RNA SEQ DATA ANALYSIS

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Rna Seq Data Analysis

Author : Eija Korpelainen
ISBN : 9781466595019
Genre : Mathematics
File Size : 60.72 MB
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The State of the Art in Transcriptome AnalysisRNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript le
Category: Mathematics

Multi Perspective Quality Control Of Illumina Rna Sequencing Data Analysis

Author :
ISBN : OCLC:1051963835
Genre :
File Size : 82.92 MB
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Abstract Quality control (QC) is a critical step in RNA sequencing (RNA-seq). Yet, it is often ignored or conducted on a limited basis. Here, we present a multi-perspective strategy for QC of RNA-seq experiments. The QC of RNA-seq can be divided into four related stages: (1) RNA quality, (2) raw read data (FASTQ), (3) alignment and (4) gene expression. We illustrate the importance of conducting QC at each stage of an RNA-seq experiment and demonstrate our recommended RNA-seq QC strategy. Furthermore, we discuss the major and often neglected quality issues associated with the three major types of RNA-seq: mRNA, total RNA and small RNA. This RNA-seq QC overview provides comprehensive guidance for researchers who conduct RNA-seq experiments.
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Statistical Methods For The Analysis Of Rna Sequencing Data

Author : Man-Kee Maggie Chu
ISBN : OCLC:1067211046
Genre :
File Size : 66.72 MB
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The next generation sequencing technology, RNA-sequencing (RNA-seq), has an increasing popularity over traditional microarrays in transcriptome analyses. Statistical methods used for gene expression analyses with these two technologies are di erent because the array-based technology measures intensities using continuous distributions, whereas RNA-seq provides absolute quantification of gene expression using counts of reads. There is a need for reliable statistical methods to exploit the information from the rapidly evolving sequencing technologies and limited work has been done on expression analysis of time-course RNA-seq data. Functional clustering is an important method for examining gene expression patterns and thus discovering co-expressed genes to better understand the biological systems. Clusteringbased approaches to analyze repeated digital gene expression measures are in demand. In this dissertation, we propose a model-based clustering method for identifying gene expression patterns in time-course RNA-seq data. Our approach employs a longitudinal negative binomial mixture model to postulate the over-dispersed time-course gene count data. The e ectiveness of the proposed clustering method is assessed using simulated data and is illustrated by real data from time-course genomic experiments. Due to the complexity and size of genomic data, the choice of good starting values is an important issue to the proposed clustering algorithm. There is a need for a reliable initialization strategy for cluster-wise regression specifically for time-course discrete count data. We modify existing common initialization procedures to suit our model-based clustering algorithm and the procedures are evaluated through a simulation study on artificial datasets and are applied to real genomic examples to identify the optimal initialization method. Another common issue in gene expression analysis is the presence of missing values in the datasets. Various treatments to missing values in genomic datasets have been developed but limited work has been done on RNA-seq data. In the current work, we examine the performance of various imputation methods and their impact on the clustering of time-course RNA-seq data. We develop a cluster-based imputation method which is specifically suitable for dealing with missing values in RNA-seq datasets. Simulation studies are provided to assess the performance of the proposed imputation approach.
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Computational Methods For Next Generation Sequencing Data Analysis

Author : Ion Mandoiu
ISBN : 9781118169483
Genre : Computers
File Size : 35.53 MB
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Aiming to foster future collaborations between researchers in algorithms, bioinformatics, and molecular biology, this book serves as an up-to-date survey of the most important recent developments and computational challenges in various application areas of next-generation sequencing technologies. Offering helpful insight from renowned experts, the book covers topics such as NGS error correction, road mapping, variant detection and genotyping, characterization of structural variants with NGS, genome-assisted transcriptome reconstruction, small RNA analysis, and much more.
Category: Computers

Next Generation Sequencing Data Analysis

Author : Xinkun Wang
ISBN : 9781482217896
Genre : Mathematics
File Size : 36.48 MB
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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
Category: Mathematics

Statistical Analysis Of Next Generation Sequencing Data

Author : Somnath Datta
ISBN : 9783319072128
Genre : Medical
File Size : 78.91 MB
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Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.
Category: Medical

Applications Of Rna Seq And Omics Strategies

Author : Fabio Marchi
ISBN : 9789535135036
Genre : Medical
File Size : 37.36 MB
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The large potential of RNA sequencing and other "omics" techniques has contributed to the production of a huge amount of data pursuing to answer many different questions that surround the science's great unknowns. This book presents an overview about powerful and cost-efficient methods for a comprehensive analysis of RNA-Seq data, introducing and revising advanced concepts in data analysis using the most current algorithms. A holistic view about the entire context where transcriptome is inserted is also discussed here encompassing biological areas with remarkable technological advances in the study of systems biology, from microorganisms to precision medicine.
Category: Medical

Genome Analysis

Author : Maria S. Poptsova
ISBN : 9781908230683
Genre : Science
File Size : 85.25 MB
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In recent years there have been tremendous achievements made in DNA sequencing technologies and corresponding innovations in data analysis and bioinformatics that have revolutionized the field of genome analysis. In this book, an impressive array of expert authors highlight and review current advances in genome analysis. This volume provides an invaluable, up-to-date and comprehensive overview of the methods currently employed for next-generation sequencing (NGS) data analysis, highlights their problems and limitations, demonstrates the applications and indicates the developing trends in various fields of genome research. The first part of the book is devoted to the methods and applications that arose from, or were significantly advanced by, NGS technologies: the identification of structural variation from DNA-seq data; whole-transcriptome analysis and discovery of small interfering RNAs (siRNAs) from RNA-seq data; motif finding in promoter regions, enhancer prediction and nucleosome sequence code discovery from ChiP-Seq data; identification of methylation patterns in cancer from MeDIP-seq data; transposon identification in NGS data; metagenomics and metatranscriptomics; NGS of viral communities; and causes and consequences of genome instabilities. The second part is devoted to the field of RNA biology with the last three chapters devoted to computational methods of RNA structure prediction including context-free grammar applications. An essential book for everyone involved in sequence data analysis, next-generation sequencing, high-throughput sequencing, RNA structure prediction, bioinformatics and genome analysis.
Category: Science