This document outlines the TUXEDO protocol for analyzing RNA-Seq data. It describes the basic steps as: 1) Alignment of RNA-Seq reads to a reference genome or transcriptome using splice-aware aligners like TopHat. 2) Quantification of gene and transcript expression levels using Cufflinks. 3) Quality control checks and 4) Detection of differential expression between conditions using Cuffdiff. Key points covered include RNA-Seq methodology, gene expression, alignment formats, FPKM normalization, and quality control metrics.

1. RNA-Seq analysis pipeline For
Unraveling Traits in crops:
TUXEDO protocol
By Abdulsalam Toyin

2. OUTLINE
RNA-Seq
Biology of Gene Expression
Concept of Genes and Transcripts
Alignment
RNA-Seq specific aligners
Tophat, STAR
SAM/BAM format
 Quantification
cufflinks(gene level, isoform level, splicing…..)
Alternative methods
 Post-Alignment Quality Control
 Other analysis with RNA-Seq

3. RNA-Seq
Diagram Describes a
summary of the RNA-Seq
Technique: (1) RNA is
isolated from a panel of
tissues or treatments. (2) A
pool of core tissues (e.g.,
leaf, root, flower, fruit) are
used to create the
reference cDNA library
which is then sequenced
accordingly.

4. RNA-Seq
• RNA-seq basically seeks to answer the following question:
 What genes are expressed in a sample?
What transcripts are expressed for each gene?
What are the genes, and transcripts?: what are the expression
levels?
How do expression levels and splicing pattern differ between
two conditions?

5. Biology of Gene Expression
The process of gene
expression is very
complex. “Genes takes a
body of their own in
process called gene
expression”. The process
starts with transcription
during which RNA-
polymerase creates a copy
of the gene, nucleotide by
nucleotide as a single
stranded molecule.
While in the
nucleus(highly unstable):5’
capping, 3’ cleavage and
addition of long polyA tail
to stabilize.

6. Genes are encoded in
the genome occupying
a specific location.
Genes consist of:
Informative block
“Exon” and non
informative block
“intron”. During
transcription the
introns are spliced out
and the exons are glued
together for form
mRNA.

7. RNA-Seq Pipeline

8. Mapping
 How to match millions of reads(~100 character fragments) with a reference
sequence of billions of characters.

9. Mapping for RNA-Seq
 The transcript may result from splicing and the mapping strategy must account for
this.
 Either we map to the transcriptome or we map to the genome with a “splice-aware”
aligner.
 Tophat is one of the first and by far the most popular aligner for RNA-Seq data
 it was built “on top” of bowtie

10. Mapping for RNA-Seq
 It breaks the reads into pieces, maps first to the genome and then extends to
“possible splice junctions”.
 We can also Pass an annotation file(GTF format) as arguments.
 Extract transcript, build an index
 Map to the “transcriptome” and then to the genome.

11. Tophat
 Using annotation:
 Improves accuracy, mostly around splicing junctions.
 “bias” in favor of known transcripts
 less power to detect novel transcripts or novel isoforms
 Beware if you have an incomplete annotation
 At the mapping step, it’s better to keep multi-mappers(within a reasonable limit,
10, 20 hits) Tophat provides an options control multiple mappings.

12. SAM/BAM FORMAT
 Composed of two parts
 Header to describe the source of the data, the reference sequence, the method of alignment and so on.
 Alignments to describe the reads, the location and the nature of the alignments.

13. SAM/BAM FORMAT

14. Visualization in IGV

15. Quantification
 In general, we want more than just alignment
 in theory, RNA-Seq is a quantifying assay, and we want to measure gene expression.

16. Cufflinks
 Based on alignment there are two goals:
 Transcript assembly
 Transcript quantification

17. Cufflinks
 Assembly: Try to find the
minimal number of paths in a
graph to fully represent the
alignment.
 Quantification: Estimate the
most likely abundance of the
difference isoforms.

18. Unit of Abundance
 We can only measure relative measures based on X number of reads in library, Y map to
geneA and Z map to geneB. If we change X then both Y and Z will change.
 Now if gene A and gene B have the same no of reads mapped
 Do they have similar expression levels?
 if gene B and gene A have the same size, Yes
 otherwise, No because a longer gene will receive more reads than a shorter gene

19. FPKM
Fragment per Kilobase per millions of reads.
 Not annotated gene size, but effective size
 Effective length: number of possible start site on transcript(depends on the
estimated fragment size)
 Millions of reads; millions of mapped reads(not millions of sequenced reads)
 FPKM let you compare the expression of gene between samples(because it
account for differences in library depth)
 It also lets you compare the expression of two genes within the same
sample (because it account difference in gene length).

20. Cufflinks and Cuffdiff
 In addition to estimating expression, cufflinks output gene expression(more or less
the sum of the different isoforms)
 cufflinks contains a method called cuffdiff for differential expression.
 cuffdiff estimate the isoform expression in two groups(which can be composed of
multiple replicates) and performs statistical test for;
 Differential gen expression
 Differential transcript levels
 Alternative splicing
 Differential usage of transcription start sites

21. Quality Control
 A number of matrices are important to look at:
Ribosomal contamination
Map the entire library against a set of ribosomal RNA
sequence and count the number reads mapping
 Numbers of reads mapping, and number of reads
mapping uniquely.
 Distribution of expression. Few high expressed,
some mid-expressed many low expression genes.

22. Quality Control
 Other matrices:
 Duplication rate(based on the location of the alignment) might bias the
estimation of the gene expression
% of the reads mapping of CDS, UTR, intron and intergenic regions,
obviously, the more on CDS and UTR the better.
Unsupervised clustering to verify that the samples cluster according to
biological differences and not according to experimental batches.
Hierarchical Clustering
PCA, MDS plot

23. Differential Expression

24. THANK YOU