1. Exploring the Human Transcriptome
Claudia Neuhauser
University of Minnesota
Informatics Institute

2. From DNA to Proteins
Source: Wikipedia (

3. RNA: Ribonucelic Acid Types of RNA
Ribosomal RNA (rRNA): catalytic component of ribosomes (about 80-85%)
Transfer RNA (tRNA): transfers amino acids to polypeptide chain at the ribosomal site of protein synthesis (about 15%)
Messenger RNA (mRNA): carries information about a protein sequence to the ribosomes (about 5%)
Other types
miRNA, siRNA,snRNA, dsRNA,…

4. RNA: Ribonucelic Acid Types of RNA
Ribosomal RNA (rRNA): catalytic component of ribosomes (about 80-85%)
Transfer RNA (tRNA): transfers amino acids to polypeptide chain at the ribosomal site of protein synthesis (about 15%)
Messenger RNA (mRNA): carries information about a protein sequence to the ribosomes (about 5%)
Other types
miRNA, siRNA,snRNA, dsRNA,…

5. Transcriptome
The transcriptome is the set of all RNA produced in a cell (or population of cells)
The transcriptome of a cell varies over time and with environmental conditions
The mRNA transcripts reflect which genes are actively expressed
Microarray technology
RNA-seq technology

6. Exploring Transcriptomes
Both microarray and RNA-seq
compare mRNA and provide quantification of gene transcripts
From: Functional Genomics (G. Meroni and F. Petrera
Accessed through INTECH (

7. Comparing Microarray and RNA-Seq
Wang, Zhong, Mark Gerstein, and Michael Snyder. "RNA-Seq: a revolutionary tool for transcriptomics." Nature Reviews Genetics 10.1 (2009):

8. RNA seq Experiment
By Boraas (Own work) [Public domain], via Wikimedia Commons

9. RNA seq Alignment

10. Malone, John H. , and Brian Oliver
Malone, John H., and Brian Oliver. "Microarrays, deep sequencing and the true measure of the transcriptome." BMC biology 9.1 (2011): 34.

11. Figure 4: Correlation of gene expression based on RPKM by RNA-Seq and protein abundance by label-free method(A) MS1 based quantification by msInspect plotted against RPKM, log transformed. (B) Normalized MS2 spectral counts (NSAF)) plotted against RPKM, log transformed. Data for mouse mitochondrial genes in brainstem tissue. Protein abundance by msInspect is based on top 3 normalized peptide area intensities.
Source: Ning, Kang, Damian Fermin, and Alexey I. Nesvizhskii. "Comparative analysis of different label-free mass spectrometry based protein abundance estimates and their correlation with RNA-Seq gene expression data." Journal of proteome research 11.4 (2012):

12. Resources Recount Ensembl RefSeq Expression Atlas
Online resource of RNA-seq gene count datasets from 18 different studies
Ensembl
Genome database (automated gene annotation system)
RefSeq
NCBI Reference Sequence Database (manually curated)
Expression Atlas
Information on gene expression patterns under different biological conditions

13. The Data
ReCount
“ReCount is an online resource consisting of RNA-seq gene count datasets built using the raw data from 18 different studies. […] By taking care of several preprocessing steps and combining many datasets into one easily-accessible website, we make finding and analyzing RNA-seq data considerably more straightforward.”

14. From ReCount to Excel I
Wang, ET, et al. (2008):
Count tables can be accessed by clicking on the “link”
Ctrl-a
Ctrl-c
Open Excel
Click on Cell A1
Ctrl-v

15. From ReCount to Excel II
Click on the Data tab in your spreadsheet and click on Text to Columns in the ribbon under Data Tools. The Convert to Columns Wizard will guide you through the next steps.
Your original data are separated by spaces. Click on Delimited to choose the original data type, and click Next.
Click Space in the Delimiters box. You should see how the data will be displayed in the data preview. If it looks correct, click Finish.
Save your file or use the ones uploaded to the site.

16. The Data Gene ID Reads gene SRX003935 SRX003921 SRX003924 SRX003923 1
Sample ID
gene
SRX003935
SRX003921
SRX003924
SRX003923 1
ENSG 12 22 16 2
ENSG 3
ENSG 25 13 74 26 4
ENSG 65 19 5
ENSG 8 6
ENSG 7
ENSG
ENSG 33
125 88 9
ENSG 10
ENSG
339
269
404
253

17. Exercises 1 & 2: The Wang et al. Data
Open the Heart tab
Explore the genes
Pick a gene ID and search in your browser for the gene ID
Explore the gene on the Ensemble website
Explore the read count distribution
What percentage of genes are expressed?
What is the distribution of read counts?
Detailed instructions are in workbook

18. From Raw Counts to Interpretation
What affects the magnitude of the number of reads assigned to a specific gene?
Exon model
Expression level
Length of gene
Sequencing depth

19. Normalizing Raw Counts I
Raw Data
Similar number of reads but different lengths
To compare genes within a sample, divide raw count by length of gene

20. Normalizing Raw Counts II
Find the total number of reads N
For gene i, calculate
These numbers are very small
The median is around 4x10-10
Multiply by 109=1,000,000,000
This new quantity is called RPKM (or FPKM)
Reads per kilobase pair per million mapped reads

21. Normalizing Raw Counts III
Calculating RPKM
This quantity can be used for within sample analysis
Note: gene annotation and length come from an ‘exon model’

22. Exercise 3 Heart Length tab Calculate RPKM
Plot RPKM as a function of length
Find genes that are strongly expressed in the heart and go to the Expression Atlas to confirm
Detailed instructions are in workbook

23. Exercise 4
The Heart-Liver tab has RNA-seq read counts for two tissue types, the heart and the liver. We will use this data set to learn about differential expression.
How many genes are expressed in both the heart and the liver, in one but not the other, and in neither tissue?

24. Normalizing Raw Counts IV
To compare across samples, we need to account for sequencing depth
For each sample, find the total number of reads
For gene i in sample k, calculate
Sum over all genes i in sample to obtain normalizing factor Λk

25. Normalizing Raw Counts V
For each gene i in sample k, divide λik by Λk
This quantity, called relative abundance, can be used to compare across samples

26. Exercise 5
The Heart Liver Length tab has an additional column (Column C) with the length of each gene. We will compare relative importance of each gene.
Determine the total number of reads N for each tissue.
Calculate relative abundance for each tissue
Graph the cumulative distribution function of the relative abundance as a function of the number of genes.
Detailed instructions are in workbook

27. Exercise 6 Calculate the log fold change ‘=ABS(LOG(ratio,2))’
Graph the log fold change as a function of relative abundance for each tissue type