Duke NGS Course (Summer 2015) 1.0
  • Site
      • Assigned Reading
      • Lecture notes
      • How to Claim and Access Your Virtual Machine
      • Installing R, RStudio and IPython notebook with the R kernel
      • Basic R in the Jupyter Notebook and RStudio
      • Introduction to R
      • Preparing Data for Analysis
      • Working with Data
      • Grouping and Aggregation
      • Hypothesis Testing and Power Calculations
      • Probability distributions and Random Number Genereation
      • Writing Custom Functions
      • Functional Programming
      • Linear Regression
      • Using R for supervised learning
      • Unsupervised Learning
      • Unsupervised Learning and NGS
      • Multiple Testing
      • Counting Models/Discrete Distributions
      • Generalized Linear Models
      • Base Graphics
      • Comparing Base Graphics with ggplot2
      • Coding Exercises
      • Introduction to DESeq2
  • Page
      • Assigned Reading
        • Biology
        • Statistics
        • Bioinformatics
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Assigned Reading¶

Biology¶

  • Quantitative bacterial transcriptomics with RNA-seq
  • Studying bacterial transcriptomes using RNA-seq
  • Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes
  • pH Regulates Genes for Flagellar Motility, Catabolism, and Oxidative Stress in Escherichia coli K-12†

Statistics¶

  • Statistical Design and Analysis of RNA Sequencing Data
  • Statistical Challenges in Preprocessing in Microarray Experiments in Cancer
  • Statistical Considerations for Analysis of Microarray Experiments
  • Differential expression analysis for sequence count data

Bioinformatics¶

  • Ultrafast and memory-efficient alignment of short DNA sequences to the human genome
  • Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples
  • Fast gapped-read alignment with Bowtie 2
  • TopHat: discovering splice junctions with RNA-Seq
  • Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

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