Journal of Biosciences and Medicines, 2013, 1, 1-5 JBM
doi:10.4236/jbm.2013.11001 Published Online February 2013 (
Published Online Febru ar y 2013 in SciRes.
Isoform Inference From RN A-Seq Samples Based on Gene
Structures on Chromosomes
Yan Ji, Jia Wei
R&D Information, AstraZeneca, Shanghai, China
Email: yan.ji@ as,
Received 2013.
The emerging RNA-Seq technology makes it possible
to infer splicing variants from millions of short
sequence reads. Here we present a method to i dentify
isoforms by their specific signatures on ch romo some s
including both exons and junctions. By applying this
method to a RNA-Seq dataset of gastric cancer, we
showed that our method is more accurate and
sensitive than other isoform inference tools such as
RSEM and Cufflinks. By constructing a network
from gene list identified by our method but misse d by
other tools, we found that some cancer-related genes
enriched in network modules have significant
implications for cancer drug discovery.
Keywords: RNA-Seq; isoform inference; cancer genes;
Cuffli nks; RSEM
Nextgeneration sequencing (NGS) platforms have
been widely available recently [1]. A massively parallel
sequencing technology termed RNA-Seq has made it
possible to sequence cDNA derived from cellular RNA
[2]. Recently, many studies have applied RNA-Seq to
various biological and medical studies. Quantification
of alternative slicing in tissues [3] and new transcript
identification [4] have all benefited from this new tech-
nology. To fully enable RNA-Seq technology to solve
biological problems, powerful computational tools are
required. Since identifying disease driven differentially
expressed isoforms is extremely important for drug re-
search and development, we focus on inferring iso-
for ms fr o m RN A-Seq datasets.
In this work, we present an accurate and sensitive
method to identify isoforms b y their specific signatures.
Expression values of an exon or junction can be calculated
by counting the number of reads of a RNA-Seq dataset
falling withi n a n e xon o r sp annin g a j uncti o n if b ot h the
genome sequence and gene structure annotation are
available. For each isoform, there must be single exon
(junction) or a combination of exons (junctions) which
uniquely belong to the isoform. We term such exons
(junctions) as specific exons (junctions) of an isoform.
Comparing with other isoform inference tools such as
RSEM and Cufflinks, our method has two significant
advantages [5, 6]. First, for all the isoforms in the gene
structure annotations, our method can determine whether
or not they are expressed in a RNA-Seq dataset. In the
result section, we show that our method has the
capabilit y to identify isofor ms which are missed by both
RSEM and Cufflinks. Second, because some exons or
junctions of an isoform may be overlapped by other
isoforms, the estimation of the expression value of an
isoform is always affected by expression levels of other
isoforms. Therefore, instead of using expression values
of an isofo rm, using expression values of specific exons
or junctions of an isoform calculated in our method are
better choice for finding differently expressed isoforms
by comparing samples from patient and healthy donors.
2.1. The Desc rip tion o f Our method for
Inf erring Iso for ms from RN A-Seq Datasets
First, RPKM expression values of both exons and junc-
tions are calculated. In this step, two files are needed
including a BAM file of the mapping result against
reference genome and an annotation file in GFF format
containing the structure information of genes on
ch r omosomes describing the relationship between exons
and is ofor ms. The RPK M exp re ssio n val ue o f an e xon i s
calculated by the read counts mapped to a chromosome
region of the exon normalized by the total read counts in
a sample and the length of the exon. The RPKM
expres sio n value of a junct ion is calculated in th e same way
except normalization by an extension length spanning
the j unct ion p osit io n. Second, a set o f e xons or j unct io ns
specific for an isoform are used to determine whether an
isoform exists in a sample or not. The rationale to
determine the expre ssion status of an iso form in a sample
Y. Ji et al. / Journal of Biosciences and Medicines 1 (2013) 1-5
Copyright © 2013 SciRes. JBM
is described as following. If more than sixty percent of
exons in an isoform are not expressed or with ver y s mall
expr ession va lues, the e xpr ess io n stat us o f the i sofo r m is
assigned a term “no expression”. Then, if either the set
of specific exons or the set of specific junctions of an
isoform are all expressed in a sample, the expression
status of the isoform is assigned a term “existence”.
Then, if one or more of specific exons or junctions are
not expressed and they are located in the 5’ end of the
isoform, the expression status of the isoform is assigned
a term “uncertainty”. It is because that in the process of
RNA- Seq sample preparation, a small fraction of
sequences at the 5’ end of an isoform may be degraded
or lost due to various reasons. Otherwise the expression
status of the isoform is assigned a term “absence”.
2.2. Datasets
We apply this method to a Gas tric cancer sample o btained
from gastric cancer patient. mRNA was fragmented and
plus- and minus- strand cDNA were synthesized for
illumina pair-end sequencing. A 300-bp fragment size
was selected by gel excision and the sample was
sequenced twice to avoid technical variance. There are
totally 30,121,416 and 17,510,256 read pairs for each
replica. We use TopHat with reference genome hg19 and
UCSC gene structure annotation to align the two RNA-
Seq datasets [7].
3. Results
In the UCSC gene structure annotation, there are 22920
genes. Our method is designed to determine expression
status of isoforms of all the genes having at least two
isoforms. That is, 18967 isoforms of 7209 genes can be
processed by our method. Because we only processed
genes mapped by RNA-Seq reads, our r esult s o n the t wo
RNA-Seq datasets, as shown in Figure 1, determined
expre ssio n stat us of 16151 isoforms of 5891 genes.
Fi g ure 1 . Comparisons of expression status of isoform between our method and RSEM. Level 1 is the evidence used to in-
fer isofo rm by our method: exon, junction and no expression. Level 2 is the type of isoform expression status inferred by
our method: existence, uncertainty and absence. Level 3 is the expression level of an isoform inferred from their expression
values calcu lated b y RSEM: i sofor ms marked with ‘yes’ ar e taken as express ed iso forms if their exp ression values are b ig-
ger than 0.3; isoforms marked with ‘no’ are taken to be absent in a RNA-seq dataset if their expression valu es are no bigger
than 0.3 .
Y. Ji et al. / Journal of Biosciences and Medicines 1 (2013) 1-5
Copyright © 2013 SciRes. JBM
3.1. Comparisons with Cufflinks and RSEM
The method based on specific exons or junctions has its
advantages over current popular isoform quantification
tools, such as RSEM and Cufflinks. In Figure 1, we
showed comparisons of expression status of isoforms
inferred by our method and the two other tools. First, t he
two isoform expression quantification tools quantified
isoforms of some genes marked as ‘no expression’ by
our method with big expression values. ANO7 and
TB XAS 1 are two examples. As sho wn i n Fi gu re 2, mo st
of exons of the two genes are not expressed. We guess
that the two tools quantify expressions of isoforms by
total read counts even if only a small fraction of exons
such as UTR exons were expressed. So it is necessary
for our method to check expression profiles of exons
before isoform quantification. Second, some isoforms
are marked as “existence” by our method, but they were
quantified by the two tools with very small expression
value s. Fo r exa mpl e, as s ho w n in Fi gu re 3, while RSEM
and cufflinks quantified the isoform NM_001135685
with 3.93E-05 and 0, there were 10 or 15 reads spanning
its specific junctions with RPKM expression values 1.6
or 2.4. For NM_000548, its unique specific exon has
expression value 15.03, while RSEM and cufflinks
quantified it as 5.05E-23 and 0. Third, some isoforms are
marked as “absence” by our method, but they were
quantified by the two tools with big expression values.
For example, as shown in Figure 4, while RSEM and
Cufflinks quantified the isoform NM_001135730 with
big expression values 5.66 and 4.66, there are no reads
spanning its specific junction.
3.2. Biological Significance of Identified
Isof orm s
Our method marked 1860 isoforms with ‘existence’
while they we re determined to be absent according to
their expressio n value s quantifi ed by RSEM . Expre ssion
values of these isoforms are smaller than 0.3 (e xpre ssio n
values of 62% of these isoforms were zero). Actually, b y
comparing expression values of isoforms and those of
their specific exons or junctions, we found that expression
values of most of these isoforms were under-estimated.
There are 468 cancer-related isoforms out of 1860 isoforms.
By using Analyze network algorithm with default settings
in GeneGo (, biological net-
works were constructed from cancer-related iso forms. In
Table 1, some networks with annotated GO Processes
were shown. In the first network, NM_003376 is the
isoform b of VEGFA (vascula r endothelial growth factor
A) which plays a major role in vascular cell migration
and proliferation. It mediates the vascular permeability,
mitogenesis and angiogenesis. Inhibition of VEGF-A
Figure 2. Visualization of the coverage of genes mapped by reads of two RNA-Seq datasets. Isoforms of the two genes
(ANO7 and TBXAS1) are marked with ‘no expression’ by our method while they are quantified with big expression values
by RSEM and Cufflinks.
Y. Ji et al. / Journal of Biosciences and Medicines 1 (2013) 1-5
Copyright © 2013 SciRes. JBM
Figure 3. Visualization of the coverage of genes mapped by reads of two RNA-Seq datasets. As shown in ‘a’,
NM_001135685 has two specific junctions highlighted by arrows. As shown in ‘b’, NM_000548 has a specific exon. The
two isoforms are marked with ‘existence’ by our method while they are quantified with very small or zero expression val-
ues by RSEM and Cufflinks.
Figure 4. Visualization of the coverage of genes mapped by reads of two RNA-Seq datasets. NM_001135730 has a specific junction
highlighted by an arrow. It is marked with ‘absence’ by our method while it is quantified with big expression values by both RSEM and
promoter may be a useful approach for treating diseases
in which aberrant angiogensis is present such as
inflammatory diseases, cancer and diabetic retinopathy
among others [8]. In the second network, NM_021872 is
the smallest isoform of CDC25B (M-phase inducer
phosphatase 2) which plays a role in endogenous tyrosine
Y. Ji et al. / Journal of Biosciences and Medicines 1 (2013) 1-5
Copyright © 2013 SciRes. JBM
Table 1. Networks constructed from cancer related iso fo r ms
Index Key Network Objects GO Process es
integrin-mediated signaling pathway, cell-mat r ix adhesi on,
prote in tr ans po r t w ith in lip id bilaye r , calcium -independent cell-ma trix adhesion,
cell mi gr ation involved in spro uting angiogenesis
CDC25B, VAV-2, al ph a-1/beta-1 integrin, ZO-2
posi tive regulati on of cell prolifera tion
Dyn a m in -2, TFIID, MALT1, PDK (PDPK1), Al-
protein phosphorylation, cell-cell signaling, synaptic transmission,
phosp horylation, signal transduction
adenylate cyclase-modulating G-protein coupled receptor signaling pathway
phosphatase activity and G2/M phase transition of cell
cycle. Blocking CDC25B expression leads to inhibition
of cell proliferation, migration and invasion. CDC25B
may be a potential marker and therapeutic target for
hepatocellular carcinoma and pancreatic cancer [9]. In
the third network, NM_002613 is the longer isoform of
PDPK1 (3-phosphoinositide-dependent protein kinase 1)
which is involved in the regulation of cell proliferation,
cell survival and signal transduction. PDK1-expressing
MCF-7 cells showed up-regulation of genes of the Wnt
signaling pathway and down-regula tion of p utati ve t umor
suppressor genes. In the fourth network, NM_203351 is
the longest isoform of MAP3K3 (mitogen-activated
protein kinase kinase kinase 3) which is involved in
regulating interleukin 1 receptor (IL-1R), toll-like receptor
4 (TLR4), SAPK and ERK signaling pathways. MEKK3
may be a therapeutic target in controlling the apoptosis
resistance of some cancers [10].
Therefore, accurately inferring isoforms from RNA-
Seq datasets of tumor samples and identifying tumor
driven isoforms have important implications in the field
of cancer drug res earch and development.
We have developed a method for inferring isoforms from
RNA-Seq samples. Its advantages over current isoform
inference tools have been illustrated in previous sections.
Its limitations are that complete sequences and gene
structure annotation of transcriptome of targeted species
have to be available. With advances of sequencing
technologies and genome biology, our method can be
applied to more species. Furthermor e , our method can
detect isoform switching if multiple disease and no rmal
samp l e s are available. If expression status of isoforms
are different between disease and normal samples,
isoform switching can be identi fied . If expression status
of isofo r ms ar e both marked with ‘e xis tenc e’, expr essio n
of specific exons or junctions between samples can be
used to determine whether isoforms are differentially
This work was supported in part by a project from
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