Bioinformatics
1
Introduction to RNA sequencing (RNA_seq)
1.1
Downloading and setting up conda environments.
1.2
Install Miniconda, or Anaconda.
1.2.1
Conda terminal commands.
1.2.2
Anaconda/Miniconda install Summary of Steps.
1.2.3
Conda activate error
1.3
Alignment Procedures and files.
1.3.1
Using Tophat (early version)
1.3.2
Using Tophat
1.3.3
Using STAR
1.3.4
Custom/personal way to Download the Mus_musculus_NCBI_build37.2.tar.gz file.
1.3.5
Get the Sample files.
1.3.6
Starting to analize fastq.gz files
1.3.7
Trimming with Trimmomatic
1.3.8
Using STAR to map genes
2
File Download (or SRA), QC, and Trimming
2.1
Trimmomatic
2.2
Indexing with STAR
2.3
Microbial contamination
2.4
Feature counts.
2.5
Setting R environment.
3
Methods
4
RNA Seq Differentially Expressed, GO Enrichment, and Pathway analysis
4.1
Background
4.2
Cleaning Data
4.2.1
Programs used?
4.2.2
Acquiring sequences
4.2.3
FastQC
4.2.4
Trimming with Trimmomatic
4.2.5
Alignment
4.2.6
Assembling transcripts with Cufflinks
4.2.7
Checking for Contamination
4.2.8
Counting Transcripts
4.3
Differentially Expressed Sequence Identification
4.3.1
Analyzing Reads Counts
4.4
Interactions cause a difference between the lfc betwen pooled data, e.g. p53+/+ (control and IR) and p53-/- (control and IR)
4.4.1
Exploratory Data Analysis
4.4.2
Identification of Differentially Expressed Genes
4.4.3
Gene Annotations
4.5
7. GO Enrichment analysis using GOstats
4.5.1
GO Enrichment analysis of downregulated genes
4.6
8. Pathway analysis using expression data
4.6.1
Prepare data
4.6.2
KEGG pathways
4.6.3
Pathway and regulation of genes for Oxidative phosphorylation.
4.6.4
Pathway and regulation of genes for Glycosylphosphatidylinositol(GPI)-anchor biosynthesis.
4.6.5
Pathway and regulation of genes for RNA polymerase.
4.6.6
Pathway and regulation of genes for Nucleotide excision repair.
4.6.7
Pathway and regulation of genes for Non-homologous end-joining.
5
RNA-Seq-MKdata
5.1
PCA
5.2
Differential expression
5.3
RNA-seq (ScienceparkSG)
5.4
False discovery rates FDR / Benjamini-Hocheberg method
5.5
Volcano plot
5.6
Heatmap & normalization
5.7
MA plots
5.8
Eastern New Mexico University ENMU method
6
R code modules to be used for stand-alone analysis.
6.1
Local installation
6.2
Install packages
6.3
Global variables
6.4
Read data and pre-process
6.5
Hierarchical clustering
6.6
PCA
6.7
K-means clustering
6.8
Differential expression
6.9
Pathway analysis
6.10
Genome-wide view
6.11
Biclustering
6.12
Co-expression network
7
WGCNA RNA-seq Blog
7.1
Introduction to the WGCNA Rpackage
7.1.1
WGCNA Rpackage Installation procedure.
7.1.2
Final installation workflow:
7.1.3
WGCNA tutorial.
7.1.4
3.4 Gene expression analysis
7.2
RNA-seq Analisys with Kaku’s Data sample.
7.2.1
Data Analysis.
8
Youtube references.
8.1
Anaconda set up, Robust Data Science Environment with Miniconda and Conda-Forge.
8.2
Quality control & preprocessing of raw reads
8.3
Trimmomatic.
8.4
Building Genome Index and Aligning with STAR
9
External Ref
References
Published with bookdown
Bioinformatics_RNA-seq
Chapter 9
External Ref
We have finished a nice book.