Direct comparisons of Illumina vs. Roche 454 sequencing technologies on the same microbial community DNA sample

Next-generation sequencing (NGS) is commonly used in metagenomic studies of complex microbial communities but whether or not different NGS platforms recover the same diversity from a sample and their assembled sequences are of comparable quality remain unclear. We compared the two most frequently used platforms, the Roche 454 FLX Titanium and the Illumina Genome Analyzer (GA) II, on the same DNA sample obtained from a complex freshwater planktonic community. Despite the substantial differences in read length and sequencing protocols, the platforms provided a comparable view of the community sampled. For instance, derived assemblies overlapped in ~90% of their total sequences and in situ abundances of genes and genotypes (estimated based on sequence coverage) correlated highly between the two platforms (R(2)>0.9). Evaluation of base-call error, frameshift frequency, and contig length suggested that Illumina offered equivalent, if not better, assemblies than Roche 454. The results from metagenomic samples were further validated against DNA samples of eighteen isolate genomes, which showed a range of genome sizes and G+C% content. We also provide quantitative estimates of the errors in gene and contig sequences assembled from datasets characterized by different levels of complexity and G+C% content. For instance, we noted that homopolymer-associated, single-base errors affected ~1% of the protein sequences recovered in Illumina contigs of 10× coverage and 50% G+C; this frequency increased to ~3% when non-homopolymer errors were also considered. Collectively, our results should serve as a useful practical guide for choosing proper sampling strategies and data possessing protocols for future metagenomic studies.     

ReQON: a Bioconductor package for recalibrating quality scores from next-generation sequencing data

ABSTRACT: BACKGROUND: Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results. RESULTS: Here we present ReQON, a tool that recalibrates the base quality scores from an input BAM file of aligned sequencing data using logistic regression. ReQON also generates diagnostic plots showing the effectiveness of the recalibration. We show that ReQON produces quality scores that are both more accurate, in the sense that they more closely correspond to the probability of a sequencing error, and do a better job of discriminating between sequencing errors and non-errors than the original quality scores. We also compare ReQON to other available recalibration tools and show that ReQON is less biased and performs favorably in terms of quality score accuracy. CONCLUSION: ReQON is an open source software package, written in R and available through Bioconductor, for recalibrating base quality scores for next-generation sequencing data. ReQON produces a new BAM file with more accurate quality scores, which can improve the results of downstream analysis, and produces several diagnostic plots showing the effectiveness of the recalibration.     

Pollux: platform independent error correction of single and mixed genomes

Background

Second-generation sequencers generate millions of relatively short, but error-prone, reads. These errors make sequence assembly and other downstream projects more challenging. Correcting these errors improves the quality of assemblies and projects which benefit from error-free reads.

Results

We have developed a general-purpose error corrector that corrects errors introduced by Illumina, Ion Torrent, and Roche 454 sequencing technologies and can be applied to single- or mixed-genome data. In addition to correcting substitution errors, we locate and correct insertion, deletion, and homopolymer errors while remaining sensitive to low coverage areas of sequencing projects. Using published data sets, we correct 94% of Illumina MiSeq errors, 88% of Ion Torrent PGM errors, 85% of Roche 454 GS Junior errors. Introduced errors are 20 to 70 times more rare than successfully corrected errors. Furthermore, we show that the quality of assemblies improves when reads are corrected by our software.

Conclusions

Pollux is highly effective at correcting errors across platforms, and is consistently able to perform as well or better than currently available error correction software. Pollux provides general-purpose error correction and may be used in applications with or without assembly.     

Use of low-coverage, large-insert, short-read data for rapid and accurate generation of enhanced-quality draft Pseudomonas genome sequences

Next-generation genomic technology has both greatly accelerated the pace of genome research as well as increased our reliance on draft genome sequences. While groups such as the Genomics Standards Consortium have made strong efforts to promote genome standards there is a still a general lack of uniformity among published draft genomes, leading to challenges for downstream comparative analyses. This lack of uniformity is a particular problem when using standard draft genomes that frequently have large numbers of low-quality sequencing tracts. Here we present a proposal for an "enhanced-quality draft" genome that identifies at least 95% of the coding sequences, thereby effectively providing a full accounting of the genic component of the genome. Enhanced-quality draft genomes are easily attainable through a combination of small- and large-insert next-generation, paired-end sequencing. We illustrate the generation of an enhanced-quality draft genome by re-sequencing the plant pathogenic bacterium Pseudomonas syringae pv. phaseolicola 1448A (Pph 1448A), which has a published, closed genome sequence of 5.93 Mbp. We use a combination of Illumina paired-end and mate-pair sequencing, and surprisingly find that de novo assemblies with 100x paired-end coverage and mate-pair sequencing with as low as low as 2-5x coverage are substantially better than assemblies based on higher coverage. The rapid and low-cost generation of large numbers of enhanced-quality draft genome sequences will be of particular value for microbial diagnostics and biosecurity, which rely on precise discrimination of potentially dangerous clones from closely related benign strains.     

The Fast Changing Landscape of Sequencing Technologies and Their Impact on Microbial Genome Assemblies and Annotation

Background

The emergence of next generation sequencing (NGS) has provided the means for rapid and high throughput sequencing and data generation at low cost, while concomitantly creating a new set of challenges. The number of available assembled microbial genomes continues to grow rapidly and their quality reflects the quality of the sequencing technology used, but also of the analysis software employed for assembly and annotation.

Methodology/Principal Findings

In this work, we have explored the quality of the microbial draft genomes across various sequencing technologies. We have compared the draft and finished assemblies of 133 microbial genomes sequenced at the Department of Energy-Joint Genome Institute and finished at the Los Alamos National Laboratory using a variety of combinations of sequencing technologies, reflecting the transition of the institute from Sanger-based sequencing platforms to NGS platforms. The quality of the public assemblies and of the associated gene annotations was evaluated using various metrics. Results obtained with the different sequencing technologies, as well as their effects on downstream processes, were analyzed. Our results demonstrate that the Illumina HiSeq 2000 sequencing system, the primary sequencing technology currently used for de novo genome sequencing and assembly at JGI, has various advantages in terms of total sequence throughput and cost, but it also introduces challenges for the downstream analyses. In all cases assembly results although on average are of high quality, need to be viewed critically and consider sources of errors in them prior to analysis.

Conclusion

These data follow the evolution of microbial sequencing and downstream processing at the JGI from draft genome sequences with large gaps corresponding to missing genes of significant biological role to assemblies with multiple small gaps (Illumina) and finally to assemblies that generate almost complete genomes (Illumina+PacBio).     

Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies

The advent of next generation sequencing has coincided with a growth in interest in using these approaches to better understand the role of the structure and function of the microbial communities in human, animal, and environmental health. Yet, use of next generation sequencing to perform 16S rRNA gene sequence surveys has resulted in considerable controversy surrounding the effects of sequencing errors on downstream analyses. We analyzed 2.7×10(6) reads distributed among 90 identical mock community samples, which were collections of genomic DNA from 21 different species with known 16S rRNA gene sequences; we observed an average error rate of 0.0060. To improve this error rate, we evaluated numerous methods of identifying bad sequence reads, identifying regions within reads of poor quality, and correcting base calls and were able to reduce the overall error rate to 0.0002. Implementation of the PyroNoise algorithm provided the best combination of error rate, sequence length, and number of sequences. Perhaps more problematic than sequencing errors was the presence of chimeras generated during PCR. Because we knew the true sequences within the mock community and the chimeras they could form, we identified 8% of the raw sequence reads as chimeric. After quality filtering the raw sequences and using the Uchime chimera detection program, the overall chimera rate decreased to 1%. The chimeras that could not be detected were largely responsible for the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes. The number of spurious OTUs and phylotypes increased with sequencing effort indicating that comparison of communities should be made using an equal number of sequences. Finally, we applied our improved quality-filtering pipeline to several benchmarking studies and observed that even with our stringent data curation pipeline, biases in the data generation pipeline and batch effects were observed that could potentially confound the interpretation of microbial community data.     

ConDeTri--a content dependent read trimmer for Illumina data

During the last few years, DNA and RNA sequencing have started to play an increasingly important role in biological and medical applications, especially due to the greater amount of sequencing data yielded from the new sequencing machines and the enormous decrease in sequencing costs. Particularly, Illumina/Solexa sequencing has had an increasing impact on gathering data from model and non-model organisms. However, accurate and easy to use tools for quality filtering have not yet been established. We present ConDeTri, a method for content dependent read trimming for next generation sequencing data using quality scores of each individual base. The main focus of the method is to remove sequencing errors from reads so that sequencing reads can be standardized. Another aspect of the method is to incorporate read trimming in next-generation sequencing data processing and analysis pipelines. It can process single-end and paired-end sequence data of arbitrary length and it is independent from sequencing coverage and user interaction. ConDeTri is able to trim and remove reads with low quality scores to save computational time and memory usage during de novo assemblies. Low coverage or large genome sequencing projects will especially gain from trimming reads. The method can easily be incorporated into preprocessing and analysis pipelines for Illumina data. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://code.google.com/p/condetri.     

ANGLE: a sequencing errors resistant program for predicting protein coding regions in unfinished cDNA

In the process of making full-length cDNA, predicting protein coding regions helps both in the preliminary analysis of genes and in any succeeding process. However, unfinished cDNA contains artifacts including many sequencing errors, which hinder the correct evaluation of coding sequences. Especially, predictions of short sequences are difficult because they provide little information for evaluating coding potential. In this paper, we describe ANGLE, a new program for predicting coding sequences in low quality cDNA. To achieve error-tolerant prediction, ANGLE uses a machine-learning approach, which makes better expression of coding sequence maximizing the use of limited information from input sequences. Our method utilizes not only codon usage, but also protein structure information which is difficult to be used for stochastic model-based algorithms, and optimizes limited information from a short segment when deciding coding potential, with the result that predictive accuracy does not depend on the length of an input sequence. The performance of ANGLE is compared with ESTSCAN on four dataset each of them having a different error rate (one frame-shift error or one substitution error per 200-500 nucleotides) and on one dataset which has no error. ANGLE outperforms ESTSCAN by 9.26% in average Matthews's correlation coefficient on short sequence dataset (< 1000 bases). On long sequence dataset, ANGLE achieves comparable performance.     

An Integrated Pipeline for de Novo Assembly of Microbial Genomes

Remarkable advances in DNA sequencing technology have created a need for de novo genome assembly methods tailored to work with the new sequencing data types. Many such methods have been published in recent years, but assembling raw sequence data to obtain a draft genome has remained a complex, multi-step process, involving several stages of sequence data cleaning, error correction, assembly, and quality control. Successful application of these steps usually requires intimate knowledge of a diverse set of algorithms and software. We present an assembly pipeline called A5 (Andrew And Aaron''s Awesome Assembly pipeline) that simplifies the entire genome assembly process by automating these stages, by integrating several previously published algorithms with new algorithms for quality control and automated assembly parameter selection. We demonstrate that A5 can produce assemblies of quality comparable to a leading assembly algorithm, SOAPdenovo, without any prior knowledge of the particular genome being assembled and without the extensive parameter tuning required by the other assembly algorithm. In particular, the assemblies produced by A5 exhibit 50% or more reduction in broken protein coding sequences relative to SOAPdenovo assemblies. The A5 pipeline can also assemble Illumina sequence data from libraries constructed by the Nextera (transposon-catalyzed) protocol, which have markedly different characteristics to mechanically sheared libraries. Finally, A5 has modest compute requirements, and can assemble a typical bacterial genome on current desktop or laptop computer hardware in under two hours, depending on depth of coverage.     

Circumventing heterozygosity: sequencing the amplified genome of a single haploid Drosophila melanogaster embryo

Heterozygosity is a major challenge to efficient, high-quality genomic assembly and to the full genomic survey of polymorphism and divergence. In Drosophila melanogaster lines derived from equatorial populations are particularly resistant to inbreeding, thus imposing a major barrier to the determination and analyses of genomic variation in natural populations of this model organism. Here we present a simple genome sequencing protocol based on the whole-genome amplification of the gynogenetically derived haploid genome of a progeny of females mated to males homozygous for the recessive male sterile mutation, ms(3)K81. A single "lane" of paired-end sequences (2 × 76 bp) provides a good syntenic assembly with >95% high-quality coverage (more than five reads). The amplification of the genomic DNA moderately inflates the variation in coverage across the euchromatic portion of the genome. It also increases the frequency of chimeric clones. But the low frequency and random genomic distribution of the chimeric clones limits their impact on the final assemblies. This method provides a solid path forward for population genomic sequencing and offers applications to many other systems in which small amounts of genomic DNA have unique experimental relevance.     

NGmerge: merging paired-end reads via novel empirically-derived models of sequencing errors

Background

Advances in Illumina DNA sequencing technology have produced longer paired-end reads that increasingly have sequence overlaps. These reads can be merged into a single read that spans the full length of the original DNA fragment, allowing for error correction and accurate determination of read coverage. Extant merging programs utilize simplistic or unverified models for the selection of bases and quality scores for the overlapping region of merged reads.

Results

We first examined the baseline quality score - error rate relationship using sequence reads derived from PhiX. In contrast to numerous published reports, we found that the quality scores produced by Illumina were not substantially inflated above the theoretical values, once the reference genome was corrected for unreported sequence variants. The PhiX reads were then used to create empirical models of sequencing errors in overlapping regions of paired-end reads, and these models were incorporated into a novel merging program, NGmerge. We demonstrate that NGmerge corrects errors and ambiguous bases better than other merging programs, and that it assigns quality scores for merged bases that accurately reflect the error rates. Our results also show that, contrary to published analyses, the sequencing errors of paired-end reads are not independent.

Conclusions

We provide a free and open-source program, NGmerge, that performs better than existing read merging programs. NGmerge is available on GitHub (https://github.com/harvardinformatics/NGmerge) under the MIT License; it is written in C and supported on Linux.

     

Identifying the causes and consequences of assembly gaps using a multiplatform genome assembly of a bird‐of‐paradise

Genome assemblies are currently being produced at an impressive rate by consortia and individual laboratories. The low costs and increasing efficiency of sequencing technologies now enable assembling genomes at unprecedented quality and contiguity. However, the difficulty in assembling repeat‐rich and GC‐rich regions (genomic “dark matter”) limits insights into the evolution of genome structure and regulatory networks. Here, we compare the efficiency of currently available sequencing technologies (short/linked/long reads and proximity ligation maps) and combinations thereof in assembling genomic dark matter. By adopting different de novo assembly strategies, we compare individual draft assemblies to a curated multiplatform reference assembly and identify the genomic features that cause gaps within each assembly. We show that a multiplatform assembly implementing long‐read, linked‐read and proximity sequencing technologies performs best at recovering transposable elements, multicopy MHC genes, GC‐rich microchromosomes and the repeat‐rich W chromosome. Telomere‐to‐telomere assemblies are not a reality yet for most organisms, but by leveraging technology choice it is now possible to minimize genome assembly gaps for downstream analysis. We provide a roadmap to tailor sequencing projects for optimized completeness of both the coding and noncoding parts of nonmodel genomes.     

Separation of nearly identical repeats in shotgun assemblies using defined nucleotide positions, DNPs

An increasingly important problem in genome sequencing is the failure of the commonly used shotgun assembly programs to correctly assemble repetitive sequences. The assembly of non-repetitive regions or regions containing repeats considerably shorter than the average read length is in practice easy to solve, while longer repeats have been a difficult problem. We here present a statistical method to separate arbitrarily long, almost identical repeats, which makes it possible to correctly assemble complex repetitive sequence regions. The differences between repeat units may be as low as 1% and the sequencing error may be up to ten times higher. The method is based on the realization that a comparison of only a part of all overlapping sequences at a time in a data set does not generate enough information for a conclusive analysis. Our method uses optimal multi-alignments consisting of all the overlaps of each read. This makes it possible to determine defined nucleotide positions, DNPs, which constitute the differences between the repeat units. Differences between repeats are distinguished from sequencing errors using statistical methods, where the probabilities of obtaining certain combinations of candidate DNPs are calculated using the information from the multi-alignments. The use of DNPs and combinations of DNPs will allow for optimal and rapid assemblies of repeated regions. This method can solve repeats that differ in only two positions in a read length, which is the theoretical limit for repeat separation. We predict that this method will be highly useful in shotgun sequencing in the future.     

Individual genome assembly from complex community short-read metagenomic datasets

Assembling individual genomes from complex community metagenomic data remains a challenging issue for environmental studies. We evaluated the quality of genome assemblies from community short read data (Illumina 100 bp pair-ended sequences) using datasets recovered from freshwater and soil microbial communities as well as in silico simulations. Our analyses revealed that the genome of a single genotype (or species) can be accurately assembled from a complex metagenome when it shows at least about 20 × coverage. At lower coverage, however, the derived assemblies contained a substantial fraction of non-target sequences (chimeras), which explains, at least in part, the higher number of hypothetical genes recovered in metagenomic relative to genomic projects. We also provide examples of how to detect intrapopulation structure in metagenomic datasets and estimate the type and frequency of errors in assembled genes and contigs from datasets of varied species complexity.     

Types and frequencies of sequencing errors in methyl-filtered and high c0t maize genome survey sequences

The Maize Genome Sequencing Consortium has deposited into GenBank more than 850,000 maize (Zea mays) genome survey sequences (GSSs) generated via two gene enrichment strategies, methylation filtration and high-C(0)t (HC) fractionation. These GSSs are a valuable resource for generating genome assemblies and the discovery of single nucleotide polymorphisms and nearly identical paralogs. Based on the rate of mismatches between 183 GSSs (105 methylation filtration + 78 HC) and 10 control genes, the rate of sequencing errors in these GSSs is 2.3 x 10(-3). As expected many of these errors were derived from insufficient vector trimming and base-calling errors. Surprisingly, however, some errors were due to cloning artifacts. These G.C to A.T transitions are restricted to HC clones; over 40% of HC clones contain at least one such artifact. Because it is not possible to distinguish the cloning artifacts from biologically relevant polymorphisms, HC sequences should be used with caution for the discovery of single nucleotide polymorphisms or paramorphisms. The average rate of sequencing errors was reduced 6-fold (to 3.6 x 10(-4)) by applying more stringent trimming parameters. This trimming resulted in the loss of only 11% of the bases (15,469/144,968). Due to redundancy among GSSs this more stringent trimming reduced coverage of promoters, exons, and introns by only 0%, 1%, and 4%, respectively. Hence, at the cost of a very modest loss of gene coverage, the quality of these maize GSSs can approach Bermuda standards, even prior to assembly.     

Efficiency and power as a function of sequence coverage, SNP array density, and imputation

High coverage whole genome sequencing provides near complete information about genetic variation. However, other technologies can be more efficient in some settings by (a) reducing redundant coverage within samples and (b) exploiting patterns of genetic variation across samples. To characterize as many samples as possible, many genetic studies therefore employ lower coverage sequencing or SNP array genotyping coupled to statistical imputation. To compare these approaches individually and in conjunction, we developed a statistical framework to estimate genotypes jointly from sequence reads, array intensities, and imputation. In European samples, we find similar sensitivity (89%) and specificity (99.6%) from imputation with either 1× sequencing or 1 M SNP arrays. Sensitivity is increased, particularly for low-frequency polymorphisms (MAF < 5%), when low coverage sequence reads are added to dense genome-wide SNP arrays--the converse, however, is not true. At sites where sequence reads and array intensities produce different sample genotypes, joint analysis reduces genotype errors and identifies novel error modes. Our joint framework informs the use of next-generation sequencing in genome wide association studies and supports development of improved methods for genotype calling.     

Detecting selective sweeps from pooled next-generation sequencing samples

Due to its cost effectiveness, next-generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for characterizing variation in population samples. Because Pool-Seq provides genome-wide SNP frequency data, it is possible to use them for demographic inference and/or the identification of selective sweeps. Here, we introduce a statistical method that is designed to detect selective sweeps from pooled data by accounting for statistical challenges associated with Pool-Seq, namely sequencing errors and random sampling among chromosomes. This allows for an efficient use of the information: all base calls are included in the analysis, but the higher credibility of regions with higher coverage and base calls with better quality scores is accounted for. Computer simulations show that our method efficiently detects sweeps even at very low coverage (0.5× per chromosome). Indeed, the power of detecting sweeps is similar to what we could expect from sequences of individual chromosomes. Since the inference of selective sweeps is based on the allele frequency spectrum (AFS), we also provide a method to accurately estimate the AFS provided that the quality scores for the sequence reads are reliable. Applying our approach to Pool-Seq data from Drosophila melanogaster, we identify several selective sweep signatures on chromosome X that include some previously well-characterized sweeps like the wapl region.