[PDF] High Performance In Memory Genome Data Analysis eBook
High Performance In Memory Genome Data Analysis Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of High Performance In Memory Genome Data Analysis book. This book definitely worth reading, it is an incredibly well-written.
Recent achievements in hardware and software developments have enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of data, such as diagnoses, therapies, and human genome data. This book shares the latest research results of applying in-memory data management to personalized medicine, changing it from computational possibility to clinical reality. The authors provide details on innovative approaches to enabling the processing, combination, and analysis of relevant data in real-time. The book bridges the gap between medical experts, such as physicians, clinicians, and biological researchers, and technology experts, such as software developers, database specialists, and statisticians. Topics covered in this book include - amongst others - modeling of genome data processing and analysis pipelines, high-throughput data processing, exchange of sensitive data and protection of intellectual property. Beyond that, it shares insights on research prototypes for the analysis of patient cohorts, topology analysis of biological pathways, and combined search in structured and unstructured medical data, and outlines completely new processes that have now become possible due to interactive data analyses.
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.
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
A Practical Guide to the Highly Dynamic Area of Massively Parallel SequencingThe development of genome and transcriptome sequencing technologies has led to a paradigm shift in life science research and disease diagnosis and prevention. Scientists are now able to see how human diseases and phenotypic changes are connected to DNA mutation, polymorphi
This book constitutes the refereed proceedings of the 31st International Conference, ISC High Performance 2016 [formerly known as the International Supercomputing Conference] held in Frankfurt, Germany, in June 2016. The 25 revised full papers presented in this book were carefully reviewed and selected from 60 submissions. The papers cover the following topics: Autotuning and Thread Mapping; Data Locality and Decomposition; Scalable Applications; Machine Learning; Datacenters andCloud; Communication Runtime; Intel Xeon Phi; Manycore Architectures; Extreme-scale Computations; and Resilience.
High Performance Computational Methods for Biological Sequence Analysis presents biological sequence analysis using an interdisciplinary approach that integrates biological, mathematical and computational concepts. These concepts are presented so that computer scientists and biomedical scientists can obtain the necessary background for developing better algorithms and applying parallel computational methods. This book will enable both groups to develop the depth of knowledge needed to work in this interdisciplinary field. This work focuses on high performance computational approaches that are used to perform computationally intensive biological sequence analysis tasks: pairwise sequence comparison, multiple sequence alignment, and sequence similarity searching in large databases. These computational methods are becoming increasingly important to the molecular biology community allowing researchers to explore the increasingly large amounts of sequence data generated by the Human Genome Project and other related biological projects. The approaches presented by the authors are state-of-the-art and show how to reduce analysis times significantly, sometimes from days to minutes. High Performance Computational Methods for Biological Sequence Analysis is tremendously important to biomedical science students and researchers who are interested in applying sequence analyses to their studies, and to computational science students and researchers who are interested in applying new computational approaches to biological sequence analyses.
Genome sequence comparisons of exponentially growing data sets form the foundation for the comparative analysis tools provided by community biological data resources such as the Integrated Microbial Genome (IMG) system at the Joint Genome Institute (JGI). We present an example of how ScalaBLAST, a high-throughput sequence analysis program harnesses increasingly critical high-performance computing to perform sequence analysis which is a critical component of maintaining a state-of-the-art sequence data repository. The Integrated Microbial Genomes (IMG) system1 is a data management and analysis platform for microbial genomes hosted at the JGI. IMG contains both draft and complete JGI genomes integrated with other publicly available microbial genomes of all three domains of life. IMG provides tools and viewers for interactive analysis of genomes, genes and functions, individually or in a comparative context. Most of these tools are based on pre-computed pairwise sequence similarities involving millions of genes. These computations are becoming prohibitively time consuming with the rapid increase in the number of newly sequenced genomes incorporated into IMG and the need to refresh regularly the content of IMG in order to reflect changes in the annotations of existing genomes. Thus, building IMG 2.0 (released on December 1st 2006) entailed reloading from NCBI's RefSeq all the genomes in the previous version of IMG (IMG 1.6, as of September 1st, 2006) together with 1,541 new public microbial, viral and eukaryal genomes, bringing the total of IMG genomes to 2,301. A critical part of building IMG 2.0 involved using PNNL ScalaBLAST software for computing pairwise similarities for over 2.2 million genes in under 26 hours on 1,000 processors, thus illustrating the impact that new generation bioinformatics tools are poised to make in biology. The BLAST algorithm2, 3 is a familiar bioinformatics application for computing sequence similarity, and has become a workhorse in large-scale genomics projects. The rapid growth of genome resources such as IMG cannot be sustained without more powerful tools such as ScalaBLAST that use more effectively large scale computing resources to perform the core BLAST calculations. ScalaBLAST is a high performance computing algorithm designed to give high throughput BLAST results on high-end supercomputers. Other parallel sequence comparison applications have been developed4-6. However problems with scaling generally prevent these applications from being used for very large searches. ScalaBLAST7 is the first BLAST application to be both highly scaleable against the size of the database as well as the number of computer processors on high-end hardware and on commodity clusters. ScalaBLAST achieves high throughput by parsing a large collection of query sequences into independent subgroups. These smaller tasks are assigned to independent process groups. Efficient scaling is achieved by (transparently to the user) sharing only one copy of the target database across all processors using the Global Array toolkit 8, 9, which provides software implementation of shared memory interface. ScalaBLAST was initially deployed on the 1,960 processor MPP2 cluster in the Wiliam R. Wiley Environmental Molecular Sciences Laboratory at Pacific Northwest National Laboratory, and has since been ported to a variety of linux-based clusters and shared memory architectures, including SGI Altix, AMD opteron, and Intel Xeon-based clusters. Future targets include IBM BlueGene, Cray, and SGI Altix XE architectures. The importance of performing high-throughput calculations rapidly lies in the rate of growth of sequence data. For a genome sequencing center to provide multiple-genome comparison capabilities, it must keep pace with exponentially growing collection of protein data, both from its own genomes, and from the public genome information as well. As sequence data continues to grow exponentially, this challenge will only increase with time. Solving the BLAST throughput challenge for centralized data resources like IMG has the potential to unlock the power of emerging analysis methods which, until recently, were limited by the availability of multiple genome comparison data. Fig. 1 illustrates how the run-time achieved by efficient scaling in ScalaBLAST enabled the IMG all vs. all BLAST calculations to complete in roughly 1 day. Note that to keep pace with growing IMG database, we will have to double the number of processors used in these calculations during the upcoming year. Grid-based solutions for improving throughput for BLAST searches has become a popular and attractive option for some centers. The Institute for Genome Research (http://www.tigr.org/), for instance, has implemented a grid-based BLAST tool allowing users to submit requests to be farmed out to available computers on an on-demand basis.
Contemporary High Performance Computing: From Petascale toward Exascale focuses on the ecosystems surrounding the world’s leading centers for high performance computing (HPC). It covers many of the important factors involved in each ecosystem: computer architectures, software, applications, facilities, and sponsors. The first part of the book examines significant trends in HPC systems, including computer architectures, applications, performance, and software. It discusses the growth from terascale to petascale computing and the influence of the TOP500 and Green500 lists. The second part of the book provides a comprehensive overview of 18 HPC ecosystems from around the world. Each chapter in this section describes programmatic motivation for HPC and their important applications; a flagship HPC system overview covering computer architecture, system software, programming systems, storage, visualization, and analytics support; and an overview of their data center/facility. The last part of the book addresses the role of clouds and grids in HPC, including chapters on the Magellan, FutureGrid, and LLGrid projects. With contributions from top researchers directly involved in designing, deploying, and using these supercomputing systems, this book captures a global picture of the state of the art in HPC.
This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.