RNA-seq in practice: Hands-on workshop on RCAC systems: All Images

RNA-seq in practice: Hands-on workshop on RCAC systems

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RNA-seq in practice: Hands-on workshop on RCAC systems

Introduction to RNA-seqWhere are we heading towards in this workshop?

Figure 1

Illustration of part of the central dogma of molecular biology, where DNA is transcribed to RNA, and intronic sequences are spliced out

Figure 2

Illustration of the major experimental steps of an RNA-seq experiment

Figure 3

A classification of many different factors affecting measurements obtained from an experiment into treatment, biological, technical and error effects

Figure 4

Illustration of a set of reads generated by a sequencer, and genomic and transcriptomic reference sequences

Figure 5

An example MA plot An example heatmap

Downloading and organizing files

Figure 1

Basic gene annotation in GTF format

Figure 2

Reference genome file in FASTA format

Figure 3

Transcript sequences file in FASTA format

Figure 4

FASTQ files from GEO

Figure 5

FASTQ files from SRA Run Selector

Quality control of RNA-seq reads

Figure 1

Per base sequence quality for a typical RNA-seq dataset

Figure 2

Per tile sequence quality plot

Figure 3

Distribution of per sequence quality scores

Figure 4

Per base sequence content for RNA-seq reads

Figure 5

Per sequence GC content distribution

Figure 6

Per base N content plot

Figure 7

Sequence length distribution for RNA-seq data

Figure 8

Sequence duplication levels

Figure 9

Adapter content across the read

Figure 10

Example MultiQC summary across all samples

A. Genome-based quantification (STAR + featureCounts)

B. Transcript-based quantification (Kallisto)

Gene-level QC and differential expression (DESeq2)

Figure 1

Open OnDemand interface

Figure 2

Distribution of gene biotypes in annotation

Figure 3

Total assigned reads per sample

Figure 4

Size factors vs library size

Figure 5

Average counts vs variance before transformation

Figure 6

average counts vs variance after transformation

Figure 7

Euclidean distance heatmap

Figure 8

PCA plot showing sample clustering

Figure 9

Dispersion estimates from DESeq2

Figure 10

Volcano plot of differential expression results

B. Differential expression using DESeq2 (Kallisto pathway)

Figure 1

Raw count distributions

Figure 2

Count density distributions

Figure 3

Sample distance heatmap

Figure 4

PCA plot of Kallisto-quantified samples

Figure 5

Volcano plot of differential expression results

Figure 6

Volcano plot of differential expression results

Gene set enrichment analysis

Figure 1

GO Biological Process enrichment dotplot for upregulated genes

Figure 2

GO Biological Process enrichment barplot for upregulated genes

Figure 3

Simplified GO Biological Process enrichment dotplot

Figure 4

Simplified GO Biological Process enrichment barplot

Figure 5

GO Biological Process enrichment map showing term relationships

Figure 6

KEGG pathway enrichment barplot

Figure 7

MSigDB Hallmark gene set enrichment dotplot

Figure 8

GSEA dotplot for GO Biological Process showing activated and suppressed gene sets

Figure 9

GSEA enrichment plot showing running enrichment score for top gene set