TILLING: practical single-nucleotide mutation discovery

In the post-genomic sequencing era, an expanding portfolio of genomic technologies has been applied to the study of gene function. Reverse genetics approaches that provide targeted inactivation of genes identified by sequence analysis include TILLING (for Targeting Local Lesions IN Genomes). TILLING searches the genomes of mutagenized organisms for mutations in a chosen gene, typically single base-pair substitutions. This review covers practical aspects of the technology, ranging from building the mutagenized population to mutation discovery, and discusses possible improvements to current protocols and the impact of new genomic methods for mutation discovery in relation to the future of the TILLING approach.     

Understanding the molecular machinery of genetics through 3D structures

Detailed knowledge of the three-dimensional structures of biological molecules has had an enormous impact on all areas of biological science, including genetics, as structure can reveal the fine details of how molecules perform their biological functions. Here we consider how changes in protein sequence affect the corresponding 3D structure, and describe how structural information about proteins, DNA and chromatin has shed light on gene regulatory mechanisms and the storage and transmission of epigenetic information. Finally, we describe how structure determination is benefiting from the high-throughput technologies of the worldwide structural genomics projects.     

Proteomics contributions to epigenetic drug discovery

The combined activity of epigenetic features, which include histone post-translational modifications, DNA methylation, and nucleosome positioning, regulates gene expression independently from changes in the DNA sequence, defining how the shared genetic information of an organism is used to generate different cell phenotypes. Alterations in epigenetic processes have been linked with a multitude of diseases, including cancer, fueling interest in the discovery of drugs targeting the proteins responsible for writing, erasing, or reading histone and DNA modifications. Mass spectrometry (MS)-based proteomics has emerged as a versatile tool that can assist drug discovery pipelines from target validation, through target deconvolution, to monitoring drug efficacy in vivo. Here, we provide an overview of the contributions of MS-based proteomics to epigenetic drug discovery, describing the main approaches that can be used to support different drug discovery pipelines and highlighting how they contributed to the development and characterization of epigenetic drugs.     

Plant genome sequencing: applications for crop improvement

DNA sequencing technology is undergoing a revolution with the commercialization of second generation technologies capable of sequencing thousands of millions of nucleotide bases in each run. The data explosion resulting from this technology is likely to continue to increase with the further development of second generation sequencing and the introduction of third generation single‐molecule sequencing methods over the coming years. The question is no longer whether we can sequence crop genomes which are often large and complex, but how soon can we sequence them? Even cereal genomes such as wheat and barley which were once considered intractable are coming under the spotlight of the new sequencing technologies and an array of new projects and approaches are being established. The increasing availability of DNA sequence information enables the discovery of genes and molecular markers associated with diverse agronomic traits creating new opportunities for crop improvement. However, the challenge remains to convert this mass of data into knowledge that can be applied in crop breeding programs.     

The near demise and subsequent revival of classical genetics for investigating Caenorhabditis elegans embryogenesis: RNAi meets next-generation DNA sequencing

Molecular genetic investigation of the early Caenorhabditis elegans embryo has contributed substantially to the discovery and general understanding of the genes, pathways, and mechanisms that regulate and execute developmental and cell biological processes. Initially, worm geneticists relied exclusively on a classical genetics approach, isolating mutants with interesting phenotypes after mutagenesis and then determining the identity of the affected genes. Subsequently, the discovery of RNA interference (RNAi) led to a much greater reliance on a reverse genetics approach: reducing the function of known genes with RNAi and then observing the phenotypic consequences. Now the advent of next-generation DNA sequencing technologies and the ensuing ease and affordability of whole-genome sequencing are reviving the use of classical genetics to investigate early C. elegans embryogenesis.     

Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data

Single nucleotide polymorphisms (SNPs) represent the most widespread type of DNA sequence variation in the human genome and they have recently emerged as valuable genetic markers for revealing the genetic architecture of complex traits in terms of nucleotide combination and sequence. Here, we extend an algorithmic model for the haplotype analysis of SNPs to estimate the effects of genetic imprinting expressed at the DNA sequence level. The model provides a general procedure for identifying the number and types of optimal DNA sequence variants that are expressed differently due to their parental origin. The model is used to analyze a genetic data set collected from a pain genetics project. We find that DNA haplotype GAC from three SNPs, OPRKG36T (with two alleles G and T), OPRKA843G (with alleles A and G), and OPRKC846T (with alleles C and T), at the kappa-opioid receptor, triggers a significant effect on pain sensitivity, but with expression significantly depending on the parent from which it is inherited (p = 0.008). With a tremendous advance in SNP identification and automated screening, the model founded on haplotype discovery and statistical inference may provide a useful tool for genetic analysis of any quantitative trait with complex inheritance.     

Comparative genomics: the key to understanding the Human Genome Project

The sequencing of the human genome is well underway. Technology has advanced, such that the total genomic sequence is possible, along with an extensive catalogue of genes via comprehensive cDNA libraries. With the recent completion of the Saccharomyces cerevisiae sequencing project and the imminent completion of that of Caenorhabditis elegans, the most frequently asked question is how much can sequence data alone tell us? The answer is that that a DNA sequence taken in isolation from a single organism reveals very little. The vast majority of DNA in most organisms is noncoding. Protein coding sequences or genes cannot function as isolated units without interaction with noncoding DNA and neighboring genes. This genomic environment is specific to each organism. In order to understand this we need to look at similar genes in different organisms, to determine how function and position has changed over the course of evolution. By understanding evolutionary processes we can gain a greater insight into what makes a gene and the wider processes of genetics and inheritance. Comparative genomics (with model organisms), once the poor relation of the human genome project, is starting to provide the key to unlock the DNA code.     

Physical mapping of the rice genome with BACs

The development of genetics in this century has been catapulted forward by several milestones: rediscovery of Mendel's laws, determination of DNA as the genetic material, discovery of the double-helix structure of DNA and its implications for genetic behavior, and most recently, analysis of restriction fragment length polymorphisms (RFLPs). Each of these milestones has generated a huge wave of progress in genetics. Consequently, our understanding of organismal genetics now extends from phenotypes to their molecular genetic basis. It is now clear that the next wave of progress in genetics will hinge on genome molecular physical mapping, since a genome physical map will provide an invaluable, readily accessible system for many detailed genetic studies and isolation of many genes of economic or biological importance. Recent development of large-DNA fragment (>100 kb) manipulation and cloning technologies, such as pulsed-field gel electrophoresis (PFGE), and yeast artificial chromosome (YAC) and bacterial artificial chromosome (BAC) cloning, has provided the powerful tools needed to generate molecular physical maps for higher-organism genomes. This chapter will discuss (1) an ideal physical map of plant genome and its applications in plant genetic and biological studies, (2) reviews on physical mapping of the genomes of Caenorhabditis elegans, Arabidopsis thaliana, and man, (3) large-insert DNA libraries: cosmid, YAC and BAC, and genome physical mapping, (4) physical mapping of the rice genome with BACs, and (5) perspectives on the physical mapping of the rice genome with BACs.     

High throughput sequencing approaches to mutation discovery in the mouse

Phenotype-driven approaches in mice are powerful strategies for the discovery of genes and gene functions and for unravelling complex biological mechanisms. Traditional methods for mutation discovery are reliable and robust, but they can also be laborious and time consuming. Recently, high-throughput sequencing (HTS) technologies have revolutionised the process of forward genetics in mice by paving the way to rapid mutation discovery. However, successful application of HTS for mutation discovery relies heavily on the sequencing approach employed and strategies for data analysis. Here we review current HTS applications and resources for mutation discovery and provide an overview of the practical considerations for HTS implementation and data analysis.     

Detection of DNA sequence polymorphisms by enzymatic amplification and direct genomic sequencing.

The discovery of RFLPs and their utilization as genetic markers has revolutionized research in human molecular genetics. However, only a fraction of the DNA sequence polymorphisms in the human genome affect the length of a restriction fragment and hence result in an RFLP. Polymorphisms that are not detected as RFLPs are typically passed over in the screening process though they represent a potentially important source of informative genetic markers. We have used a rapid method for the detection of naturally occurring DNA sequence variations that is based on enzymatic amplification and direct sequencing of genomic DNA. This approach can detect essentially all useful sequence variations within the region screened. We demonstrate the feasibility of the technique by applying it to the human retinoblastoma susceptibility locus. We screened 3,712 bp of genomic DNA from each of nine individuals and found four DNA sequence polymorphisms. At least one of these DNA sequence polymorphisms was informative in each of three families with hereditary retinoblastoma that were not informative with any of the known RFLPs at this locus. We believe that direct sequencing is a reasonable alternative to other methods of screening for DNA sequence polymorphisms and that it represents a step forward for obtaining informative markers at well-characterized loci that have been minimally informative in the past.     

Systems genetics in “-omics” era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP–SNP genetic interactions, LD block–block interactions, miRNA–mRNA interactions, DNA–protein interactions, protein–protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate “-omics” datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.     

Gene discovery using mutagen-induced polymorphisms and deep sequencing: application to plant disease resistance

Next-generation sequencing technologies are accelerating gene discovery by combining multiple steps of mapping and cloning used in the traditional map-based approach into one step using DNA sequence polymorphisms existing between two different accessions/strains/backgrounds of the same species. The existing next-generation sequencing method, like the traditional one, requires the use of a segregating population from a cross of a mutant organism in one accession with a wild-type (WT) organism in a different accession. It therefore could potentially be limited by modification of mutant phenotypes in different accessions and/or by the lengthy process required to construct a particular mapping parent in a second accession. Here we present mapping and cloning of an enhancer mutation with next-generation sequencing on bulked segregants in the same accession using sequence polymorphisms induced by a chemical mutagen. This method complements the conventional cloning approach and makes forward genetics more feasible and powerful in molecularly dissecting biological processes in any organisms. The pipeline developed in this study can be used to clone causal genes in background of single mutants or higher order of mutants and in species with or without sequence information on multiple accessions.     

Progress in functional genomics approaches to antifungal drug target discovery

Antifungal drug discovery is starting to benefit from the enormous advances in the genomics field, which have occurred in the past decade. As traditional drug screening on existing targets is not delivering the long-awaited potent antifungals, efforts to use novel genetics and genomics-based strategies to aid in the discovery of novel drug targets are gaining increased importance. The current paradigm in antifungal drug target discovery focuses on basically two main classes of targets to evaluate: genes essential for viability and virulence or pathogenicity factors. Here we report on recent advances in genetics and genomics-based technologies that will allow us not only to identify and validate novel fungal drug targets, but hopefully in the longer run also to discover potent novel therapeutic agents. Fungal pathogens have typically presented significant obstacles when subjected to genetics, but the creativity of scientists in the anti-infectives field and the cross-talk with scientists in other areas is now yielding exciting new tools and technologies to tackle the problem of finding potent, specific and non-toxic antifungal therapeutics.     

Role of genomics in the potential restoration of the American chestnut

The development of genomic tools will enhance traditional tree breeding technologies leading to more certain and timely recovery of the American chestnut, a keystone heritage tree of the eastern United States. Major efforts are being made in gene discovery, genetic marker development, construction of a BAC-based physical map, and DNA transformation technology. A strategy of map-based cloning, association genetics, and genetic engineering, combined with traditional and marker-assisted backcross breeding is proposed for the long-term genetic restoration of this iconic tree species.     

Detection of single nucleotide polymorphisms

Single nucleotide polymorphism (SNP) detection technologies are used to scan for new polymorphisms and to determine the allele(s) of a known polymorphism in target sequences. SNP detection technologies have evolved from labor intensive, time consuming, and expensive processes to some of the most highly automated, efficient, and relatively inexpensive methods. Driven by the Human Genome Project, these technologies are now maturing and robust strategies are found in both SNP discovery and genotyping areas. The nearly completed human genome sequence provides the reference against which all other sequencing data can be compared. Global SNP discovery is therefore only limited by the amount of funding available for the activity. Local, target, SNP discovery relies mostly on direct DNA sequencing or on denaturing high performance liquid chromatography (dHPLC). The number of SNP genotyping methods has exploded in recent years and many robust methods are currently available. The demand for SNP genotyping is great, however, and no one method is able to meet the needs of all studies using SNPs. Despite the considerable gains over the last decade, new approaches must be developed to lower the cost and increase the speed of SNP detection.     

Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection

Background

DNA sequence comparison is a well-studied problem, in which two DNA sequences are compared using a weighted edit distance. Recent DNA sequencing technologies however observe an encoded form of the sequence, rather than each DNA base individually. The encoded DNA sequence may contain technical errors, and therefore encoded sequencing errors must be incorporated when comparing an encoded DNA sequence to a reference DNA sequence.

Results

Although two-base encoding is currently used in practice, many other encoding schemes are possible, whereby two ore more bases are encoded at a time. A generalized k-base encoding scheme is presented, whereby feasible higher order encodings are better able to differentiate errors in the encoded sequence from true DNA sequence variants. A generalized version of the previous two-base encoding DNA sequence comparison algorithm is used to compare a k-base encoded sequence to a DNA reference sequence. Finally, simulations are performed to evaluate the power, the false positive and false negative SNP discovery rates, and the performance time of k-base encoding compared to previous methods as well as to the standard DNA sequence comparison algorithm.

Conclusions

The novel generalized k-base encoding scheme and resulting local alignment algorithm permits the development of higher fidelity ligation-based next generation sequencing technology. This bioinformatic solution affords greater robustness to errors, as well as lower false SNP discovery rates, only at the cost of computational time.     

Automation of Molecular-Based Analyses: A Primer on Massively Parallel Sequencing

Recent advances in genetics have been enabled by new genetic sequencing techniques called massively parallel sequencing (MPS) or next-generation sequencing. Through the ability to sequence in parallel hundreds of thousands to millions of DNA fragments, the cost and time required for sequencing has dramatically decreased. There are a number of different MPS platforms currently available and being used in Australia. Although they differ in the underlying technology involved, their overall processes are very similar: DNA fragmentation, adaptor ligation, immobilisation, amplification, sequencing reaction and data analysis. MPS is being used in research, translational and increasingly now also in clinical settings. Common applications include sequencing of whole genomes, whole exomes or targeted genes for disease-causing gene discovery, genetic diagnosis and targeted cancer therapy. Even though the revolution that is occurring with MPS is exciting due to its increasing use, improving and emerging technologies and new applications, significant challenges still exist. Particularly challenging issues are the bioinformatics required for data analysis, interpretation of results and the ethical dilemma of ‘incidental findings’.     

Using mouse forward genetics to define novel target space

Rapid advances in genomics technologies have identified a wealth of new therapeutic targets, but typically these targets are weakly validated with only circumstantial evidence to link them to human disease. The next challenge is testing gene-to-disease connections in a relevant animal model, a time-consuming and uncertain process using conventional reverse-genetic approaches such as knockout and transgenic mice. By contrast, forward genetics proceeds by measuring a physiological process that is relevant to disease, then identifying the gene products that impinge on this process. This ‘phenotype-first’ approach solves the bottleneck of target validation by using clinically relevant assays in a mammalian whole-animal system as a discovery platform. As an unbiased approach to gene discovery and validation, forward genetics will identify novel drug targets and increase the success rate of drug development.