The document provides an overview of the history and techniques of transcriptome analysis. It discusses how RNA was separated from DNA with the formulation of the central dogma in 1958. Key developments include the discoveries of messenger RNA, transfer RNA, and ribosomal RNA in the 1960s. The document outlines techniques such as serial analysis of gene expression (SAGE) and RNA sequencing (RNA-seq) that allow comprehensive analysis of gene expression patterns. It provides details on the basic steps and advantages of SAGE and describes how next generation sequencing revolutionized transcriptome analysis through massive parallel sequencing.

1. TRANSCRIPTOME ANALYSIS

2. TRANSCRIPTOME: A BRIEF
HISTORY
 Transcriptomics is the study of RNA, single-stranded nucleic acid, which
was not separated from the DNA world until the central dogma was
formulated by Francis Crick in 1958, i.e., the idea that genetic information
is transcribed from DNA to RNA and then translated from RNA into
protein.
 In 1961, Jacob and Monod proposed a model that the protein-coding gene
is transcribed into a special short-lived intermediate associated with the
ribosome, which was designated as messenger RNA (mRNA).
 A short, stable RNA, transfer RNA (tRNA), was identified as the
predicted “adaptor”.
 Shortly, ribosomal RNA (rRNA) involved in protein synthesis was
purified.
 During RNA splicing, the introns are cut out from the primary transcripts
and degraded, while the exons are reassembled into different mature
messenger RNAs (mRNAs) (alternative splicing).

3.  The discovery of the split gene was a complete surprise and had
revolutionized our understanding of the architecture of genes.
 Since the late 1970s, Altman and Cech revealed respectively that
RNA can function as a catalyst. In 1982, Kruger put forward the
“ribozyme” concept, demonstrating that RNA could act as both
genetic material (like DNA) and a biological catalyst (like protein
enzymes).
 In the early 1990s, it was observed by a number of scientists
independently that RNA inhibited gene expression in plants and
fungi with unknown mechanism. In 1998, Fire and Mello found
that double-stranded RNAs (dsRNAs) could recognize specific
mRNA sequence and then led to the degradation of the target
mRNAs, which was known as RNA interference (RNAi).

4.  Further studies indicated that the actual molecules that
directly caused RNAi were short dsRNA fragments of
21–25 base pair, called small interfering RNA
(siRNA).
 In 1977, Sharp and Roberts showed that the mRNA
sequence of adenovirus displayed discontinuous
distribution in the genome, and therefore first
suggested that a typical eukaryotic gene consists of
exons, the protein- coding sequence, and introns, the
non-coding sequence; the protein-coding sequence was
interrupted by the non-coding sequence.

5. TRANSCRIPTOME
 Transcriptome is the whole set of RNAs
transcribed by the genome from a specific tissue
or cell type at a developmental stage and/or under
a certain physiological condition.
 After the genome has been sequenced,
transcriptome analysis allows us to understand
the expression of genome at the transcription
level, which provides information on gene
structure, regulation of gene expression, gene
product function, and genome dynamics.

6. TRANSCRIPT FORMATION

7. STEPS OF TRANSCRIPTION

8. STRUCTURE OF RNA

9. TYPE OF RNA AND THEIR
ROLEThe central dogma of molecular biology explains that DNA codes for RNA, which
codes for proteins. In The Central Dogma, we can learn about the important roles of
messenger RNA, transfer RNA and ribosomal RNA in the protein-building process.
But RNA does more than just build proteins. RNA has many jobs in the cell,
including jobs that have been traditionally associated with DNA and proteins.

10. POSITIONAL INFORMATION
INTEGRATION ON THE
TRANSCRIPTOME
 The recent explosion of high-throughput
sequencing methods applied to RNA
molecules is allowing us to go beyond the
description of sequence variants and their
relative abundances, as measured by RNA-
seq.
 One can now probe for RNA engagement
in polysomes, for ribosomes, RNA binding
proteins and microRNAs binding sites, for
RNA secondary structure and for RNA
methylation.
 These descriptors produce a steadily
growing multidimensional array of
positional information on RNA sequences,
whose effective integration only would
bring to decipher the regulatory interplay
occurring between proteins, RNAs and
their modifications on the transcriptome.
 This interplay ultimately dictates the
degree of mRNA availability to translation,
and thus the occurrence of cell phenotypes.

11. NORTHERN BLOTTING
 The northern blot is a technique used in molecular biology research to study
gene expression by detection of RNA (or isolated mRNA) in a sample.
 The quantity of mRNA transcript for a single gene directly reflects how much
transcription of that gene has occurred.
 Tracking of that quantity will therefore indicate how vigorously a gene is
transcribed, or expressed.
 To visualize differences in the quantity of mRNA produced by different groups
of cells or at different times, researchers often use the method known as a
Northern blot.
 For this method, researchers must first isolate mRNA from a biological
sample by exposing the cells within it to a protease, which is an enzyme that
breaks down cell membranes and releases the genetic material in the cells.
 Next, the mRNA is separated from the DNA, proteins, lipids, and other
cellular contents.

12. NORTHERN BLOTTING
 The different fragments of mRNA are then separated
from one another via gel electrophoresis (a technique
that separates molecules by passing an electrical
current through a gel medium containing the
molecules) and transferred to a filter or other solid
support using a technique known as blotting.
 To identify the mRNA transcripts produced by a
particular gene, the researchers next incubate the
sample with a short piece of single-stranded RNA or
DNA (also known as a probe) that is labeled with a
radioactive molecule.
 Designed to be complementary to mRNA from the
gene of interest, the probe will bind to this sequence.
 Later, when the filter is placed against X-ray film, the
radioactivity in the probe will expose the film, thereby
making marks on it.
 The intensity of the resulting marks, called bands, tells
researchers how much mRNA was in the sample,
which is a direct indicator of how strongly the gene of

14. DRAW BACK AND
MODIFICATION
 Until recently, scientists studied gene expression by looking at only one
or very few gene transcripts at a time.
 Thankfully, new techniques now make large-scale studies of gene
expression possible.
 One such technique is SAGE (serial analysis of gene expression). A
method for measuring the expression patterns of many genes at once,
SAGE not only allows scientists to analyze thousands of gene transcripts
simultaneously, but it also enables them to determine which genes are
active in different tissues or at different stages of cellular development.
 Serial analysis of gene expression (SAGE) is a powerful tool that allows
the analysis of overall gene expression patterns with digital analysis.
Because SAGE does not require a preexisting clone, it can be used to
identify and quantitate new genes as well as known genes.

15. SAGE
 SAGE invented at Johns Hopkins University in USA (Oncology Center) by
Dr. Victor Velculescu in 1995.
 Serial analysis of gene expression (SAGE) is an approach that allows rapid
and detailed analysis of overall gene expression patterns.
 SAGE identifies and counts the mRNA transcripts in a cell with the help of
short snippets of the genetic code, called tags.
 In most cases, each tag contains enough information to uniquely identify a
transcript.
 The name "serial analysis" refers to the fact that tags are read sequentially as
a continuous string of information.
 SAGE provides quantitative and comprehensive expression profiling in a
given cell population.
 The basic steps of the SAGE technique are:

16. THE BASIC STEPS OF THE
SAGE TECHNIQUE ARE
OUTLINED BELOWCapturing mRNA
Rewriting mRNA into cDNA
Cutting tags from each cDNA
Linking tags together in chains for sequencing
Copying and reading the chains
Identifying and counting the tags

17. SAGE FLOWCHART…17
1. Isolate mRNA.
2. (a) Add biotin-labeled dT primer:
(b) Synthesize ds cDNA.
3. (a) Bind to streptavidin-coated beads.
(b) Cleave with “anchoring enzyme”.
B
B
B

18. (c) Discard loose fragments.
18
4. (a) Divide into two pools and add linker sequences
(b) Ligate.
B

19. 5. Cleave with “tagging enzyme”
19
6. Combine pools and ligate.
7. Amplify ditags, then cleave with anchoring
enzyme.
B

20. 8. Ligate ditags.
9. Sequence and record the tags and frequencies.
20

21. SAGE IN DETAILS…
Trapping of RNA with beads
 mRNA’s end with a long string of “A” (Adenine)
 Molecules that consist of 20 or so dT’s acts like a
attractant to capture mRNAs.
 Coating of microscopic magnetic beads with
“TTTTT” tails is done.
 A magnet is used to withdraw the bead and the
mRNA is isolated.
21

22. 22
Microscopic
bead coated
with TTTT’s

23. 23
23
Microscopic
bead coated
with TTTT’s

24. cDNA synthesis
 ds cDNA is synthesized from the extracted
mRNA by means of biotinylated oligo (dT)
primer.
 cDNA synthesis is immobilized to streptavidin
beads.
24

25. 25
B
B
B
B
B
B
B
Biotinylated oligo dT (primers)
Streptavidin
beads
B
B
B

26. Enzymatic cleavage of cDNA
 The cDNA molecule is cleaved with a restriction
enzyme.
 Type II restriction enzyme used (E.g. NlaIII.)
 Average length of cDNA – 256bp with sticky
ends created.
26

27. 27
B
B
B
Nla III
(Restriction
enzyme)
B

28. Ligation of Linkers to bound cDNA
 Captured cDNA are then ligated to linkers at their
ends.
 Linkers must contain:
 NlaIII 4-nucleotide cohesive overhang.
 Type IIs recognition sequence.
 PCR primer sequence.
28

29. 29
B
Linkers
B
B
B
Pool A Pool B

30. Cleaving with tagging enzyme
 Tagging enzyme, (usually BsmF1) cleave DNA,
releasing the linker-adapted SAGE tag from each
cDNA.
 Repair of ends to make blunt ended tags using
DNA polymerase (Klenow fragments) and
dNTPs.
30

31. 31
B
B
Bsm FI
(tagging Enzyme)
Linker adapted
SAGE tag

32. Formation of Ditags
 The left thing is the collection of short tags taken
from each molecule.
 Two groups of cDNAs are ligated to each other,
to create a “ditag” with linkers on either end.
 Two tags are linked together using T4 DNA
ligase.
32

33. 33

34. PCR amplification of Ditags
 The linker-ditag-linker constructs are amplified
by PCR using primers specific to the linkers.
34

35. 35
PCR Amplification

36. Isolation of Ditags
 The cDNA is again digested by the Anchoring
enzyme (AE)
 Breaking the linker off right where it was added
in beginning.
 This leaves a “sticky” end with the sequence
GTAC (or CAGT on the other strand) at each end
of the ditag.
36

37. Nla III
(Anchoring enzyme)
37
37
37
37

38. Concatamerization of Ditags
 Tags are combined into much longer molecules,
called concatamers.
 Each ditag is having an AE site, allowing the
scientist and the computer to recognize where one
ends and the next begins.
38

39. 39
Concatemirize

40. Cloning Concatamers and Sequencing…
 Lots of copies are required – so the concatamers are inserted into bacteria,
which act like living “copy machines” to create millions of copies from
original.
 Copies are then sequenced, using machines that can read the nucleotides
in DNA. The result is a long list of nucleotides that has to be analyzed by
computer.
 Analysis will do several things: count the tags, determine which one come
from the same RNA molecule, and figure out which ones come from
known, well studied genes and which ones are new.
Vast amount of data is produced, which must be shifted and ordered for useful
information to become apparent.
SAGE reference databases:
 SAGE map
 SAGE Genie
http://www.ncbi.nlm.nih.gov/cgap
40

41. HOW DOES THE DATA
LOOK LIKE?
41

42. 42 From Tags to Genes…
 Collect sequence records from GenBank.
 Assign sequence orientation (by finding poly-A tail)
 Assign UniGene identifier to each sequence with a SAGE tag.
 Record (for each tag-gene pair)
 Advantages:
 mRNA sequence does not need to be known prior, so genes of variants
which are not known can be discovered.
 Its more accurate as it involves direct counting of the number of
transcripts.

43. Problems
 Length of gene tag is extremely short (13
or 14bp), so if the tag is derived from an
unknown gene, it is difficult to analyze
with such a short sequence.
 Type II restriction enzyme does not yield
same length fragments.
 mRNA levels and protein expression do
not are always correlate.
Need of SAGE
 Compared to other techniques for measuring
gene expression, SAGE offers a significant
advantage because it measures the
expression of both known and unknown
genes.
 Sometimes, when analyzing SAGE data,
computers cannot find matches for certain
tags in their sequence databases that means
a lack of matches indicates that the mRNA
used to produce these tags is associated with
genes that have not been studied before.
 In this way, SAGE has been used to
discover new genes involved in a variety of
diseases.
43

44. RNA-SEQ/ NGS
 RNA sequencing (RNASeq) is revolutionizing the study of the
transcriptome.
 It is providing visibility to previously undetected changes occurring in
disease states, in response to therapeutics, under different environmental
conditions and across a broad range of other study designs.
 Compared with Sanger sequencing, the core of NGS is massive parallel
sequencing.
 Development of nanotechnology makes it possible to sequence hundreds
of thousands of DNA molecules simultaneously.
 The prototype of NGS is massive parallel signature sequencing (MPSS),
which applies four rounds of restriction enzyme digestion and ligation
reactions to determine the nucleotide sequence of cDNA ends generating a
17–20 bp sequence as the fingerprint of a corresponding RNA.
 MPSS is used to digitize the quantitative transcriptome with the capacity
to produce more than 100000 signatures at a time.

45.  However, due to the nature of digestion and ligation reactions, a large
fraction of the sequence signatures obtained is not long enough to be
unique fingerprints of RNA molecules.
 Overcoming the limits of MPSS, Illumina, Roche, Lifescientific, and other
companies developed their own platforms with considerable improvement
on the throughput, reading length, and sequencing accuracy. Based on
these platforms, the RNA-seq methodology became the most convenient
and cost effective tool for transcriptome analysis.
 RNASeq allows researchers to detect both known and novel features in a
single assay, enabling the detection of transcript isoforms, gene fusions,
single nucleotide variants, allele specific gene expression and other
features without the limitation of prior knowledge.
 High throughput sequencing also called Next Generation Sequencing
(NGS) have the capacity to sequence full genomes. Bacteriophage fX174,
was the first genome to be sequenced, a viral genome with only 5,368
base pairs (bp).

46. RNA-Seq or Transcriptome Sequencing
Sequencing technologies applicable to RNA-Seq
High throughput
• Illumina HiSeq 2500
• Illumina Next-Seq 500
• Illumina MiSeq
• Illumina X Ten
“Lower” throughput
• Roche 454
Low throughput
• Sanger
Illumina…

47. Martin J.A. and Wang Z., Nat. Rev. Genet. (2011) 12:671–682
From RNA -> sequence data

48. Martin J.A. and Wang Z., Nat. Rev. Genet. (2011) 12:671–682
From RNA -> sequence data
Ready for sequencing

49. Library Preparation
DNA
(0.1-5.0 μg)
1 2 3 7 8 94 5 6
T G T A C G A T …
ILLUMINA SEQUENCING TECHNOLOGY WORKFLOW
C
C
C
C
A
A
A
T
T
G
G
G
G
Sequencing
Single molecule array
Cluster Growth
Image Acquisition Base Calling
5’
5’3’
T
G
T
A
C
G
A
T
C
A
C
C
C
G
A
T
C
G
A
A
49Alvaro Hernandez

50. Martin J.A. and Wang Z., Nat. Rev. Genet. (2011) 12:671–682
From RNA -> sequence data

51. Other Technologies to Study Expression
1. Expressed Sequence Tags (ESTs)
2. RT-PCR
3. DNA Microarray’s
4. Bioinformatics

52. EST AND MICROARRAY
 Sanger sequencing of EST or cDNA library provided
information for genome annotation in the early days of
genome research.
 Due to the limitations on throughput and cost, it is
impossible to achieve transcriptome quantitative
analysis using EST methods.
 With serial analysis of gene expression (SAGE) and
CAGE, respectively, multiple 3′ and 5′ cDNA ends
were concatenated to be one clone.
 Therefore, multiple sequence tags can be recovered
from one Sanger sequencing reaction, which overcomes
those limits and makes quantitative analysis possible.
 However, due to the high cost of Sanger sequencing
and the difficulty to map the short sequence (~20 bp)
tags to genome, CAGE and SAGE were replaced by
DNA microarray shortly.
• Short, annotated sequences at 3’ or 5’ end
• Can be used to determine the number of genes/genome
• Can be used to identify novel genes

53. DNA MICROARRAYS
 DNA microarray or chip method is based on nucleic acid
hybridization.
 Fluorescent labeled cDNAs incubate with oligonucleotide probes
on the chip, then the abundance of RNA is determined by
measuring fluorescence density.
 High-density gene chip allowed relatively low cost gene
expression profiling.
 Specific microarrays were designed according to the purpose of
the experiment, such as arrays to detect different isoforms from
alternative splicing.
 In addition, the genome tiling array is an unbiased design, without
prior knowledge of genome transcription information, using a set
of overlapping oligonucleotide probes for the detection of whole
genome expression with the resolution up to a few nucleotides.
 However, for large genomes, tiling array is expensive. Another
limiting factor of hybridization methodology is high background,
because it is unable to distinguish RNA molecules sharing high
sequence similarity.
• High-throughput
• Allows for simultaneous detection of genome-wide expression
• Can provide relative quantitative information about expression…

54. Microarray vs. SAGE

55. QRT-PCR
• Can be used to both detect &
quantify gene expression
• Not high-throughput
• Can only be used on a limited scale

56. BIOINFORMATICS ANALYSIS
Data alignment
We need to align the sequence data to our genome of interest
 If aligning RNA-Seq data to the genome, always pick a slice-aware aligner
TopHat2, MapSplice, SOAPSplice, Passion, SpliceMap, RUM, ABMapper, CRAC,
GSNAP, HMMSplicer, Olego, BLAT
 There are excellent aligners available that are not splice-aware. These are useful
for aligning directly to an already available transcriptome (gene models, so you
are not worrying about introns). However, be aware that you will lose isoform
information.
Bowtie2, BWA, Novoalign (not free), SOAPaligner
Sequence formats
• FASTA
• FASTQ
Alignment formats
• SAM/BAM
Feature formats
• GFF (– Gene feature
format)
• GTF(Gene transfer format)
File formats
A brief note

57. STATUS OF TRANSCRIPTOME
ANALYSIS IN INDIA
 Plant Biotechnology Division, CSIR- Indian Institute
of Integrative Medicine, Sanat Nagar, Srinagar, J&K,
India.
 Academy of Science and Innovative Research
(AcSIR), Anusandhan Bhawan, New Delhi, India.
 Division of Biotechnology, CSIR- Institute of
Himalayan Bioresource Technology, Palampur, India
 School of Biotechnology, University of Jammu,
Jammu, Jammu & Kashmir, India,
 National Institute of Plant Genome Research (NIPGR),
Aruna Asaf Ali Marg, New Delhi, India

58. APPLICATION
 Transcriptome analysis will further reveal the regulation network of biological
processes and eventually give some guidance in disease diagnosis and crop
improvement.
 RNA-Seq is a powerful approach that enables researchers to discover, profile
and quantify RNA transcripts in the whole transcriptome. As this method does
not use predesigned probes or primers, it becomes an unbiased hypothesis free
approach providing researchers with applications that are not possible by
traditional microarray based methods.
 RNA SEQUENCING IS USED WIDELY FOR:
 Studies of gene expression and discovery of novel transcripts and isoforms
 SNP discovery in the coding portions of the genome
 Denovo transcriptome assembly
 Basis for expression quantification in RNA-Seq

59. CONCLUSION
To analyze differences between gene expression patterns of cancer cells and their normal counter parts.
Examining which transcripts are present in a cell.
Allows rapid, detailed analysis of thousands of transcripts in a cell.
By comparing different types of cells, generate profiles that will help to understand healthy cells and what
goes wrong in diseases.
By comparing different types of cells, generate profiles that will help to understand healthy cells and what
goes wrong in diseases.
To identify downstream targets of oncogenes and tumor suppresser genes.
Studied the tumors of pancreatic and colon tumors.
Zhang et al.(1997)Science, 276(5316), 1268-1272.
59