1. An Overview of Next-generation Genome Sequencing Platforms

Chandra Shekhar Pareek

The next-generation genome sequencing platforms are principally based on the immobilization of the DNA samples onto a solid support, cyclic sequencing reactions using automated fluidics devices, and detection of molecular events by imaging. This chapter reviews the platform-specific sequencing chemistries, instrument specifications, and general workflows and procedures of both the popular second generation sequencing platforms: GS FLX by 454 Life Technologies / Roche Applied Science, genome analyser by Solexa / Illumina, SOLiD by Applied Biosystems, and the third generation sequencing platforms: semiconductor sequencing by Ion Torrents / Life Technologies, PacBio RS by Pacific Biosciences, HeliScope™ single molecule sequencer by Helicos Bioscience Cooperation, Nano pore sequencing by Oxford Nanopore Technologies.

2. Attomole-Level Genomics with Single-molecule Direct DNA, cDNA and RNA Sequencing Technologies

Fatih Ozsolak

With the introduction of next generation sequencing (NGS) technologies in 2005, the domination of microarrays in genomics quickly came to an end due to NGS' superior technical performance and cost advantages. By enabling genetic analysis capabilities that were not possible previously, NGS technologies have started to play an integral role in all areas of biomedical research. This chapter outlines the low-quantity DNA/cDNA sequencing capabilities and applications developed with the Helicos single molecule DNA sequencing technology.

3. SNP Assessment on Draft Genomes from Next Generation Sequencing Data

Michael Piechotta and Christoph Dieterich

The number of genome projects has dramatically increased with the advent of high-throughput sequencing technologies. Typically, most assemblies remain in draft status. We present two methods for robust polymorphism detection on draft genomes. The first application, ACCUSA identifies true SNPs on draft genomes by considering the reference base call quality. We exemplify the advantages of ACCUSA by using publicly available sequencing data from yeast strains. Furthermore, we present a novel method ACCUSA2 that allows to identify SNPs between sequenced samples and show that ACCUSA2 outperforms a state of the art SNP caller in an in silico benchmark.

4. Processing Large-scale Small RNA Datasets in Silico

Daniel Mapleson, Irina Mohorianu, Helio Pais, Matthew Stocks, Leighton Folkes and Vincent Moulton

The latest advances in next generation sequencing technologies have resulted in a dramatic increase in the total number of sequences that can be produced per experiment as well as a significant decrease in sequencing error and bias. These improvements have driven forward both in silico and in vivo analyses in small RNA (sRNA) research. Until recently, the majority of existing sRNA computational methods focused on the analysis of a particular class of sRNAs, the micro RNAs. However there are several less well characterised classes of sRNAs present in plants, animals and other organisms that may have important biological function. This has prompted the development of novel data-driven approaches for sRNA analysis that are designed to cope with the increase in both the number of sequences and the diversity of information that is extracted. This chapter reviews these approaches and consists of three main sections. First, we consider the steps required to produce sRNA libraries. After this, a typical workflow for pre-processing the output from sequencing machines is presented. This includes an outline of the state of the art for adaptor removal, read filtering and selection, read mapping, and various approaches to normalise the read abundances. We then present the main computational techniques for sRNA analysis. More specifically, we discuss qualitative statistics for sample checking, biogenesis driven approaches for identification of known and novel sRNAs, and methods for predicting their function. We also give an overview of how correlation tools, developed to predict the types of interactions between sRNAs and their target genes, can refine information from target prediction tools. The chapter concludes with some remarks on how in silico sRNA research might evolve in the near future.

5. Utility of High Throughput Sequence Data in Rare Variant Detection

Viacheslav Y. Fofanov, Tudor Constantin, Heather Koshinsky and Eureka Genomics

Rare variants (sequence variants present in <1% of the sequence data) detection through the analysis of High Throughput Sequence (HTS) data is an exciting new field. HTS data enables genotype-based, primer and probe-independent detection of variant sequences that are present in low frequency in the population. This exciting technical advance may find clinical, epidemiological, forensic and quality control applications. However, because the field is new, terminology is not yet consistent, sources of potential error are not characterized, controlled or quantitated, and models to test experimental concepts are not common. Herein, we attempt (1) to provide some terminology framework, (2) review variant detection through traditional approaches and through examination of HTS data, (3) propose a minimum coverage model to test how much HTS data is needed for reliable rare variant detection, (4) examine sources of error that contribute to false positive rare variant detection and (5) potential approaches to minimize these errors.

6. Detecting Break Points of Insertions and Deletions from Paired-end Short Reads

Kai Ye and Zemin Ning

There is a strong demand in the genomics community to develop effective algorithms to reliably identify all types of genomic variants. Indel and structure variant detection using next-generation sequencing (NGS) data is difficult, and identification of large and complex structural variations is extremely challenging. When applied to NGS data, split-read methods have recently demonstrated their power both in pinpointing the precise breakpoints and in efficient use of computer memory and time, as compared with the read-depth, read-pair and assembly approaches. Pindel and its recent improvements as well as other split-read approaches are reviewed in this article. As each current method can only capture a subset of variant types with a high degree of confidence, a complete software package is needed in the field in order to integrate all types of signals for identifying all genetic variants of different types and size ranges.

7. Novel Insights from Re-sequencing of Human Exomes Through NGS

Jun Li, Tao Jiang, Xu Yang and Jun Wang

The advent of exome sequencing has opened a new door of NGS application in human diseases research. As an efficient and cost-effective method, exome sequencing using NGS platforms has brought human disease research into a new era. Since many human diseases are caused by mutations in exons, exome sequencing presents a powerful approach to study human diseases. Several exome capture kits have been developed to promote exome sequencing. The wide application of exome sequencing in recent years has significantly boosted scientific discoveries. Through bioinformatics analysis after exome sequencing, many genes have been found related to human diseases. As NGS price decreases rapidly, exome sequencing is expected to become a routine method for academic and clinical use.

8. Insights on Plant Development Using NGS Technologies

Ying Wang and Yuling Jiao

Despite being a recent breakthrough technology, Next-generation sequencing (NGS) has already found its application in plant developmental biology. This review summarizes recent applications of NGS in understanding key questions in plant development, such as organ formation and genetic information transmission in germline cells. NGS has been used to study gene expression, transcription factor (TF) binding, and epigenetic regulation, which includes DNA methylation, histone modification, and profiles of small RNA (smRNA) expression. NGS has been combined with traditional and novel technologies to address these types of questions that have intrigued plant developmental biologists. This review will cover recent advances in cell-specific analysis of transcription by NGS, and their applications to understand organ formation and development in two model species Arabidopsis thaliana and maize (Zea mays).

9. Next Generation Sequencing and the Future of Microbial Metagenomics

Andreas Wilke, Peter Larsen and Jack A Gilbert

Microbial ecology is going through a turbulent time, which is to be expected for a relatively young field. Here we explore the impact of one technique, microbial metagenomics, the study of sequencing DNA fragments from multiple microbial genomes in an assemblage of organisms, on the field of microbial ecology. We take a clear exploration of the reasons for looking at communities of microorganisms, and the potential for new discoveries of ecological importance, such as ubiquitous species distribution, and environmental community structuring factors. We explore the importance of community-driven research initiatives to help tackle the immensity of global microbial dynamics, and discuss the financial and practical implications of data generation and bioinformatics. Finally we highlight the role of microbial ecosystem modeling in helping to synthesize theories across domains. Microbial ecology has been, and will continue to be advanced through the application of careful experimental design and sophisticated technology. Metagenomics has a continuing role to play in this discovery, and as researchers grow ask more complex questions, this form of data generation will continue to help to answer those questions.

10. Next Generation Sequencing, Metagenomes and the Human Microbiome

Karen E. Nelson, Ramana Madupu, Sebastian Szpakowski, Johannes Goll, Konstantinos Krampis and Barbara A. Methé

Since 2003 there has been an explosion in the quantity of sequencing data generated through the study of whole microbial communities and their genetic content. These metagenomic surveys are accelerating the field of Microbial Ecology, revealing new species, pathways, functional capabilities, lateral gene transfer and enabling studies of microbial evolution. Venter and colleagues embarked on the first large-scale metagenomic study with the surveys of the Sargasso Sea, and the subsequent years have delivered significant improvements in high throughput sequencing technologies, coupled with a reduction of sequencing costs, which collectively has accelerated the rate and ease with which a range of environments are being studied with this approach. The end result has been a democratization of sequencing and now both large and small labs as well as individual investigators are capable of high-throughput metagenomic studies on any environment of choice. However, management, analysis and interpretation of these data sets remain an ongoing challenge requiring continual improvement as technologies progress.