1,539 research outputs found

    Weighted pooling—practical and cost-effective techniques for pooled high-throughput sequencing

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    Motivation: Despite the rapid decline in sequencing costs, sequencing large cohorts of individuals is still prohibitively expensive. Recently, several sophisticated pooling designs were suggested that can identify carriers of rare alleles in large cohorts with a significantly smaller number of pools, thus dramatically reducing the cost of such large-scale sequencing projects. These approaches use combinatorial pooling designs where each individual is either present or absent from a pool. One can then infer the number of carriers in a pool, and by combining information across pools, reconstruct the identity of the carriers

    Implementation of two high through-put techniques in a novel application: detecting point mutations in large EMS mutated plant populations

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    Background - The establishment of mutant populations together with the strategies for targeted mutation detection has been applied successfully to a large number of organisms including many species in the plant kingdom. Considerable efforts have been invested into research on tomato as a model for berry-fruit plants. With the progress of the tomato sequencing project, reverse genetics becomes an obvious and achievable goal. Results - Here we describe the treatment of Solanum lycopersicum seeds with 1% EMS and the development of a new mutated tomato population. To increase targeted mutant detection throughput an automated seed DNA extraction has been combined with novel mutation detection platforms for TILLING in plants. We have adapted two techniques used in human genetic diagnostics: Conformation Sensitive Capillary Electrophoresis (CSCE) and High Resolution DNA Melting Analysis (HRM) to mutation screening in DNA pools. Classical TILLING involves critical and time consuming steps such as endonuclease digestion reactions and gel electrophoresis runs. Using CSCE or HRM, the only step required is a simple PCR before either capillary electrophoresis or DNA melting curve analysis. Here we describe the development of a mutant tomato population, the setting up of two polymorphism detection platforms for plants and the results of the first screens as mutation density in the populations and estimation of the false-positives rate when using HRM to screen DNA pools. Conclusion - These results demonstrate that CSCE and HRM are fast, affordable and sensitive techniques for mutation detection in DNA pools and therefore allow the rapid identification of new allelic variants in a mutant population. Results from the first screens indicate that the mutagen treatment has been effective with an average mutation detection rate per diploid genome of 1.36 mutation/kb/1000 line

    Effective detection of rare variants in pooled DNA samples using Cross-pool tailcurve analysis

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    Sequencing targeted DNA regions in large samples is necessary to discover the full spectrum of rare variants. We report an effective Illumina sequencing strategy utilizing pooled samples with novel quality (Srfim) and filtering (SERVIC4E) algorithms. We sequenced 24 exons in two cohorts of 480 samples each, identifying 47 coding variants, including 30 present once per cohort. Validation by Sanger sequencing revealed an excellent combination of sensitivity and specificity for variant detection in pooled samples of both cohorts as compared to publicly available algorithms

    Diagnostic applications of next generation sequencing: working towards quality standards

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    Over the past 6 years, next generation sequencing (NGS) has been established as a valuable high-throughput method for research in molecular genetics and has successfully been employed in the identification of rare and common genetic variations. All major NGS technology companies providing commercially available instruments (Roche 454, Illumina, Life Technologies) have recently marketed bench top sequencing instruments with lower throughput and shorter run times, thereby broadening the applications of NGS and opening the technology to the potential use for clinical diagnostics. Although the high expectations regarding the discovery of new diagnostic targets and an overall reduction of cost have been achieved, technological challenges in instrument handling, robustness of the chemistry and data analysis need to be overcome. To facilitate the implementation of NGS as a routine method in molecular diagnostics, consistent quality standards need to be developed. Here the authors give an overview of the current standards in protocols and workflows and discuss possible approaches to define quality criteria for NGS in molecular genetic diagnostics

    DNA Sequencing

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    This book illustrates methods of DNA sequencing and its application in plant, animal and medical sciences. It has two distinct sections. The one includes 2 chapters devoted to the DNA sequencing methods and the second includes 6 chapters focusing on various applications of this technology. The content of the articles presented in the book is guided by the knowledge and experience of the contributing authors. This book is intended to serve as an important resource and review to the researchers in the field of DNA sequencing

    TILLING & EcoTILLING in tomato : harvesting artificial and natural genetic diversity

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    Mutations in genes controlling specific traits of interest are widely used in plant breeding to introduce particular characteristics in commercial varieties. The establishment of a large mutant collections and the identification of mutations in candidate genes controling traits of interest represents an important resource for plant breeding. Chemical mutagenesis with EMS produces G/C to A/T transitions and is a well-characterized method to generate a saturated mutant population. The TILLING (Targeting Induced Local Lesions IN Genomes) process enables the identification of mutations in specific genes of interest and produces heteromorphic as well as knock-out alleles. In order to apply the TILLING strategy to tomato, we have created two new populations of 8025 M2 and 6600 M3 lines respectively derived from seed treatment with 1% EMS of Solanum esculentum (var. TPAADASU). To screen the collection for mutations in selected genes, two novel high-throughput SNP detection technologies were adapted. Firstly, Conformation Sensitive Capillary Electrophoresis (CSCE) and secondly, high resolution DNA-melting analysis (HRM). Our results indicate a mutation frequency of 1,1 mutation per 1kb per 1000 plants. Mutations in genes involved in carotenoid biosynthesis and tolerance to salinity stress, Psy1 and ProDh, were identified. Characterisation of the mutant lines, carrying knock-out or amino acid substitution alleles, using plant growth assays, metabolic profiling and gene expression studies allowed the demonstration of the TILLING strategy as an efficient method for plant breeding purposes and plant biology research. EcoTILLING was set up using the HRM based SNP screening method to screen the EU-SOL and CBSG tomato core collections for candidates genes involved in sugar metabolism. 13 haplotypes over 6 target genes were identified and related to the studied trait, total soluble sugars. We show that HRM is a fast and cost effective method to unravel natural genetic diversity in candidate genes. </p

    Identification of rare alleles and their carriers using compressed se(que)nsing

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    Identification of rare variants by resequencing is important both for detecting novel variations and for screening individuals for known disease alleles. New technologies enable low-cost resequencing of target regions, although it is still prohibitive to test more than a few individuals. We propose a novel pooling design that enables the recovery of novel or known rare alleles and their carriers in groups of individuals. The method is based on a Compressed Sensing (CS) approach, which is general, simple and efficient. CS allows the use of generic algorithmic tools for simultaneous identification of multiple variants and their carriers. We model the experimental procedure and show via computer simulations that it enables the recovery of rare alleles and their carriers in larger groups than were possible before. Our approach can also be combined with barcoding techniques to provide a feasible solution based on current resequencing costs. For example, when targeting a small enough genomic region (∌100 bp) and using only ∌10 sequencing lanes and ∌10 distinct barcodes per lane, one recovers the identity of 4 rare allele carriers out of a population of over 4000 individuals. We demonstrate the performance of our approach over several publicly available experimental data sets

    Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems

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    Summary 1. Significant advances in both mathematical and molecular approaches in ecology offer unprecedented opportunities to describe and understand ecosystem functioning. Ecological networks describe interactions between species, the underlying structure of communities and the function and stability of ecosystems. They provide the ability to assess the robustness of complex ecological communities to species loss, as well as a novel way of guiding restoration. However, empirically quantifying the interactions between entire communities remains a significant challenge. 2. Concomitantly, advances in DNA sequencing technologies are resolving previously intractable questions in functional and taxonomic biodiversity and provide enormous potential to determine hitherto difficult to observe species interactions. Combining DNA metabarcoding approaches with ecological network analysis presents important new opportunities for understanding large-scale ecological and evolutionary processes, as well as providing powerful tools for building ecosystems that are resilient to environmental change. 3. We propose a novel ‘nested tagging’ metabarcoding approach for the rapid construction of large, phylogenetically structured species-interaction networks. Taking tree–insect–parasitoid ecological networks as an illustration, we show how measures of network robustness, constructed using DNA metabarcoding, can be used to determine the consequences of tree species loss within forests, and forest habitat loss within wider landscapes. By determining which species and habitats are important to network integrity, we propose new directions for forest management. 4. Merging metabarcoding with ecological network analysis provides a revolutionary opportunity to construct some of the largest, phylogenetically structured species-interaction networks to date, providing new ways to: (i) monitor biodiversity and ecosystem functioning; (ii) assess the robustness of interacting communities to species loss; and (iii) build ecosystems that are more resilient to environmental change

    Computational Methods for Sequencing and Analysis of Heterogeneous RNA Populations

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    Next-generation sequencing (NGS) and mass spectrometry technologies bring unprecedented throughput, scalability and speed, facilitating the studies of biological systems. These technologies allow to sequence and analyze heterogeneous RNA populations rather than single sequences. In particular, they provide the opportunity to implement massive viral surveillance and transcriptome quantification. However, in order to fully exploit the capabilities of NGS technology we need to develop computational methods able to analyze billions of reads for assembly and characterization of sampled RNA populations. In this work we present novel computational methods for cost- and time-effective analysis of sequencing data from viral and RNA samples. In particular, we describe: i) computational methods for transcriptome reconstruction and quantification; ii) method for mass spectrometry data analysis; iii) combinatorial pooling method; iv) computational methods for analysis of intra-host viral populations

    Computational methods for the analysis of next generation sequencing data

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    Recently, next generation sequencing (NGS) technology has emerged as a powerful approach and dramatically transformed biomedical research in an unprecedented scale. NGS is expected to replace the traditional hybridization-based microarray technology because of its affordable cost and high digital resolution. Although NGS has significantly extended the ability to study the human genome and to better understand the biology of genomes, the new technology has required profound changes to the data analysis. There is a substantial need for computational methods that allow a convenient analysis of these overwhelmingly high-throughput data sets and address an increasing number of compelling biological questions which are now approachable by NGS technology. This dissertation focuses on the development of computational methods for NGS data analyses. First, two methods are developed and implemented for detecting variants in analysis of individual or pooled DNA sequencing data. SNVer formulates variant calling as a hypothesis testing problem and employs a binomial-binomial model to test the significance of observed allele frequency by taking account of sequencing error. SNVerGUI is a GUI-based desktop tool that is built upon the SNVer model to facilitate the main users of NGS data, such as biologists, geneticists and clinicians who often lack of the programming expertise. Second, collapsing singletons strategy is explored for associating rare variants in a DNA sequencing study. Specifically, a gene-based genome-wide scan based on singleton collapsing is performed to analyze a whole genome sequencing data set, suggesting that collapsing singletons may boost signals for association studies of rare variants in sequencing study. Third, two approaches are proposed to address the 3’UTR switching problem. PolyASeeker is a novel bioinformatics pipeline for identifying polyadenylation cleavage sites from RNA sequencing data, which helps to enhance the knowledge of alternative polyadenylation mechanisms and their roles in gene regulation. A change-point model based on a likelihood ratio test is also proposed to solve such problem in analysis of RNA sequencing data. To date, this is the first method for detecting 3’UTR switching without relying on any prior knowledge of polyadenylation cleavage sites
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