Detection of genomic variation by selection of a 9 mb DNA region and high throughput sequencing

DNA Methylation Analysis: Choosing the Right Method

In the burgeoning field of epigenetics, there are several methods available to determine the methylation status of DNA samples. However, choosing the method that is best suited to answering a particular biological question still proves to be a difficult task. This review aims to provide biologists, particularly those new to the field of epigenetics, with a simple algorithm to help guide them in the selection of the most appropriate assay to meet their research needs. First of all, we have separated all methods into two categories: those that are used for: (1) the discovery of unknown epigenetic changes; and (2) the assessment of DNA methylation within particular regulatory regions/genes of interest. The techniques are then scrutinized and ranked according to their robustness, high throughput capabilities and cost. This review includes the majority of methods available to date, but with a particular focus on commercially available kits or other simple and straightforward solutions that have proven to be useful.

Comprehensive mutation analysis for congenital muscular dystrophy: a clinical PCR-based enrichment and next-generation sequencing panel

The congenital muscular dystrophies (CMDs) comprise a heterogeneous group of heritable muscle disorders with often difficult to interpret muscle pathology, making them challenging to diagnose. Serial Sanger sequencing of suspected CMD genes, while the current molecular diagnostic method of choice, can be slow and expensive. A comprehensive panel test for simultaneous screening of mutations in all known CMD-associated genes would be a more effective diagnostic strategy. Thus, the CMDs are a model disorder group for development and validation of next-generation sequencing (NGS) strategies for diagnostic and clinical care applications. Using a highly multiplexed PCR-based target enrichment method (RainDance) in conjunction with NGS, we performed mutation detection in all CMD genes of 26 samples and compared the results with Sanger sequencing. The RainDance NGS panel showed great consistency in coverage depth, on-target efficiency, versatility of mutation detection, and genotype concordance with Sanger sequencing, demonstrating the test's appropriateness for clinical use. Compared to single tests, a higher diagnostic yield was observed by panel implementation. The panel's limitation is the amplification failure of select gene-specific exons which require Sanger sequencing for test completion. Successful validation and application of the CMD NGS panel to improve the diagnostic yield in a clinical laboratory was shown.

Characterization of H3K9me3- and H4K20me3-associated circulating nucleosomal DNA by high-throughput sequencing in colorectal cancer

Modified histone tails in nucleosomes circulating in the blood bear the potential as cancer biomarkers. Recently, using chromatin immunopecipitation (ChIP)-related quantitative PCR, we described reduced plasma levels of the two pericentric heterochromatin-specific histone methylation marks H3K9me3 and H4K20me3 in patients with colorectal cancer (CRC). Here, by utilizing ChIP-related high-throughput sequencing, we further characterized these modifications in circulation. Plasma DNA from nucleosomes immunoprecipitated by H3K9me3- and H4K20me3-specific antibodies from patients with CRC (N = 15) and healthy subjects (N = 15) was subjected to the Roche 454 FLX sequencing, and the generated array of ChIP-enriched sequences were compared to the human reference genome. The total number of nucleosomes, of sequence reads and of diverse DNA repetitive elements were statistically compared between the study groups. Total nucleosome amount was not different in both groups. Concerning both histone modifications, lower numbers of sequence reads were detected in CRC patients as compared with healthy controls (medians in H3K9me3: 32 vs. 61; p < 0.01; in H4K20me3: 54 vs. 88; p < 0.01). Size of fragments was not different in both groups. Most abundant sequences were repetitive LINE and SINE elements while simple repeats, LTR, DNA, SAT, and low complexity elements were less frequent. Best discrimination between both groups was achieved by total number of H3K9me3 reads (AUC 0.90) and H3K9me3 LINE elements L1 (AUC 0.93) und L2 (AUC 0.91). The present results confirm earlier findings of lower H3K9me3 levels in CRC and show LINE elements to be the most frequent and best discriminative markers on modified histones.

Assessment of target enrichment platforms using massively parallel sequencing for the mutation detection for congenital muscular dystrophy

Sequencing individual genes by Sanger sequencing is a time-consuming and costly approach to resolve clinically heterogeneous genetic disorders. Panel testing offers the ability to efficiently and cost-effectively screen all of the genes for a particular genetic disorder. We assessed the analytical sensitivity and specificity of two different enrichment technologies, solution-based hybridization and microdroplet-based PCR target enrichment, in conjunction with next-generation sequencing (NGS), to identify mutations in 321 exons representing 12 different genes involved with congenital muscular dystrophies. Congenital muscular dystrophies present diagnostic challenges due to phenotypic variability, lack of standard access to and inherent difficulties with muscle immunohistochemical stains, and a general lack of clinician awareness. NGS results were analyzed across several parameters, including sequencing metrics and genotype concordance with Sanger sequencing. Genotyping data showed that both enrichment technologies produced suitable calls for use in clinical laboratories. However, microdroplet-based PCR target enrichment is more appropriate for a clinical laboratory, due to excellent sequence specificity and uniformity, reproducibility, high coverage of the target exons, and the ability to distinguish the active gene versus known pseudogenes. Regardless of the method, exons with highly repetitive and high GC regions are not well enriched and require Sanger sequencing for completeness. Our study demonstrates the successful application of targeted sequencing in conjunction with NGS to screen for mutations in hundreds of exons in a genetically heterogeneous human disorder.

Genetic mapping of quantitative phenotypic traits in Saccharomyces cerevisiae

Saccharomyces cerevisiae has become a favorite production organism in industrial biotechnology presenting new challenges to yeast engineers in terms of introducing advantageous traits such as stress tolerances. Exploring subspecies diversity of S. cerevisiae has identified strains that bear industrially relevant phenotypic traits. Provided that the genetic basis of such phenotypic traits can be identified inverse engineering allows the targeted modification of production strains. Most phenotypic traits of interest in S. cerevisiae strains are quantitative, meaning that they are controlled by multiple genetic loci referred to as quantitative trait loci (QTL). A straightforward approach to identify the genetic basis of quantitative traits is QTL mapping which aims at the allocation of the genetic determinants to regions in the genome. The application of high-density oligonucleotide arrays and whole-genome re-sequencing to detect genetic variations between strains has facilitated the detection of large numbers of molecular markers thus allowing high-resolution QTL mapping over the entire genome. This review focuses on the basic principle and state of the art of QTL mapping in S. cerevisiae. Furthermore we discuss several approaches developed during the last decade that allow down-scaling of the regions identified by QTL mapping to the gene level. We also emphasize the particular challenges of QTL mapping in nonlaboratory strains of S. cerevisiae.

Multiplex Chromosomal Exome Sequencing Accelerates Identification of ENU-Induced Mutations in the Mouse

Forward genetic screens in Mus musculus have proved powerfully informative by revealing unsuspected mechanisms governing basic biological processes. This approach uses potent chemical mutagens, such as N-ethyl-N-nitrosourea (ENU), to randomly induce mutations in mice, which are then bred and phenotypically screened to identify lines that disrupt a specific biological process of interest. Although identifying a mutation using the rich resources of mouse genetics is straightforward, it is unfortunately neither fast nor cheap. Here we show that detecting newly induced causal variants in a forward genetic screen can be accelerated dramatically using a methodology that combines multiplex chromosome-specific exome capture, next-generation sequencing, rapid mapping, sequence annotation, and variation filtering. The key innovation of our method is multiplex capture and sequence that allows the simultaneous survey of both mutant, parental, and background strains in a single experiment. By comparing variants identified in mutant offspring with those found in dbSNP, the unmutagenized background strains, and parental lines, induced causative mutations can be distinguished immediately from preexisting variation or experimental artifact. Here we demonstrate this approach to find the causative mutations induced in four novel ENU lines identified from a recent ENU screen. In all four cases, after applying our method, we found six or fewer putative mutations (and sometimes only a single one). Determining the causative variant was then easily achieved through standard segregation approaches. We have developed this process into a community resource that will speed up individual labs’ ability to identify the genetic lesion in mutant mouse lines; all of our reagents and software tools are open source and available to the broader scientific community.

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications

BACKGROUND

The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering.

RESULTS

In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function.

CONCLUSIONS

With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk.

Phenotype ontologies for mouse and man: bridging the semantic gap

A major challenge of the post-genomic era is coding phenotype data from humans and model organisms such as the mouse, to permit the meaningful translation of phenotype descriptions between species. This ability is essential if we are to facilitate phenotype-driven gene function discovery and empower comparative pathobiology. Here, we review the current state of the art for phenotype and disease description in mice and humans, and discuss ways in which the semantic gap between coding systems might be bridged to facilitate the discovery and exploitation of new mouse models of human diseases.