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RNA-seq Data Analysis

The State of the Art in Transcriptome Analysis RNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but;

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Bioinformatics Research and Applications

interaction; HPC and CryoEM; machine and deep learning; data analysis and methodology; analysis and visualization tools; and RNA-Seq data analysis.;

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Hidden Treasures in Contemporary RNA Sequencing

or a transcriptome of the sample is available, mapping-based RNA-seq analysis protocols align the RNA-seq reads to the reference sequences;

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Practical Guide to ChIP-seq Data Analysis

epigenetic marks, is among the most widely used methods in molecular biology. Practical Guide to ChIP-seq Data Analysis will guide readers through;

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Practical Guide to ChIP-seq Data Analysis

epigenetic marks, is among the most widely used methods in molecular biology. Practical Guide to ChIP-seq Data Analysis will guide readers through;

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Echinoderms Part B

presenting chapters on Echinoderm Genome Databases, analysis of gene regulatory networks, using ATAC-seq and RNA-seq to increase resolution in GRN;

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Deep Sequencing Data Analysis

deep sequencing data field. Chapters guide readers through techniques for processing RNA-seq data, microbiome analysis, deep learning;

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Next-Generation Sequencing Data Analysis

Sequencing Data Analysis shows how next-generation sequencing (NGS) technologies are applied to transform nearly all aspects of biological research;

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Application of Omics, AI and Blockchain in Bioinformatics Research

discovery, RNA-seq analysis, tensor decomposition and Boolean network.;

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Stem Cell Transcriptional Networks

-cell RNA-Seq data, analysis of 3D chromatin architecture, interpretation of large-scale interaction networks, transcriptional networks;

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Computational Genomics with R

genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and;

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Computational Genomics with R

visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will;

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Omics Approaches, Technologies And Applications

fingerprinting, RNA Seq and NMR spectroscopy. It entails a chapter focused on the bioinformatics resources for analysis of the omics data. In summary;

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Introduction to Bioinformatics with R

to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered;

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Introduction to Bioinformatics with R

to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered;

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Molecular Neuroscience

analysis of gene expression (e.g., RNA-Seq), and constructing and imaging fluorescent proteins. The manual includes protocols developed;

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Algorithms for Computational Biology

on genomics, phylogenetics, and RNA-Seq and other biological processes.;

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Computational Methods for Next Generation Sequencing Data Analysis

analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software;

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Algorithms for Computational Biology

selected from 22 submissions. They were organized in topical sections on genomics, phylogenetics, and RNA-Seq and other biological processes;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

differential equations for wearable computing, genetic analysis of function-valued traits, RNA-seq data analysis, causal networks for genetic;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

of function-valued traits, RNA-seq data analysis, causal networks for genetic methylation analysis, gene expression and methylation deconvolution;

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Data Analysis for Omic Sciences: Methods and Applications

of High-Throughput RNA Sequencing Data, Analysis of High-Throughput DNA Bisulfite Sequencing Data, Data Quality Assessment in Untargeted LC-MS;

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Measuring Gene Expression

, reporter genes, microarrays, and proteomics, as well as a section on RNA isolation and analysis. There is an overview of each method: its pros and;

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Analysis of Microarray Gene Expression Data

technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the;

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Next Generation Sequencing and Data Analysis

applications. The reader learns how to perform Next Generation Sequencing data analysis, how to interpret and visualize the data, and acquires;

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RNA Motifs and Regulatory Elements

RNA Motifs and Regulatory Elements is the new edition of the successful book, Regulatory RNA . It alerts the reader to the importance;

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Epstein Barr Virus

purification-mass spectrometry methods; 3D telomere FISH; transcription analysis using high-throughput RNA sequencing, qPCR and nuclear run-on assay;

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