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  1. The transcriptional diversity of 25 <i>Drosophila</i> cell lines [electronic resource]

    Washington, D.C. : United States. Dept. of Energy. ; Oak Ridge, Tenn. : distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2010

    Drosophila melanogaster cell lines are important resources for cell biologists. In this article, we catalog the expression of exons, genes, and unannotated transcriptional signals for 25 lines. Unannotated transcription is substantial (typically 19% of euchromatic signal). Conservatively, we identify 1405 novel transcribed regions; 684 of these appear to be new exons of neighboring, often distant, genes. Sixty-four percent of genes are expressed detectably in at least one line, but only 21% are detected in all lines. Each cell line expresses, on average, 5885 genes, including a common set of 3109. Expression levels vary over several orders of magnitude. Major signaling pathways are well represented: most differentiation pathways are ‘‘off’’ and survival/growth pathways ‘‘on.’’ Roughly 50% of the genes expressed by each line are not part of the common set, and these show considerable individuality. Thirty-one percent are expressed at a higher level in at least one cell line than in any single developmental stage, suggesting that each line is enriched for genes characteristic of small sets of cells. Most remarkable is that imaginal disc-derived lines can generally be assigned, on the basis of expression, to small territories within developing discs. These mappings reveal unexpected stability of even fine-grained spatial determination. No two cell lines show identical transcription factor expression. We conclude that each line has retained features of an individual founder cell superimposed on a common ‘‘cell line‘‘ gene expression pattern. We report the transcriptional profiles of 25 Drosophila melanogaster cell lines, principally by whole-genome tiling microarray analysis of total RNA, carried out as part of the modENCODE project. The data produced in this study add to our knowledge of the cell lines and of the Drosophila transcriptome in several ways. We summarize the expression of previously annotated genes in each of the 25 lines with emphasis on what those patterns reveal about the origins of the lines and the stability of spatial expression patterns. In addition, we offer an initial analysis of previously unannotated transcripts in the cell lines.

    Online OSTI

  2. Computational methods for next generation sequencing data analysis

    Hoboken, New Jersey : John Wiley & Sons, 2016.

    Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: -Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms -Discusses the mathematical and computational challenges in NGS technologies -Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

    Online Wiley Online Library

  3. Data production and analysis in population genomics : methods and protocols

    New York : Humana Press, c2012.

    Population genomics is a recently emerged discipline, which aims at understanding how evolutionary processes influence genetic variation across genomes. Today, in the era of cheaper next-generation sequencing, it is no longer as daunting to obtain whole genome data for any species of interest and population genomics is now conceivable in a wide range of fields, from medicine and pharmacology to ecology and evolutionary biology. However, because of the lack of reference genome and of enough a priori data on the polymorphism, population genomics analyses of populations will still involve higher constraints for researchers working on non-model organisms, as regards the choice of the genotyping/sequencing technique or that of the analysis methods. Therefore, Data Production and Analysis in Population Genomics purposely puts emphasis on protocols and methods that are applicable to species where genomic resources are still scarce. It is divided into three convenient sections, each one tackling one of the main challenges facing scientists setting up a population genomics study. The first section helps devising a sampling and/or experimental design suitable to address the biological question of interest. The second section addresses how to implement the best genotyping or sequencing method to obtain the required data given the time and cost constraints as well as the other genetic resources already available, Finally, the last section is about making the most of the (generally huge) dataset produced by using appropriate analysis methods in order to reach a biologically relevant conclusion. Written in the successful Methods in Molecular Biology(TM) series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, advice on methodology and implementation, and notes on troubleshooting and avoiding known pitfalls. Authoritative and easily accessible, Data Production and Analysis in Population Genomics serves a wide readership by providing guidelines to help choose and implement the best experimental or analytical strategy for a given purpose.

    Online SpringerLink

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