Identification of Optimum Sequencing Depth Especially for De Novo Genome Assembly of Small Genomes Using Next Generation Sequencing Data

Next Generation Sequencing (NGS) is a disruptive technology that has found widespread acceptance in the life sciences research community. The high throughput and low cost of sequencing has encouraged researchers to undertake ambitious genomic projects, especially in de novo genome sequencing. Currently, NGS systems generate sequence data as short reads and de novo genome assembly using these short reads is computationally very intensive. Due to lower cost of sequencing and higher throughput, NGS systems now provide the ability to sequence genomes at high depth. However, currently no report is available highlighting the impact of high sequence depth on genome assembly using real data sets and multiple assembly algorithms. Recently, some studies have evaluated the impact of sequence coverage, error rate and average read length on genome assembly using multiple assembly algorithms, however, these evaluations were performed using simulated datasets. One limitation of using simulated datasets is that variables such as error rates, read length and coverage which are known to impact genome assembly are carefully controlled. Hence, this study was undertaken to identify the minimum depth of sequencing required for de novo assembly for different sized genomes using graph based assembly algorithms and real datasets. Illumina reads for E.coli (4.6 MB) S.kudriavzevii (11.18 MB) and C.elegans (100 MB) were assembled using SOAPdenovo, Velvet, ABySS, Meraculous and IDBA-UD. Our analysis shows that 50X is the optimum read depth for assembling these genomes using all assemblers except Meraculous which requires 100X read depth. Moreover, our analysis shows that de novo assembly from 50X read data requires only 6–40 GB RAM depending on the genome size and assembly algorithm used. We believe that this information can be extremely valuable for researchers in designing experiments and multiplexing which will enable optimum utilization of sequencing as well as analysis resources.     

Random Tagging Genotyping by Sequencing (rtGBS), an Unbiased Approach to Locate Restriction Enzyme Sites across the Target Genome

Genotyping by sequencing (GBS) is a restriction enzyme based targeted approach developed to reduce the genome complexity and discover genetic markers when a priori sequence information is unavailable. Sufficient coverage at each locus is essential to distinguish heterozygous from homozygous sites accurately. The number of GBS samples able to be pooled in one sequencing lane is limited by the number of restriction sites present in the genome and the read depth required at each site per sample for accurate calling of single-nucleotide polymorphisms. Loci bias was observed using a slight modification of the Elshire et al. method: some restriction enzyme sites were represented in higher proportions while others were poorly represented or absent. This bias could be due to the quality of genomic DNA, the endonuclease and ligase reaction efficiency, the distance between restriction sites, the preferential amplification of small library restriction fragments, or bias towards cluster formation of small amplicons during the sequencing process. To overcome these issues, we have developed a GBS method based on randomly tagging genomic DNA (rtGBS). By randomly landing on the genome, we can, with less bias, find restriction sites that are far apart, and undetected by the standard GBS (stdGBS) method. The study comprises two types of biological replicates: six different kiwifruit plants and two independent DNA extractions per plant; and three types of technical replicates: four samples of each DNA extraction, stdGBS vs. rtGBS methods, and two independent library amplifications, each sequenced in separate lanes. A statistically significant unbiased distribution of restriction fragment size by rtGBS showed that this method targeted 49% (39,145) of BamH I sites shared with the reference genome, compared to only 14% (11,513) by stdGBS.     

Multi-Sample Pooling and Illumina Genome Analyzer Sequencing Methods to Determine Gene Sequence Variation for Database Development

Determination of sequence variation within a genetic locus to develop clinically relevant databases is critical for molecular assay design and clinical test interpretation, so multisample pooling for Illumina genome analyzer (GA) sequencing was investigated using the RET proto-oncogene as a model. Samples were Sanger-sequenced for RET exons 10, 11, and 13–16. Ten samples with 13 known unique variants (“singleton variants” within the pool) and seven common changes were amplified and then equimolar-pooled before sequencing on a single flow cell lane, generating 36 base reads. For comparison, a single “control” sample was run in a different lane. After alignment, a 24-base quality score-screening threshold and 3` read end trimming of three bases yielded low background error rates with a 27% decrease in aligned read coverage. Sequencing data were evaluated using an established variant detection method (percent variant reads), by the presented subtractive correction method, and with SNPSeeker software. In total, 41 variants (of which 23 were singleton variants) were detected in the 10 pool data, which included all Sanger-identified variants. The 23 singleton variants were detected near the expected 5% allele frequency (average 5.17%±0.90% variant reads), well above the highest background error (1.25%). Based on background error rates, read coverage, simulated 30, 40, and 50 sample pool data, expected singleton allele frequencies within pools, and variant detection methods; ≥30 samples (which demonstrated a minimum 1% variant reads for singletons) could be pooled to reliably detect singleton variants by GA sequencing.     

An Improved Genotyping by Sequencing (GBS) Approach Offering Increased Versatility and Efficiency of SNP Discovery and Genotyping

Highly parallel SNP genotyping platforms have been developed for some important crop species, but these platforms typically carry a high cost per sample for first-time or small-scale users. In contrast, recently developed genotyping by sequencing (GBS) approaches offer a highly cost effective alternative for simultaneous SNP discovery and genotyping. In the present investigation, we have explored the use of GBS in soybean. In addition to developing a novel analysis pipeline to call SNPs and indels from the resulting sequence reads, we have devised a modified library preparation protocol to alter the degree of complexity reduction. We used a set of eight diverse soybean genotypes to conduct a pilot scale test of the protocol and pipeline. Using ApeKI for GBS library preparation and sequencing on an Illumina GAIIx machine, we obtained 5.5 M reads and these were processed using our pipeline. A total of 10,120 high quality SNPs were obtained and the distribution of these SNPs mirrored closely the distribution of gene-rich regions in the soybean genome. A total of 39.5% of the SNPs were present in genic regions and 52.5% of these were located in the coding sequence. Validation of over 400 genotypes at a set of randomly selected SNPs using Sanger sequencing showed a 98% success rate. We then explored the use of selective primers to achieve a greater complexity reduction during GBS library preparation. The number of SNP calls could be increased by almost 40% and their depth of coverage was more than doubled, thus opening the door to an increase in the throughput and a significant decrease in the per sample cost. The approach to obtain high quality SNPs developed here will be helpful for marker assisted genomics as well as assessment of available genetic resources for effective utilisation in a wide number of species.     

Variant Identification in Multi-Sample Pools by Illumina Genome Analyzer Sequencing

Multi-sample pooling and Illumina Genome Analyzer (GA) sequencing allows high throughput sequencing of multiple samples to determine population sequence variation. A preliminary experiment, using the RET proto-oncogene as a model, predicted ≤30 samples could be pooled to reliably detect singleton variants without requiring additional confirmation testing. This report used 30 and 50 sample pools to test the hypothesized pooling limit and also to test recent protocol improvements, Illumina GAIIx upgrades, and longer read chemistry. The SequalPrep^TM method was used to normalize amplicons before pooling. For comparison, a single ‘control’ sample was run in a different flow cell lane. Data was evaluated by variant read percentages and the subtractive correction method which utilizes the control sample. In total, 59 variants were detected within the pooled samples, which included all 47 known true variants. The 15 known singleton variants due to Sanger sequencing had an average of 1.62±0.26% variant reads for the 30 pool (expected 1.67% for a singleton variant [unique variant within the pool]) and 1.01±0.19% for the 50 pool (expected 1%). The 76 base read lengths had higher error rates than shorter read lengths (33 and 50 base reads), which eliminated the distinction of true singleton variants from background error. This report demonstrated pooling limits from 30 up to 50 samples (depending on error rates and coverage), for reliable singleton variant detection. The presented pooling protocols and analysis methods can be used for variant discovery in other genes, facilitating molecular diagnostic test design and interpretation.     

Targeted single molecule sequencing methodology for ovarian hyperstimulation syndrome

Background

One of the most significant issues surrounding next generation sequencing is the cost and the difficulty assembling short read lengths. Targeted capture enrichment of longer fragments using single molecule sequencing (SMS) is expected to improve both sequence assembly and base-call accuracy but, at present, there are very few examples of successful application of these technologic advances in translational research and clinical testing. We developed a targeted single molecule sequencing (T-SMS) panel for genes implicated in ovarian response to controlled ovarian hyperstimulation (COH) for infertility.

Results

Target enrichment was carried out using droplet-base multiplex polymerase chain reaction (PCR) technology (RainDance®) designed to yield amplicons averaging 1 kb fragment size from candidate 44 loci (99.8% unique base-pair coverage). The total targeted sequence was 3.18 Mb per sample. SMS was carried out using single molecule, real-time DNA sequencing (SMRT® Pacific Biosciences®), average raw read length = 1178 nucleotides, 5% of the amplicons >6000 nucleotides). After filtering with circular consensus (CCS) reads, the mean read length was 3200 nucleotides (97% CCS accuracy). Primary data analyses, alignment and filtering utilized the Pacific Biosciences® SMRT portal. Secondary analysis was conducted using the Genome Analysis Toolkit for SNP discovery l and wANNOVAR for functional analysis of variants. Filtered functional variants 18 of 19 (94.7%) were further confirmed using conventional Sanger sequencing. CCS reads were able to accurately detect zygosity. Coverage within GC rich regions (i.e.VEGFR; 72% GC rich) was achieved by capturing long genomic DNA (gDNA) fragments and reading into regions that flank the capture regions. As proof of concept, a non-synonymous LHCGR variant captured in two severe OHSS cases, and verified by conventional sequencing.

Conclusions

Combining emulsion PCR-generated 1 kb amplicons and SMRT DNA sequencing permitted greater depth of coverage for T-SMS and facilitated easier sequence assembly. To the best of our knowledge, this is the first report combining emulsion PCR and T-SMS for long reads using human DNA samples, and NGS panel designed for biomarker discovery in OHSS.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1451-2) contains supplementary material, which is available to authorized users.     

A general coverage theory for shotgun DNA sequencing.

The classical theory of shotgun DNA sequencing accounts for neither the placement dependencies that are a fundamental consequence of the forward-reverse sequencing strategy, nor the edge effect that arises for small to moderate-sized genomic targets. These phenomena are relevant to a number of sequencing scenarios, including large-insert BAC and fosmid clones, filtered genomic libraries, and macro-nuclear chromosomes. Here, we report a model that considers these two effects and provides both the expected value of coverage and its variance. Comparison to methyl-filtered maize data shows significant improvement over classical theory. The model is used to analyze coverage performance over a range of small to moderately-sized genomic targets. We find that the read pairing effect and the edge effect interact in a non-trivial fashion. Shorter reads give superior coverage per unit sequence depth relative to longer ones. In principle, end-sequences can be optimized with respect to template insert length; however, optimal performance is unlikely to be realized in most cases because of inherent size variation in any set of targets. Conversely, single-stranded reads exhibit roughly the same coverage attributes as optimized end-reads. Although linking information is lost, single-stranded data should not pose a significant assembly liability if the target represents predominantly low-copy sequence. We also find that random sequencing should be halted at substantially lower redundancies than those now associated with larger projects. Given the enormous amount of data generated per cycle on pyro-sequencing instruments, this observation suggests devising schemes to split each run cycle between twoor more projects. This would prevent over-sequencing and would further leverage the pyrosequencing method.     

Is the whole greater than the sum of its parts? De novo assembly strategies for bacterial genomes based on paired-end sequencing

Background

Whole genome sequence construction is becoming increasingly feasible because of advances in next generation sequencing (NGS), including increasing throughput and read length. By simply overlapping paired-end reads, we can obtain longer reads with higher accuracy, which can facilitate the assembly process. However, the influences of different library sizes and assembly methods on paired-end sequencing-based de novo assembly remain poorly understood.

Results

We used 250 bp Illumina Miseq paired-end reads of different library sizes generated from genomic DNA from Escherichia coli DH1 and Streptococcus parasanguinis FW213 to compare the assembly results of different library sizes and assembly approaches. Our data indicate that overlapping paired-end reads can increase read accuracy but sometimes cause insertion or deletions. Regarding genome assembly, merged reads only outcompete original paired-end reads when coverage depth is low, and larger libraries tend to yield better assembly results. These results imply that distance information is the most critical factor during assembly. Our results also indicate that when depth is sufficiently high, assembly from subsets can sometimes produce better results.

Conclusions

In summary, this study provides systematic evaluations of de novo assembly from paired end sequencing data. Among the assembly strategies, we find that overlapping paired-end reads is not always beneficial for bacteria genome assembly and should be avoided or used with caution especially for genomes containing high fraction of repetitive sequences. Because increasing numbers of projects aim at bacteria genome sequencing, our study provides valuable suggestions for the field of genomic sequence construction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1859-8) contains supplementary material, which is available to authorized users.     

SMRT sequencing only de novo assembly of the sugar beet (Beta vulgaris) chloroplast genome

Background

Third generation sequencing methods, like SMRT (Single Molecule, Real-Time) sequencing developed by Pacific Biosciences, offer much longer read length in comparison to Next Generation Sequencing (NGS) methods. Hence, they are well suited for de novo- or re-sequencing projects. Sequences generated for these purposes will not only contain reads originating from the nuclear genome, but also a significant amount of reads originating from the organelles of the target organism. These reads are usually discarded but they can also be used for an assembly of organellar replicons. The long read length supports resolution of repetitive regions and repeats within the organelles genome which might be problematic when just using short read data. Additionally, SMRT sequencing is less influenced by GC rich areas and by long stretches of the same base.

Results

We describe a workflow for a de novo assembly of the sugar beet (Beta vulgaris ssp. vulgaris) chloroplast genome sequence only based on data originating from a SMRT sequencing dataset targeted on its nuclear genome. We show that the data obtained from such an experiment are sufficient to create a high quality assembly with a higher reliability than assemblies derived from e.g. Illumina reads only. The chloroplast genome is especially challenging for de novo assembling as it contains two large inverted repeat (IR) regions. We also describe some limitations that still apply even though long reads are used for the assembly.

Conclusions

SMRT sequencing reads extracted from a dataset created for nuclear genome (re)sequencing can be used to obtain a high quality de novo assembly of the chloroplast of the sequenced organism. Even with a relatively small overall coverage for the nuclear genome it is possible to collect more than enough reads to generate a high quality assembly that outperforms short read based assemblies. However, even with long reads it is not always possible to clarify the order of elements of a chloroplast genome sequence reliantly which we could demonstrate with Fosmid End Sequences (FES) generated with Sanger technology. Nevertheless, this limitation also applies to short read sequencing data but is reached in this case at a much earlier stage during finishing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0726-6) contains supplementary material, which is available to authorized users.     

Potential of genotyping-by-sequencing for genomic selection in livestock populations

Background

Next-generation sequencing techniques, such as genotyping-by-sequencing (GBS), provide alternatives to single nucleotide polymorphism (SNP) arrays. The aim of this work was to evaluate the potential of GBS compared to SNP array genotyping for genomic selection in livestock populations.

Methods

The value of GBS was quantified by simulation analyses in which three parameters were varied: (i) genome-wide sequence read depth (x) per individual from 0.01x to 20x or using SNP array genotyping; (ii) number of genotyped markers from 3000 to 300 000; and (iii) size of training and prediction sets from 500 to 50 000 individuals. The latter was achieved by distributing the total available x of 1000x, 5000x, or 10 000x per genotyped locus among the varying number of individuals. With SNP arrays, genotypes were called from sequence data directly. With GBS, genotypes were called from sequence reads that varied between loci and individuals according to a Poisson distribution with mean equal to x. Simulated data were analyzed with ridge regression and the accuracy and bias of genomic predictions and response to selection were quantified under the different scenarios.

Results

Accuracies of genomic predictions using GBS data or SNP array data were comparable when large numbers of markers were used and x per individual was ~1x or higher. The bias of genomic predictions was very high at a very low x. When the total available x was distributed among the training individuals, the accuracy of prediction was maximized when a large number of individuals was used that had GBS data with low x for a large number of markers. Similarly, response to selection was maximized under the same conditions due to increasing both accuracy and selection intensity.

Conclusions

GBS offers great potential for developing genomic selection in livestock populations because it makes it possible to cover large fractions of the genome and to vary the sequence read depth per individual. Thus, the accuracy of predictions is improved by increasing the size of training populations and the intensity of selection is increased by genotyping a larger number of selection candidates.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0102-z) contains supplementary material, which is available to authorized users.     

How to infer reliable diploid genotypes from NGS or traditional sequence data: from basic probability to experimental optimization

The use of diploid sequence markers is still challenging despite the good quality of the information they provide. There is a common problem to all sequencing approaches [traditional cloning and sequencing of PCR amplicons as well as next-generation sequencing (NGS)]: when no variation is found within the sequences from a given individual, homozygozity can never be asserted with certainty. As a consequence, sequence data from diploid markers are mostly analysed at the population (not the individual level) particularly in animal studies. This study aims at contributing to solve this. Using the Bayes theorem and the binomial law, useful results are derived, among which: (i) the number of sequence reads per individual (or sequencing depth) which is required to ensure, at a given probability threshold, that some heterozygotes are not considered erroneously as homozygotes, as a function of the observed heterozygozity (H(o) ) of the locus in the population; (ii) a way of estimating H(o) from low coverage NGS data; (iii) a way of testing the null hypothesis that a genetic marker corresponds to a single and diploid locus, in the absence of data from controlled crosses; (iv) strategies for characterizing sequence genotypes in populations minimizing the average number of sequence reads per individual; (v) a rationale to decide which are the variations that one needs to consider along the sequence, as a function of the sequencing depth affordable, the level of polymorphism desired and the risk of sequencing error. For traditional sequencing technology, optimal strategies appear surprisingly different from the usual empirical ones. The average number of sequence reads required to obtain 99% of fully determined genotypes never exceeds six, this value corresponding to the worst situation when H(o) equals 0.6. This threshold value of H(o) is strikingly stable when the tolerated proportion of nonfully resolved genotypes varies in a reasonable range. These results do not rely on the Hardy-Weinberg equilibrium assumption or on diallelism of nucleotidic sites.     

An integrative probabilistic model for identification of structural variation in sequencing data

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model which can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50-90% improvement in specificity on deletions and a 50% improvement on inversions.     

A novel method for the multiplexed target enrichment of MinION next generation sequencing libraries using PCR-generated baits

The enrichment of targeted regions within complex next generation sequencing libraries commonly uses biotinylated baits to capture the desired sequences. This method results in high read coverage over the targets and their flanking regions. Oxford Nanopore Technologies recently released an USB3.0-interfaced sequencer, the MinION. To date no particular method for enriching MinION libraries has been standardized. Here, using biotinylated PCR-generated baits in a novel approach, we describe a simple and efficient way for multiplexed enrichment of MinION libraries, overcoming technical limitations related with the chemistry of the sequencing-adapters and the length of the DNA fragments. Using Phage Lambda and Escherichia coli as models we selectively enrich for specific targets, significantly increasing the corresponding read-coverage, eliminating unwanted regions. We show that by capturing genomic fragments, which contain the target sequences, we recover reads extending targeted regions and thus can be used for the determination of potentially unknown flanking sequences. By pooling enriched libraries derived from two distinct E. coli strains and analyzing them in parallel, we demonstrate the efficiency of this method in multiplexed format. Crucially we evaluated the optimal bait size for large fragment libraries and we describe for the first time a standardized method for target enrichment in MinION platform.     

Paralogs are revealed by proportion of heterozygotes and deviations in read ratios in genotyping‐by‐sequencing data from natural populations

Whole‐genome duplications have occurred in the recent ancestors of many plants, fish, and amphibians, resulting in a pervasiveness of paralogous loci and the potential for both disomic and tetrasomic inheritance in the same genome. Paralogs can be difficult to reliably genotype and are often excluded from genotyping‐by‐sequencing (GBS) analyses; however, removal requires paralogs to be identified which is difficult without a reference genome. We present a method for identifying paralogs in natural populations by combining two properties of duplicated loci: (i) the expected frequency of heterozygotes exceeds that for singleton loci, and (ii) within heterozygotes, observed read ratios for each allele in GBS data will deviate from the 1:1 expected for singleton (diploid) loci. These deviations are often not apparent within individuals, particularly when sequence coverage is low; but, we postulated that summing allele reads for each locus over all heterozygous individuals in a population would provide sufficient power to detect deviations at those loci. We identified paralogous loci in three species: Chinook salmon (Oncorhynchus tshawytscha) which retains regions with ongoing residual tetrasomy on eight chromosome arms following a recent whole‐genome duplication, mountain barberry (Berberis alpina) which has a large proportion of paralogs that arose through an unknown mechanism, and dusky parrotfish (Scarus niger) which has largely rediploidized following an ancient whole‐genome duplication. Importantly, this approach only requires the genotype and allele‐specific read counts for each individual, information which is readily obtained from most GBS analysis pipelines.     

Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences

Microbial community profiling using 16S rRNA gene sequences requires accurate taxonomy assignments. ‘Universal'' primers target conserved sequences and amplify sequences from many taxa, but they provide variable coverage of different environments, and regions of the rRNA gene differ in taxonomic informativeness—especially when high-throughput short-read sequencing technologies (for example, 454 and Illumina) are used. We introduce a new evaluation procedure that provides an improved measure of expected taxonomic precision when classifying environmental sequence reads from a given primer. Applying this measure to thousands of combinations of primers and read lengths, simulating single-ended and paired-end sequencing, reveals that these choices greatly affect taxonomic informativeness. The most informative sequence region may differ by environment, partly due to variable coverage of different environments in reference databases. Using our Rtax method of classifying paired-end reads, we found that paired-end sequencing provides substantial benefit in some environments including human gut, but not in others. Optimal primer choice for short reads totaling 96 nt provides 82–100% of the confident genus classifications available from longer reads.     

An analysis of the feasibility of short read sequencing

Several methods for ultra high-throughput DNA sequencing are currently under investigation. Many of these methods yield very short blocks of sequence information (reads). Here we report on an analysis showing the level of genome sequencing possible as a function of read length. It is shown that re-sequencing and de novo sequencing of the majority of a bacterial genome is possible with read lengths of 20–30 nt, and that reads of 50 nt can provide reconstructed contigs (a contiguous fragment of sequence data) of 1000 nt and greater that cover 80% of human chromosome 1.     

Haplotype Estimation Using Sequencing Reads

High-throughput sequencing technologies produce short sequence reads that can contain phase information if they span two or more heterozygote genotypes. This information is not routinely used by current methods that infer haplotypes from genotype data. We have extended the SHAPEIT2 method to use phase-informative sequencing reads to improve phasing accuracy. Our model incorporates the read information in a probabilistic model through base quality scores within each read. The method is primarily designed for high-coverage sequence data or data sets that already have genotypes called. One important application is phasing of single samples sequenced at high coverage for use in medical sequencing and studies of rare diseases. Our method can also use existing panels of reference haplotypes. We tested the method by using a mother-father-child trio sequenced at high-coverage by Illumina together with the low-coverage sequence data from the 1000 Genomes Project (1000GP). We found that use of phase-informative reads increases the mean distance between switch errors by 22% from 274.4 kb to 328.6 kb. We also used male chromosome X haplotypes from the 1000GP samples to simulate sequencing reads with varying insert size, read length, and base error rate. When using short 100 bp paired-end reads, we found that using mixtures of insert sizes produced the best results. When using longer reads with high error rates (5–20 kb read with 4%–15% error per base), phasing performance was substantially improved.