Expression of conjoined genes: another mechanism for gene regulation in eukaryotes.
ABSTRACT From the ENCODE project, it is realized that almost every base of the entire human genome is transcribed. One class of transcripts resulting from this arises from the conjoined gene, which is formed by combining the exons of two or more distinct (parent) genes lying on the same strand of a chromosome. Only a very limited number of such genes are known, and the definition and terminologies used for them are highly variable in the public databases. In this work, we have computationally identified and manually curated 751 conjoined genes (CGs) in the human genome that are supported by at least one mRNA or EST sequence available in the NCBI database. 353 representative CGs, of which 291 (82%) could be confirmed, were subjected to experimental validation using RT-PCR and sequencing methods. We speculate that these genes are arising out of novel functional requirements and are not merely artifacts of transcription, since more than 70% of them are conserved in other vertebrate genomes. The unique splicing patterns exhibited by CGs reveal their possible roles in protein evolution or gene regulation. Novel CGs, for which no transcript is available, could be identified in 80% of randomly selected potential CG forming regions, indicating that their formation is a routine process. Formation of CGs is not only limited to human, as we have also identified 270 CGs in mouse and 227 in drosophila using our approach. Additionally, we propose a novel mechanism for the formation of CGs. Finally, we developed a database, ConjoinG, which contains detailed information about all the CGs (800 in total) identified in the human genome. In summary, our findings reveal new insights about the functionality of CGs in terms of another possible mechanism for gene regulation and genomic evolution and the mechanism leading to their formation.
- [Show abstract] [Hide abstract]
ABSTRACT: Metastatic cancer of unknown primary (CUP) accounts for up to 5% of all new cancer cases, with a 5-year survival rate of only 10%. Accurate identification of tissue of origin would allow for directed, personalized therapies to improve clinical outcomes. Our objective was to use transcriptome sequencing (RNA-Seq) to identify lineage-specific biomarker signatures for the cancer types that most commonly metastasize as CUP (colorectum, kidney, liver, lung, ovary, pancreas, prostate, and stomach). RNA-Seq data of 17,471 transcripts from a total of 3,244 cancer samples across 26 different tissue types were compiled from in-house sequencing data and publically available International Cancer Genome Consortium and The Cancer Genome Atlas datasets. Robust cancer biomarker signatures were extracted using a 10-fold cross-validation method of log transformation, quantile normalization, transcript ranking by area under the receiver operating characteristic curve, and stepwise logistic regression. The entire algorithm was then repeated with a new set of randomly generated training and test sets, yielding highly concordant biomarker signatures. External validation of the cancer-specific signatures yielded high sensitivity (92.0% ± 3.15%; mean ± standard deviation) and specificity (97.7% ± 2.99%) for each cancer biomarker signature. The overall performance of this RNA-Seq biomarker-generating algorithm yielded an accuracy of 90.5%. In conclusion, we demonstrate a computational model for producing highly sensitive and specific cancer biomarker signatures from RNA-Seq data, generating signatures for the top eight cancer types responsible for CUP to accurately identify tumor origin.Neoplasia. 11/2014;
- [Show abstract] [Hide abstract]
ABSTRACT: Chimeric RNAs originating from two or more different genes are known to exist not only in cancer, but also in normal tissues, where they can play a role in human evolution. However, the exact mechanism of their formation is unknown. Here, we use RNA sequencing data from 462 healthy individuals representing 5 human populations to systematically identify and in depth characterize 81 RNA tandem chimeric transcripts, 13 of which are novel. We observe that 6 out of these 81 chimeras have been regarded as cancer-specific. Moreover, we show that a prevalence of long introns at the fusion breakpoint is associated with the chimeric transcripts formation. We also find that tandem RNA chimeras have lower abundances as compared to their partner genes. Finally, by combining our results with genomic data from the same individuals we uncover intronic genetic variants associated with the chimeric RNA formation. Taken together our findings provide an important insight into the chimeric transcripts formation and open new avenues of research into the role of intronic genetic variants in post-transcriptional processing events.PLoS ONE 01/2014; 9(8):e104567. · 3.53 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Mammalian splicing regulatory protein RNA-binding motif protein 4 (RBM4) has an alanine repeat-containing C-terminal domain (CAD) that confers both nuclear- and splicing speckle-targeting activities. Alanine-repeat expansion has pathological potential. Here we show that the alanine-repeat tracts influence the subnuclear targeting properties of the RBM4 CAD in cultured human cells. Notably, truncation of the alanine tracts redistributed a portion of RBM4 to paraspeckles. The alanine-deficient CAD was sufficient for paraspeckle targeting. On the other hand, alanine-repeat expansion reduced the mobility of RBM4 and impaired its splicing activity. We further took advantage of the putative coactivator activator (CoAA)-RBM4 conjoined splicing factor, CoAZ, to investigate the function of the CAD in subnuclear targeting. Transiently expressed CoAZ formed discrete nuclear foci that emerged and subsequently separated-fully or partially-from paraspeckles. Alanine-repeat expansion appeared to prevent CoAZ separation from paraspeckles, resulting in their complete colocalization. CoAZ foci were dynamic but, unlike paraspeckles, were resistant to RNase treatment. Our results indicate that the alanine-rich CAD, in conjunction with its conjoined RNA-binding domain(s), differentially influences the subnuclear localization and biogenesis of RBM4 and CoAZ. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.Nucleic Acids Research 11/2014; · 8.81 Impact Factor
Expression of Conjoined Genes: Another Mechanism for
Gene Regulation in Eukaryotes
Tulika Prakash, Vineet K. Sharma, Naoki Adati, Ritsuko Ozawa, Naveen Kumar, Yuichiro Nishida,
Takayoshi Fujikake, Tadayuki Takeda, Todd D. Taylor*
MetaSystems Research Team, Computational Systems Biology Research Group, Advanced Computational Sciences Department, RIKEN Advanced Science Institute (ASI),
From the ENCODE project, it is realized that almost every base of the entire human genome is transcribed. One class of
transcripts resulting from this arises from the conjoined gene, which is formed by combining the exons of two or more
distinct (parent) genes lying on the same strand of a chromosome. Only a very limited number of such genes are known,
and the definition and terminologies used for them are highly variable in the public databases. In this work, we have
computationally identified and manually curated 751 conjoined genes (CGs) in the human genome that are supported by at
least one mRNA or EST sequence available in the NCBI database. 353 representative CGs, of which 291 (82%) could be
confirmed, were subjected to experimental validation using RT-PCR and sequencing methods. We speculate that these
genes are arising out of novel functional requirements and are not merely artifacts of transcription, since more than 70% of
them are conserved in other vertebrate genomes. The unique splicing patterns exhibited by CGs reveal their possible roles
in protein evolution or gene regulation. Novel CGs, for which no transcript is available, could be identified in 80% of
randomly selected potential CG forming regions, indicating that their formation is a routine process. Formation of CGs is not
only limited to human, as we have also identified 270 CGs in mouse and 227 in drosophila using our approach. Additionally,
we propose a novel mechanism for the formation of CGs. Finally, we developed a database, ConjoinG, which contains
detailed information about all the CGs (800 in total) identified in the human genome. In summary, our findings reveal new
insights about the functionality of CGs in terms of another possible mechanism for gene regulation and genomic evolution
and the mechanism leading to their formation.
Citation: Prakash T, Sharma VK, Adati N, Ozawa R, Kumar N, et al. (2010) Expression of Conjoined Genes: Another Mechanism for Gene Regulation in
Eukaryotes. PLoS ONE 5(10): e13284. doi:10.1371/journal.pone.0013284
Editor: Pawel Michalak, University of Texas Arlington, United States of America
Received April 30, 2010; Accepted September 14, 2010; Published October 12, 2010
Copyright: ? 2010 Prakash et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding support from operational expenditure fund of RIKEN is duly acknowledged. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Eukaryotic transcription is a highly complex process typically
accomplished by interaction of several proteins and regulatory
sequences at different levels to generate a variety of gene products.
The ENCODE project recently uncovered complex patterns of
dispersed regulation and pervasive transcription for at least 1% of
the human genome . Subsequently, the long-standing conven-
tional definition of a gene is fading and it is now realized that the
genome is full of overlapping and other complex transcripts .
One such intriguing example is the read-through transcript or
conjoined or co-transcribed gene (see Table S1 for a list of alternative
and proposed names). A ‘‘conjoined gene’’ (CG) is defined as a gene,
which gives rise to transcripts by combining at least part of one
exon from each of two or more distinct known (parent) genes
which lie on the same chromosome, are in the same orientation,
and often (95%) translate independently into different proteins. In
some cases, the transcripts formed by CGs are translated to form
chimeric or completely novel proteins. Currently, only 34 CGs are
described in the NCBI Entrez Gene database, including well-
known examples such as TRIM6-TRIM34 and NME1-NME2 (see
http://metasystems.riken.jp/conjoing/faqs#ques2 for a complete
list). This ‘‘lack of annotation’’ indicates that this is either a rare
phenomenon or that this type of gene has not yet been well
characterized in the human genome due to the lack of consensus
within the genome annotation community. Also, the use of
different gene names to address such transcripts compounds the
problem of their identification.
The most widely used resources for accessing human genome
annotation information include NCBI Entrez Gene (http://www.
ncbi.nlm.nih.gov/sites/entrez?db=gene), the UCSC Genome
(http://uswest.ensembl.org/index.html), and the Vertebrate Ge-
nome Annotation (Vega) database (http://vega.sanger.ac.uk/
Homo_sapiens/index.html). Even now, seven years after the
completion of the human genome, there exist many discrepancies
for annotating human genes (including CGs) among these
resources, thus somewhat limiting the identification of CGs. For
example, CG FPGT-TNNI3K is not reported in either NCBI or
UCSC although both parent genes, FPGT and TNNI3K, are
present, while in Vega and Ensembl this CG is reported as
TNNI3K and the parent gene FPGT is not present at all. To add to
the confusion, the locus representing the FPGT gene in NCBI and
UCSC is represented as a variant of TNNI3K in Vega and
PLoS ONE | www.plosone.org1October 2010 | Volume 5 | Issue 10 | e13284
Ensembl (see Figure S1). Furthermore, the descriptive contents
and the terms used to address CGs across the various resources are
not uniform and could be misleading, making it difficult to find
and to search for detailed information about them. For example,
in plants, read-through transcription commonly refers to genes
having multiple polyadenylation sites leading to transcripts that
extend for variable distances into the 39 flanking region of the
genes . Confusingly, many known CGs are also termed ‘‘read-
through transcripts’’ in these databases. Similarly, ‘‘fusion gene’’ is
another misnomer for CGs because the commonly understood
definition of a fusion gene is a hybrid gene formed from two
previously separate genes that occurs as the result of a
translocation, interstitial deletion, or chromosomal inversion.
Thus, we propose the term ‘conjoined gene’ (CG) for these specialized
genes that give rise to transcripts by joining the exons of two or
more distinct parent genes during transcription. For clarity, it is
important to assign these genes with unique and meaningful
names, as is done by the HUGO Gene Nomenclature Committee
(http://www.genenames.org/) for all other genes in the human
During the gene annotation of human chromosome 11, eleven
CGs were identified by our group . Concurrently, three other
research groups independently analyzed the human genome and
identified unique CGs using mRNA and EST information
available in the public databases [5,6,7]. Recently, in two cancer
cell lines, MCF7 (breast) and HCT116 (colon), 70 putative CGs
were identified using Paired-End diTags . Confusingly, the
definition of ‘conjoined genes’ in these analyses is variable,
including transcripts formed by combining the exons of two genes
in opposite orientations and fused transcripts formed by
chromosomal translocations or other rearrangements such as
insertions or deletions. Similarly, Li and Zhao et al.  recently
identified nearly 31,000 ‘‘chimeric RNAs’’ in the human genome,
however, transcripts formed by CGs were not included in their
analysis. According to Denoeud et al. , more than 150 CGs
were found in the 1% of the human genome analyzed for the
ENCODE project; thus, there ought to be many more instances of
CGs than have yet been found in the entire genome. Therefore,
there is still a need for systematic scanning of the human genome
for the identification of more true instances of ‘‘conjoined genes’’.
In the present study, we report the findings of our ‘‘Conjoin’’
algorithm for the identification of CGs in any genome given the
availability of its mRNA or EST sequences, along with its
reference gene annotation in the NCBI database. Detailed
examination of these genes provides useful insights about their
roles in the human genome. Furthermore, we have developed a
comprehensive database with more uniform and descriptive
annotation for the CGs to provide the research community a
specialized resource to visualize, examine, and study the
characteristics of these genes.
Computational identification of CGs
To estimate the total number of CGs in the human genome,
alignments of the known genes, mRNAs, and ESTs to the human
genome generated by UCSC (Human assembly (hg18) March,
2006) were used. The algorithm ‘‘Conjoin’’ was developed and
applied to the entire human genome (see Text S1 for the details).
This resulted in 623 and 942 CG candidates from the mRNA and
EST data, respectively. In a manual curation step, false positives
arising out of misalignments of genes from the same gene family,
poor quality sequences, and short EST sequences were removed.
We also removed the false positive cases arising from splice
variants of single gene loci with multiple gene names (see Figure S2
for an example). As a result we obtained 317 and 434 CGs
supported by at least one mRNA or EST sequence, respectively.
These 751 CGs connected a total of 1,451 known, separate parent
genes, with at least one case found on each chromosome.
Interestingly, more than one third of the CGs are formed by
parent genes belonging to the functional class of ‘‘Cellular
Processes and Signaling’’ as defined in the clusters of orthologous
groups for eukaryotes (KOGs)  (see Text S2). In addition, 11%
(83/751) of the CGs involved parent genes with related functions,
such as TRIM6-TRIM34, CCL15-CCL14, TNFSF12-TNFSF13, etc.
Figure 1 shows an example of a known CG, NME1-NME2, in the
NCBI Entrez Gene database on chromosome 17 formed by
connecting parent genes NME1 and NME2. Only 12% (88/751) of
CGs are supported by more than one transcript, suggesting that
these genes are either relatively rare, or that they are not
ubiquitously expressed and are limited to only certain tissue types.
Interestingly, when multiple transcripts were detected for a CG,
alternative splicing was observed in 61% of the cases. Out of the
34 CGs listed in the NCBI Entrez Gene database, 30 were
identified by our approach. For the remaining four cases, no
aligned mRNA or EST sequences connecting the parent genes
were found in the version of data used from the UCSC Genome
Browser at the time of this study.
A vast majority (98%) of CGs are formed by only two parent
genes, with only a few cases where three (13) or four (4) genes are
involved. As expected, the number of CGs per chromosome is
significantly correlated with the average gene density and amount
of transcriptome data available for each chromosome. However,
an inverse correlation was observed for the average distance
between genes on each chromosome (see Text S3), implying that
genes lying closer on the genome have a higher tendency for
forming CGs. Nevertheless, these observations ruled out the
possibility of any bias towards certain chromosomes over others.
Splicing patterns of exons
For the 317 CGs supported by at least one mRNA sequence, we
evaluated the exon splicing patterns (see Methods). An interesting
pattern was observed in 42% of conjoined mRNAs, where a new
intron was created which spans the terminal exon, the 39-UTR of
the upstream (59-) gene, followed by the intergenic region between
the two parent genes, and then the 59-UTR and initial exon of the
downstream (39-) gene (http://metasystems.riken.jp/conjoing/
faqs#ques5). Similar observations were also reported by Akiva
et al. . In addition, in 46% of the CGs, novel exons were
included from the intergenic or intronic regions, or from the
flanking regions of the upstream (59-) or the downstream (39-)
parent genes. New splice sites have been used in 58% of the CGs;
however, in the majority of these cases (85%) splicing occurred at
canonical sites (GT-AG). In almost all the CGs (99%), in which
splice sites remain conserved from the parent genes, splicing
occurred at canonical sites.
Experimental validation of conjoined genes
We attempted to experimentally validate 353 out of 751 CGs
using RT-PCR and sequencing methods in 16 human tissues (see
Methods). The CGs in 291 out of 353 (82%) cases were confirmed
by sequencing the expected CG transcript in at least one tissue.
One representative example is shown in Figure 2. This CG,
ZC3H10-ESYT1, is supported by one EST sequence (DB062879)
in the NCBI GenBank database and was confirmed by sequencing
of the RT-PCR product. PCR products of kidney, skeletal muscle,
spleen, and testis were selected for sequencing because they
contained representative bands of obtained PCR products.
Conjoined Genes in Eukaryotes
PLoS ONE | www.plosone.org2 October 2010 | Volume 5 | Issue 10 | e13284
Obtained sequences revealed alternative splicing and novel exons,
as well as tissue-specific expression. The expected mRNAs could
not be sequenced for the remaining 62 CGs. This could be due to
non-expression of these genes in the tissues used for validation or
because they are expressed at very low and undetectable levels.
Interestingly, alternative splicing was observed in 63% (184/291)
of the CGs, many of which also harbor novel exons. For the
remaining 37% of the CGs, either only a single sequence was
obtained by our experiments (for example, CG PHOSPHO2-
KLHL23), or all of the sequences exhibited the same splicing
pattern (for example, CG TGIF2-C20orf24), so alternative splicing
could not be detected.
These experiments also revealed important information about
CG expression patterns. Among the tissues examined, brain and
reproductive tissues, including testis, prostate, and uterus, are
particularly enriched in CG sequences (http://metasystems.riken.
jp/conjoing/faqs#ques11). The reproductive tissues exhibit dis-
tinct polyadenylation signal biases , which possibly confer them
RNA diversity. Mammalian brain has been identified as the tissue
that expresses the greatest number of alternative mRNA isoforms
. In addition, a large variety of non-coding RNAs have been
associated with the central nervous system and impart high
complexity to the brain by acting as gene expression regulators
. This is likely to be related to the fact that the brain is
populated by thousands of highly specialized unique cell types that
undergo dynamic changes. Therefore, finding a large number of
transcripts encoded by CGs which can increase both protein-
coding and non-coding RNA diversity in these tissues is not so
surprising, and a similar role of imparting functional diversity and
gene expression regulation by CGs cannot be ruled out.
Since 69% (202/291) of CGs were found to be widely expressed
of CGs is expected to be a more widespread phenomenon
occurring in most tissues. However, 31% (89/291) of CGs showed
tissue-specific expression, suggesting selective expression for many
of them. Certainly, these CGs could be expressed in tissues other
than the ones we examined. Using the tissue expression data from
the NCBI UniGene database (http://www.ncbi.nlm.nih.gov/
unigene), about 76% of the conjoined and participating parent
genes were found to be expressed in tissues from various tumors.
Although several fusion genes, particularly those produced by
chromosomal rearrangements, have been found to be involved in
carcinogenesis , only a few CGs have been shown to be
expressed in cancer tissues, including RBM6-RBM5 , HHLA1-
OC90 , and LY75-CD302 . Therefore, studying the role of
these CGs in both normal and cancerous cells in more detail may
provide important insights about the diseased conditions.
Comparison of our findings with those of others
In early 2006, Akiva et al.  and Parra et al.  independently
analyzed the human genome and identified 212 and 127 CGs,
respectively, using mRNA and EST information available in the
public databases. At the same time, another analysis done by Kim
et al. resulted in the identification of 258 unique CGs in the
human genome . On close examination, we found that some of
these genes have now become obsolete due to the lack of
appropriate alignments of the CG mRNAs with the parent genes,
revision of the coordinates of the participating parent genes in the
current version of the human genome databases, or different
chromosomal location or opposite orientation of the parent genes.
As a result, only 193, 123, and 83 CGs remain valid in the Akiva,
Kim, and Parra datasets, respectively. To estimate the total
number of unique CGs, we compared the 751 CGs identified in
this study with these three datasets (Figure 3). 232 out of 751 CGs
Figure 1. CG formed by parent genes NME1 and NME2 on chromosome 17. This CG is found in the NCBI Entrez Gene database (NME1-NME2).
Two mRNA sequences (DQ109675, and BC107894) and 37 EST sequences (two are shown here, BI603164 and BQ277261) support this CG. The
upstream (59-) and downstream (39-) parent genes are shown in red and blue colors, respectively. The CG transcripts and CDSs are shown in magenta
and grey colors, respectively. Exons and introns are shown as boxes and lines, respectively. Arrows indicate the strand of the transcript.
Conjoined Genes in Eukaryotes
PLoS ONE | www.plosone.org3 October 2010 | Volume 5 | Issue 10 | e13284
were included in at least one other dataset, with 106 being
confirmed in at least one human tissue by our experiments. The
remaining 519 (69%) CGs were uniquely identified by our method
only, 185 of which were experimentally validated. Forty-three
CGs were uniquely identified by at least one of the Akiva, Kim, or
Parra analyses, but not by us.
We also compared the 13 chimeric transcripts obtained by RT-
PCR by Denoeud et al.  in the ENCODE region to our
dataset. Only two were found in common, and five were doubtful
by the criteria mentioned above. Six CGs in the ENCODE study
were uniquely identified by them. The sequences used to confirm
these CGs were not found in the version of the human genome
data from UCSC that was used for our analysis. Thus, adding the
unique CGs identified by Akiva, Kim, Parra and the ENCODE
analysis to ours results in 800 unique CGs identified in the entire
human genome to date. This clearly indicates that there may still
be many more instances of CGs in the genome and that the
existing genomic information is not yet sufficient to identify all of
Functional roles of conjoined genes
Protein evolution by chimeric proteins.
of gene fusion in eukaryotic evolution has been previously
Figure 2. Example of experimentally verified CG, ZC3H10-ESYT1, on chromosome 12. (A) PCR products amplified from human tissues. This
CG (based on EST accession DB062879) was confirmed by RT-PCR in 16 human tissues. It showed various patterns in each tissue. Arrowheads indicate
the bands which were cloned and sequenced. Marker: OneSTEP Ladder 100 (0.1–2 kbp) (NIPPON GENE CO., LTD., Toyama, Japan). (B) Confirmed CG
regions displayed on the UCSC Genome Browser (hg18). The upper tracks (black) represent the seed EST and the identified CG sequences, and the
lower tracks (blue) show the RefSeq genes annotated in this region.
Figure 3. Overlap of the CGs identified by our approach versus
those identified by others. Twelve CGs are common among all four
datasets. 519 CGs were uniquely identified by our approach. (Akiva et al
, Parra et al , and Kim et al  (ChimerDB)).
Conjoined Genes in Eukaryotes
PLoS ONE | www.plosone.org4 October 2010 | Volume 5 | Issue 10 | e13284
demonstrated . The specialized splicing patterns observed in
CGs can result in novel proteins, which may play a role in the
additional complexity of the human genome as demonstrated in
case of chimeric protein Kua-UBE2V1 . For open reading
frame (ORF) prediction, only those CGs were selected for which at
least one mRNA sequence was found and a reasonable ORF could
be obtained (297 CGs, 409 mRNAs, see Methods). Transcripts
arising from 16% of the selected CGs used conserved reading
frames of translation from all the parent genes, thereby forming
chimeric proteins by joining the domains of their respective parent
genes (Table 1). It is well known in prokaryotes that the vast
majority of gene pairs whose orthologs are fused are either part of
the same complex, or function in the same pathway . Thus
CG formation can result in co-regulation of gene expression of
functionally related proteins such as for TNFSF12-TNFSF13 
and TRIM6-TRIM34 .
Gene regulation by CG expression.
perform other regulatory roles, such as altering the expression of
parent genes. They can achieve this by using altogether different
frames of translation in the conjoined transcript with respect to all
the parent genes, thus preventing their expression by forming a
novel protein or non- protein-coding transcript (5% of CG
mRNAs). Alternatively, the CG transcript may be translated using
the conserved reading frame from only one parent gene. The
sequence corresponding to the other parent gene is then either
translated in a different reading frame or remains as an
untranslated region (79% of CG mRNAs) (Table 1). In either of
these scenarios, expression of one of the parent genes is affected. In
some mouse tissues, CG Ankhd1-Eif4ebp3 shows a similar
expression pattern as that of Ankhd1, whereas the expression of
Eif4ebp3 is significantly lower in these tissues . A similar role of
gene regulation is expected for some other CGs such as INS-IGF2,
ZFP91-CNTF, MUTED-TXNDC5, etc., in which the CG ORF is
similar to only one of the parent gene’s ORF. Interestingly, in a
slightly larger number of CGs (58%), the predicted ORF was more
like that of the downstream (39-) parent gene as compared to the
upstream (59-) parent gene (42%).
Protein expression, however, is highly controlled by the
Nonsense Mediated Decay (NMD) mechanism . Thus, it is
not surprising that 18% of CGs are expected to undergo NMD
due to the appearance of a premature stop codon as a result of
frame-shift caused by altered exon-intron splicing with respect to
the parent genes, or by the inclusion of novel exons. Even in these
cases, expression of the parent genes may be temporally regulated
by the formation of a conjoined transcript.
Alternatively, CGs can
Occurrence of conjoined genes in other genomes.
functional role of CGs can be further strengthened if they are
shown to survive through selective evolutionary pressure. Hence,
we examined the conservation of CG ‘junction exons’ across 23
other vertebrate genomes using BLAT (e#1026). The ‘junction
exons’ can be defined as the exons which contain DNA sequence
from both participating parent genes, that is to say, the last exon of
the upstream (59-) gene and the first exon of the downstream (39-)
gene, in the transcripts formed by the CGs. In cases where the last
and first exons of the two parent genes, respectively, form separate
exons in the CG transcripts, with or without any novel exons
between them, the entire region was used.
More than 70% of the human CG junction exons were found to
be conserved across eight other vertebrate genomes (http://
metasystems.riken.jp/conjoing/faqs#ques4). No significant con-
servation was observed in the lower-order vertebrates including
zebrafish, X. tropicalis, medaka, stickleback, lamprey, tetraodon,
and fugu although a large number of mRNA and EST sequences
were available for all these organisms. Among the higher-order
vertebrates, maximum conservation of CG junction exons was
observed in the chimpanzee genome. We observed a large
decrease in the number of conserved human CG junction exons
from chimpanzee to macaque and orangutan. This could be due
to the poor quality of the transcriptome annotation for these
genomes. With the advancement in high-throughput transcrip-
tome sequencing technologies, such as RNAseq, more RNA
sequence data is expected to be available in the near future,
leading to the detection of additional conserved human CGs in
these and other genomes. Nevertheless, all other genomes showed
much less conservation of human CG junction exons. Therefore, it
is evident from our analysis that CG conservation does not depend
on the amount of sequence data available for any given genome;
instead, it is correlated with the order of complexity of the
Conservation of CGs across several different vertebrate
genomes implies that their formation is not only limited to
human. To further explore this possibility, we applied the
‘Conjoin’ algorithm on three eukaryotic genomes namely, mouse,
fruit fly, and dog. Interestingly, 270 and 227 CGs were identified
in mouse and fruit fly respectively, whereas no CGs could be
detected in the dog genome using the currently available mRNA
and EST data from UCSC. The junction exons from the mouse
and fruit fly CGs were extracted as described above for human
CGs, and were searched in the human mRNA and EST datasets
using BLAT (e#1026). Only 0.03% of the mouse CG junction
Table 1. Reading frames used for the formation of the CG ORFs.
Reading frames used by CGProtein Product% CG (## of CGs/Total Selected)Examples
Same as all parent genes
Chimeric16% (48/297) NME1-NME2
Same as one of the parent
genes (partially conserved)
Similar to one of the
79% (234/297) ARID4B-RBM34, OMA1-DAB1
Different than all parent
Novel or non-coding 5% (15/297)DCDC1-DCDC5
Conjoined Genes in Eukaryotes
PLoS ONE | www.plosone.org5 October 2010 | Volume 5 | Issue 10 | e13284
exons were found conserved in human, whereas no fruit fly CG
junction exons were detected. Identification of CGs in the mouse
and fruit fly genomes further emphasizes that CGs are not mere
artifacts of the transcription process, but that they likely have
well-defined roles in either gene regulation or protein complexity
Identification of CGs from the regions where no prior CG
transcript evidence was available
We found that the median distance between parent genes
forming CGs is 10 kb, except for a few outliers such as DOCK5-
PPP2R2A, LASP1-PPP1R1B, FIP1L1-PDGFRA, and MATR3-
PURA, which are formed by parent genes that lie as far apart as
700–800 kb, bypassing exons of several internally located genes.
Since the median distance between genes in the entire human
genome is roughly 64 kb, this clearly indicates that CGs are
preferentially formed by genes lying much closer to each other. In
total, there are about 3,000 gene pairs, arising from 5,050 unique
genes (20% of all the known human genes), which lie less than
10 kb apart on the same strand that could potentially encode CGs.
Therefore, the possibility of finding more CGs in the human
genome cannot be ruled out.
To test this possibility, we randomly selected ten pairs of genes
(test cases) from the human genome which satisfied the following
‘minimum’ criterion for formation of CGs; they are (i) on the same
chromosome, (ii) on the same strand, and (iii) less than 10 kb
apart. Since the most common pattern of CG splicing is to exclude
the terminal exon of the upstream (59-) gene and the initial exon of
the downstream gene (39-), we designed PCR primer sequences for
these regions from the second-to-last exon of the upstream (59-)
gene and the second exon of the downstream (39-) gene. We then
performed RT-PCR, followed by sequencing, of the positive PCR
fragments. Surprisingly, for 80% (eight out of ten) of the test cases,
we successfully verified the expected CG mRNA joining the two
parent genes (Table 2). Like most other CGs, alternative splicing
with the inclusion of novel exons was also observed in these test
cases. This clearly indicates that more than 20% of the human
protein-coding genes are capable of forming CGs, and the
formation of CGs is an innate process in the genome which does
not necessarily take place under any special conditions such as the
diseased state. However, the existing transcript information for the
human genome is still not comprehensive enough to detect all
possible gene variants and there lies the great possibility of finding
many more CGs and other novel genes  using other more
Development of ConjoinG: Database of Human
Because of the lack of uniformity in the annotation of CGs
across the various human genome resources (UCSC, GenBank,
Ensembl, Vega), we realized that there was a need for a dedicated
and comprehensive repository for such genes. This repository
could also act as an important resource to link the information
about these genes from the other databases. Therefore, we
developed a comprehensive database, called ConjoinG, which
harbors detailed information about each of the 751 unique human
CGs identified by our method and 49 CGs identified by other
approaches. The ConjoinG database has several useful function-
alities such as visualization of the CG mRNAs and ESTs with
respect to the parent genes in their genomic context, similarity of
the CG coding DNA sequences (CDSs) with those of the parent
genes, conserved regions of the CG ‘junction exons’ in other
vertebrate genomes, etc. In addition, the database harbors details
about the experiments used to confirm the CGs, including primers
used for RT-PCR, sequences of the PCR products mapped back
on the genome, tissues in which the CGs were found expressed,
and so on. This database also includes several simple and
advanced query options and can be accessed freely at http://
The mammalian transcriptome is much more complex than
previously thought. Several recent studies suggest that most of the
mammalian genome is transcribed, yet thousands of transcripts do
not encode for proteins . These non-protein-coding genes,
along with some CGs, play a variety of regulatory functional roles.
In this analysis we report the identification of 751 CGs, and for the
first time experimental confirmation of the existence of 82% (291
out of 353 representatives) of CGs in 16 human tissues. Some of
the CGs also overlapped with those identified in other studies
(Figure 3), but a large majority of them were uniquely identified by
our method only.
Recently, induced chromosomal proximity has been shown to
give rise to gene fusions in some cancer cells . Chromosomal
folding is known to bring loci which are far apart in the linear
sequence in close proximity in the three dimensional space of the
Table 2. Ten test cases in human for which no prior evidence existed.
Gene SymbolStrand Chromosome CG Confirmed?
ABHD14A ACY13 YesYes
3 Yes Yes
ACCN2 SMARCD112 YesNo
20 No NA
Conjoined Genes in Eukaryotes
PLoS ONE | www.plosone.org6 October 2010 | Volume 5 | Issue 10 | e13284
transcription factories . Thus, the likely role of chromosomal
folding, giving rise to distinct loops, for the formation of some CGs
cannot be ruled out. Based on physical properties, the estimated
minimum length of a typical chromatin loop is more than 10 kb;
however, shorter loops are also possible . Interestingly, most
genes in close proximity in the transcription factories where they
may form the CG transcript. In a few recent studies, two different
models have been proposed for the formation of chimeric RNAs
(not including CGs) in the transcription factories. First, the SHS
(short homologous sequence) mediated ‘‘transcriptional slippage’’
model has been described for the formation of interchromosomal
chimeric RNAs . Only a very small fraction of CGs (10%) were
found to harbor such SHSs ($4 bps) at their junction. In the second
model, ‘‘trans-splicing’’ mediated formation of chimeric RNAs was
proposed [30,31]. However, both of these models suffer from their
own limitations. In the ‘‘transcriptional slippage’’ model, the
presence of an SHS element at the junction seems essential;
whereas in the ‘‘trans-splicing’’ model, the appearance of novel
exonsfound inmanyof the CGscannot be explained. Thus, a single
model is not sufficient to explain the formation of CGs.
From our observations, we propose a third mechanism of
‘‘transcriptional run over and intergenic splicing’’ to be operating,
especially in the formation of CGs which harbor novel exons from
the intergenic regions. In our proposed mechanism, the transcrip-
tional machinery first transcribes the upstream (59) gene but
somehow escapes its termination signal, it then continues
transcribing the downstream (39-) gene as part of the same
conjoined transcript. This escape from termination of the upstream
(59-) gene seems unlikely to be inadvertent; rather, it appears to be
well-regulated by some yet unknown trans-factors, as cis-regulatory
elements,exceptfor simple repeats, showed nounusualtrendsinthe
flanking regions of CGs (see Text S4). In prokaryotic genomes,
several transcription anti-termination factors are known which help
in the escape of the termination signals and consequently lead to
transcriptional read-through into the next gene . The
identification of CGs suggests the presence of similar factors in
eukaryotic genomes as well, which are less well explored to date. In
addition to anti-termination factors, the presence of multiple
polyadenylation sites might also aid in the formation of CGs.
Recently, the use of alternative polyadenylation sites has been
realized as another mechanism for generating a variety of mRNA
transcripts . The choice of polyadenylation site is found to be
affected by several factors, such as tissue specificity or differing cell
types [13,33]. Also, it has been demonstrated that almost half of the
genes in the human genome are alternatively polyadenylated .
Therefore, the formation of CG transcripts, with the help of yet
unidentified anti-termination factors, in addition to alternative
polyadenylation, appears likely and requires further investigation.
Since it is evident that CGs are not merely artifacts of
transcription, then they must be the result of some specific
genomic requirements and have well-defined functional roles. This
idea is further strengthened by the fact that some CGs have
endured purifying evolutionary selective pressure and are
conserved in other vertebrate genomes such as chimpanzee,
macaque, mouse, and dog. In addition, formation of CGs is not
only limited to human, as we have also identified unique CGs in,
among others, the mouse and drosophila genomes, indicating
towards their functional relevance. In addition to protein
evolution, CGs can be responsible for gene regulation by
preventing the expression of at least one or more of the parent
genes. The fact that around 18% of the CG transcripts are
expected to undergo NMD clearly suggests that some of these
transcripts exist only transiently, and that the predicted resultant
proteins are never actually expressed.
Although more than 20% of the human protein-coding genes
are capable of forming CGs, we could identify only 800 CGs in
our analysis, which is the highest number of CGs shown so far in
the entire human genome. The limitation to our approach for the
identification of additional CGs is the availability of mRNA or
EST sequences which support the CGs in the publicly available
databases. The fact that most CGs, including some well-known
examples such as TNFSF12-TNFSF13 and TMEM189-UBE2V1,
are supported by only a single mRNA or EST sequence indicates
towards very low or tissue-specific expression of CGs. Therefore,
more sophisticated techniques may be required to identify
additional CGs in the human or other genomes.
To understand the complex systems biology of higher organisms
it is critical to determine the role of CGs, such as has been done for
other unusual types of transcripts, including ncRNAs identified in
the human genome. However, the lack of a specific resource
dedicated to these specialized CGs, compounded with the use of
different terminologies for addressing them across currently
available public genomic resources, greatly restricts this task.
Therefore, the ConjoinG database should be particularly useful in
this scenario. Identification of the underlying mechanism(s)
controlling the formation of CGs in human and other vertebrate
genomes remains, and is a target for future studies.
Clearly from this and other similar analyses, the same loci in a
genome canoperatein a multi-functional mannersuchthat an exon
of one gene can be the intron of another gene, or even an intergenic
region for some other genes. These observations have once again
put the definition of a ‘gene’ in question. Traditionally, a gene is a
stretch of DNA that encodes for a type of protein that has a function
in an organism. However, keeping in mind the other important
regulatory functional roles performed by CGs, their annotation in
the genome is equally important and should not be ignored.
Computational identification of conjoined genes
Alignments of ‘UCSC genes’, ‘Human mRNAs’ and ‘Spliced
ESTs’ tracks (Human Genome assembly (hg18) March, 2006 build
the UCSC Genome Browser database for the human, mouse, dog,
and fruit fly genomes. The reference sequences for these genomes
were also downloaded from the same resource. An automated Perl
algorithm ‘‘Conjoin’’ was developed for the identification of conjoined
genes (see Text S1 for details). The algorithm is based on the
positional comparisonfrom thealignments of theknowngenestothe
mRNA and EST sequences. The algorithm identifies all those
mRNA and EST sequences which align to two or more different
genes as defined in the NCBI RefSeq or UCSC Genes database.
False positive cases arising out of misalignments and alternative
splicing of the same loci were removed by manual curation.
Experimental verification of conjoined genes
To validate the CGs, we used human poly(A)+RNA from 16
different human tissues including brain, heart, kidney, liver, lung,
pancreas, prostate, skeletal muscle, spleen, stomach, testis, uterus,
fetal brain, fetal kidney, fetal liver, and fetal lung (Clontech
Laboratories, Inc., Mountain View, CA, USA), which were
reverse-transcribed with oligo(dT)20using the ThermoScript RT-
PCR System for First-Strand cDNA Synthesis (Invitrogen Corp.,
Carlsbad, CA, USA). Two sets of primers, external (first) and
internal (nested) primers, were designed from the genomic
sequences corresponding to the CG mRNAs and ESTs using the
Conjoined Genes in Eukaryotes
PLoS ONE | www.plosone.org7 October 2010 | Volume 5 | Issue 10 | e13284
Primer3  software. RT-PCR was basically performed with
TaKaRa Ex Taq (Takara Bio Inc., Otsu, Shiga, Japan) and both
first and nested primer sets to detect the expected CG mRNA or
EST. The PCR products were introduced into the pT7Blue-2 T-
Vector (Novagen, San Diego, CA, USA) and then transformed
into E. coli strain, DH5 alpha. Colony PCR was performed using
the E. coli clones carrying the PCR products originating from a few
selected tissues in which CG sequences were expected to be
transcribed. These PCR products were subsequently subjected to
sequencing with the BigDye Terminator v3.1 Cycle Sequencing
Kit (Applied Biosystems LLC, Foster City, CA, USA) on an
Applied Biosystems 3130xl Genetic Analyzer and a 3730xl DNA
Analyzer (Applied Biosystems LLC).
Analysis of conjoined gene ORFs
For analysis of the splicing patterns and ORFs encoded by the
CGs, only those candidates were selected for which at least one
mRNA sequence was found so that a full-length sequence could be
expected. Out of the 751 CGs, only 317 were found with at least
one supporting mRNA sequence, for a total of 426 mRNA
sequences. For the analysis of CG ORFs, the predicted proteins
corresponding to all the parent genes and 264 out of 426 CG
mRNAs were obtained from the NCBI GenBank database (http://
www.ncbi.nlm.nih.gov/Genbank/).Forthe remainingCG mRNAs
the longest ORF starting with a methionine, if found, was used. For
17 CG mRNAs, either reliable ORFs could not be predicted or the
predicted proteins corresponding to the parent gene(s) were not
found, so they were excluded from this analysis, leaving a total of
409 CG mRNAs (297 CGs). Alignments of the parent genes and
CG proteins were generated using the Emboss Needle program
(http://emboss.sourceforge.net/) with default options.
Conservation of conjoined genes
To measure the conservation of CGs in other species, the
mRNA and EST libraries of 23 other vertebrate genomes were
downloaded from the UCSC Genome Browser database. BLAT
(version 33) was used to align the ‘junction exons’ from the CGs to
the vertebrate genome mRNA and EST sequences using an E-
value cut off of less than 1026. In addition, only matches where
greater than 90% of the junction exon sequence was conserved
with greater than 90% sequence identity were considered.
Development of ConjoinG database
Open Source LAMP (Red Hat Enterprise Linux 4) Technology,
and Perl (version 5.8.5) were used for development of the GUI
and back-end database called ‘Conjoindb’ (http://metasystems.
riken.jp/conjoing/). The web server was developed using the
Apache HTTP Server (version 2.2.8). Client-side scripting was done
done using PHP and XML. The external application BLAST 
was integrated for additional analysis.
Entrez Gene database, the UCSC Genome Browser, the Vertebrate
Genome Annotation (Vega) database, the Ensembl Genome
Status of conjoined gene FPGT-TNNI3K in the NCBI
Browser, and the ConjoinG database. The CG FPGT-TNNI3K is
not reported in either NCBI or UCSC, although both parent genes,
FPGT and TNNI3K, are present (shown by red block arrows), while
inVega and Ensembl this CG is reported as TNNI3K(shown by red
block arrow), and the parent gene FPGT is not present at all. The
locus representing the FPGT gene in NCBI and UCSC is
represented as a variant of TNNI3K in Vega and Ensembl.
Found at: doi:10.1371/journal.pone.0013284.s001 (4.76 MB
variants of the same gene on chromosome 2 (UGT1A complex
locus). The members of this family have different names, so the
mRNA or EST sequences aligning in this region are falsely
predicted as possible CG candidates. For example, mRNA
accession AF030310 (shown in red) will be predicted as a CG
transcript combining many members of the UGT1A complex
locus. Such false positive cases were removed during the manual
curation step of our analysis.
Found at: doi:10.1371/journal.pone.0013284.s002 (0.13 MB
Example of a false positive case due to gene name
Found at: doi:10.1371/journal.pone.0013284.s003 (0.03 MB
Alternative names used for conjoined genes.
Found at: doi:10.1371/journal.pone.0013284.s004 (0.31 MB
Identification of conjoined genes in the human genome.
Found at: doi:10.1371/journal.pone.0013284.s005 (0.25 MB
Functional analysis of the parent genes.
Found at: doi:10.1371/journal.pone.0013284.s006 (0.10 MB
Chromosomal distribution of the conjoined genes.
regions of CGs.
Found at: doi:10.1371/journal.pone.0013284.s007 (0.06 MB
Distribution of cis-regulatory elements in the upstream
We are deeply grateful to Emi Abe, Yuko Sano, Mayumi Kato, Reina
Okumura, and Maki Mushiake (Genome Annotation and Comparative
Analysis Team, RIKEN Genomic Sciences Center), for experimental
contribution. In addition, we thank Takujiro Katayama (Hitachi
Government and Public Corporation System Engineering, Ltd) and
Chiharu Kawagoe (Hitachi, Ltd) for providing technical support. We also
thank Naoko Kobayashi and Yui Bando for their administrative assistance.
The sequences generated in this study were deposited in the DNA Data
Bank of Japan (DDBJ)/EMBL/GenBank under the accession numbers
FY210627 - FY214045.
Conceived and designed the experiments: TP TDT. Performed the
experiments: TP VKS NA RO TT TDT. Analyzed the data: TP VKS YN
TF TDT. Contributed reagents/materials/analysis tools: TP NK TDT.
Wrote the paper: TP TDT. Developed the database and designed the web
1. Birney E, Stamatoyannopoulos JA, Dutta A, Guigo R, Gingeras TR, et al.
(2007) Identification and analysis of functional elements in 1% of the human
genome by the ENCODE pilot project. Nature 447: 799–816.
2. Pearson H (2006) Genetics: what is a gene? Nature 441: 398–401.
3. Xing A, Moon BP, Mills KM, Falco SC, Li Z (2010) Revealing frequent
alternative polyadenylation and widespread low-level transcription read-
through of novel plant transcription terminators. Plant Biotechnol J 8:
Conjoined Genes in Eukaryotes
PLoS ONE | www.plosone.org8 October 2010 | Volume 5 | Issue 10 | e13284
4. Taylor TD, Noguchi H, Totoki Y, Toyoda A, Kuroki Y, et al. (2006) Human
chromosome 11 DNA sequence and analysis including novel gene identification.
Nature 440: 497–500.
5. Akiva P, Toporik A, Edelheit S, Peretz Y, Diber A, et al. (2006) Transcription-
mediated gene fusion in the human genome. Genome Res 16: 30–36.
6. Parra G, Reymond A, Dabbouseh N, Dermitzakis ET, Castelo R, et al. (2006)
Tandem chimerism as a means to increase protein complexity in the human
genome. Genome Res 16: 37–44.
7. Kim N, Kim P, Nam S, Shin S, Lee S (2006) ChimerDB–a knowledgebase for
fusion sequences. Nucleic Acids Res 34: D21–D24.
8. Ruan Y, Ooi HS, Choo SW, Chiu KP, Zhao XD, et al. (2007) Fusion transcripts
and transcribed retrotransposed loci discovered through comprehensive
transcriptome analysis using Paired-End diTags (PETs). Genome Res 17:
9. Li X, Zhao L, Jiang H, Wang W (2009) Short homologous sequences are
strongly associated with the generation of chimeric RNAs in eukaryotes. J Mol
Evol 68: 56–65.
10. Denoeud F, Kapranov P, Ucla C, Frankish A, Castelo R, et al. (2007) Prominent
use of distal 59 transcription start sites and discovery of a large number of
additional exons in ENCODE regions. Genome Res 17: 746–759.
11. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, et al. (2003)
The COG database: an updated version includes eukaryotes. BMC Bioinfor-
matics 4: 41.
12. Zhang H, Lee JY, Tian B (2005) Biased alternative polyadenylation in human
tissues. Genome Biol 6: R100–112.
13. Licatalosi DD, Darnell RB (2010) RNA processing and its regulation: global
insights into biological networks. Nat Rev Genet 11: 75–87.
14. Qureshi IA, Mehler MF (2009) Regulation of non-coding RNA networks in the
nervous system–what’s the REST of the story? Neurosci Lett 466: 73–80.
15. Kaye FJ (2009) Mutation-associated fusion cancer genes in solid tumors. Mol
Cancer Ther 8: 1399–1408.
16. Wang K, Ubriaco G, Sutherland LC (2007) RBM6-RBM5 transcription-
induced chimeras are differentially expressed in tumours. BMC Genomics 8:
17. Kowalski PE, Freeman JD, Mager DL (1999) Intergenic splicing between a
HERV-H endogenous retrovirus and two adjacent human genes. Genomics 57:
18. Kato M, Khan S, Gonzalez N, O’Neill BP, McDonald KJ, et al. (2003)
Hodgkin’s lymphoma cell lines express a fusion protein encoded by
intergenically spliced mRNA for the multilectin receptor DEC-205 (CD205)
and a novel C-type lectin receptor DCL-1. J Biol Chem 278: 34035–34041.
19. Stechmann A, Cavalier-Smith T (2002) Rooting the eukaryote tree by using a
derived gene fusion. Science 297: 89–91.
20. Thomson TM, Lozano JJ, Loukili N, Carrio R, Serras F, et al. (2000) Fusion of
the human gene for the polyubiquitination coeffector UEV1 with Kua, a newly
identified gene. Genome Res 10: 1743–1756.
21. Snel B, Bork P, Huynen M (2000) Genome evolution. Gene fusion versus gene
fission. Trends Genet 16: 9–11.
22. Pradet-Balade B, Medema JP, Lopez-Fraga M, Lozano JC, Kolfschoten GM,
et al. (2002) An endogenous hybrid mRNA encodes TWE-PRIL, a functional
cell surface TWEAK-APRIL fusion protein. EMBO J 21: 5711–5720.
23. Li X, Li Y, Stremlau M, Yuan W, Song B, et al. (2006) Functional replacement
of the RING, B-box 2, and coiled-coil domains of tripartite motif 5alpha
(TRIM5alpha) by heterologous TRIM domains. J Virol 80: 6198–6206.
24. Poulin F, Brueschke A, Sonenberg N (2003) Gene fusion and overlapping
reading frames in the mammalian genes for 4E-BP3 and MASK. J Biol Chem
25. Brogna S, Wen J (2009) Nonsense-mediated mRNA decay (NMD) mechanisms.
Nat Struct Mol Biol 16: 107–113.
26. Yada T, Takagi T, Totoki Y, Sakaki Y, Takaeda Y (2003) DIGIT: a novel gene
finding program by combining gene-finders. Pac Symp Biocomput 375-387.
27. Carninci P (2006) Tagging mammalian transcription complexity. Trends Genet
28. Mani RS, Tomlins SA, Callahan K, Ghosh A, Nyati MK, et al. (2009) Induced
chromosomal proximity and gene fusions in prostate cancer. Science 326: 1230.
29. Gondor A, Ohlsson R (2009) Chromosome crosstalk in three dimensions. Nature
30. Gingeras TR (2009) Implications of chimaeric non-co-linear transcripts. Nature
31. Li H, Wang J, Mor G, Sklar J (2008) A neoplastic gene fusion mimics trans-
splicing of RNAs in normal human cells. Science 321: 1357–1361.
32. Friedman DI, Court DL (1995) Transcription antitermination: the lambda
paradigm updated. Mol Microbiol 18: 191–200.
33. Lutz CS (2008) Alternative polyadenylation: a twist on mRNA 39 end formation.
ACS Chem Biol 3: 609–617.
34. Tian B, Hu J, Zhang H, Lutz CS (2005) A large-scale analysis of mRNA
polyadenylation of human and mouse genes. Nucleic Acids Res 33: 201–212.
35. Rozen S, Skaletsky H (2000) Primer3 on the WWW for general users and for
biologist programmers. Methods Mol Biol 132: 365–86.
36. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local
alignment search tool. J Mol Biol 215: 403–410.
Conjoined Genes in Eukaryotes
PLoS ONE | www.plosone.org9 October 2010 | Volume 5 | Issue 10 | e13284