneTack: ab initio detection of frameshifts in metagenomic sequences

Summary: Frameshift (FS) prediction is important for analysis and biological interpretation of metagenomic sequences. Since a genomic context of a short metagenomic sequence is rarely known, there is not enough data available to estimate parameters of species-specific statistical models of protein-coding and non-coding regions. The challenge of ab initio FS detection is, therefore, two fold: (i) to find a way to infer necessary model parameters and (ii) to identify positions of frameshifts (if any). Here we describe a new tool, MetaGeneTack, which uses a heuristic method to estimate parameters of sequence models used in the FS detection algorithm. It is shown on multiple test sets that the MetaGeneTack FS detection performance is comparable or better than the one of earlier developed program FragGeneScan.

Availability and implementation: MetaGeneTack is available as a web server at http://exon.gatech.edu/GeneTack/cgi/metagenetack.cgi. Academic users can download a standalone version of the program from http://exon.gatech.edu/license_download.cgi.

Contact:borodovsky@gatech.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Current opportunities and challenges in microbial metagenome analysis--a bioinformatic perspective

Metagenomics has become an indispensable tool for studying the diversity and metabolic potential of environmental microbes, whose bulk is as yet non-cultivable. Continual progress in next-generation sequencing allows for generating increasingly large metagenomes and studying multiple metagenomes over time or space. Recently, a new type of holistic ecosystem study has emerged that seeks to combine metagenomics with biodiversity, meta-expression and contextual data. Such 'ecosystems biology' approaches bear the potential to not only advance our understanding of environmental microbes to a new level but also impose challenges due to increasing data complexities, in particular with respect to bioinformatic post-processing. This mini review aims to address selected opportunities and challenges of modern metagenomics from a bioinformatics perspective and hopefully will serve as a useful resource for microbial ecologists and bioinformaticians alike.

Short-read reading-frame predictors are not created equal: sequence error causes loss of signal

Background

Gene prediction algorithms (or gene callers) are an essential tool for analyzing shotgun nucleic acid sequence data. Gene prediction is a ubiquitous step in sequence analysis pipelines; it reduces the volume of data by identifying the most likely reading frame for a fragment, permitting the out-of-frame translations to be ignored. In this study we evaluate five widely used ab initio gene-calling algorithms—FragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal—for accuracy on short (75–1000 bp) fragments containing sequence error from previously published artificial data and “real” metagenomic datasets.

Results

While gene prediction tools have similar accuracies predicting genes on error-free fragments, in the presence of sequencing errors considerable differences between tools become evident. For error-containing short reads, FragGeneScan finds more prokaryotic coding regions than does MetaGeneAnnotator, MetaGeneMark, Orphelia, or Prodigal. This improved detection of genes in error-containing fragments, however, comes at the cost of much lower (50%) specificity and overprediction of genes in noncoding regions.

Conclusions

Ab initio gene callers offer a significant reduction in the computational burden of annotating individual nucleic acid reads and are used in many metagenomic annotation systems. For predicting reading frames on raw reads, we find the hidden Markov model approach in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are better suited for higher-quality sequences such as assembled contigs.

Gene and translation initiation site prediction in metagenomic sequences

MOTIVATION

Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample; consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data.

RESULTS

We present MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify genes in short, anonymous coding sequences with a high degree of accuracy. The novel value of the method consists of enhanced translation initiation site identification, ability to identify sequences that use alternate genetic codes and confidence values for each gene call. We compare the results of MetaProdigal with other methods and conclude with a discussion of future improvements.

AVAILABILITY

The Prodigal software is freely available under the General Public License from http://code.google.com/p/prodigal/.

A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as “noise” or “error”) within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms.

Sequence quality (referred to alternatively as the level of sequencing error or noise) is a primary concern to all sequence-dependent investigations. This is particularly true in the field of metagenomics where automated tools (e.g. annotation pipelines like MG-RAST) rely on high fidelity sequence data to derive meaningful biological inferences, and is exacerbated by the capacity of next generation sequencing platforms that continue to expand at a rate greater than Moore's law. We demonstrate that the most commonly utilized means to assess sequencing error exhibit severe limitations with respect to analysis of metagenomic data. Furthermore, we introduce a method (DRISEE) that accounts for these limitations through the application of a novel approach to assess sequencing error. DRISEE-based analyses reveal previously unobserved levels of sequencing error. DRISEE provides a platform independent measure of sequencing error that objectively assesses the quality of entire sequence samples. This assessment can be used to exclude low quality samples from computationally expensive analyses (e.g. annotation). It can also be used to evaluate the relative fidelity of analyses after they have been performed (e.g. annotation of error prone samples is less reliable than that of samples with low levels of sequencing error).

Next-generation sequencing: ready for the clinics?

Next-generation sequencing (NGS) has transformed genomic research by decreasing the cost of sequencing and increasing the throughput. Now, the focus is on using NGS technology for diagnostics and therapeutics. In this review, we discuss the possible clinical applications of NGS and the potential of some of the current systems to transition to the clinic. Clinical use of NGS technologies will enable the identification of causative mutations for rare genetic disorders through whole-genome or targeted genome resequencing, rapid pathogen screening and cancer diagnosis along with the identification of appropriate therapy. Routine clinical use of NGS technologies is appealing, but mandates high accuracy, simple assays, small inexpensive instruments, flexible throughput, short run times and most importantly, easy data analysis as well as interpretation. A number of NGS systems launched recently have least some of these characteristics, namely, small instruments, flexible throughput and short run time, but still face a few challenges. Moreover, simplified data analysis tools will need to be developed to minimize the requirement of sophisticated bioinformatics support in clinics. In summary, for successful transition of NGS to clinic, a sustained collaboration between research labs, clinical practitioners and vendors offering sequencing based genetic tests is required.

Novel high-rank phylogenetic lineages within a sulfur spring (Zodletone Spring, Oklahoma), revealed using a combined pyrosequencing-sanger approach

The utilization of high-throughput sequencing technologies in 16S rRNA gene-based diversity surveys has indicated that within most ecosystems, a significant fraction of the community could not be assigned to known microbial phyla. Accurate determination of the phylogenetic affiliation of such sequences is difficult due to the short-read-length output of currently available high-throughput technologies. This fraction could harbor multiple novel phylogenetic lineages that have so far escaped detection. Here we describe our efforts in accurate assessment of the novelty and phylogenetic affiliation of selected unclassified lineages within a pyrosequencing data set generated from source sediments of Zodletone Spring, a sulfide- and sulfur-rich spring in southwestern Oklahoma. Lineage-specific forward primers were designed for 78 putatively novel lineages identified within the pyrosequencing data set, and representative nearly full-length small-subunit (SSU) rRNA gene sequences were obtained by pairing those primers with reverse universal bacterial primers. Of the 78 lineages tested, amplifiable products were obtained for 52, 32 of which had at least one nearly full-length sequence that was representative of the lineage targeted. Analysis of phylogenetic affiliation of the obtained Sanger sequences identified 5 novel candidate phyla and 10 novel candidate classes (within Fibrobacteres, Planctomycetes, and candidate phyla BRC1, GN12, TM6, TM7, LD1, WS2, and GN06) in the data set, in addition to multiple novel orders and families. The discovery of multiple novel phyla within a pilot study of a single ecosystem clearly shows the potential of the approach in identifying novel diversities within the rare biosphere.