Evaluation of a transposase protocol for rapid generation of shotgun high-throughput sequencing libraries from nanogram quantities of DNA

Construction of DNA fragment libraries for next-generation sequencing can prove challenging, especially for samples with low DNA yield. Protocols devised to circumvent the problems associated with low starting quantities of DNA can result in amplification biases that skew the distribution of genomes in metagenomic data. Moreover, sample throughput can be slow, as current library construction techniques are time-consuming. This study evaluated Nextera, a new transposon-based method that is designed for quick production of DNA fragment libraries from a small quantity of DNA. The sequence read distribution across nine phage genomes in a mock viral assemblage met predictions for six of the least-abundant phages; however, the rank order of the most abundant phages differed slightly from predictions. De novo genome assemblies from Nextera libraries provided long contigs spanning over half of the phage genome; in four cases where full-length genome sequences were available for comparison, consensus sequences were found to match over 99% of the genome with near-perfect identity. Analysis of areas of low and high sequence coverage within phage genomes indicated that GC content may influence coverage of sequences from Nextera libraries. Comparisons of phage genomes prepared using both Nextera and a standard 454 FLX Titanium library preparation protocol suggested that the coverage biases according to GC content observed within the Nextera libraries were largely attributable to bias in the Nextera protocol rather than to the 454 sequencing technology. Nevertheless, given suitable sequence coverage, the Nextera protocol produced high-quality data for genomic studies. For metagenomics analyses, effects of GC amplification bias would need to be considered; however, the library preparation standardization that Nextera provides should benefit comparative metagenomic analyses.     

Impact of Pre-Analytical Variables on Cancer Targeted Gene Sequencing Efficiency

Tumor specimens are often preserved as formalin-fixed paraffin-embedded (FFPE) tissue blocks, the most common clinical source for DNA sequencing. Herein, we evaluated the effect of pre-sequencing parameters to guide proper sample selection for targeted gene sequencing. Data from 113 FFPE lung tumor specimens were collected, and targeted gene sequencing was performed. Libraries were constructed using custom probes and were paired-end sequenced on a next generation sequencing platform. A PCR-based quality control (QC) assay was utilized to determine DNA quality, and a ratio was generated in comparison to control DNA. We observed that FFPE storage time, PCR/QC ratio, and DNA input in the library preparation were significantly correlated to most parameters of sequencing efficiency including depth of coverage, alignment rate, insert size, and read quality. A combined score using the three parameters was generated and proved highly accurate to predict sequencing metrics. We also showed wide read count variability within the genome, with worse coverage in regions of low GC content like in KRAS. Sample quality and GC content had independent effects on sequencing depth, and the worst results were observed in regions of low GC content in samples with poor quality. Our data confirm that FFPE samples are a reliable source for targeted gene sequencing in cancer, provided adequate sample quality controls are exercised. Tissue quality should be routinely assessed for pre-analytical factors, and sequencing depth may be limited in genomic regions of low GC content if suboptimal samples are utilized.     

Systematic bias in high-throughput sequencing data and its correction by BEADS

Genomic sequences obtained through high-throughput sequencing are not uniformly distributed across the genome. For example, sequencing data of total genomic DNA show significant, yet unexpected enrichments on promoters and exons. This systematic bias is a particular problem for techniques such as chromatin immunoprecipitation, where the signal for a target factor is plotted across genomic features. We have focused on data obtained from Illumina's Genome Analyser platform, where at least three factors contribute to sequence bias: GC content, mappability of sequencing reads, and regional biases that might be generated by local structure. We show that relying on input control as a normalizer is not generally appropriate due to sample to sample variation in bias. To correct sequence bias, we present BEADS (bias elimination algorithm for deep sequencing), a simple three-step normalization scheme that successfully unmasks real binding patterns in ChIP-seq data. We suggest that this procedure be done routinely prior to data interpretation and downstream analyses.     

GPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene group

Background

Massively parallel sequencing technology is revolutionizing approaches to genomic and genetic research. Since its advent, the scale and efficiency of Next-Generation Sequencing (NGS) has rapidly improved. In spite of this success, sequencing genomes or genomic regions with extremely biased base composition is still a great challenge to the currently available NGS platforms. The genomes of some important pathogenic organisms like Plasmodium falciparum (high AT content) and Mycobacterium tuberculosis (high GC content) display extremes of base composition. The standard library preparation procedures that employ PCR amplification have been shown to cause uneven read coverage particularly across AT and GC rich regions, leading to problems in genome assembly and variation analyses. Alternative library-preparation approaches that omit PCR amplification require large quantities of starting material and hence are not suitable for small amounts of DNA/RNA such as those from clinical isolates. We have developed and optimized library-preparation procedures suitable for low quantity starting material and tolerant to extremely high AT content sequences.

Results

We have used our optimized conditions in parallel with standard methods to prepare Illumina sequencing libraries from a non-clinical and a clinical isolate (containing ~53% host contamination). By analyzing and comparing the quality of sequence data generated, we show that our optimized conditions that involve a PCR additive (TMAC), produces amplified libraries with improved coverage of extremely AT-rich regions and reduced bias toward GC neutral templates.

Conclusion

We have developed a robust and optimized Next-Generation Sequencing library amplification method suitable for extremely AT-rich genomes. The new amplification conditions significantly reduce bias and retain the complexity of either extremes of base composition. This development will greatly benefit sequencing clinical samples that often require amplification due to low mass of DNA starting material.     

Investigating and Correcting Plasma DNA Sequencing Coverage Bias to Enhance Aneuploidy Discovery

Pregnant women carry a mixture of cell-free DNA fragments from self and fetus (non-self) in their circulation. In recent years multiple independent studies have demonstrated the ability to detect fetal trisomies such as trisomy 21, the cause of Down syndrome, by Next-Generation Sequencing of maternal plasma. The current clinical tests based on this approach show very high sensitivity and specificity, although as yet they have not become the standard diagnostic test. Here we describe improvements to the analysis of the sequencing data by reducing GC bias and better handling of the genomic repeats. We show substantial improvements in the sensitivity of the standard trisomy 21 statistical tests, which we measure by artificially reducing read coverage. We also explore the bias stemming from the natural cleavage of plasma DNA by examining DNA motifs and position specific base distributions. We propose a model to correct this fragmentation bias and observe that incorporating this bias does not lead to any further improvements in the detection of fetal trisomy. The improved bias corrections that we demonstrate in this work can be readily adopted into existing fetal trisomy detection protocols and should also lead to improvements in sub-chromosomal copy number variation detection.     

Modeling and correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring

Background

Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias.

Results

We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting GC-bias at SCNAs level. Pre-SCNAClonal first corrects GC bias using Markov chain Monte Carlo probability model, then accurately locates baseline DNA segments (not containing any SCNAs) with a hierarchy clustering model. We show Pre-SCNAClonal’s superiority to exsiting GC-bias correction methods at any level of subclonal population.

Conclusions

Pre-SCNAClonal could be run independently as well as serving as pre-processing/gc-correction step in conjuntion with exsiting SCNA-based subclonal inferring tools.

     

Summarizing Specific Profiles in Illumina Sequencing from Whole-Genome Amplified DNA

Advances in both high-throughput sequencing and whole-genome amplification (WGA) protocols have allowed genomes to be sequenced from femtograms of DNA, for example from individual cells or from precious clinical and archived samples. Using the highly curated Caenorhabditis elegans genome as a reference, we have sequenced and identified errors and biases associated with Illumina library construction, library insert size, different WGA methods and genome features such as GC bias and simple repeat content. Detailed analysis of the reads from amplified libraries revealed characteristics suggesting that majority of amplified fragment ends are identical but inverted versions of each other. Read coverage in amplified libraries is correlated with both tandem and inverted repeat content, while GC content only influences sequencing in long-insert libraries. Nevertheless, single nucleotide polymorphism (SNP) calls and assembly metrics from reads in amplified libraries show comparable results with unamplified libraries. To utilize the full potential of WGA to reveal the real biological interest, this article highlights the importance of recognizing additional sources of errors from amplified sequence reads and discusses the potential implications in downstream analyses.     

Identification and prevention of a GC content bias in SAGE libraries

Serial Analysis of Gene Expression (SAGE) is becoming a widely used gene expression profiling method for the study of development, cancer and other human diseases. Investigators using SAGE rely heavily on the quantitative aspect of this method for cataloging gene expression and comparing multiple SAGE libraries. We have developed additional computational and statistical tools to assess the quality and reproducibility of a SAGE library. Using these methods, a critical variable in the SAGE protocol was identified that has the potential to bias the Tag distribution relative to the GC content of the 10 bp SAGE Tag DNA sequence. We also detected this bias in a number of publicly available SAGE libraries. It is important to note that the GC content bias went undetected by quality control procedures in the current SAGE protocol and was only identified with the use of these statistical analyses on as few as 750 SAGE Tags. In addition to keeping any solution of free DiTags on ice, an analysis of the GC content should be performed before sequencing large numbers of SAGE Tags to be confident that SAGE libraries are free from experimental bias.     

Read count approach for DNA copy number variants detection

MOTIVATION: The advent of high-throughput sequencing technologies is revolutionizing our ability in discovering and genotyping DNA copy number variants (CNVs). Read count-based approaches are able to detect CNV regions with an unprecedented resolution. Although this computational strategy has been recently introduced in literature, much work has been already done for the preparation, normalization and analysis of this kind of data. RESULTS: Here we face the many aspects that cover the detection of CNVs by using read count approach. We first study the characteristics and systematic biases of read count distributions, focusing on the normalization methods designed for removing these biases. Subsequently, we compare the algorithms designed to detect the boundaries of CNVs and we investigate the ability of read count data to predict the exact number of DNA copy. Finally, we review the tools publicly available for analysing read count data. To better understand the state of the art of read count approaches, we compare the performance of the three most widely used sequencing technologies (Illumina Genome Analyzer, Roche 454 and Life Technologies SOLiD) in all the analyses that we perform.     

Advantages of Single-Molecule Real-Time Sequencing in High-GC Content Genomes

Next-generation sequencing has become the most widely used sequencing technology in genomics research, but it has inherent drawbacks when dealing with high-GC content genomes. Recently, single-molecule real-time sequencing technology (SMRT) was introduced as a third-generation sequencing strategy to compensate for this drawback. Here, we report that the unbiased and longer read length of SMRT sequencing markedly improved genome assembly with high GC content via gap filling and repeat resolution.     

seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing

Background

Deviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.

Results

We introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.

Conclusions

seqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-178) contains supplementary material, which is available to authorized users.     

Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and Genome Analyzer systems

Background

The generation and analysis of high-throughput sequencing data are becoming a major component of many studies in molecular biology and medical research. Illumina's Genome Analyzer (GA) and HiSeq instruments are currently the most widely used sequencing devices. Here, we comprehensively evaluate properties of genomic HiSeq and GAIIx data derived from two plant genomes and one virus, with read lengths of 95 to 150 bases.

Results

We provide quantifications and evidence for GC bias, error rates, error sequence context, effects of quality filtering, and the reliability of quality values. By combining different filtering criteria we reduced error rates 7-fold at the expense of discarding 12.5% of alignable bases. While overall error rates are low in HiSeq data we observed regions of accumulated wrong base calls. Only 3% of all error positions accounted for 24.7% of all substitution errors. Analyzing the forward and reverse strands separately revealed error rates of up to 18.7%. Insertions and deletions occurred at very low rates on average but increased to up to 2% in homopolymers. A positive correlation between read coverage and GC content was found depending on the GC content range.

Conclusions

The errors and biases we report have implications for the use and the interpretation of Illumina sequencing data. GAIIx and HiSeq data sets show slightly different error profiles. Quality filtering is essential to minimize downstream analysis artifacts. Supporting previous recommendations, the strand-specificity provides a criterion to distinguish sequencing errors from low abundance polymorphisms.     

采用诱导环化酶制备Illumina Mate-paired DNA文库

新一代测序技术(NGS)的文库制备方法在基因组的拼装中起着重要作用。但是NGS技术制备的普通DNA文库片段只有500 bp左右,难以满足复杂基因组的从头(de novo)拼装要求。三代测序技术的读长可以达到20 kb,但是其高错误率及测序成本过高使得其又不易推广。因此二代测序的Mate-paired文库制备技术一直在基因组的de novo拼装中扮演着非常重要的角色。目前主流的NGS平台Illumina制备的Mate-paired文库的片段范围只有2~5 kb,为了得到更长的可用于Illumina平台测序的Mate-paired文库,本研究首次整合并优化了Illumina和Roche/454两种测序平台的Mate-paired文库制备技术,采用诱导环化酶来提高基因组长片段DNA的环化效率,成功建立了20 kb Mate-paired文库制备技术,并已将该技术应用于人类基因组20 kb Mate-paired文库制备。该技术为Illumina平台制备长片段Mate-paired库提供了方法指导。     

Synthetic oligodeoxyribonucleotides showing abnormal mobilities on polyacrylamide gel electrophoresis

We found that synthetic DNA fragments containing a GCGAAAGC sequence showed higher mobilities than oligonucleotides without the sequence on denaturing polyacrylamide gel electrophoresis. For example, the fragment, GCGAAAGCT (9mer), showed higher mobility than the corresponding 8mer (CGAAAGCT). In addition, on Maxam-Gilbert sequencing, a 21mer containing the GCGAAAGC sequence showed an abnormal pattern, which were similar to those due to compression observed on sequencing of DNAs with high GC contents, as recently reported. It was suggested that this compression was due to the increased mobilities of the specific fragments with the GCGAAAGC sequence and that these fragments took on abnormal conformations.     

Generation of long insert pairs using a Cre-LoxP Inverse PCR approach

Large insert mate pair reads have a major impact on the overall success of de novo assembly and the discovery of inherited and acquired structural variants. The positional information of mate pair reads generally improves genome assembly by resolving repeat elements and/or ordering contigs. Currently available methods for building such libraries have one or more of limitations, such as relatively small insert size; unable to distinguish the junction of two ends; and/or low throughput. We developed a new approach, Cre-LoxP Inverse PCR Paired-End (CLIP-PE), which exploits the advantages of (1) Cre-LoxP recombination system to efficiently circularize large DNA fragments, (2) inverse PCR to enrich for the desired products that contain both ends of the large DNA fragments, and (3) the use of restriction enzymes to introduce a recognizable junction site between ligated fragment ends and to improve the self-ligation efficiency. We have successfully created CLIP-PE libraries up to 22 kb that are rich in informative read pairs and low in small fragment background. These libraries have demonstrated the ability to improve genome assemblies. The CLIP-PE methodology can be implemented with existing and future next-generation sequencing platforms.