Multilocus sequence typing of Salmonella strains by high-throughput sequencing of selectively amplified target genes

Rapid development of next generation sequencing (NGS) technologies in recent years has made whole genome sequencing of bacterial genomes widely accessible. However, it is often unnecessary or not feasible to sequence the whole genome for most applications of genetic analyses in bacteria. Selectively capturing defined genomic regions followed by NGS analysis could be a promising approach for high-resolution molecular typing of a large set of strains. In this study, we describe a novel and straightforward PCR-based target-capturing method, hairpin-primed multiplex amplification (HPMA), which allows for simultaneous amplification of numerous target genes. To test the feasibility of NGS-based strain typing using HPMA, 20 target gene sequences were simultaneously amplified with barcode tagging in each of 41 Salmonella strains. The amplicons were then pooled and analyzed by 454 pyrosequencing. Analysis of the sequence data, as an extension of multilocus sequence typing (MLST), demonstrated the utility and potential of this novel typing method, MLST-seq, as a high-resolution strain typing method. With the rapidly increasing sequencing capacity of NGS, MLST-seq or its variations using different target enrichment methods can be expected to become a high-resolution typing method in the near future for high-throughput analysis of a large collection of bacterial strains.     

HAPHPIPE: Haplotype Reconstruction and Phylodynamics for Deep Sequencing of Intrahost Viral Populations

Deep sequencing of viral populations using next-generation sequencing (NGS) offers opportunities to understand and investigate evolution, transmission dynamics, and population genetics. Currently, the standard practice for processing NGS data to study viral populations is to summarize all the observed sequences from a sample as a single consensus sequence, thus discarding valuable information about the intrahost viral molecular epidemiology. Furthermore, existing analytical pipelines may only analyze genomic regions involved in drug resistance, thus are not suited for full viral genome analysis. Here, we present HAPHPIPE, a HAplotype and PHylodynamics PIPEline for genome-wide assembly of viral consensus sequences and haplotypes. The HAPHPIPE protocol includes modules for quality trimming, error correction, de novo assembly, alignment, and haplotype reconstruction. The resulting consensus sequences, haplotypes, and alignments can be further analyzed using a variety of phylogenetic and population genetic software. HAPHPIPE is designed to provide users with a single pipeline to rapidly analyze sequences from viral populations generated from NGS platforms and provide quality output properly formatted for downstream evolutionary analyses.     

Critical review of NGS analyses for de novo genotyping multigene families

The genotyping of highly polymorphic multigene families across many individuals used to be a particularly challenging task because of methodological limitations associated with traditional approaches. Next‐generation sequencing (NGS) can overcome most of these limitations, and it is increasingly being applied in population genetic studies of multigene families. Here, we critically review NGS bioinformatic approaches that have been used to genotype the major histocompatibility complex (MHC) immune genes, and we discuss how the significant advances made in this field are applicable to population genetic studies of gene families. Increasingly, approaches are introduced that apply thresholds of sequencing depth and sequence similarity to separate alleles from methodological artefacts. We explain why these approaches are particularly sensitive to methodological biases by violating fundamental genotyping assumptions. An alternative strategy that utilizes ultra‐deep sequencing (hundreds to thousands of sequences per amplicon) to reconstruct genotypes and applies statistical methods on the sequencing depth to separate alleles from artefacts appears to be more robust. Importantly, the ‘degree of change’ (DOC) method avoids using arbitrary cut‐off thresholds by looking for statistical boundaries between the sequencing depth for alleles and artefacts, and hence, it is entirely repeatable across studies. Although the advances made in generating NGS data are still far ahead of our ability to perform reliable processing, analysis and interpretation, the community is developing statistically rigorous protocols that will allow us to address novel questions in evolution, ecology and genetics of multigene families. Future developments in third‐generation single molecule sequencing may potentially help overcome problems that still persist in de novo multigene amplicon genotyping when using current second‐generation sequencing approaches.     

Parallel Tagged Next-Generation Sequencing on Pooled Samples – A New Approach for Population Genetics in Ecology and Conservation

Next-generation sequencing (NGS) on pooled samples has already been broadly applied in human medical diagnostics and plant and animal breeding. However, thus far it has been only sparingly employed in ecology and conservation, where it may serve as a useful diagnostic tool for rapid assessment of species genetic diversity and structure at the population level. Here we undertake a comprehensive evaluation of the accuracy, practicality and limitations of parallel tagged amplicon NGS on pooled population samples for estimating species population diversity and structure. We obtained 16S and Cyt b data from 20 populations of Leiopelma hochstetteri, a frog species of conservation concern in New Zealand, using two approaches – parallel tagged NGS on pooled population samples and individual Sanger sequenced samples. Data from each approach were then used to estimate two standard population genetic parameters, nucleotide diversity (π) and population differentiation (F_ST), that enable population genetic inference in a species conservation context. We found a positive correlation between our two approaches for population genetic estimates, showing that the pooled population NGS approach is a reliable, rapid and appropriate method for population genetic inference in an ecological and conservation context. Our experimental design also allowed us to identify both the strengths and weaknesses of the pooled population NGS approach and outline some guidelines and suggestions that might be considered when planning future projects.     

On the Prospects of Whole-Genome Association Mapping in Saccharomyces cerevisiae

Advances in sequencing technology have enabled whole-genome sequences to be obtained from multiple individuals within species, particularly in model organisms with compact genomes. For example, 36 genome sequences of Saccharomyces cerevisiae are now publicly available, and SNP data are available for even larger collections of strains. One potential use of these resources is mapping the genetic basis of phenotypic variation through genome-wide association (GWA) studies, with the benefit that associated variants can be studied experimentally with greater ease than in outbred populations such as humans. Here, we evaluate the prospects of GWA studies in S. cerevisiae strains through extensive simulations and a GWA study of mitochondrial copy number. We demonstrate that the complex and heterogeneous patterns of population structure present in yeast populations can lead to a high type I error rate in GWA studies of quantitative traits, and that methods typically used to control for population stratification do not provide adequate control of the type I error rate. Moreover, we show that while GWA studies of quantitative traits in S. cerevisiae may be difficult depending on the particular set of strains studied, association studies to map cis-acting quantitative trait loci (QTL) and Mendelian phenotypes are more feasible. We also discuss sampling strategies that could enable GWA studies in yeast and illustrate the utility of this approach in Saccharomyces paradoxus. Thus, our results provide important practical insights into the design and interpretation of GWA studies in yeast, and other model organisms that possess complex patterns of population structure.     

Using Next Generation RAD Sequencing to Isolate Multispecies Microsatellites for Pilosocereus (Cactaceae)

Microsatellite markers (also known as SSRs, Simple Sequence Repeats) are widely used in plant science and are among the most informative molecular markers for population genetic investigations, but the development of such markers presents substantial challenges. In this report, we discuss how next generation sequencing can replace the cloning, Sanger sequencing, identification of polymorphic loci, and testing cross-amplification that were previously required to develop microsatellites. We report the development of a large set of microsatellite markers for five species of the Neotropical cactus genus Pilosocereus using a restriction-site-associated DNA sequencing (RAD-seq) on a Roche 454 platform. We identified an average of 165 microsatellites per individual, with the absolute numbers across individuals proportional to the sequence reads obtained per individual. Frequency distribution of the repeat units was similar in the five species, with shorter motifs such as di- and trinucleotide being the most abundant repeats. In addition, we provide 72 microsatellites that could be potentially amplified in the sampled species and 22 polymorphic microsatellites validated in two populations of the species Pilosocereus machrisii. Although low coverage sequencing among individuals was observed for most of the loci, which we suggest to be more related to the nature of the microsatellite markers and the possible bias inserted by the restriction enzymes than to the genome size, our work demonstrates that an NGS approach is an efficient method to isolate multispecies microsatellites even in non-model organisms.     

Understanding genetic variation and function- the applications of next generation sequencing

Next generation sequencing (NGS) technology has had a transformatory effect upon population-level studies linking genetic variation to gene function. In this review, I briefly describe recent studies that have used top-down genome scanning and population genetic approaches to identify loci under recent selection, as well as some examples of how large NGS datasets can be deployed to detect the total level of deleterious, neutral and advantageous variation present in standing genetic variation. I then explore studies that have used some of these approaches to study gene function along with advances in sequencing populations under selection, QTL mapping techniques and emerging methodologies utilising targeted capture and NGS.     

Impact of Next Generation Sequencing Techniques in Food Microbiology

Understanding the Maxam-Gilbert and Sanger sequencing as the first generation, in recent years there has been an explosion of newly-developed sequencing strategies, which are usually referred to as next generation sequencing (NGS) techniques. NGS techniques have high-throughputs and produce thousands or even millions of sequences at the same time. These sequences allow for the accurate identification of microbial taxa, including uncultivable organisms and those present in small numbers. In specific applications, NGS provides a complete inventory of all microbial operons and genes present or being expressed under different study conditions. NGS techniques are revolutionizing the field of microbial ecology and have recently been used to examine several food ecosystems. After a short introduction to the most common NGS systems and platforms, this review addresses how NGS techniques have been employed in the study of food microbiota and food fermentations, and discusses their limits and perspectives. The most important findings are reviewed, including those made in the study of the microbiota of milk, fermented dairy products, and plant-, meat- and fish-derived fermented foods. The knowledge that can be gained on microbial diversity, population structure and population dynamics via the use of these technologies could be vital in improving the monitoring and manipulation of foods and fermented food products. They should also improve their safety.     

Estimation of population allele frequencies from next‐generation sequencing data: pool‐versus individual‐based genotyping

Molecular markers produced by next‐generation sequencing (NGS) technologies are revolutionizing genetic research. However, the costs of analysing large numbers of individual genomes remain prohibitive for most population genetics studies. Here, we present results based on mathematical derivations showing that, under many realistic experimental designs, NGS of DNA pools from diploid individuals allows to estimate the allele frequencies at single nucleotide polymorphisms (SNPs) with at least the same accuracy as individual‐based analyses, for considerably lower library construction and sequencing efforts. These findings remain true when taking into account the possibility of substantially unequal contributions of each individual to the final pool of sequence reads. We propose the intuitive notion of effective pool size to account for unequal pooling and derive a Bayesian hierarchical model to estimate this parameter directly from the data. We provide a user‐friendly application assessing the accuracy of allele frequency estimation from both pool‐ and individual‐based NGS population data under various sampling, sequencing depth and experimental error designs. We illustrate our findings with theoretical examples and real data sets corresponding to SNP loci obtained using restriction site–associated DNA (RAD) sequencing in pool‐ and individual‐based experiments carried out on the same population of the pine processionary moth (Thaumetopoea pityocampa). NGS of DNA pools might not be optimal for all types of studies but provides a cost‐effective approach for estimating allele frequencies for very large numbers of SNPs. It thus allows comparison of genome‐wide patterns of genetic variation for large numbers of individuals in multiple populations.     

QTL sequencing strategy to map genomic regions associated with resistance to ascochyta blight in chickpea

Whole‐genome sequencing‐based bulked segregant analysis (BSA) for mapping quantitative trait loci (QTL) provides an efficient alternative approach to conventional QTL analysis as it significantly reduces the scale and cost of analysis with comparable power to QTL detection using full mapping population. We tested the application of next‐generation sequencing (NGS)‐based BSA approach for mapping QTLs for ascochyta blight resistance in chickpea using two recombinant inbred line populations CPR‐01 and CPR‐02. Eleven QTLs in CPR‐01 and six QTLs in CPR‐02 populations were mapped on chromosomes Ca1, Ca2, Ca4, Ca6 and Ca7. The QTLs identified in CPR‐01 using conventional biparental mapping approach were used to compare the efficiency of NGS‐based BSA in detecting QTLs for ascochyta blight resistance. The QTLs on chromosomes Ca1, Ca4, Ca6 and Ca7 overlapped with the QTLs previously detected in CPR‐01 using conventional QTL mapping method. The QTLs on chromosome Ca4 were detected in both populations and overlapped with the previously reported QTLs indicating conserved region for ascochyta blight resistance across different chickpea genotypes. Six candidate genes in the QTL regions identified using NGS‐based BSA on chromosomes Ca2 and Ca4 were validated for their association with ascochyta blight resistance in the CPR‐02 population. This study demonstrated the efficiency of NGS‐based BSA as a rapid and cost‐effective method to identify QTLs associated with ascochyta blight in chickpea.     

Next-generation sequencing for understanding and accelerating crop domestication

Next generation Sequencing (NGS) provides a powerful tool for discovery of domestication genes in crop plants and their wild relatives. The accelerated domestication of new plant species as crops may be facilitated by this knowledge. Re-sequencing of domesticated genotypes can identify regions of low diversity associated with domestication. Species-specific data can be obtained from related wild species by whole-genome shot-gun sequencing. This sequence data can be used to design species specific polymerase chain reaction (PCR) primers. Sequencing of the products of PCR amplification of target genes can be used to explore genetic variation in large numbers of genes and gene families. Novel allelic variation in close or distant relatives can be characterized by NGS. Examples of recent applications of NGS to capture of genetic diversity for crop improvement include rice, sugarcane and Eucalypts. Populations of large numbers of individuals can be screened rapidly. NGS supports the rapid domestication of new plant species and the efficient identification and capture of novel genetic variation from related species.     

The Next Generation of Molecular Markers From Massively Parallel Sequencing of Pooled DNA Samples

Next generation sequencing (NGS) is about to revolutionize genetic analysis. Currently NGS techniques are mainly used to sequence individual genomes. Due to the high sequence coverage required, the costs for population-scale analyses are still too high to allow an extension to nonmodel organisms. Here, we show that NGS of pools of individuals is often more effective in SNP discovery and provides more accurate allele frequency estimates, even when taking sequencing errors into account. We modify the population genetic estimators Tajima''s π and Watterson''s θ to obtain unbiased estimates from NGS pooling data. Given the same sequencing effort, the resulting estimators often show a better performance than those obtained from individual sequencing. Although our analysis also shows that NGS of pools of individuals will not be preferable under all circumstances, it provides a cost-effective approach to estimate allele frequencies on a genome-wide scale.NEXT generation sequencing (NGS) is about to revolutionize biology. Through a massive parallelization, NGS provides an enormous number of reads, which permits sequencing of entire genomes at a fraction of the costs for Sanger sequencing. Hence, for the first time it has become feasible to obtain the complete genomic sequence for a large number of individuals. For several organisms, including humans, Drosophila melanogaster, and Arabidopsis thaliana, large resequencing projects are well on their way. Nevertheless, despite the enormous cost reduction, genome sequencing on a population scale is still out of reach for the budget of most laboratories. The extraction of as much statistical information as possible at cost as low as possible has therefore already attracted considerable interest. See, for instance, Jiang et al. (2009) for the modeling of sequencing errors and Erlich et al. (2009) for the efficient tagging of sequences.Current genome-wide resequencing projects collect the sequences individual by individual. To obtain full coverage of the entire genome and to have high confidence that all heterozygous sites were discovered, it is required that genomes are sequenced at a sufficiently high coverage. As many of the reads provide only redundant information, cost could be reduced by a more effective sampling strategy.In this report, we explore the potential of DNA pooling to provide a more cost-effective approach for SNP discovery and genome-wide population genetics. Sequencing a large pool of individuals simultaneously keeps the number of redundant DNA reads low and provides thus an economic alternative to the sequencing of individual genomes. On the other hand, more care has to be taken to establish an appropriate control of sequencing errors. Obviously haplotype information is not available from pooling experiments, but this will often be outweighed by the increased accuracy in population genetic inference.Focusing on biallelic loci, our analysis shows that with sufficiently large pool sizes, pooling usually outperforms the separate sequencing of individuals, both for estimating allele frequencies and for inference of population genetic parameters. When sequencing errors are not too common, pooling seems also to be a good choice for SNP detection experiments. To avoid the additional challenges encountered with individual sequencing of diploid individuals, we compare pooling with individual sequencing of haploid individuals. See Lynch (2008, 2009) for a discussion of next generation sequencing of diploid individuals. Our results for the pooling experiments should be also applicable to a diploid setting, as we are just merging pools of size 2 to a larger pool in this case, leading to a pool size of n = 2n_d for n_d diploid individuals. In the methods section, we derive several mathematical expressions that permit us to compare pooling with separate sequencing of individuals. These formulas are then applied in the results section to illustrate the differences in accuracy between the approaches. A reader who is interested only in the actual differences under several scenarios might therefore want to move directly to the results section.     

Viral Population Estimation Using Pyrosequencing

The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate-based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug-resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an expectation–maximization (EM) algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.     

Viral population estimation using pyrosequencing

The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate-based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug-resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an expectation-maximization (EM) algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.     

Assessing the Effect of Sequencing Depth and Sample Size in Population Genetics Inferences

Next-Generation Sequencing (NGS) technologies have dramatically revolutionised research in many fields of genetics. The ability to sequence many individuals from one or multiple populations at a genomic scale has greatly enhanced population genetics studies and made it a data-driven discipline. Recently, researchers have proposed statistical modelling to address genotyping uncertainty associated with NGS data. However, an ongoing debate is whether it is more beneficial to increase the number of sequenced individuals or the per-sample sequencing depth for estimating genetic variation. Through extensive simulations, I assessed the accuracy of estimating nucleotide diversity, detecting polymorphic sites, and predicting population structure under different experimental scenarios. Results show that the greatest accuracy for estimating population genetics parameters is achieved by employing a large sample size, despite single individuals being sequenced at low depth. Under some circumstances, the minimum sequencing depth for obtaining accurate estimates of allele frequencies and to identify polymorphic sites is , where both alleles are more likely to have been sequenced. On the other hand, inferences of population structure are more accurate at very large sample sizes, even with extremely low sequencing depth. This all points to the conclusion that under various experimental scenarios, in cost-limited population genetics studies, large sample sizes at low sequencing depth are desirable to achieve high accuracy. These findings will help researchers design their experimental set-ups and guide further investigation on the effect of protocol design for genetic research.     

Harvesting the Promising Fruits of Genomics: Applying Genome Sequencing Technologies to Crop Breeding

Next generation sequencing (NGS) technologies are being used to generate whole genome sequences for a wide range of crop species. When combined with precise phenotyping methods, these technologies provide a powerful and rapid tool for identifying the genetic basis of agriculturally important traits and for predicting the breeding value of individuals in a plant breeding population. Here we summarize current trends and future prospects for utilizing NGS-based technologies to develop crops with improved trait performance and increase the efficiency of modern plant breeding. It is our hope that the application of NGS technologies to plant breeding will help us to meet the challenge of feeding a growing world population.

This article is part of the PLOS Biology Collection “The Promise of Plant Translational Research.”

     

Genotyping by Genome Reducing and Sequencing for Outbred Animals

Next-generation sequencing (NGS) approaches are widely used in genome-wide genetic marker discovery and genotyping. However, current NGS approaches are not easy to apply to general outbred populations (human and some major farm animals) for SNP identification because of the high level of heterogeneity and phase ambiguity in the haplotype. Here, we reported a new method for SNP genotyping, called genotyping by genome reducing and sequencing (GGRS) to genotype outbred species. Through an improved procedure for library preparation and a marker discovery and genotyping pipeline, the GGRS approach can genotype outbred species cost-effectively and high-reproducibly. We also evaluated the efficiency and accuracy of our approach for high-density SNP discovery and genotyping in a large genome pig species (2.8 Gb), for which more than 70,000 single nucleotide polymorphisms (SNPs) can be identified for an expenditure of only $80 (USD)/sample.     

Targeted multiplex next‐generation sequencing: advances in techniques of mitochondrial and nuclear DNA sequencing for population genomics

Next‐generation sequencing (NGS) is emerging as an efficient and cost‐effective tool in population genomic analyses of nonmodel organisms, allowing simultaneous resequencing of many regions of multi‐genomic DNA from multiplexed samples. Here, we detail our synthesis of protocols for targeted resequencing of mitochondrial and nuclear loci by generating indexed genomic libraries for multiplexing up to 100 individuals in a single sequencing pool, and then enriching the pooled library using custom DNA capture arrays. Our use of DNA sequence from one species to capture and enrich the sequencing libraries of another species (i.e. cross‐species DNA capture) indicates that efficient enrichment occurs when sequences are up to about 12% divergent, allowing us to take advantage of genomic information in one species to sequence orthologous regions in related species. In addition to a complete mitochondrial genome on each array, we have included between 43 and 118 nuclear loci for low‐coverage sequencing of between 18 kb and 87 kb of DNA sequence per individual for single nucleotide polymorphisms discovery from 50 to 100 individuals in a single sequencing lane. Using this method, we have generated a total of over 500 whole mitochondrial genomes from seven cetacean species and green sea turtles. The greater variation detected in mitogenomes relative to short mtDNA sequences is helping to resolve genetic structure ranging from geographic to species‐level differences. These NGS and analysis techniques have allowed for simultaneous population genomic studies of mtDNA and nDNA with greater genomic coverage and phylogeographic resolution than has previously been possible in marine mammals and turtles.     

T-lex: a program for fast and accurate assessment of transposable element presence using next-generation sequencing data

Transposable elements (TEs) are repetitive DNA sequences that are ubiquitous, extremely abundant and dynamic components of practically all genomes. Much effort has gone into annotation of TE copies in reference genomes. The sequencing cost reduction and the newly available next-generation sequencing (NGS) data from multiple strains within a species offer an unprecedented opportunity to study population genomics of TEs in a range of organisms. Here, we present a computational pipeline (T-lex) that uses NGS data to detect the presence/absence of annotated TE copies. T-lex can use data from a large number of strains and returns estimates of population frequencies of individual TE insertions in a reasonable time. We experimentally validated the accuracy of T-lex detecting presence or absence of 768 previously identified TE copies in two resequenced Drosophila melanogaster strains. Approximately 95% of the TE insertions were detected with 100% sensitivity and 97% specificity. We show that even at low levels of coverage T-lex produces accurate results for TE copies that it can identify reliably but that the rate of 'no data' calls increases as the coverage falls below 15×. T-lex is a broadly applicable and flexible tool that can be used in any genome provided the availability of the reference genome, individual TE copy annotation and NGS data.