Strain/species identification in metagenomes using genome-specific markers

Shotgun metagenome sequencing has become a fast, cheap and high-throughput technology for characterizing microbial communities in complex environments and human body sites. However, accurate identification of microorganisms at the strain/species level remains extremely challenging. We present a novel k-mer-based approach, termed GSMer, that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes, which were then used for strain/species-level identification in metagenomes. Using 5390 sequenced microbial genomes, 8 770 321 50-mer strain-specific and 11 736 360 species-specific GSMs were identified for 4088 strains and 2005 species (4933 strains), respectively. The GSMs were first evaluated against mock community metagenomes, recently sequenced genomes and real metagenomes from different body sites, suggesting that the identified GSMs were specific to their targeting genomes. Sensitivity evaluation against synthetic metagenomes with different coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥0.25× coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs identified 45 and 74 microbial strains/species significantly associated with type 2 diabetes patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes, respectively. Our result agreed with previous studies but provided strain-level information. The approach can be directly applied to identify microbial strains/species from raw metagenomes, without the effort of complex data pre-processing.     

MetaCluster 4.0: a novel binning algorithm for NGS reads and huge number of species

Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. The difficulties of the binning problem are due to the following four factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species; (3) short NGS reads; and (4) a large number of species (can be more than a hundred). None of the existing binning tools can handle all four factors. No tools, including both AbundanceBin and MetaCluster 3.0, have demonstrated reasonable performance on a sample with more than 20 species. In this article, we introduce MetaCluster 4.0, an unsupervised binning algorithm that can accurately (with about 80% precision and sensitivity in all cases and at least 90% in some cases) and efficiently bin short reads with varying abundance ratios and is able to handle datasets with 100 species. The novelty of MetaCluster 4.0 stems from solving a few important problems: how to divide reads into groups by a probabilistic approach, how to estimate the 4-mer distribution of each group, how to estimate the number of species, and how to modify MetaCluster 3.0 to handle a large number of species. We show that Meta Cluster 4.0 is effective for both simulated and real datasets. Supplementary Material is available at www.liebertonline.com/cmb.     

How low can you go? Introducing SeXY: sex identification from low‐quantity sequencing data despite lacking assembled sex chromosomes

Accurate sex identification is crucial for elucidating the biology of a species. In the absence of directly observable sexual characteristics, sex identification of wild fauna can be challenging, if not impossible. Molecular sexing offers a powerful alternative to morphological sexing approaches. Here, we present SeXY, a novel sex‐identification pipeline, for very low‐coverage shotgun sequencing data from a single individual. SeXY was designed to utilize low‐effort screening data for sex identification and does not require a conspecific sex‐chromosome assembly as reference. We assess the accuracy of our pipeline to data quantity by downsampling sequencing data from 100,000 to 1000 mapped reads and to reference genome selection by mapping to a variety of reference genomes of various qualities and phylogenetic distance. We show that our method is 100% accurate when mapping to a high‐quality (highly contiguous N50 > 30 Mb) conspecific genome, even down to 1000 mapped reads. For lower‐quality reference assemblies (N50 < 30 Mb), our method is 100% accurate with 50,000 mapped reads, regardless of reference assembly quality or phylogenetic distance. The SeXY pipeline provides several advantages over previously implemented methods; SeXY (i) requires sequencing data from only a single individual, (ii) does not require assembled conspecific sex chromosomes, or even a conspecific reference assembly, (iii) takes into account variation in coverage across the genome, and (iv) is accurate with only 1000 mapped reads in many cases.     

Improving ancient DNA read mapping against modern reference genomes

ABSTRACT: BACKGROUND: Next-Generation Sequencing has revolutionized our approach to ancient DNA (aDNA) research, by providing complete genomic sequences of ancient individuals and extinct species. However, the recovery of genetic material from long-dead organisms is still complicated by a number of issues, including post-mortem DNA damage and high levels of environmental contamination. Together with error profiles specific to the type of sequencing platforms used, these specificities could limit our ability to map sequencing reads against modern reference genomes and therefore limit our ability to identify endogenous ancient reads, reducing the efficiency of shotgun sequencing aDNA. RESULTS: In this study, we compare different computational methods for improving the accuracy and sensitivity of aDNA sequence identification, based on shotgun sequencing reads recovered from Pleistocene horse extracts using Illumina GAIIx and Helicos Heliscope platforms. We show that the performance of the Burrows Wheeler Aligner (BWA), that has been developed for mapping of undamaged sequencing reads using platforms with low rates of indel-types of sequencing errors, can be employed at acceptable run-times by modifying default parameters in a platform-specific manner. We also examine if trimming likely damaged positions at read ends can increase the recovery of genuine aDNA fragments and if accurate identification of human contamination can be achieved using a strategy previously suggested based on best hit filtering. We show that combining our different mapping and filtering approaches can increase the number of high-quality endogenous hits recovered by up to 33%. CONCLUSIONS: We have shown that Illumina and Helicos sequences recovered from aDNA extracts could not be aligned to modern reference genomes with the same efficiency unless mapping parameters are optimized for the specific types of errors generated by these platforms and by post-mortem DNA damage. Our findings have important implications for future aDNA research, as we define mapping guidelines that improve our ability to identify genuine aDNA sequences, which in turn could improve the genotyping accuracy of ancient specimens. Our framework provides a significant improvement to the standard procedures used for characterizing ancient genomes, which is challenged by contamination and often low amounts of DNA material.     

Shotgun metagenomics of soil invertebrate communities reflects taxonomy,biomass, and reference genome properties

• Metagenomics – shotgun sequencing of all DNA fragments from a community DNA extract – is routinely used to describe the composition, structure, and function of microorganism communities. Advances in DNA sequencing and the availability of genome databases increasingly allow the use of shotgun metagenomics on eukaryotic communities. Metagenomics offers major advances in the recovery of biomass relationships in a sample, in comparison to taxonomic marker gene‐based approaches (metabarcoding). However, little is known about the factors which influence metagenomics data from eukaryotic communities, such as differences among organism groups, the properties of reference genomes, and genome assemblies.
• We evaluated how shotgun metagenomics records composition and biomass in artificial soil invertebrate communities at different sequencing efforts. We generated mock communities of controlled biomass ratios from 28 species from all major soil mesofauna groups: mites, springtails, nematodes, tardigrades, and potworms. We shotgun sequenced these communities and taxonomically assigned them with a database of over 270 soil invertebrate genomes.
• We recovered over 95% of the species, and observed relatively high false‐positive detection rates. We found strong differences in reads assigned to different taxa, with some groups (e.g., springtails) consistently attracting more hits than others (e.g., enchytraeids). Original biomass could be predicted from read counts after considering these taxon‐specific differences. Species with larger genomes, and with more complete assemblies, consistently attracted more reads than species with smaller genomes. The GC content of the genome assemblies had no effect on the biomass–read relationships. Results were similar among different sequencing efforts.
• The results show considerable differences in taxon recovery and taxon specificity of biomass recovery from metagenomic sequence data. The properties of reference genomes and genome assemblies also influence biomass recovery, and they should be considered in metagenomic studies of eukaryotes. We show that low‐ and high‐sequencing efforts yield similar results, suggesting high cost‐efficiency of metagenomics for eukaryotic communities. We provide a brief roadmap for investigating factors which influence metagenomics‐based eukaryotic community reconstructions. Understanding these factors is timely as accessibility of DNA sequencing and momentum for reference genomes projects show a future where the taxonomic assignment of DNA from any community sample becomes a reality.

     

Long-read sequence assembly: a technical evaluation in barley

Sequence assembly of large and repeat-rich plant genomes has been challenging, requiring substantial computational resources and often several complementary sequence assembly and genome mapping approaches. The recent development of fast and accurate long-read sequencing by circular consensus sequencing (CCS) on the PacBio platform may greatly increase the scope of plant pan-genome projects. Here, we compare current long-read sequencing platforms regarding their ability to rapidly generate contiguous sequence assemblies in pan-genome studies of barley (Hordeum vulgare). Most long-read assemblies are clearly superior to the current barley reference sequence based on short-reads. Assemblies derived from accurate long reads excel in most metrics, but the CCS approach was the most cost-effective strategy for assembling tens of barley genomes. A downsampling analysis indicated that 20-fold CCS coverage can yield very good sequence assemblies, while even five-fold CCS data may capture the complete sequence of most genes. We present an updated reference genome assembly for barley with near-complete representation of the repeat-rich intergenic space. Long-read assembly can underpin the construction of accurate and complete sequences of multiple genomes of a species to build pan-genome infrastructures in Triticeae crops and their wild relatives.

A greatly improved reference genome sequence of barley was assembled from accurate long reads.     

Genometa - a fast and accurate classifier for short metagenomic shotgun reads

SUMMARY: Metagenomic studies use high-throughput sequence data to investigate microbial communities in situ. However, considerable challenges remain in the analysis of these data, particularly with regard to speed and reliable analysis of microbial species as opposed to higher level taxa such as phyla. We here present Genometa, a computationally undemanding graphical user interface program that enables identification of bacterial species and gene content from datasets generated by inexpensive high-throughput short read sequencing technologies. Our approach was first verified on two simulated metagenomic short read datasets, detecting 100% and 94% of the bacterial species included with few false positives or false negatives. Subsequent comparative benchmarking analysis against three popular metagenomic algorithms on an Illumina human gut dataset revealed Genometa to attribute the most reads to bacteria at species level (i.e. including all strains of that species) and demonstrate similar or better accuracy than the other programs. Lastly, speed was demonstrated to be many times that of BLAST due to the use of modern short read aligners. Our method is highly accurate if bacteria in the sample are represented by genomes in the reference sequence but cannot find species absent from the reference. This method is one of the most user-friendly and resource efficient approaches and is thus feasible for rapidly analysing millions of short reads on a personal computer. AVAILABILITY: The Genometa program, a step by step tutorial and Java source code are freely available from http://genomics1.mh-hannover.de/genometa/ and on http://code.google.com/p/genometa/. This program has been tested on Ubuntu Linux and Windows XP/7.     

Selective Whole Genome Amplification for Resequencing Target Microbial Species from Complex Natural Samples

Population genomic analyses have demonstrated power to address major questions in evolutionary and molecular microbiology. Collecting populations of genomes is hindered in many microbial species by the absence of a cost effective and practical method to collect ample quantities of sufficiently pure genomic DNA for next-generation sequencing. Here we present a simple method to amplify genomes of a target microbial species present in a complex, natural sample. The selective whole genome amplification (SWGA) technique amplifies target genomes using nucleotide sequence motifs that are common in the target microbe genome, but rare in the background genomes, to prime the highly processive phi29 polymerase. SWGA thus selectively amplifies the target genome from samples in which it originally represented a minor fraction of the total DNA. The post-SWGA samples are enriched in target genomic DNA, which are ideal for population resequencing. We demonstrate the efficacy of SWGA using both laboratory-prepared mixtures of cultured microbes as well as a natural host–microbe association. Targeted amplification of Borrelia burgdorferi mixed with Escherichia coli at genome ratios of 1:2000 resulted in >10⁵-fold amplification of the target genomes with <6.7-fold amplification of the background. SWGA-treated genomic extracts from Wolbachia pipientis-infected Drosophila melanogaster resulted in up to 70% of high-throughput resequencing reads mapping to the W. pipientis genome. By contrast, 2–9% of sequencing reads were derived from W. pipientis without prior amplification. The SWGA technique results in high sequencing coverage at a fraction of the sequencing effort, thus allowing population genomic studies at affordable costs.     

Perm-seq: Mapping Protein-DNA Interactions in Segmental Duplication and Highly Repetitive Regions of Genomes with Prior-Enhanced Read Mapping

Segmental duplications and other highly repetitive regions of genomes contribute significantly to cells’ regulatory programs. Advancements in next generation sequencing enabled genome-wide profiling of protein-DNA interactions by chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq). However, interactions in highly repetitive regions of genomes have proven difficult to map since short reads of 50–100 base pairs (bps) from these regions map to multiple locations in reference genomes. Standard analytical methods discard such multi-mapping reads and the few that can accommodate them are prone to large false positive and negative rates. We developed Perm-seq, a prior-enhanced read allocation method for ChIP-seq experiments, that can allocate multi-mapping reads in highly repetitive regions of the genomes with high accuracy. We comprehensively evaluated Perm-seq, and found that our prior-enhanced approach significantly improves multi-read allocation accuracy over approaches that do not utilize additional data types. The statistical formalism underlying our approach facilitates supervising of multi-read allocation with a variety of data sources including histone ChIP-seq. We applied Perm-seq to 64 ENCODE ChIP-seq datasets from GM12878 and K562 cells and identified many novel protein-DNA interactions in segmental duplication regions. Our analysis reveals that although the protein-DNA interactions sites are evolutionarily less conserved in repetitive regions, they share the overall sequence characteristics of the protein-DNA interactions in non-repetitive regions.     

MetaGeniE: Characterizing Human Clinical Samples Using Deep Metagenomic Sequencing

With the decreasing cost of next-generation sequencing, deep sequencing of clinical samples provides unique opportunities to understand host-associated microbial communities. Among the primary challenges of clinical metagenomic sequencing is the rapid filtering of human reads to survey for pathogens with high specificity and sensitivity. Metagenomes are inherently variable due to different microbes in the samples and their relative abundance, the size and architecture of genomes, and factors such as target DNA amounts in tissue samples (i.e. human DNA versus pathogen DNA concentration). This variation in metagenomes typically manifests in sequencing datasets as low pathogen abundance, a high number of host reads, and the presence of close relatives and complex microbial communities. In addition to these challenges posed by the composition of metagenomes, high numbers of reads generated from high-throughput deep sequencing pose immense computational challenges. Accurate identification of pathogens is confounded by individual reads mapping to multiple different reference genomes due to gene similarity in different taxa present in the community or close relatives in the reference database. Available global and local sequence aligners also vary in sensitivity, specificity, and speed of detection. The efficiency of detection of pathogens in clinical samples is largely dependent on the desired taxonomic resolution of the organisms. We have developed an efficient strategy that identifies “all against all” relationships between sequencing reads and reference genomes. Our approach allows for scaling to large reference databases and then genome reconstruction by aggregating global and local alignments, thus allowing genetic characterization of pathogens at higher taxonomic resolution. These results were consistent with strain level SNP genotyping and bacterial identification from laboratory culture.     

Strain/Species-Specific Probe Design for Microbial Identification Microarrays

Specific identification of microorganisms in the environment is important but challenging, especially at the species/strain level. Here, we have developed a novel k-mer-based approach to select strain/species-specific probes for microbial identification with diagnostic microarrays. Application of this approach to human microbiome genomes showed that multiple (≥10 probes per strain) strain-specific 50-mer oligonucleotide probes could be designed for 2,012 of 3,421 bacterial strains of the human microbiome, and species-specific probes could be designed for most of the other strains. The method can also be used to select strain/species-specific probes for sequenced genomes in any environments, such as soil and water.     

Clustering metagenomic sequences with interpolated Markov models

Background  

Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects.     

Effective Extraction and Assembly Methods for Simultaneously Obtaining Plastid and Mitochondrial Genomes

Background

In conventional approaches to plastid and mitochondrial genome sequencing, the sequencing steps are performed separately; thus, plastid DNA (ptDNA) and mitochondrial DNA (mtDNA) should be prepared independently. However, it is difficult to extract pure ptDNA and mtDNA from plant tissue. Following the development of high-throughput sequencing technology, many researchers have attempted to obtain plastid genomes or mitochondrial genomes using high-throughput sequencing data from total DNA. Unfortunately, the huge datasets generated consume massive computing and storage resources and cost a great deal, and even more importantly, excessive pollution reads affect the accuracy of the assembly. Therefore, it is necessary to develop an effective method that can generate base sequences from plant tissue and that is suitable for all plant species. Here, we describe a highly effective, low-cost method for obtaining plastid and mitochondrial genomes simultaneously.

Results

First, we obtained high-quality DNA employing Partial Concentration Extraction. Second, we evaluated the purity of the DNA sample and determined the sequencing dataset size employing Vector Control Quantitative Analysis. Third, paired-end reads were obtained using a high-throughput sequencing platform. Fourth, we obtained scaffolds employing Two-step Assembly. Finally, we filled in gaps using specific methods and obtained complete plastid and mitochondrial genomes. To ensure the accuracy of plastid and mitochondrial genomes, we validated the assembly using PCR and Sanger sequencing. Using this method,we obtained complete plastid and mitochondrial genomes with lengths of 153,533 nt and 223,412 nt separately.

Conclusion

A simple method for extracting, evaluating, sequencing and assembling plastid and mitochondrial genomes was developed. This method has many advantages: it is timesaving, inexpensive and reproducible and produces high-quality sequence. Furthermore, this method can produce plastid and mitochondrial genomes simultaneously and be used for other plant species. Due to its simplicity and extensive applicability, this method will support research on plant cytoplasmic genomes.     

Evaluation and Validation of Assembling Corrected PacBio Long Reads for Microbial Genome Completion via Hybrid Approaches

Despite the ever-increasing output of next-generation sequencing data along with developing assemblers, dozens to hundreds of gaps still exist in de novo microbial assemblies due to uneven coverage and large genomic repeats. Third-generation single-molecule, real-time (SMRT) sequencing technology avoids amplification artifacts and generates kilobase-long reads with the potential to complete microbial genome assembly. However, due to the low accuracy (~85%) of third-generation sequences, a considerable amount of long reads (>50X) are required for self-correction and for subsequent de novo assembly. Recently-developed hybrid approaches, using next-generation sequencing data and as few as 5X long reads, have been proposed to improve the completeness of microbial assembly. In this study we have evaluated the contemporary hybrid approaches and demonstrated that assembling corrected long reads (by runCA) produced the best assembly compared to long-read scaffolding (e.g., AHA, Cerulean and SSPACE-LongRead) and gap-filling (SPAdes). For generating corrected long reads, we further examined long-read correction tools, such as ECTools, LSC, LoRDEC, PBcR pipeline and proovread. We have demonstrated that three microbial genomes including Escherichia coli K12 MG1655, Meiothermus ruber DSM1279 and Pdeobacter heparinus DSM2366 were successfully hybrid assembled by runCA into near-perfect assemblies using ECTools-corrected long reads. In addition, we developed a tool, Patch, which implements corrected long reads and pre-assembled contigs as inputs, to enhance microbial genome assemblies. With the additional 20X long reads, short reads of S. cerevisiae W303 were hybrid assembled into 115 contigs using the verified strategy, ECTools + runCA. Patch was subsequently applied to upgrade the assembly to a 35-contig draft genome. Our evaluation of the hybrid approaches shows that assembling the ECTools-corrected long reads via runCA generates near complete microbial genomes, suggesting that genome assembly could benefit from re-analyzing the available hybrid datasets that were not assembled in an optimal fashion.     

FragGeneScan: predicting genes in short and error-prone reads

The advances of next-generation sequencing technology have facilitated metagenomics research that attempts to determine directly the whole collection of genetic material within an environmental sample (i.e. the metagenome). Identification of genes directly from short reads has become an important yet challenging problem in annotating metagenomes, since the assembly of metagenomes is often not available. Gene predictors developed for whole genomes (e.g. Glimmer) and recently developed for metagenomic sequences (e.g. MetaGene) show a significant decrease in performance as the sequencing error rates increase, or as reads get shorter. We have developed a novel gene prediction method FragGeneScan, which combines sequencing error models and codon usages in a hidden Markov model to improve the prediction of protein-coding region in short reads. The performance of FragGeneScan was comparable to Glimmer and MetaGene for complete genomes. But for short reads, FragGeneScan consistently outperformed MetaGene (accuracy improved ∼62% for reads of 400 bases with 1% sequencing errors, and ∼18% for short reads of 100 bases that are error free). When applied to metagenomes, FragGeneScan recovered substantially more genes than MetaGene predicted (>90% of the genes identified by homology search), and many novel genes with no homologs in current protein sequence database.     

Single-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis

Single-cell RNA sequencing is a powerful technique that continues to expand across various biological applications. However, incomplete 3′-UTR annotations can impede single-cell analysis resulting in genes that are partially or completely uncounted. Performing single-cell RNA sequencing with incomplete 3′-UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single-cell isoform sequencing in tandem with single-cell RNA sequencing can rapidly improve 3′-UTR annotations. Using threespine stickleback fish (Gasterosteus aculeatus), we show that gene models resulting from a minimal embryonic single-cell isoform sequencing dataset retained 26.1% greater single-cell RNA sequencing reads than gene models from Ensembl alone. Furthermore, pooling our single-cell sequencing isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the single-cell isoform sequencing only dataset. In addition, isoforms identified by single-cell isoform sequencing included thousands of new splicing variants. The improved gene models obtained using single-cell isoform sequencing led to successful identification of cell types and increased the reads identified of many genes in our single-cell RNA sequencing stickleback dataset. Our work illuminates single-cell isoform sequencing as a cost-effective and efficient mechanism to rapidly annotate genomes for single-cell RNA sequencing.     

Aiming off the target: recycling target capture sequencing reads for investigating repetitive DNA

Background and AimsWith the advance of high-throughput sequencing, reduced-representation methods such as target capture sequencing (TCS) emerged as cost-efficient ways of gathering genomic information, particularly from coding regions. As the off-target reads from such sequencing are expected to be similar to genome skimming (GS), we assessed the quality of repeat characterization in plant genomes using these data.MethodsRepeat composition obtained from TCS datasets of five Rhynchospora (Cyperaceae) species were compared with GS data from the same taxa. In addition, a FISH probe was designed based on the most abundant satellite found in the TCS dataset of Rhynchospora cephalotes. Finally, repeat-based phylogenies of the five Rhynchospora species were constructed based on the GS and TCS datasets and the topologies were compared with a gene-alignment-based phylogenetic tree.Key ResultsAll the major repetitive DNA families were identified in TCS, including repeats that showed abundances as low as 0.01 % in the GS data. Rank correlations between GS and TCS repeat abundances were moderately high (r = 0.58–0.85), increasing after filtering out the targeted loci from the raw TCS reads (r = 0.66–0.92). Repeat data obtained by TCS were also reliable in developing a cytogenetic probe of a new variant of the holocentromeric satellite Tyba. Repeat-based phylogenies from TCS data were congruent with those obtained from GS data and the gene-alignment tree.ConclusionsOur results show that off-target TCS reads can be recycled to identify repeats for cyto- and phylogenomic investigations. Given the growing availability of TCS reads, driven by global phylogenomic projects, our strategy represents a way to recycle genomic data and contribute to a better characterization of plant biodiversity.     

High‐throughput sequencing and graph‐based cluster analysis facilitate microsatellite development from a highly complex genome

Despite recent advances in high‐throughput sequencing, difficulties are often encountered when developing microsatellites for species with large and complex genomes. This probably reflects the close association in many species of microsatellites with cryptic repetitive elements. We therefore developed a novel approach for isolating polymorphic microsatellites from the club‐legged grasshopper (Gomphocerus sibiricus), an emerging quantitative genetic and behavioral model system. Whole genome shotgun Illumina MiSeq sequencing was used to generate over three million 300 bp paired‐end reads, of which 67.75% were grouped into 40,548 clusters within RepeatExplorer. Annotations of the top 468 clusters, which represent 60.5% of the reads, revealed homology to satellite DNA and a variety of transposable elements. Evaluating 96 primer pairs in eight wild‐caught individuals, we found that primers mined from singleton reads were six times more likely to amplify a single polymorphic microsatellite locus than primers mined from clusters. Our study provides experimental evidence in support of the notion that microsatellites associated with repetitive elements are less likely to successfully amplify. It also reveals how advances in high‐throughput sequencing and graph‐based repetitive DNA analysis can be leveraged to isolate polymorphic microsatellites from complex genomes.     

Genome assembly using Nanopore-guided long and error-free DNA reads

Background

Long-read sequencing technologies were launched a few years ago, and in contrast with short-read sequencing technologies, they offered a promise of solving assembly problems for large and complex genomes. Moreover by providing long-range information, it could also solve haplotype phasing. However, existing long-read technologies still have several limitations that complicate their use for most research laboratories, as well as in large and/or complex genome projects. In 2014, Oxford Nanopore released the MinION® device, a small and low-cost single-molecule nanopore sequencer, which offers the possibility of sequencing long DNA fragments.

Results

The assembly of long reads generated using the Oxford Nanopore MinION® instrument is challenging as existing assemblers were not implemented to deal with long reads exhibiting close to 30% of errors. Here, we presented a hybrid approach developed to take advantage of data generated using MinION® device. We sequenced a well-known bacterium, Acinetobacter baylyi ADP1 and applied our method to obtain a highly contiguous (one single contig) and accurate genome assembly even in repetitive regions, in contrast to an Illumina-only assembly. Our hybrid strategy was able to generate NaS (Nanopore Synthetic-long) reads up to 60 kb that aligned entirely and with no error to the reference genome and that spanned highly conserved repetitive regions. The average accuracy of NaS reads reached 99.99% without losing the initial size of the input MinION® reads.

Conclusions

We described NaS tool, a hybrid approach allowing the sequencing of microbial genomes using the MinION® device. Our method, based ideally on 20x and 50x of NaS and Illumina reads respectively, provides an efficient and cost-effective way of sequencing microbial or small eukaryotic genomes in a very short time even in small facilities. Moreover, we demonstrated that although the Oxford Nanopore technology is a relatively new sequencing technology, currently with a high error rate, it is already useful in the generation of high-quality genome assemblies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1519-z) contains supplementary material, which is available to authorized users.