Comparison of sequence reads obtained from three next-generation sequencing platforms

Next-generation sequencing technologies enable the rapid cost-effective production of sequence data. To evaluate the performance of these sequencing technologies, investigation of the quality of sequence reads obtained from these methods is important. In this study, we analyzed the quality of sequence reads and SNP detection performance using three commercially available next-generation sequencers, i.e., Roche Genome Sequencer FLX System (FLX), Illumina Genome Analyzer (GA), and Applied Biosystems SOLiD system (SOLiD). A common genomic DNA sample obtained from Escherichia coli strain DH1 was applied to these sequencers. The obtained sequence reads were aligned to the complete genome sequence of E. coli DH1, to evaluate the accuracy and sequence bias of these sequence methods. We found that the fraction of "junk" data, which could not be aligned to the reference genome, was largest in the data set of SOLiD, in which about half of reads could not be aligned. Among data sets after alignment to the reference, sequence accuracy was poorest in GA data sets, suggesting relatively low fidelity of the elongation reaction in the GA method. Furthermore, by aligning the sequence reads to the E. coli strain W3110, we screened sequence differences between two E. coli strains using data sets of three different next-generation platforms. The results revealed that the detected sequence differences were similar among these three methods, while the sequence coverage required for the detection was significantly small in the FLX data set. These results provided valuable information on the quality of short sequence reads and the performance of SNP detection in three next-generation sequencing platforms.     

Family-based association studies for next-generation sequencing

An individual's disease risk is determined by the compounded action of both common variants, inherited from remote ancestors, that segregated within the population and rare variants, inherited from recent ancestors, that segregated mainly within pedigrees. Next-generation sequencing (NGS) technologies generate high-dimensional data that allow a nearly complete evaluation of genetic variation. Despite their promise, NGS technologies also suffer from remarkable limitations: high error rates, enrichment of rare variants, and a large proportion of missing values, as well as the fact that most current analytical methods are designed for population-based association studies. To meet the analytical challenges raised by NGS, we propose a general framework for sequence-based association studies that can use various types of family and unrelated-individual data sampled from any population structure and a universal procedure that can transform any population-based association test statistic for use in family-based association tests. We develop family-based functional principal-component analysis (FPCA) with or without smoothing, a generalized T(2), combined multivariate and collapsing (CMC) method, and single-marker association test statistics. Through intensive simulations, we demonstrate that the family-based smoothed FPCA (SFPCA) has the correct type I error rates and much more power to detect association of (1) common variants, (2) rare variants, (3) both common and rare variants, and (4) variants with opposite directions of effect from other population-based or family-based association analysis methods. The proposed statistics are applied to two data sets with pedigree structures. The results show that the smoothed FPCA has a much smaller p value than other statistics.     

Epigenetics meets next-generation sequencing

Next-generation sequencing is poised to unleash dramatic changes in every area of molecular biology. In the past few years, chromatin immunoprecipitation (ChIP) on tiled microarrays (ChIP-chip) has been an important tool for genome-wide mapping of DNA-binding proteins or histone modifications. Now, ChIP followed by direct sequencing of DNA fragments (ChIP-seq) offers superior data with less noise and higher resolution and is likely to replace ChIP-chip in the near future. We will describe advantages of this new technology and outline some of the issues in dealing with the data. ChIP-seq generates considerably larger quantities of data and the most challenging aspect for investigators will be computational and statistical analysis necessary to uncover biological insights hidden in the data.     

Looking for therapeutic antibodies in next-generation sequencing repositories

ABSTRACT

Recently it has become possible to query the great diversity of natural antibody repertoires using next-generation sequencing (NGS). These methods are capable of producing millions of sequences in a single experiment. Here we compare clinical-stage therapeutic antibodies to the ~1b sequences from 60 independent sequencing studies in the Observed Antibody Space database, which includes antibody sequences from NGS analysis of immunoglobulin gene repertoires. Of 242 post-Phase 1 antibodies, we found 16 with sequence identity matches of 95% or better for both heavy and light chains. There are also 54 perfect matches to therapeutic CDR-H3 regions in the NGS outputs, suggesting a nontrivial amount of convergence between naturally observed sequences and those developed artificially. This has potential implications for both the legal protection of commercial antibodies and the discovery of antibody therapeutics.     

Rapid quantification of DNA libraries for next-generation sequencing

The next-generation DNA sequencing workflows require an accurate quantification of the DNA molecules to be sequenced which assures optimal performance of the instrument. Here, we demonstrate the use of qPCR for quantification of DNA libraries used in next-generation sequencing. In addition, we find that qPCR quantification may allow improvements to current NGS workflows, including reducing the amount of library DNA required, increasing the accuracy in quantifying amplifiable DNA, and avoiding amplification bias by reducing or eliminating the need to amplify DNA before sequencing.     

Evaluation of next generation sequencing platforms for population targeted sequencing studies

Background

Next generation sequencing (NGS) platforms are currently being utilized for targeted sequencing of candidate genes or genomic intervals to perform sequence-based association studies. To evaluate these platforms for this application, we analyzed human sequence generated by the Roche 454, Illumina GA, and the ABI SOLiD technologies for the same 260 kb in four individuals.

Results

Local sequence characteristics contribute to systematic variability in sequence coverage (>100-fold difference in per-base coverage), resulting in patterns for each NGS technology that are highly correlated between samples. A comparison of the base calls to 88 kb of overlapping ABI 3730xL Sanger sequence generated for the same samples showed that the NGS platforms all have high sensitivity, identifying >95% of variant sites. At high coverage, depth base calling errors are systematic, resulting from local sequence contexts; as the coverage is lowered additional 'random sampling' errors in base calling occur.

Conclusions

Our study provides important insights into systematic biases and data variability that need to be considered when utilizing NGS platforms for population targeted sequencing studies.     

Membrane platforms for biological nanopore sensing and sequencing

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Targeted enrichment of genomic DNA regions for next-generation sequencing

In this review, we discuss the latest targeted enrichment methods and aspects of their utilization along with second-generation sequencing for complex genome analysis. In doing so, we provide an overview of issues involved in detecting genetic variation, for which targeted enrichment has become a powerful tool. We explain how targeted enrichment for next-generation sequencing has made great progress in terms of methodology, ease of use and applicability, but emphasize the remaining challenges such as the lack of even coverage across targeted regions. Costs are also considered versus the alternative of whole-genome sequencing which is becoming ever more affordable. We conclude that targeted enrichment is likely to be the most economical option for many years to come in a range of settings.     

Alta-Cyclic: a self-optimizing base caller for next-generation sequencing

Next-generation sequencing is limited to short read lengths and by high error rates. We systematically analyzed sources of noise in the Illumina Genome Analyzer that contribute to these high error rates and developed a base caller, Alta-Cyclic, that uses machine learning to compensate for noise factors. Alta-Cyclic substantially improved the number of accurate reads for sequencing runs up to 78 bases and reduced systematic biases, facilitating confident identification of sequence variants.     

Performance comparison of whole-genome sequencing platforms

Whole-genome sequencing is becoming commonplace, but the accuracy and completeness of variant calling by the most widely used platforms from Illumina and Complete Genomics have not been reported. Here we sequenced the genome of an individual with both technologies to a high average coverage of ～76×, and compared their performance with respect to sequence coverage and calling of single-nucleotide variants (SNVs), insertions and deletions (indels). Although 88.1% of the ～3.7 million unique SNVs were concordant between platforms, there were tens of thousands of platform-specific calls located in genes and other genomic regions. In contrast, 26.5% of indels were concordant between platforms. Target enrichment validated 92.7% of the concordant SNVs, whereas validation by genotyping array revealed a sensitivity of 99.3%. The validation experiments also suggested that >60% of the platform-specific variants were indeed present in the genome. Our results have important implications for understanding the accuracy and completeness of the genome sequencing platforms.     

Probing the SELEX process with next-generation sequencing

Background

SELEX is an iterative process in which highly diverse synthetic nucleic acid libraries are selected over many rounds to finally identify aptamers with desired properties. However, little is understood as how binders are enriched during the selection course. Next-generation sequencing offers the opportunity to open the black box and observe a large part of the population dynamics during the selection process.

Methodology

We have performed a semi-automated SELEX procedure on the model target streptavidin starting with a synthetic DNA oligonucleotide library and compared results obtained by the conventional analysis via cloning and Sanger sequencing with next-generation sequencing. In order to follow the population dynamics during the selection, pools from all selection rounds were barcoded and sequenced in parallel.

Conclusions

High affinity aptamers can be readily identified simply by copy number enrichment in the first selection rounds. Based on our results, we suggest a new selection scheme that avoids a high number of iterative selection rounds while reducing time, PCR bias, and artifacts.