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Genome Walking by Next Generation Sequencing Approaches  [PDF]
Mariateresa Volpicella,Claudia Leoni,Alessandra Costanza,Immacolata Fanizza,Antonio Placido,Luigi R. Ceci
Biology , 2012, DOI: 10.3390/biology1030495
Abstract: Genome Walking (GW) comprises a number of PCR-based methods for the identification of nucleotide sequences flanking known regions. The different methods have been used for several purposes: from de novo sequencing, useful for the identification of unknown regions, to the characterization of insertion sites for viruses and transposons. In the latter cases Genome Walking methods have been recently boosted by coupling to Next Generation Sequencing technologies. This review will focus on the development of several protocols for the application of Next Generation Sequencing (NGS) technologies to GW, which have been developed in the course of analysis of insertional libraries. These analyses find broad application in protocols for functional genomics and gene therapy. Thanks to the application of NGS technologies, the original vision of GW as a procedure for walking along an unknown genome is now changing into the possibility of observing the parallel marching of hundreds of thousands of primers across the borders of inserted DNA molecules in host genomes.
Evaluation of Toxicogenomics Approaches for Assessing the Risk of Nongenotoxic Carcinogenicity in Rat Liver  [PDF]
Johannes Eichner, Clemens Wrzodek, Michael R?mer, Heidrun Ellinger-Ziegelbauer, Andreas Zell
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0097678
Abstract: The current gold-standard method for cancer safety assessment of drugs is a rodent two-year bioassay, which is associated with significant costs and requires testing a high number of animals over lifetime. Due to the absence of a comprehensive set of short-term assays predicting carcinogenicity, new approaches are currently being evaluated. One promising approach is toxicogenomics, which by virtue of genome-wide molecular profiling after compound treatment can lead to an increased mechanistic understanding, and potentially allow for the prediction of a carcinogenic potential via mathematical modeling. The latter typically involves the extraction of informative genes from omics datasets, which can be used to construct generalizable models allowing for the early classification of compounds with unknown carcinogenic potential. Here we formally describe and compare two novel methodologies for the reproducible extraction of characteristic mRNA signatures, which were employed to capture specific gene expression changes observed for nongenotoxic carcinogens. While the first method integrates multiple gene rankings, generated by diverse algorithms applied to data from different subsamplings of the training compounds, the second approach employs a statistical ratio for the identification of informative genes. Both methods were evaluated on a dataset obtained from the toxicogenomics database TG-GATEs to predict the outcome of a two-year bioassay based on profiles from 14-day treatments. Additionally, we applied our methods to datasets from previous studies and showed that the derived prediction models are on average more accurate than those built from the original signatures. The selected genes were mostly related to p53 signaling and to specific changes in anabolic processes or energy metabolism, which are typically observed in tumor cells. Among the genes most frequently incorporated into prediction models were Phlda3, Cdkn1a, Akr7a3, Ccng1 and Abcb4.
Cross-Platform Toxicogenomics for the Prediction of Non-Genotoxic Hepatocarcinogenesis in Rat  [PDF]
Michael R?mer, Johannes Eichner, Ute Metzger, Markus F. Templin, Simon Plummer, Heidrun Ellinger-Ziegelbauer, Andreas Zell
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0097640
Abstract: In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.
Molecular Characterization of Transgene Integration by Next-Generation Sequencing in Transgenic Cattle  [PDF]
Ran Zhang, Yinliang Yin, Yujun Zhang, Kexin Li, Hongxia Zhu, Qin Gong, Jianwu Wang, Xiaoxiang Hu, Ning Li
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0050348
Abstract: As the number of transgenic livestock increases, reliable detection and molecular characterization of transgene integration sites and copy number are crucial not only for interpreting the relationship between the integration site and the specific phenotype but also for commercial and economic demands. However, the ability of conventional PCR techniques to detect incomplete and multiple integration events is limited, making it technically challenging to characterize transgenes. Next-generation sequencing has enabled cost-effective, routine and widespread high-throughput genomic analysis. Here, we demonstrate the use of next-generation sequencing to extensively characterize cattle harboring a 150-kb human lactoferrin transgene that was initially analyzed by chromosome walking without success. Using this approach, the sites upstream and downstream of the target gene integration site in the host genome were identified at the single nucleotide level. The sequencing result was verified by event-specific PCR for the integration sites and FISH for the chromosomal location. Sequencing depth analysis revealed that multiple copies of the incomplete target gene and the vector backbone were present in the host genome. Upon integration, complex recombination was also observed between the target gene and the vector backbone. These findings indicate that next-generation sequencing is a reliable and accurate approach for the molecular characterization of the transgene sequence, integration sites and copy number in transgenic species.
Arno Lukas,Bernd Mayer
The IIOAB Journal , 2010,
Abstract: Omics has massively permeated translational clinical research with numerous diseases being covered by Omics studies from the genome to the metabolome level. Integrating these disease specific Omics tracks appears a logical next step for building the fundament of Systems Biology and Systems Medicine. Here, coherence of individual Omics tracks regarding clinical hypothesis, samples and clinical descriptors, and finally data handling and integration become pivotal. We present a data integration, annotation and relations modeling concept for heterogeneous Omics data and workflows. With molecular features at the center of all Omics we link the result profiles from different Omics tracks characterizing a specific disease phenotype to a common human molecular reference network for allowing a seamless integration and subsequent support in interpretation of Omics screening results. Our concept rests on data structures for representing objects specified by metadata and content. For handling diverse Omics tracks a flexible structure for content is proposed allowing data representation at different levels of granularity as demanded by the type of Omics and specific type of data. Content on the molecular level includes deep annotation of molecular features on gene and protein level. Based on this annotation pair-wise relations between molecular objects are built, traversing the molecular annotation into a network of relations (molecular feature graph). Such a relation network is also built on the Omics data level, combining explicit relations derived from study setup and implicit relations generated by mining metadata and content (Omics data graph). Finally both graphs are merged utilizing the molecular feature level as common denominator, enabling a persistent integration and subsequently interpretation of Omics profiling results in the realm of a given clinical hypothesis. We present a case study on integrating transcriptomics and proteomics data on chronic kidney disease for demonstrating the feasibility of this concept.
Bioinformatics for Next Generation Sequencing Data  [PDF]
Alberto Magi,Matteo Benelli,Alessia Gozzini,Francesca Girolami,Francesca Torricelli,Maria Luisa Brandi
Genes , 2010, DOI: 10.3390/genes1020294
Abstract: The emergence of next-generation sequencing (NGS) platforms imposes increasing demands on statistical methods and bioinformatic tools for the analysis and the management of the huge amounts of data generated by these technologies. Even at the early stages of their commercial availability, a large number of softwares already exist for analyzing NGS data. These tools can be fit into many general categories including alignment of sequence reads to a reference, base-calling and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection and genome browsing. This manuscript aims to guide readers in the choice of the available computational tools that can be used to face the several steps of the data analysis workflow.
Next generation sequencing in cardiovascular diseases  [cached]
Francesca Faita,Cecilia Vecoli,Ilenia Foffa,Maria Grazia Andreassi
World Journal of Cardiology , 2012, DOI: 10.4330/wjc.v4.i10.288
Abstract: In the last few years, the advent of next generation sequencing (NGS) has revolutionized the approach to genetic studies, making whole-genome sequencing a possible way of obtaining global genomic information. NGS has very recently been shown to be successful in identifying novel causative mutations of rare or common Mendelian disorders. At the present time, it is expected that NGS will be increasingly important in the study of inherited and complex cardiovascular diseases (CVDs). However, the NGS approach to the genetics of CVDs represents a territory which has not been widely investigated. The identification of rare and frequent genetic variants can be very important in clinical practice to detect pathogenic mutations or to establish a profile of risk for the development of pathology. The purpose of this paper is to discuss the recent application of NGS in the study of several CVDs such as inherited cardiomyopathies, channelopathies, coronary artery disease and aortic aneurysm. We also discuss the future utility and challenges related to NGS in studying the genetic basis of CVDs in order to improve diagnosis, prevention, and treatment.
An Integrated SNP Mining and Utilization (ISMU) Pipeline for Next Generation Sequencing Data  [PDF]
Sarwar Azam, Abhishek Rathore, Trushar M. Shah, Mohan Telluri, BhanuPrakash Amindala, Pradeep Ruperao, Mohan A. V. S. K. Katta, Rajeev K. Varshney
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0101754
Abstract: Open source single nucleotide polymorphism (SNP) discovery pipelines for next generation sequencing data commonly requires working knowledge of command line interface, massive computational resources and expertise which is a daunting task for biologists. Further, the SNP information generated may not be readily used for downstream processes such as genotyping. Hence, a comprehensive pipeline has been developed by integrating several open source next generation sequencing (NGS) tools along with a graphical user interface called Integrated SNP Mining and Utilization (ISMU) for SNP discovery and their utilization by developing genotyping assays. The pipeline features functionalities such as pre-processing of raw data, integration of open source alignment tools (Bowtie2, BWA, Maq, NovoAlign and SOAP2), SNP prediction (SAMtools/SOAPsnp/CNS2snp and CbCC) methods and interfaces for developing genotyping assays. The pipeline outputs a list of high quality SNPs between all pairwise combinations of genotypes analyzed, in addition to the reference genome/sequence. Visualization tools (Tablet and Flapjack) integrated into the pipeline enable inspection of the alignment and errors, if any. The pipeline also provides a confidence score or polymorphism information content value with flanking sequences for identified SNPs in standard format required for developing marker genotyping (KASP and Golden Gate) assays. The pipeline enables users to process a range of NGS datasets such as whole genome re-sequencing, restriction site associated DNA sequencing and transcriptome sequencing data at a fast speed. The pipeline is very useful for plant genetics and breeding community with no computational expertise in order to discover SNPs and utilize in genomics, genetics and breeding studies. The pipeline has been parallelized to process huge datasets of next generation sequencing. It has been developed in Java language and is available at http://hpc.icrisat.cgiar.org/ISMU as a standalone free software.
Next Generation Sequencing of miRNAs – Strategies, Resources and Methods  [PDF]
Susanne Motameny,Stefanie Wolters,Peter Nürnberg,Bj?rn Schumacher
Genes , 2010, DOI: 10.3390/genes1010070
Abstract: miRNAs constitute a family of small RNA species that have been demonstrated to play a central role in regulating gene expression in many organisms. With the advent of next generation sequencing, new opportunities have arisen to identify and quantify miRNAs and elucidate their function. The unprecedented sequencing depth reached by next generation sequencing technologies makes it possible to get a comprehensive miRNA landscape but also poses new challenges for data analysis. We provide an overview of strategies used for miRNA sequencing, public miRNA resources, and useful methods and tools that are available for data analysis.
Comparative study of microarray and next generation sequencing technologies  [PDF]
Dr. Charles Edeki
International Journal of Computer Science and Mobile Computing , 2012,
Abstract: Huge efforts are being made to develop algorithms and procedures for DNA sequences. The purpose of this study is to expand understanding of how biologists, computational biologists, bioinformaticians, medical practitioners and scientists would benefit from next-generation sequencing and microarray technology in analyzing DNA and protein dataset. Microarrays techniques usage in analyzingbiological dataset (gene expression) has grown exponentially for the past two decades. Recently, next generation sequencing technologies are revolutionizing the DNA/RNA sequencing tasks. These highlyefficient parallel sequencing methods make it possible to generate billions of bases of sequence per day in a biological laboratory. These methods allow individual human genomes to be sequenced in an instant or one to two days. In this paper, a comparative study of next generation sequencing technology and microarray technology would be presented and the performance of the two techniques would be discussed.
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