RNA SEQ DATA ANALYSIS

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Rna Seq Data Analysis

Author : Eija Korpelainen
ISBN : 9781466595019
Genre : Mathematics
File Size : 36.7 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

Tribocorrosion For Materials Engineers

Author : Margaret Stack
ISBN : 9781466595002
Genre : Science
File Size : 57.38 MB
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Tribocorrosion occurs in all walks of life from energy conversion to hip joints and dental implants. However, the materials required, and control of processes to limit the tribo-corrosion degradation are often not well understood. Tribocorrosion for Materials Engineers describes the various tribocorrosion processes, including wear phenomena, the Pourbaix diagram for aqueous corrosion, and the thermodynamic stability diagram for high temperature oxidation. It highlights the work done during in the past 20 years on tribocorrosion maps where both tribology and corrosion mapping approaches are combined. The author addresses erosion, abrasion (and microscale-abrasion), fretting and sliding wear interactions with corrosion. He also traces theoretical development and validation from practical experimental data with respect to the literature. The then discusses recent mapping approaches using CFD techniques. An examination of the latest thinking on the subject, the book details tribocorrosion issues in dentistry, bio-implants such as hip joint replacements and renewable energies such as tidal and offshore wind as well as space will be addressed in addition to more conventional oil/gas environments. It also discusses best practice in testing tribocorrosion over a multi-parameter space, enabling the production of such maps, with respect to current international standards in the subject.
Category: Science

Differential Expression Analysis Of Rna Sequencing Data

Author : Yilun Zhang
ISBN : 1392212340
Genre :
File Size : 44.43 MB
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Tools for analysis of gene expression are central to research in molecular biology. Probably the most important current tool for this analysis is RNA-Seq, which uses next-generation sequencing technology. Arguably, differential expression analysis is the most critical part of analyzing RNA-Seq data. In RNA-Seq, the data from each sample consist of counts of the number of fragments mapped to each gene or exon in the target genome. With the fact that RNA-seq data are usually overdispersed, the negative binomial model stands out with a modeled variance function being a quadratic function of the mean. In the first part of my thesis, I will review some popular methods based on negative binomial distribution, and demonstrate the presence and possible reasons for the inflation of the false positive rate of these methods, including edgeR and DESeq2. In the second part, a novel method, intSEQ, is proposed that is less vulnerable to the false positive problem. The method integrates the joint likelihood function of the negative binomial model and a normal prior for the dispersion parameter on the support of the dispersion parameter. The simulation results show the proposed method has higher power with a controlled false positive rate. In the last part of this thesis, I recommend a pipeline for differential expression analysis of RNA-Seq data, which is encapsulated in an Rpackage, 'intSEQ'.
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Multi Perspective Quality Control Of Illumina Rna Sequencing Data Analysis

Author :
ISBN : OCLC:1051963835
Genre :
File Size : 34.36 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 : 56.1 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 : 80.37 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

Applications Of Rna Seq And Omics Strategies

Author : Fabio Marchi
ISBN : 9789535135036
Genre : Medical
File Size : 43.9 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