The genomic uses of a 200 year-old herbarium – Pitfalls and potentials

New Technologies, Tools and Approaches for Improving Crop Breeding

High-throughput sequencing (HTS) technologies have the potential to become one of the most significant advances in molecular diagnostics. Their use by researchers to detect and characterize plant pathogens and pests has been growing steadily for more than a decade and they are now envisioned as a routine diagnostic test to be deployed by plant pest diagnostics laboratories. Nevertheless, HTS technologies and downstream bioinformatics analysis of the generated datasets represent a complex process including many steps whose reliability must be ensured. The aim of the present guidelines is to provide recommendations for researchers and diagnosticians aiming to reliably use HTS technologies to detect plant pathogens and pests. These guidelines are generic and do not depend on the sequencing technology or platform. They cover all the adoption processes of HTS technologies from test selection to test validation as well as their routine implementation. A special emphasis is given to key elements to be considered: undertaking a risk analysis, designing sample panels for validation, using proper controls, evaluating performance criteria, confirming and interpreting results. These guidelines cover any HTS test used for the detection and identification of any plant pest (viroid, virus, bacteria, phytoplasma, fungi and fungus-like protists, nematodes, arthropods, plants) from any type of matrix. Overall, their adoption by diagnosticians and researchers should greatly improve the reliability of pathogens and pest diagnostics and foster the use of HTS technologies in plant health.

High throughput sequencing technology has great promise for biodiversity studies. However, an underlying assumption is that the primers used in these studies are universal for the prokaryotic or eukaryotic groups of interest. Full primer universality is difficult or impossible to achieve and studies using different primer sets make biodiversity comparisons problematic. The aim of this study was to design and optimize universal eukaryotic primers that could be used as a standard in future biodiversity studies. Using the alignment of all eukaryotic sequences from the publicly available SILVA database, we generated a full characterization of variable versus conserved regions in the 18S rRNA gene. All variable regions within this gene were analyzed and our results suggested that the V2, V4 and V9 regions were best suited for biodiversity assessments. Previously published universal eukaryotic primers as well as a number of self-designed primers were mapped to the alignment. Primer selection will depend on sequencing technology used, and this study focused on the 454 pyrosequencing GS FLX Titanium platform. The results generated a primer pair yielding theoretical matches to 80% of the eukaryotic and 0% of the prokaryotic sequences in the SILVA database. An empirical test of marine sediments using the AmpliconNoise pipeline for analysis of the high throughput sequencing data yielded amplification of sequences for 71% of all eukaryotic phyla with no isolation of prokaryotic sequences. To our knowledge this is the first characterization of the complete 18S rRNA gene using all eukaryotes present in the SILVA database, providing a robust test for universal eukaryotic primers. Since both in silico and empirical tests using high throughput sequencing retained high inclusion of eukaryotic phyla and exclusion of prokaryotes, we conclude that these primers are well suited for assessing eukaryote diversity, and can be used as a standard in biodiversity studies.

Automation Highlights from the Literature

High throughput sequencing technologies have become essential in studies on genomics, epigenomics, and transcriptomics. Although sequencing information has traditionally been elucidated using a low throughput technique called Sanger sequencing, high throughput sequencing technologies are capable of sequencing multiple DNA molecules in parallel, enabling hundreds of millions of DNA molecules to be sequenced at a time. This advantage allows high throughput sequencing to be used to create large data sets, generating more comprehensive insights into the cellular genomic and transcriptomic signatures of various diseases and developmental stages. Within high throughput sequencing technologies, whole exome sequencing can be used to identify novel variants and other mutations that may underlie many genetic cardiac disorders, whereas RNA sequencing can be used to analyze how the transcriptome changes. Chromatin immunoprecipitation sequencing and methylation sequencing can be used to identify epigenetic changes, whereas ribosome sequencing can be used to determine which mRNA transcripts are actively being translated. In this review, we will outline the differences in various sequencing modalities and examine the main sequencing platforms on the market in terms of their relative read depths, speeds, and costs. Finally, we will discuss the development of future sequencing platforms and how these new technologies may improve on current sequencing platforms. Ultimately, these sequencing technologies will be instrumental in further delineating how the cardiovascular system develops and how perturbations in DNA and RNA can lead to cardiovascular disease.

High-throughput sequencing technologies for cancer genomics.

Simple SummaryAbscess disease is a major disease that affects forest musk deer populations. Accurately identifying the types of pathogenic bacteria responsible for it is crucial for effective clinical treatment and the development of drugs and vaccines. This study is the first to use high-throughput 16S rRNA sequencing technology to detect the types and abundance of pathogenic bacteria in abscess disease samples at the genetic level, thereby overcoming limitations of previous methods. Microbial structure and bacterial community correlation analyses of six sequencing samples revealed that the dominant pathogenic bacteria were relatively singular and had an overwhelming abundance in the same individual. The pathogenic bacterial species differed among different individuals, and the dominant pathogenic bacteria exhibited no significant correlation with other bacteria in the pus, thus indicating that the dominant pathogenic bacteria were responsible for the production of abscess disease. The primary dominant pathogenic bacteria were Trueperella pyogenes, Fusobacterium necrophorum, and Bacteroides fragilis. While Trueperella pyogenes has been confirmed as one of the pathogenic bacteria responsible for abscess disease in forest musk deer, Fusobacterium necrophorum and Bacteroides fragilis could not be isolated or identified by previous research methods due to their obligate anaerobic characteristics. Therefore, this study is the first to report that Fusobacterium necrophorum and Bacteroides fragilis are the dominant pathogenic bacteria responsible for abscess disease in forest musk deer.Currently, researchers use bacterial culture and targeted PCR methods to classify, culture, and identify the pathogens causing abscess diseases. However, this method is limited by factors such as the type of culture medium and culture conditions, making it challenging to screen and proliferate many bacteria effectively. Fortunately, with the development of high-throughput sequencing technology, pathogen identification at the genetic level has become possible. Not only can this approach overcome the limitations of bacterial culture, but it can also accurately identify the types and relative abundance of pathogens. In this study, we used high-throughput sequencing of 16S rRNA to identify the pathogens in purulent fluid samples. Our results not only confirmed the presence of the main pathogen reported by previous researchers, Trueperella pyogenes, but also other obligate anaerobes, Fusobacterium necrophorum and Bacteroides fragilis as the dominant pathogens causing abscess diseases for the first time. Therefore, our findings suggest that high-throughput sequencing technology has the potential to replace traditional bacterial culture and targeted PCR methods.

The recent advances in high‐throughput sequencing technologies have enabled several whole cancer genomes to be sequenced. In addition, a number of large‐scale targeted resequencing studies have also been performed previously using Sanger sequencing methods. These studies have identified numerous somatic mutations in cancer genomes and provided new insights into the patterns of mutations in different cancer types. Several challenges remain in cancer genome sequencing such as accurately detecting different types of somatic mutations, the difficulty in identifying driver mutations, bioinformatics and analytical challenges in analysing the sequencing data and the cost of whole genome resequencing restricting the studies to a few genomes. However, cancer genome sequencing will eventually emerge as a routine tool to dissect the cancer genomes especially with the arrival of third generation sequencing technologies. The cancer genome resequencing studies have so far produced encouraging results to stimulate further studies to sequence more cancer genomes. These studies have made a significant contribution to the understanding of the somatic mutational profile of various cancers. Key Concepts: The genetic alterations of cancer occurring at the DNA sequence level can be classified as germline or somatic. Somatic mutations can occur in the cancer genome in several different forms such as single and double nucleotide variants or base substitutions, small insertion–deletions (indels) and larger structural chromosomal alterations. The recent advances in dissecting the somatic mutational profile of cancer genomes have been driven by high‐throughput or next‐generation sequencing (NGS) technologies which have enabled several whole cancer genomes to be sequenced for the first time. The involvement of somatic mutations in cancer initiation and progression, in addition to germline variations, is well recognised. Cancer genomes are characterised by their genomic instability which results in the occurrence of numerous somatic mutations which has proved challenging to investigate. Although a large number of somatic mutations have been detected in cancer genomes, only a small subset is predicted to be ‘driver’ mutations and the remainder considered ‘passenger’ mutations. Driver mutations are the mutations that initiate and drive oncogenesis steps, such as cell proliferation, tumour growth, angiogenesis, tissue invasion and metastasis. Several challenges remain in cancer genome sequencing such as to accurately detect different types of somatic mutations, the difficulty in identifying driver mutations, bioinformatics and analytical challenges and the cost for whole genome resequencing has restricted the studies to a few genomes. Currently there are no major obstacles in cataloging somatic mutations in cancer genomes. The real challenge lies in data interpretation and how the data can be used to discover new drugs or molecular markers for clinical applications. The ultimate goals of cancer genome sequencing are to improve the clinical management of patients and the creation of personalised medicine through the development of new therapeutic agents which are tailored to the individual based on their genetic information.

Application of high-throughput sequencing technology in HIV drug resistance detection

The adaptive immune system is essential in defending the host against diverse and rapidly evolving pathogens, or controlling diseases such as cancer. To perform its duty, the adaptive immunity depends on enormously diverse repertoires of B- and T-cell receptors (BCRs and TCRs). In light of the rapid advancement in high-throughput sequencing (HTSeq) technologies, it is now possible to study the properties of these repertoires, which is central to the development of vaccines, new prognostic markers, and treatments for cancer and autoimmune diseases. One challenge in extracting biologically meaningful information from HTSeq data comes from the fact that this data is both complex and massive. We can anticipate that additional improvements in HTSeq technologies will generate even larger datasets with hundreds of millions of sequenced reads from potentially hundreds or thousands of individuals. To meet these challenges, we need new computational methods. Furthermore, the biological processes that contribute to the diversity of BCR repertoires are stochastic in nature. This calls for the use of probabilistic modeling to accurately describe these processes. I begin this thesis with an introduction of the most relevant concepts of B-cell mediated immunity (chapter 1). This is followed by general introduction of probabilistic modeling for Bayes inference (chapter 2). The main result of this thesis are computational methods, which are summarized in two publications (chapter 3). In the first publication (section 3.1), I introduce IgGeneUsage, a computational tool for probabilistic detection of differential Ig gene usage under different biological conditions (e.g. infected vs. healthy subjects). We know that V(D)J recombination of different germline-encoded Ig genes is an important component that contributes to the enormous diversity of BCR repertoires. Detection of disrupted usage of Ig genes has previously been reported e.g. in chronic lymphocytic leukemia, where specific Ig gene disruptions may be used as prognostic markers for different diseases. Despite the importance of this feature, most analyses of differential Ig gene usage are either qualitative, or rely on inadequate statistical methods. IgGeneUsage employs a hierarchical probabilistic model for Bayes inference, and is able to cope with complex and noisy Ig gene usage data. The results reported by IgGeneUsage are statistically sound, and easy to interpret by non-statisticians. The performance of IgGeneUsage was compared against tools that are commonly used for differential Ig gene usage, such as the Welch’s t-test (t-test) and Wilcoxon signed-rank test (U-test). This evaluation was performed based on publicly available data of human BCR repertoires, where biologically replicated datasets were available for each repertoire. The evaluation revealed that IgGeneUsage generates consistent results in each replicate, whereas the t- and U-test produce divergent results. In the second publication (section 3.2), I introduce the results of a collaborative project in which we examined the effects of chronic Hepatitis C Virus (HCV) infection on the human BCR repertoire. This involved diverse computational analyses based on HTSeq data of human immunoglobulin heavy chain VDJ rearrangements, obtained from different B-cell populations in healthy and HCV infected individuals. In patients infected with HCV, our analyses revealed large perturbations such as aberrant Ig gene usage, clonal expansions, and changes in CDR3 length. To perform these analyses, we have developed numerous computational methods for the different stages of BCR repertoire profiling.

High throughput sequencing technology has dramatically improved the efficiency of DNA sequencing, and decreased the costs to a great extent. Meanwhile, this technology usually has advantages of better specificity, higher sensitivity and accuracy. Therefore, it has been applied to the research on genetic variations, transcriptomics and epigenomics. Recently, this technology has been widely employed in the studies of transposable elements and has achieved fruitful results. In this review, we summarize the application of high throughput sequencing technology in the fields of transposable elements, including the estimation of transposon content, preference of target sites and distribution, insertion polymorphism and population frequency, identification of rare copies, transposon horizontal transfers as well as transposon tagging. We also briefly introduce the major common sequencing strategies and algorithms, their advantages and disadvantages, and the corresponding solutions. Finally, we envision the developing trends of high throughput sequencing technology, especially the third generation sequencing technology, and its application in transposon studies in the future, hopefully providing a comprehensive understanding and reference for related scientific researchers.

Infectious diseases are among the leading causes of human morbidity and mortality, with the greatest burden felt in the pediatric population. For any infectious disease, only a fraction of the exposed individuals develop clinical symptoms. These inter-individual differences can be due to variation in pathogen virulence or in host susceptibility. The recent advent of high-throughput sequencing (HTS) technology has enabled studies of both human and pathogen genetic factors that have the potential to influence infectious diseases pathogenesis and alter clinical presentation. In this thesis, I present a set of genomic studies that used HTS to dissect the genetic basis of life-threatening infections with Pseudomonas aeruginosa (P. aeruginosa) and respiratory syncytial virus (RSV). This work provides conclusive evidence for the role of rare human genetic variants in susceptibility to life-threatening P. aeruginosa and RSV infections in previously healthy children. Furthermore, in an attempt to determine the role of viral genetic factors in severe presentations of RSV infection, I established a framework for exploring RSV genetic variation using HTS technology and bioinformatic analysis. Together, theses studies demonstrate that current genomic technology, bioinformatic analysis and functional follow-up have the potential to give us novel insight into the molecular basis of host-pathogen interactions and infectious disease pathogenesis.

High-throughput sequencing (HTS) technologies generate millions of sequence reads from DNA/RNA molecules rapidly and cost-effectively, enabling single investigator laboratories to address a variety of 'omics' questions in nonmodel organisms, fundamentally changing the way genomic approaches are used to advance biological research. One major challenge posed by HTS is the complexity and difficulty of data quality control (QC). While QC issues associated with sample isolation, library preparation and sequencing are well known and protocols for their handling are widely available, the QC of the actual sequence reads generated by HTS is often overlooked. HTS-generated sequence reads can contain various errors, biases and artefacts whose identification and amelioration can greatly impact subsequent data analysis. However, a systematic survey on QC procedures for HTS data is still lacking. In this review, we begin by presenting standard 'health check-up' QC procedures recommended for HTS data sets and establishing what 'healthy' HTS data look like. We next proceed by classifying errors, biases and artefacts present in HTS data into three major types of 'pathologies', discussing their causes and symptoms and illustrating with examples their diagnosis and impact on downstream analyses. We conclude this review by offering examples of successful 'treatment' protocols and recommendations on standard practices and treatment options. Notwithstanding the speed with which HTS technologies - and consequently their pathologies - change, we argue that careful QC of HTS data is an important - yet often neglected - aspect of their application in molecular ecology, and lay the groundwork for developing a HTS data QC 'best practices' guide.

Objective To identify the possible pathogens of a patient with upper respiratory infection using high throughput sequencing technology and to optimize the methods for sample pre-treatment. Methods After pre-treating the sample with Benzonase Nuclease and/or ribosomal RNA (rRNA) probes, RNA was amplified with sequence independent single primer amplification (SISPA). The amplicons were used to prepare the sequencing library. Then the high throughput sequencing and bioinformatics analysis were performed. Results After resembled reads were blast against viruses library, we found the presence of influenza B virus sequences. Furthurmore, phylogenetic analysis showed that the influenza B virus belonged to Yamagat linage. Treatment with nuclease and ribosomal RNA (rRNA) probes provided more target sequences and higher coverage for the high throughput sequencing. Conclusions Combination of using nuclease and ribosomal RNA (rRNA) probes to treat sample and high throughput sequencing can rapidly identify the pathogen of unknown infection. Key words: Influenza B virus; high throughput sequencing; nuclease; ribosomal RNA

High throughput, (or next generation) sequencing technologies have opened new and exciting opportunities in the use of DNA sequences. The new emerging technologies mark the beginning of a new era of high throughput short read sequencing: they have the potential to assemble a bacterial genome during a single experiment and at a moderate cost. In this paper, we address the problem of efficiently mapping millions of degenerate and weighted sequences to a reference genome with respect to whether they occur exactly once in the genome or not, and by taking probability scores into consideration. In particular, we define and solve the Massive Exact and Approximate Unique Pattern Matching problem for degenerate and weighted sequences derived from high throughput sequencing technologies.