Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform

Recent advancements in next-generation sequencing (NGS) technology have ushered in significant improvements in sequencing speed and data throughput, thereby enabling the simultaneous analysis of a greater number of samples within a single sequencing run. This technology has proven particularly valuable in the context of microbial community profiling, offering a powerful tool for characterizing the microbial composition at the species level within a given sample. This profiling process typically involves the sequencing of 16S ribosomal RNA (rRNA) gene fragments. By scaling up the analysis to accommodate a substantial number of samples, sometimes as many as 2,000, it becomes possible to achieve cost-efficiency and minimize the introduction of potential batch effects. Our study was designed with the primary objective of devising an approach capable of facilitating the comprehensive analysis of 1,711 samples sourced from diverse origins, including oropharyngeal swabs, mouth cavity swabs, dental swabs, and human fecal samples. This analysis was based on data obtained from 16S rRNA metagenomic sequencing conducted on the Illumina MiSeq and HiSeq sequencing platforms. We have designed a custom set of 10-base pair indices specifically tailored for the preparation of libraries from amplicons derived from the V3-V4 region of the 16S rRNA gene. These indices are instrumental in the analysis of the microbial composition in clinical samples through sequencing on the Illumina MiSeq and HiSeq platforms. The utilization of our custom index set enables the consolidation of a significant number of libraries, enabling the efficient sequencing of these libraries in a single run. The unique array of 10-base pair indices that we have developed, in conjunction with our sequencing methodology, will prove highly valuable to laboratories engaged in sequencing on Illumina platforms or utilizing Illumina-compatible kits.

Over the past 10 years, microbial ecologists have largely abandoned sequencing 16S rRNA genes by the Sanger sequencing method and have instead adopted highly parallelized sequencing platforms. These new platforms, such as 454 and Illumina's MiSeq, have allowed researchers to obtain millions of high quality, but short sequences. The result of the added sequencing depth has been significant improvements in experimental design. The tradeoff has been the decline in the number of full-length reference sequences that are deposited into databases. To overcome this problem, we tested the ability of the PacBio Single Molecule, Real-Time (SMRT) DNA sequencing platform to generate sequence reads from the 16S rRNA gene. We generated sequencing data from the V4, V3-V5, V1-V3, V1-V5, V1-V6, and V1-V9 variable regions from within the 16S rRNA gene using DNA from a synthetic mock community and natural samples collected from human feces, mouse feces, and soil. The mock community allowed us to assess the actual sequencing error rate and how that error rate changed when different curation methods were applied. We developed a simple method based on sequence characteristics and quality scores to reduce the observed error rate for the V1-V9 region from 0.69 to 0.027%. This error rate is comparable to what has been observed for the shorter reads generated by 454 and Illumina's MiSeq sequencing platforms. Although the per base sequencing cost is still significantly more than that of MiSeq, the prospect of supplementing reference databases with full-length sequences from organisms below the limit of detection from the Sanger approach is exciting.

The advances in next generation sequencing (NGS) technologies have tremendous impacts on the studies of structural and functional genomics. Sequencing‐based approaches like ChIP‐Seq and RNA‐Seq have started taking the place of microarray experiments to study protein–DNA ( deoxyribonucleic acid ) interactions and transcriptomic profiling, respectively. The arrival of NGS technologies has also enabled several whole human genome resequencing studies to be completed efficiently at an affordable price. The major strengths of NGS technologies are their ultra high‐throughput production, characterized by their ability to generate several hundred megabases to tens of gigabases of sequencing data per instrument run, and more importantly, the steep reduction in cost compared to the traditional Sanger sequencing method. Hence, NGS technologies have rapidly become the primary choice for large scale as well as genome‐wide sequencing studies. The new sequencing‐based approaches to explore structural and functional genomics have produced important information and significantly expanded our knowledge in these areas. Key concepts The rapid developments in sequencing technologies have transformed the approaches in the studies of structural and functional genomics. The arrival of next generation sequencing (NGS) technologies has started substituting traditional Sanger sequencing method in many large‐scale or genome‐wide sequencing studies. Shortly after the first next generation sequencer was introduced by Roche® 454 Life Science, the Genome Sequencer 20 (GS 20) System (it was subsequently replaced by GS FLX System), another two biotechnology companies also marketed their sequencing platforms: Illumina® Genome Analyzer (GA) and Applied Biosystems® (ABI) Supported Oligonucleotide Ligation Detection System (SOLiD). The major attractions of NGS technologies are their ultra high‐throughput production, characterized by their ability to produce gigabases of sequencing data per instrument run, and more importantly, the steep reduction in cost compared to the traditional sequencing method. Previously, the molecular genomics studies mainly relied on microarray technologies such as gene expression microarrays and the ChIP‐chip method (i.e. chromatin immunoprecipitation coupled with microarray) for genome‐wide interrogation. The NGS technologies have been used in various research areas besides the standard sequencing applications such as whole genome sequencing; they have also been increasingly applied in detecting structural variations (paired‐end mapping), studies of protein–DNA interactions and histone modifications (ChIP‐Seq) and transcriptomic profiling of messenger RNAs (mRNAs) and noncoding RNAs (RNA‐Seq). Sequencing‐based approaches have already yielded numerous novel and important findings in research areas like genome‐wide mapping of histone modifications and protein–DNA interactions, discovery of genetic variations and transcriptomics studies even though the approaches are still new and maturing. The NGS technologies have shown their potential of being dominant in future genomics studies. This is evident from several international projects using NGS technologies like the ENCODE Project, 1000 Genomes Project and cancers sequencing project by the International Cancer Genome Consortium. It is only a matter of time before achieving the goal of $1000 per whole genome sequencing. This should not be too far from now given the progresses in the development of third generation sequencing technologies. Although the $1000 genome will technically make sequencing of thousands of human genomes a reality, the substantial cost that will be incurred for data storage, powerful computational packages and analytical softwares has to be borne in mind. However, beyond affordability, what are left behind are the bioinformatics challenges in processing and analysing the huge amount of sequencing data.

Over the past 10 years, microbial ecologists have largely abandoned sequencing 16S rRNA genes by the Sanger sequencing method and have instead adopted highly parallelized sequencing platforms. These new platforms, such as 454 and Illumina’s MiSeq, have allowed researchers to obtain millions of high quality but short sequences. The result of the added sequencing depth has been significant improvements in experimental design. The tradeoff has been the decline in the number of full-length reference sequences that are deposited into databases. To overcome this problem, we tested the ability of the PacBio Single Molecule, Real-Time (SMRT) DNA sequencing platform to generate sequence reads from the 16S rRNA gene. We generated sequencing data from the V4, V3–V5, V1–V3, V1–V5, V1–V6, and V1–V9 variable regions from within the 16S rRNA gene using DNA from a synthetic mock community and natural samples collected from human feces, mouse feces, and soil. The mock community allowed us to assess the actual sequencing error rate and how that error rate changed when different curation methods were applied. We developed a simple method based on sequence characteristics and quality scores to reduce the observed error rate for the V1–V9 region from 0.69 to 0.027%. This error rate is comparable to what has been observed for the shorter reads generated by 454 and Illumina’s MiSeq sequencing platforms. Although the per base sequencing cost is still significantly more than that of MiSeq, the prospect of supplementing reference databases with full-length sequences from organisms below the limit of detection from the Sanger approach is exciting.

High-throughput sequencing (HTS) refers to the simultaneous sequencing of millions of fragments of DNA, which can be either assembled to reconstitute a genome, or aligned to an existing reference genome. The protocol can be extended to assay a wide variety of biological states of the cell, including DNA copy number, mRNA abundance and various properties of chromatin. HTS experiments allow for these biological states to be quantified as read counts at genome-wide scale with a single experiment. Though the experiments are expensive and often datasets are produced with limited sample size, information can be shared across thousands of genomic ranges in order to obtain robust models which control for technical biases. In this thesis, I present three statistical models for analyzing HTS read count data, aimed at answering concise biological questions. First, a hidden Markov model is developed for detecting copy number variants (CNVs) in individual samples while controlling for technical artifacts, such as variation in read counts due to local GC-content. Applied to a study of 248 male patients with X-linked intellectual disability, the model predicts 16 large CNVs, of which 10 candidate disease-causing CNVs were tested and all experimentally validated. The proposed software is then compared with state-of-the-art segmentation algorithms on normalized data, showing higher sensitivity while controlling the total rate of predicted CNVs. Second, improvements for parameter estimation are made for a statistical model of differential gene expression from RNA-Seq data. The improvements involve the use of empirical Bayes priors – priors estimated using the observations from all genes – in order to moderate otherwise noisy estimates of dispersion and fold changes for individual genes. The improved model shows increased sensitivity and more robust estimation of fold change in comparison with other differential expression software packages for RNA-Seq. Finally, a hierarchical Bayes model is used to associate transcription factor binding with chromatin and sequence features in regions of accessible chromatin. The hierarchical model incorporates three levels of parameters: one for individual experiments, one for experiments of the same cell type and one across all cell types. The model parameters are used to generate hypotheses regarding the DNA-binding behavior of a transcription factor, the glucocorticoid receptor. In summary, this thesis describes a set of statistical methods for HTS read count data which can be used across various biological domains. The methods form a framework for robust estimation of variables and hypothesis testing.%%%%Mit Hochdurchsatz-Sequenzierverfahren (HTS) bezeichnet man das gleichzeitige Sequenzieren von Millionen von DNA-Fragmenten, welche entweder zur Genomrekonstrution genutzt oder auf ein bestehendes Referenzgenom aligniert werden konnen. Das Protokoll kann erweitert werden, um verschiedene biologische Zustande der Zelle, wie z.B. die Anzahl an DNA-Kopien, mRNA-Abundanzen oder…

Next-generation sequencing (NGS) technology has extraordinarily enhanced the scope of research in the life sciences. To broaden the application of NGS to systems that were previously difficult to study, we present protocols for processing faecal and swab samples into amplicon libraries amenable to Illumina sequencing. We developed and tested a novel metagenomic DNA extraction approach using solid phase reversible immobilization (SPRI) beads on Western Bluebird (Sialia mexicana) samples stored in RNAlater. Compared with the MO BIO PowerSoil Kit, the current standard for the Human and Earth Microbiome Projects, the SPRI-based method produced comparable 16S rRNA gene PCR amplification from faecal extractions but significantly greater DNA quality, quantity and PCR success for both cloacal and oral swab samples. We furthermore modified published protocols for preparing highly multiplexed Illumina libraries with minimal sample loss and without post-adapter ligation amplification. Our library preparation protocol was successfully validated on three sets of heterogeneous amplicons (16S rRNA gene amplicons from SPRI and PowerSoil extractions as well as control arthropod COI gene amplicons) that were sequenced across three independent, 250-bp, paired-end runs on Illumina's MiSeq platform. Sequence analyses revealed largely equivalent results from the SPRI and PowerSoil extractions. Our comprehensive strategies focus on maximizing efficiency and minimizing costs. In addition to increasing the feasibility of using minimally invasive sampling and NGS capabilities in avian research, our methods are notably not avian-specific and thus applicable to many research programmes that involve DNA extraction and amplicon sequencing.

High-resolution HLA typings obtained for high-throughput NGS using the Illumina MiSeq platform

BackgroundUnderstanding the community structure of microbes is typically accomplished by sequencing 16S ribosomal RNA (16S rRNA) genes. These community data can be represented by constructing a phylogenetic tree and comparing it with other samples using statistical methods. However, owing to high computational complexity, these methods are insufficient to effectively analyze the millions of sequences produced by new sequencing technologies such as pyrosequencing.ResultsWe introduce a web tool named VITCOMIC (VIsualization tool for Taxonomic COmpositions of MIcrobial Community) that can analyze millions of bacterial 16S rRNA gene sequences and calculate the overall taxonomic composition for a microbial community. The 16S rRNA gene sequences of genome-sequenced strains are used as references to identify the nearest relative of each sample sequence. With this information, VITCOMIC plots all sequences in a single figure and indicates relative evolutionary distances.ConclusionsVITCOMIC yields a clear representation of the overall taxonomic composition of each sample and facilitates an intuitive understanding of differences in community structure between samples. VITCOMIC is freely available at http://mg.bio.titech.ac.jp/vitcomic/.

A number of next-generation sequencing (NGS) technologies such as Roche/454, Illumina and AB SOLiD have recently become available. These technologies are capable of generating hundreds of thousands or tens of millions of short DNA sequence reads at a relatively low cost. These NGS technologies, now referred as second-generation sequencing (SGS) technologies, are being utilized for de novo sequencing, genome re-sequencing, and whole genome and transcriptome analysis. Now, new generation of sequencers, based on the 'next-next' or third-generation sequencing (TGS) technologies like the Single-Molecule Real-Time (SMRT™) Sequencer, Heliscope™ Single Molecule Sequencer, and the Ion Personal Genome Machine™ are becoming available that are capable of generating longer sequence reads in a shorter time and at even lower costs per instrument run. Ever declining sequencing costs and increased data output and sample throughput for NGS and TGS sequencing technologies enable the plant genomics and breeding community to undertake genotyping-by-sequencing (GBS). Data analysis, storage and management of large-scale second or TGS projects, however, are essential. This article provides an overview of different sequencing technologies with an emphasis on forthcoming TGS technologies and bioinformatics tools required for the latest evolution of DNA sequencing platforms.

to explore the cutting-edge research on oral health and systemic diseases. 13Our first volume collated 17 insightful papers exploring the use of microbial meta-omics data to 14 better understand the complex biological context of health and disease. These contributions 15 provided a comprehensive overview of the current state of knowledge and future directions in the 16 field of oral microbiome research using NGS technologies. NGS technologies hold immense 17 potential for developing strategies to modulate the oral microbiome for improved overall health. A 18 key theme emerging from these studies is the significant impact of systemic conditions, such as 19 hypertension and hyperglycemia, and dysbiosis in distant body sites, like the gut, on the oral 20 microbiota via the oral-gut axis. This intricate connection between the oral cavity and the gut 21 warrants significant attention. In this volume, Lin et al investigated the presence of oral bacteria 22 in the gut of patients without any history of intestinal disorders. Their research give compelling 23 evidence for the transmission of oral taxa, commonly residing on the tongue dorsum, to the 24 rectum. Notably, they discovered that the translocation of oral bacteria to the rectum was 25 significantly more prevalent in participants with advanced age, hypertension, and those utilizing 26 proton pump inhibitors. 27This volume also features an interesting perspective by Filardo et al that explores the human 28 microbiome as a "hidden organ", with important contribution to host functions and significant 29 influence on human health. The review provides insights into human microbiome profiling, 30 describing various metagenomic methods, including 16S rRNA gene sequencing and its 31 associated biases (partial or full-length of the 16S rRNA gene) for the accurate identification of 32 bacteria to the species level, as well as biases associated with different sequencing platforms, 33 that may lead to the underestimation of the biodiversity of the microbiota being studied. 34For a more comprehensive picture, shotgun metagenomics offers a deeper taxonomic and 35 functional characterisation of the microbiome (de Cena et al., 2021, Kifle et al., 2024). While first 36 and second-generation sequencing technologies revolutionised the field by enabling higher-resolution analyses, further advancements have brought about third-generation platforms like 38 PacBio and Oxford Nanopore Technologies. These platforms offer long-read sequencing, which, 39 when combined with short-read methods (although admittedly more expensive), can significantly 40 improve coverage and assembly performance, facilitating the detection of low-abundance 41 species. This combined approach would provide a more thorough characterisation of the 42 microbiota and offer a clearer picture of microbial interactions during the transition to dysbiosis 43 (de Cena et al., 2021). However, the high cost of combining these sequencing approaches 44 remains a significant obstacle. This cost disparity leads to a major limitation in the current data which facilitates collaboration, enables new discoveries, and saves time and resources. However, 76 challenges such as incomplete metadata, incompatible software, and lack of standardisation 77 hinder data reuse. Implementing "FAIR" principles can address these issues by ensuring data is 78 "findable, accessible, interoperable, and reusable". This can lead to more efficient data handling, 79 faster insights, and reduced research costs, through the deployment of a user-friendly framework 80 that promotes inter-disciplinary collaboration. Furthermore, we argue that future studies require 81 much more detailed metadata. Research on the oral microbiome and systemic health should 82 include comprehensive data on the donor's oral health, including periodontal disease and caries, 83 as well as well-characterised and reproducible disease diagnoses. However, there are some 84 concerns regarding General Data Protection Regulation (GDPR) for data privacy with regards to 85 patients' data information, which may limit such implementation in healthcare research. 86In conclusion, the oral microbiome undeniably impacts overall human health. A deeper 87 understanding of host-microbe interactions can inform targeted strategies for disease prevention 88 and treatment. This knowledge can guide the development of novel preventive strategies through 89 microbiome modulation. Moreover, achieving predictive health and disease models for 90 personalised therapies focused on restoring a healthy microbiome will necessitate collaboration 91 between microbiologists and clinicians to strengthen the connection between biological and 92 clinical characteristics. 93 94 95

Objective: To compare the performance of Miseq and Ion Torrent PGM platforms and library construction method for next-generation sequencing (NGS) technology for formalin-fixed and paraffin-embedded (FFPE) samples. Methods: A total of 204 FFPE cancer samples including 100 non-small cell lung cancers at the First Affiliated Hospital of Zhejiang University, and 104 colorectal cancers at West China Hospital of Sichuan University were retrospectively selected from January 2013 to December 2016. By using the same samples, DNA was extracted, and the same amount of DNA was used for library construction with the same kit, and sequenced on Miseq and Ion Torrent PGM respectively, after passing the quality control. Any discordant mutations between two platforms were validated by amplified refractory mutation system-polymerase chain reaction (ARMS-PCR) method and Sanger sequencing. Results: A total of 204 FFPE samples were included and 197 samples were successfully analyzed by both platforms. The number of reads generated by the samples on Miseq platform sequencing was higher than PGM platform (median 391 634 vs. 298 030, P<0.01). Alignment with human reference genome showed that the mapping rate of Miseq platform was higher than PGM platform (median 100.0% vs. 99.7%, P<0.01). The median sequence depth of samples on Miseq was higher than PGM platform (median 853× vs. 698×, P<0.01). A total of 236 mutations were detected by two platforms, of which 221 were detected on both platforms, with a 93.6% concordance. Miseq platform detected 11 mutations not detected on PGM platform, while PGM platform detected 4 more mutations not detected on Miseq platform. With validation by ARMS-PCR and Sanger sequencing, Miseq platform was more reliable for low-frequency mutations. The main reasons for the discordant mutations between two platforms were that mutation frequency on undetected platform was lower than mutation reporting range (5%) and FFPE samples were stored for a long time. Conclusions: Compared with PGM, Miseq platform shows higher sequencing quality in terms of the number of reads, alignment results and coverage depth, and the test results are more reliable. In clinical practice, the appropriate platform should be chosen based on sample size and actual throughput requirements to aid in the molecular characterization of tumors.

Quantification of JAK2 V167F in Patients with Myeloproliferative Neoplasms: Comparison of NGS and Digital PCR with Quantitative PCR

Comparative analysis of two next-generation sequencing platforms for analysis of antimicrobial resistance genes

Rapidly developing next-generation sequencing (NGS) technologies produce a large amount of data across the whole human genome and allow a large number of DNA samples to be analyzed simultaneously. Screening cell-free fetal DNA (cffDNA) obtained from maternal blood using NGS technologies has provided new opportunities for non-invasive prenatal diagnosis (NIPD) of fetal aneuploidies. One of the major challenges to the analysis of fetal abnormalities is the development of accurate and reliable algorithms capable of analyzing large numbers of short sequence reads. Several such algorithms have recently been developed. Here, we provide a review of recent NGS-based NIPD methods as well as the available algorithms for short-read sequence analysis. We furthermore introduce the practical application of these algorithms for the detection of different types of fetal aneuploidies, and compare the performance, cost and complexity of each approach for clinical deployment. Our review identifies several main technologies and trends in NGS-based NIPD. The main considerations for clinical development for NIPD and screening tests using DNA sequencing are: accuracy, intellectual property, cost and the ability to screen for a wide range of chromosomal abnormalities and genetic defects. The cost of the diagnostic test depends on the sequencing method, diagnostic algorithm and volume of the tests. If the cost of sequencing equipment and reagents remains at or around current levels, targeted approaches for sequencing-based aneuploidy testing and SNP-based methods are preferred.

same way in each person, targeted therapy is personalized to the individual. Targeted therapy at molecule level potentiallyis less harm to normal cells, thus has fewer side effects, with improved efficacy and quality of life. A number of targeted therapies have been implemented into standard of care, such as the selective BRAF inhibitor vemurafenib, Bcr-Abl1 drugs imatinib and nilotinib, EGFR antagonist serlotinib and gefitinib, HER2 therapeutic antibody trastuzumab, and ALK inhibitor crizotinib. In these cases, genetic biomarkers as companion diagnostics able to stratify patient responders have been crucial to the successful outcome of targeted therapies [2,3]. As we understand more and more about cancer biology, we are now able to develop drugs that selectively target the molecular drivers of tumorigenesis. Cancer genomes are extremely heterogeneous and most probably would have to be scanned multiple times to monitor disease progression, as tumor cells are constantly evolving and developing resistance to anti-cancer drugs. Next-generation sequencing (NGS) technologies, in this respect, have the ability to massively parallelize the sequencing of millions of DNA templates, simultaneously interrogate thousands of clinically-actionable mutations, revolutionizing the way in which we detect, treat and manage cancer patients [4-7].Over the past few years, the data derived from NGS provided tremendous insights into the pathophysiology of cancer genomics, simultaneously providing new diagnostic and therapeutic information, as well as identifying specific lesions for targeted therapy. The advantages of NGS in genome medicine are many, including surveillance of the whole genome for the detection of all types of mutations. Major barriers for clinical implementation include high cost of equipment and reagents, the requirement for relatively large batch sizes for each run, large data sets, the expense complexity of analysis and interpretation, and the findings with unknown clinical significance. However, we have already observed an enormous decrease in cost during the past few years with the same level of information being delivered faster. Further, focused gene panels for targeted cancer sequencing significantly narrow the scope of a sequencing project, reducing cost and data analysis burdens. Because it assesses a limited set of genes, targeted cancer sequencing allows for deeper coverage of those genes and higher sensitivity to call variants in rare tumor subclones confidently. In this aspect, targeted gene sequencinghas become a preferable testing platform which is more cost effective, the results are easy to interpret, and most findings are clinically actionable for cancer-relevant genes. NGS panels for cancer-associated genes known in leukemia, as well as lung, colon, breast, melanoma and other cancers are currently provided by some commercial laboratories.Since its introduction in 2007, NGS technology has already made extraordinary advances, producing data on an unprecedented scale, this technique is now driving the generation of knowledge (especially in oncology) to new dimensions [8,9]. It’s not feasible for every cancer patient to receive whole genome sequencing and analysis, but it’s clear that there’s already a place for it in the medical toolbox. While the task of deciphering the cancer genome remains ongoing, we are already beginning to see clinical application of NGS-based genetic testing. And as NGS becomes faster and less expensive, it’s sure to be used more frequently and with greater benefit. Most importantly, the convergence of NGS technologies, bioinformatics power and algorithms, and other genomics advancements brings precision medicine closer than ever to a cost- and time-effective reality, and along with it the promise of enormous improvements in drug discovery, diagnostics, and personalized treatment of patients. To put the power of NGS into perspective, the Human Genome Project, based on Sanger sequencing, required 5 years and $300 million to sequence a single genome. Today, the same task can be achieved by NGS in a matter of weeks for a mere $5000 [10] (Table 1). It will be only a matter of time before the long sought-after goal of the $1000 genome set by the U.S. National Institutes of Health will be achieved. NGS innovation allows clinicians to make the best possible therapeutic choices, minimize the use of ineffective therapies and enhance enrollment in clinical trials appropriate for the individual patient. As personalized medicine, targeted therapies and treatments become the norm, the future of NGS-based genetic testing’s role in cancer patient care is expanding. And the role of the clinical laboratory is becoming more prominent as a resource for understanding each patient individually at the genetic level. Cost, turnaround time, and interpretability of NGS data will evolve and improve rapidly, as in many other technology sectors. We are at the dawn of an incredible precision medicine era.