Skip to main content
  • Research article
  • Open access
  • Published:

In silico discovery of human natural antisense transcripts



Several high-throughput searches for ppotential natural antisense transcripts (NATs) have been performed recently, but most of the reports were focused on cis type. A thorough in silico analysis of human transcripts will help expand our knowledge of NATs.


We have identified 568 NATs from human RefSeq RNA sequences. Among them, 403 NATs are reported for the first time, and at least 157 novel NATs are trans type. According to the pairing region of a sense and antisense RNA pair, hNATs are divided into 6 classes, of which about 87% involve 5' or 3' UTR sequences, supporting the regulatory role of UTRs. Among a total of 535 NAT pairs related with splice variants, 77.4% (414/535) have their pairing regions affected or completely eliminated by alternative splicing, suggesting significant relationship of alternative splicing and antisense-directed regulation. The extensive occurrence of splice variants in hNATs and other multiple pairing patterns results in a one-to-many relationship, allowing the formation of complex regulation networks. Based on microarray data from Stanford Microarray Database, two hNAT pairs were found to display significant inverse expression patterns before and after insulin injection.


NATs might carry out more extensive and complex functions than previously thought. Combined with endogenous micro RNAs, hNATs could be regarded as a special group of transcripts contributing to the complex regulation networks.


Natural antisense transcripts (NATs) are endogenous ones that exhibit complementary sequences to transcripts of a known function, or sense transcripts. NATs were first described in prokaryotes [1], and they were found to down-regulate the expression of sense transcripts involved in diverse biological functions, such as transposition, plasmid replication and gene expression [2]. Since the discovery of first NAT in human [3], an increasing number of NATs in mammalian organisms have been reported to be related to genomic imprinting [4], RNA interference [5], alternative splicing [6], X-inactivation [7] and RNA editing [8]. It has been shown that NATs could perform two non-exclusive major functions: template for translation and regulation of sense gene expression, and the latter may occur at the level of transcription, maturation, transport, stability and/or translation [9].

As NATs seemed to exist so extensively, independent genome-wide searches for potential NATs were performed recently with data from RefSeq, UniGene or some specially constructed EST database. Due to differences in data sources and/or criteria used in searches, the number of reported human NATs (hNATs) varies greatly, from hundreds [10, 11] to thousands [12, 13].

There are two different types of NATs, cis and trans. A cis NAT is transcribed from the opposite strand of the same genomic locus as its sense RNA, thus the pair displays perfect sequence complementarity. In contrast, a trans NAT is transcribed from a genomic locus different from its sense counterpart and the pair may display imperfect complementarity [9]. All early works in human, mouse, and drosophila revealed only cis NATs [1416]. Although increasing evidence suggests that trans NATs might perform more significant and versatile functions than previously expected [11], most of the high-throughput searches were focused on cis NATs but overlooked trans ones [10, 12, 13]. The bias towards cis type also existed in Kiyosawa et al's survey on mouse genome [17]. Lehner et al. [11] investigated both cis and trans NATs based on available mRNAs, and found 372 human NATs including 80 putative trans ones. However, we believe that they underestimated the prevalence of hNATs since many known antisense RNAs are non-protein coding.

In this work, we carried out a thorough in silico analysis of human RNA sequences from the RefSeq database [18] and identified 568 hNATs, among which 403 NATs were reported for the first time. Out of the 313 novel NATs that have genomic mapping data available, we determined that 157 are trans NATs. We also classified the 568 NATs according to pairing regions and found that 87% cases involved UTR sequences of one or both transcripts. We noticed that among a total of 535 NAT pairs related with splice variants, 77.4% (414/535) have their pairing regions affected or completely eliminated by alternative splicing, suggesting significant relationship of alternative splicing and antisense-directed regulation. We proposed that the one-to-many relationship of sense and antisense transcript pairs, caused by the extensive occurrence of splice variants in hNATs and other multiple pairing patterns, may lead to a probable regulatory network. Furthermore, using Stanford Microarray Database (SMD) [19], we found two hNAT pairs displaying a significant inverse expression pattern before and after insulin injection.

Results and Discussion

Identification of hNATs from RefSeq with BLASTN

Previous large-scale hNATs searches have significantly expanded our knowledge about the prevalence of hNAT [1013]. Particularly, Chen et al. [13] predicted that about 20% of the human genes formed sense-antisense transcript pairs.

In this work, we studied hNATs with RefSeq [18], which is a manually curated database that collects comprehensive, non-redundant sets of sequences, and identified 568 hNATs including both cis and trans types (see Table 1 for summary and Additional Table 1 for detailed information of each pair). Although we found limited hNATs compared to previous reports, sequence accession number matching showed that 403 out of the 568 NAT pairs did not appear in any of the published data sets [1013], indicating that antisense regulation may be even more widespread in the mammalian genome than appreciated previously.

Table 1 Summary of hNAT pairs

The reason that previous studies did not find these entries might be due to different starting data and/or the criteria used. Most of these studies identified NATs based on genomic locus overlapping. As a result, they tend to find cis NATs, but overlooked trans ones [10, 12, 13]. For example, Yelin et al. [12] mainly collected sequences that span intron(s) from human expressed sequences (mRNAs and expressed-sequence tags (ESTs)), and predicted 8.4% of human transcription clusters formed cis NAT pairs. Similarly, Chen et al. [13] investigated sense-orientation-reliable ESTs from UniGene, and got the largest cis hNATs data set reported to date. Non-polyadenylated transcripts were not included in their study, which may partly explain why they did not find the novel cis pairs in this work. We did not exclude the possibility that some of the novel cis pairs in this paper were actually included in Chen et al.'s results [13] since our comparison was based on accession number only and there are cases that a sequence entry acquires a different primary accession number in following releases due to sequence split or merge. Lehner et al. [11] identified both trans and cis hNATs, but they only considered mRNA, and ignored non-coding RNA which occurs in many known functional NAT examples. Moreover, the newer version of RefSeq database that we used in this work contains nearly twice as many entries of human mRNA (25,827) as that in the previous version (12,897) used by Lehner et al. [11]. As a result, we were able to identify more novel hNATs, particularly trans type NATs. The 568 hNATs are summarized in Table 1. Out of the 568 entries, 473 have genomic mapping data available in the Genome database. Thus we determined that 312 NATs are cis type and 161 are trans type (see Additional Table 1 for detailed information). One hundred and fifty seven out of the 161 trans NATs were reported for the first time. The breadth and importance of trans NATs-directed gene regulation are coming into focus as more trans encoded NATs, particularly small nucleolar RNAs (snoRNAs) and micro RNAs (miRNAs), are discovered [2022]. To date, underlying mechanisms of trans NATs are relatively less understood than cis ones. The identification of more novel trans NATs in this work may shed some light on this field.

It is noticeable that Lehner et al. concluded that 51 out of their 80 trans hNATs were likely to be chimeric mRNA containing sequences from two different chromosomal loci due to artifacts of cDNA library construction and chromosomal rearrangements [11]. However, after carefully checking the trans hNATs they reported, we found that 21 out of the 23 RefSeq trans hNAT pairs thought to be involving suspected chimeras can now be mapped to certain loci, thus are true trans NATs. Among these 21 NATs, the chromosomal location information of 4 entries was modified in the Genome database, suggesting that the presence of trans NATs did affect the genome assembly and gene localization as people worried about. It seems that the temporal unfinished human genome assembly was a major obstacle in their survey on trans hNATs.

Our data do not cover all of previously reported ones [1013]. This is due to our choice of using the RefSeq database and the stringent criteria for BLASTN (e-value cutoff of 1e-9 and identity above 98%). RefSeq includes both mRNAs and non-mRNAs, but it has a bias towards mRNA because of the convenience of identifying mRNA. There are 25827 human mRNA and 148 human non-mRNA entries in the release of RefSeq we used. Thus, our choice of using RefSeq helped to find coding antisense RNAs, while underestimated the non-coding antisense RNAs. For example, the first reported hNAT over the c-myc locus, a non-coding antisense transcript, cannot be found in our result [3].

Eighteen out of the 568 (3.2%) hNAT pairs we found involved non-coding RNAs, which is significantly higher than the expected 1.1%, assuming no bias for mRNA or non-mRNA to form NAT pairs. The expected value is estimated as 1–99.43%*99.43%, where 99.43% is the percentage of mRNA entries in the starting dataset. This indicates that NATs tend to be non-coding RNAs, supporting Chen et al.'s observations [13], while disagreeing with Lehner et al.'s assumption [11]. As new non-coding RNAs are being discovered, we believe that the number of reported NAT pairs will rise further.

Even though coding transcripts have fewer propensities to form NAT pairs in contrast to non-coding ones, they have been recently reported more frequently than before [9, 23, 24]. Since coding NATs function as both template and regulator, they may lead to more complex gene regulation, as the THRA (c-erbAα) and NR1D1 (Rev-erbAα) NAT pair example demonstrates below.

Classification of hNATs based on the pairing region

Assuming that the complementarity of sense and antisense RNA pair is the major basis of the regulation function of antisense RNAs [9], we analyzed the overlapping length and location of these 568 hNATs. It was found that overlapping lengths varied from 34 bp to 1810 bp (see Additional Table 1), similar to previously reported [1012]. We classified the 550 hNAT pairs that have CDS location data into 6 types according to the pairing region: 1) 5'UTR vs. 5'UTR, 2) 5'UTR vs. CDS, 3) 5'UTR vs. 3'UTR, 4) CDS vs. CDS, 5) CDS vs. 3'UTR, 6) 3'UTR vs. 3'UTR (Table 2). More than 87% of the 550 NAT pairs involved 5' or 3' UTR, that is, types 1, 2, 3, 5 and 6, supporting the significance of UTR in antisense-mediated regulation [9, 25, 26].

Table 2 Classification of hNAT pairs based on pairing region

According to our data, the subtype 6-cis, or 3'UTR vs. 3'UTR-cis, is the most common form of overlapping. While, analysis of adjacent gene sets in S. cerevisiae suggested that there might be evolutionary pressure to select against convergent genes [27], and the overlapping arrangement of convergent genes restricted the elongation of both transcripts, resulting in a severe reduction in mRNA accumulation, termed as transcriptional collision [28]. This collision effect could be a direct physical impediment to the transcription machinery or an indirect effect caused by supercoiling changes to the DNA template during transcription, while seemed unrelated with interference of antisense RNAs [28]. The precise biological implication of the convergent arrangement of genes in yeast and mammalian genomes remain to be elucidated. The 6-cis dataset including 116 hNAT pairs might provide basis for future in-depth investigation.

The subtype 1-cis, or 5'UTR vs. 5'UTR-cis NAT pairs, could be involved in bidirectional transcription driven by a divergent promoter, which seems to be a preferred structure in prokaryotes for gene regulation such as transcription coupling [29]. Transcription coupling is different from the traditional concept of antisense phenomena, and the choice between transcription coupling and antisense transcript-directed inhibition may depend on the overlapping length and involved transcription factor binding sites. Since bi-directional transcription in eukaryotes is not as common as in prokaryotes, the precise organization and its significance of the 20 members of type 1-cis are intriguing. An in-depth analysis is still in progress.

Splice variants involved in hNATs

Since the wide existence of alternative splicing has been well established [30], and there is evidence for the involvement of NATs in alternative splicing [31, 32], we also investigated the splice variants involved in hNATs. In the present 568 NATs pairs, 63 genes involved in 168 NAT pairs have splice variants (see Additional Table 2A). Forty-nine of the 63 genes involved in 121 pairs have pairing regions unaffected by alternative splicing; while the other 14 genes involved in 47 pairs have variable pairing regions due to alternative splicing. As an example for the latter case, the SPAG8 gene has two splice variants, NM_012436 and NM_172312, and they may pair with antisense transcript NPR2 with 308 bp overlap of 99% identity, and 110 bp overlap of 100% identity, respectively. It is conceivable that alternative splicing can also make the whole pairing region lost. In case of the IL18BP gene (see Additional Table 2A), it has four splice variants, but only three of them have antisense transcript NUMA1. To see how frequently this can happen, we checked the rest 400 hNAT pairs (568-168) using AceView [33] and found that additional 367 NAT pairs involved splicing variants (see Additional Table 2B). In these cases, only one of the transcript variants pairs with its countertranscript while the others lose the pairing region because of alternative splicing. Therefore these cases were not contained in Additional Table 2A. Among the 535 NAT pairs (168+367) related with splice variants, 22.6% (121/535) have splicing variants sharing the same pairing region, 77.4% (414/535) have their pairing regions affected or completely eliminated by alternative splicing. The remarkably high percentage of the latter suggested significant relationship of alternative splicing and antisense-directed regulation. Taking the THRA (c-erbAα) and NR1D1 (Rev-erbAα) pair (No. 472 in the Additional Table 1) as an example, the sense transcript c-erbAα encodes two structurally related proteins R-erbAα1 and R-erbAα2 by alternative splicing [23]. The antisense rev-erbAα transcript is complementary to the last exon of r-erbAα2 mRNA but not to the r-erbAα1 mRNA. It was indicated that rev-erbAα messenger prevents sense r-erbAα primary transcript splicing into r-erbAα2 mRNA by RNA masking, thus tilting the balance towards R-erbAα1 synthesis and ultimately modulating cellular response to hormone [6, 22, 23, 31, 34]. The inhibition of splicing by NAT complementary to sequences remote from the splice site is specific and efficient, which might be attributed to blocking of regulatory elements within the exon essential for exon selection and intron removal, disruption of pre-mRNA secondary structure important for splicing, or disruption of RNP structure required for assembly of a functional RNA-splicing complex [22, 35]. As a result, NAT dictates the way how a sense transcript is differentially spliced. A further analysis of the 414 NATs pairs involving splicing variants with different overlaps may help uncover underlying mechanisms.

Also in the above example, the antisense transcript encodes protein Rev-erbAα (NR1D1) which happens to belong to the thyroid/steroid hormone receptor family, same as products of sense transcripts [9]. This example illustrates the dual roles of some NATs: template for translation and regulator of sense gene expression. Such a gene structure is quite exquisite and economic to organize functionally related genes.

One-to-many relationship in hNATs

Initially, NAT pairs were considered to have one-to-one relationship. However, an antisense transcript might form duplex with more than one splice variants of a sense gene, as the examples given in previous section. In theory, an antisense transcript has also the potential to pair with more than one paralogous sense transcripts. Finally, more than one antisense transcripts may pair with one sense transcript at different parts of its sequence. In the 568 hNAT pairs, besides the 168 entries that form one-to-many relationship caused by alternative splicing, additional 97 hNAT pairs were found to have one-to-many relationship due to different pairing regions. That makes 47% ((97+168)/568) of the total, allowing NATs to form complex regulation networks. Figure 1 is an example of such a network including 29 genes involved in 40 hNATs. Interestingly, all hNAT pairs with genomic mapping data are trans type. In fact, hNAT pairs in this paper formed 38 such networks in various sizes (see Additional Table 3). It is worth noting that these networks mainly involve trans NATs, same as the example in Figure 1. The trans relationship seems more flexible, thus provides greater chance for transcripts to "communicate" with each other. In addition to RNA masking mentioned above, there is now strong evidence that the interaction of antisense partners can also affect gene expression via the activation of dsRNA-dependent pathways [22, 36]. These might include RNA interference (RNAi)-dependent gene silencing. It was reported that several different micro RNAs could regulate the expression of the same target mRNA based on complementarity between micro RNA and mRNA [21]. It seems that the one-to-many relationship, notably in transcriptional regulation, might be common as the list of known one-to-many regulatory interactions becomes more comprehensive. From this perspective, the biological significance of trans NATs deserves to be further investigated systematically.

Figure 1
figure 1

Human NAT interaction network constituted by one-to-many relationship. Gene symbol represents a gene. Boldface gene symbols represent genes with variants which could pair with a same countertranscript. A dashed line indicates an uncertain type due to lacking genomic mapping data; a solid line with bi-directional arrows indicates a trans type. There is no cis-NAT shown in this figure.

Identification of two hNAT pairs with inverse expression pattern

There have been reports that sense and antisense transcripts show inverse expression pattern, as well as examples of coordinated regulation of both transcripts [9, 10, 12, 37]. It is more intriguing that sense and antisense transcripts are differentially expressed depending on tissue types or development stages [25, 38, 39]. All these phenomena illustrated that the underlying mechanism is complicated and confusing. In this work we focused on identifying inverse expression pattern under certain condition for the hNAT pairs we found, considering that this pattern is the most common one for sense-antisense expression.

Microarray is a high-throughput technology for analyzing gene expression and has been recently applied to study NATs [12]. In this work, we used data from SMD [19] for in silico expression analysis of the hNATs we found. Rome et al. [40]designed a microarray of 29308 cDNA probes to evaluate gene expression pattern of skeletal muscle cells from six independent volunteers before and after insulin injection. We found that 150 out of the 568 NATs reported in this work had representing probes in the array, so we selected the expression data of 121 NATs with no less than 3 repeat samples for analysis. T-test and multiplicity adjustment showed that relative quantity (RQ) values of two NAT pairs, SARM1/MGC9564 (No. 550 in Additional Tables 1 and 4) and HARS/WDR55 (No. 443 in Additional Tables 1 and 4), varied significantly after insulin injection, showing the two pairs displayed inverse expression pattern. The calculation result is presented in Additional Table 4.

For the SARM1/MGC9564 pair, RQ value rose after insulin injection, indicating SARM1 is relatively up-regulated in contrast to MGC9564. SARM1 mRNA encodes a conserved protein with a SAM motif and is highly expressed in liver and kidney [41]. It was reported that a 0.4 kb antisense transcript was coordinately expressed with the SARM1 gene, but this is apparently not the same NAT as we found [41], suggesting that the SARM1 gene has at least two antisense partners. MGC9564 is an experimentally supported full-length mRNA (2096 bp) with a predicted coding sequence. According to the pairing data, SARM1 mRNA and MGC9564 mRNA possibly form a CDS vs. 3'UTR cis NAT pair.

For the second pair, HARS/WDR55, the relative quantity decreased after insulin injection, indicating HARS is down-regulated relative to WDR55. HARS mRNA codes for histidyl-tRNA synthetase, which is essential for the incorporation of histidine into proteins [42]. The WDR55 cDNA was cloned recently [43], and has not yet been subjected to final review in the latest release of NCBI RefSeq database. Based on the chromosomal location information, the two genes are mapped closely (5q31.3), but do not overlap. They may form a 3' UTR vs. 3' UTR trans NAT pair. This is a novel pair that we reported for the first time.

The apparent inverse expression pattern of the above two hNAT pairs suggests their possible regulatory roles in skeletal muscle cells after insulin injection. Further experimental studies are needed to unravel the underlying mechanism.


Through a systematic analysis of hNATs using RefSeq dataset, we identified 568 hNATs. Even though the total number is less than those reported by Yelin et al. [12] and Chen et al. [13], many novel hNATs, particularly trans NATs, were discovered in this work. Among the NAT pairs involving splice variants, a remarkably high percentage (77.4%) have their pairing regions affected or completely eliminated by alternative splicing, suggesting significant relationship of alternative splicing and antisense-directed regulation. The extensive occurrence of splice variants in hNATs and other multiple pairing patterns results in one-to-many relationship, allowing the formation of complex regulation networks. It seemed that trans NATs bring more flexibility and complexity to antisense-directed gene regulation. Furthermore, out of the 121 hNATs with expression data available, we found that the expression pattern of two NAT pairs in the skeletal muscle cells showed significant inverse relationship before and after insulin injection.

In summary, NATs might carry out more extensive and complex functions than previously thought. Combined with endogenous micro RNAs, hNATs could be regarded as a special group of transcripts contributing to the complex regulation networks.


Data source

Human transcript sequences (25,827 in total) were extracted from RefSeq release 2 [18]. Genomic mapping data were taken from the Genome database version 35.1 at NCBI [44]. Alternative splicing information was obtained from AceView [33]. Microarray data used for in silico expression analysis were downloaded from Stanford Microarray Database (SMD) [19].

Search for hNATs

The BLASTN program was used to identify putative hNATs with an e-value cutoff of 1e-9 and identity threshold of 98%. Query sequences were all human RNA sequences extracted from the RefSeq database, and the subject database was made up with reverse complement sequences of all query sequences. Option -S was set to be 1 to avoid the program reverse-complementing query sequences automatically. After getting BLASTN hits, we compared pairing sequence segments to the repetitive sequence database, RepBase [45], and confirmed that they contained no known repetitive sequence. We also used ClustalW to align all pairing segments to make sure there was no novel repeats. These two steps are necessary to eliminate the possibility of repetitive element-induced prevalence of hNATs.

Classification of hNATs according to genomic location and pairing region

Human NATs were divided into cis and trans types according to relative genome location using genomic mapping data from the Genome database [44]. That is, the NAT transcribed from the opposite strand of the same genomic locus as its sense RNA is cis, while the NAT transcribed from a genomic locus different from its sense counterpart is trans. Based on the pairing region of sense and antisense transcripts, we also defined 6 types: 1) 5'UTR vs. 5'UTR, 2) 5'UTR vs. CDS, 3) 5'UTR vs. 3'UTR, 4) CDS vs. CDS, 5) CDS vs. 3'UTR, and 6) 3'UTR vs. 3'UTR, where the "CDS" contains "CDS", "CDS+3'UTR", "CDS+5' UTR" and "5'UTR+CDS+3' UTR".

Expression analysis of hNATs using SMD data

Gene expression data of human skeletal muscle cells before and after insulin injection are available in SMD [19, 40]. Since human cDNA probes in these data are identified with UniGene cluster IDs, antisense transcripts can be mapped to corresponding probes based on the relationship between UniGene cluster ID and RefSeq accession number (ACC), by which expression data of hNATs were retrieved. Human NATs with data from 3 or more repeat samples were selected for statistical analysis. The ratio of sense to antisense transcript expression levels represents the relative quantity (RQ) of a NAT pair, RQ = S/A. The change of the ratio of a pair after insulin injection is expressed as the ratio of two relative quantities, R = RQa/RQb, where RQb and RQa are relative quantities before and after insulin injection, respectively. This R value is used to check if a NAT pair shows an inverse expression pattern under the given condition. T-test with a significance level of 0.05 and Bonferroni's adjustment for multiplicity were used to evaluate the significance of an R value, that is, the change of a NAT pair in expression pattern.


  1. Lacatena RM, Cesareni G: Base pairing of RNA I with its complementary sequence in the primer precursor inhibits ColE1 replication. Nature 1981, 294(5842):623–626. 10.1038/294623a0

    Article  CAS  PubMed  Google Scholar 

  2. Wagner EG, Simons RW: Antisense RNA control in bacteria, phages, and plasmids. Annu Rev Microbiol 1994, 48: 713–742. 10.1146/annurev.mi.48.100194.003433

    Article  CAS  PubMed  Google Scholar 

  3. Bentley DL, Groudine M: A block to elongation is largely responsible for decreased transcription of c-myc in differentiated HL60 cells. Nature 1986, 321: 702–706. 10.1038/321702a0

    Article  CAS  PubMed  Google Scholar 

  4. Rougeulle C, Heard E: Antisense RNA in imprinting: spreading silence through Air. Trends Genet 2002, 18: 434–437. 10.1016/S0168-9525(02)02749-X

    Article  CAS  PubMed  Google Scholar 

  5. Brantl S: Antisense-RNA regulation and RNA interference. Biochim Biophys Acta 2002, 1575(1–3):15–25.

    Article  CAS  PubMed  Google Scholar 

  6. Hastings ML, Milcarek C, Martincic K, Peterson ML, Munroe SH: Expression of the thyroid hormone receptor gene, erbAalpha, in B lymphocytes: alternative mRNA processing is independent of differentiation but correlates with antisense RNA levels. Nucleic Acids Res 1997, 25: 4296–4300. 10.1093/nar/25.21.4296

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Lee JT, Davidow LS, Warshawsky D: Tsix, a gene antisense to Xist at the X-inactivation centre. Nat Genet 1999, 21: 400–404. 10.1038/7734

    Article  CAS  PubMed  Google Scholar 

  8. Peters NT, Rohrbach JA, Zalewski BA, Byrkett CM, Vaughn JC: RNA editing and regulation of Drosophila 4f-rnp expression by sas-10 antisense readthrough mRNA transcripts. Rna 2003, 9(6):698–710. 10.1261/rna.2120703

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Vanhee-Brossollet C, Vaquero C: Do natural antisense transcripts make sense in eukaryotes? Gene 1998, 211: 1–9. 10.1016/S0378-1119(98)00093-6

    Article  CAS  PubMed  Google Scholar 

  10. Shendure J, Church GM: Computational discovery of sense-antisense transcription in the human and mouse genomes. Genome Biol 2002, 3(9):RESEARCH0044. 10.1186/gb-2002-3-9-research0044

    Article  PubMed Central  PubMed  Google Scholar 

  11. Lehner B, Williams G, Campbell RD, Sanderson CM: Antisense transcripts in the human genome. Trends Genet 2002, 18(2):63–65. 10.1016/S0168-9525(02)02598-2

    Article  CAS  PubMed  Google Scholar 

  12. Yelin R, Dahary D, Sorek R, Levanon EY, Goldstein O, Shoshan A, Diber A, Biton S, Tamir Y, Khosravi R, et al.: Widespread occurrence of antisense transcription in the human genome. Nat Biotechnol 2003, 21(4):379–386. 10.1038/nbt808

    Article  CAS  PubMed  Google Scholar 

  13. Chen J, Sun M, Kent WJ, Huang X, Xie H, Wang W, Zhou G, Shi RZ, Rowley JD: Over 20% of human transcripts might form sense-antisense pairs. Nucleic Acids Res 2004, 32: 4812–4820. 10.1093/nar/gkh818

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Anderson S, Bankier AT, Barrell BG, de Bruijn MH, Coulson AR, Drouin J, Eperon IC, Nierlich DP, Roe BA, Sanger F, et al.: Sequence and organization of the human mitochondrial genome. Nature 1981, 290(5806):457–465. 10.1038/290457a0

    Article  CAS  PubMed  Google Scholar 

  15. Bibb MJ, Van Etten RA, Wright CT, Walberg MW, Clayton DA: Sequence and gene organization of mouse mitochondrial DNA. Cell 1981, 26(2 Pt 2):167–180. 10.1016/0092-8674(81)90300-7

    Article  CAS  PubMed  Google Scholar 

  16. Spencer CA, Gietz RD, Hodgetts RB: Overlapping transcription units in the dopa decarboxylase region of Drosophila. Nature 1986, 322(6076):279–281. 10.1038/322279a0

    Article  CAS  PubMed  Google Scholar 

  17. kiyosawa H, Yamanaka I, Osato N, Kondo S, Hayashizaki Y: Antisense transcripts with FANTOM2 clone set and their implications for gene regulation. Genome Res 2003, 13: 1324–1334. 10.1101/gr.982903

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. RefSeq[]

  19. Stanford Microarray Database (SMD)[]

  20. Kiss T: Small nucleolar RNAs: an abundant group of noncoding RNAs with diverse cellular functions. Cell 2002, 109(2):145–148. 10.1016/S0092-8674(02)00718-3

    Article  CAS  PubMed  Google Scholar 

  21. Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004, 116(2):281–297. 10.1016/S0092-8674(04)00045-5

    Article  CAS  PubMed  Google Scholar 

  22. Lavorgna G, Dahary D, Lehner B, Sorek R, Sanderson CM, Casari G: In search of antisense. Trends Biochem Sci 2004, 29(2):88–94. 10.1016/j.tibs.2003.12.002

    Article  CAS  PubMed  Google Scholar 

  23. Miyajima N, Horiuchi R, Shibuya Y, Fukushige S, Matsubara K, Toyoshima K, Yamamoto T: Two erbA homologs encoding proteins with different T3 binding capacities are transcribed from opposite DNA strands of the same genetic locus. Cell 1989, 57(1):31–39. 10.1016/0092-8674(89)90169-4

    Article  CAS  PubMed  Google Scholar 

  24. Potter SS, Branford WW: Evolutionary conservation and tissue-specific processing of Hoxa 11 antisense transcripts. Mamm Genome 1998, 9(10):799–806. 10.1007/s003359900870

    Article  CAS  PubMed  Google Scholar 

  25. Ross J: Control of messenger RNA stability in higher eukaryotes. Trends Genet 1996, 12(5):171–175. 10.1016/0168-9525(96)10016-0

    Article  CAS  PubMed  Google Scholar 

  26. Lipman DJ: Making (anti)sense of non-coding sequence conservation. Nucleic Acids Res 1997, 25(18):3580–3583. 10.1093/nar/25.18.3580

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Dujon B: The yeast genome project: what did we learn? Trends Genet 1996, 12: 263–270. 10.1016/0168-9525(96)10027-5

    Article  CAS  PubMed  Google Scholar 

  28. Prescott EM, Proudfoot NJ: Transcriptional collision between convergent genes in budding yeast. Proc Natl Acad USA 2002, 99: 8796–8801. 10.1073/pnas.132270899

    Article  CAS  Google Scholar 

  29. Yamada M, Kabir MS, Tsunedomi R: Divergent promoter organization may be a preferred structure for gene control in Escherichia coli. J Mol Microbiol Biotechnol 2003, 6: 206–210. 10.1159/000077251

    Article  CAS  PubMed  Google Scholar 

  30. Ast G: How did alternative splicing evolve? Nat Rev Genet 2004, 5: 773–782. 10.1038/nrg1451

    Article  CAS  PubMed  Google Scholar 

  31. Munroe SH, Lazar MA: Inhibition of c-erbA mRNA splicing by a naturally occurring antisense RNA. J Biol Chem 1991, 266(33):22083–22086.

    CAS  PubMed  Google Scholar 

  32. Sureau A, Soret J, Guyon C, Gaillard C, Dumon S, Keller M, Crisanti P, Perbal B: Characterization of multiple alternative RNAs resulting from antisense transcription of the PR264/SC35 splicing factor gene. Nucleic Acids Res 1997, 25: 4513–4522. 10.1093/nar/25.22.4513

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. AceView[]

  34. Lazar MA, Hodin RA, Darling DS, Chin WW: A novel member of the thyroid/steroid hormone receptor family is encoded by the opposite strand of the rat c-erbA alpha transcriptional unit. Mol Biol Cell 1989, 9: 1128–1136.

    Article  CAS  Google Scholar 

  35. Munroe SH: Antisense RNA inhibits splicing of pre-mRNA in vitro. Embo J 1988, 7(8):2523–2532.

    PubMed Central  CAS  PubMed  Google Scholar 

  36. Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993, 75(5):843–854. 10.1016/0092-8674(93)90529-Y

    Article  CAS  PubMed  Google Scholar 

  37. Dolnick BJ: Naturally occurring antisense RNA. Pharmacol Ther 1997, 75(3):179–184. 10.1016/S0163-7258(97)00050-8

    Article  CAS  PubMed  Google Scholar 

  38. Chen X, Cheung ST, So S, Fan ST, Barry C, Higgins J, Lai KM, Ji J, Dudoit S, Ng IO, et al.: Gene expression patterns in human liver cancers. Mol Biol Cell 2002, 13(6):1929–1939. 10.1091/mbc.02-02-0023.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Murphy PR, Knee RS: Identification and characterization of an antisense RNA transcript (gfg) from the human basic fibroblast growth factor gene. Mol Endocrinol 1994, 8(7):852–859. 10.1210/me.8.7.852

    CAS  PubMed  Google Scholar 

  40. Rome S, Clement K, Rabasa-Lhoret R, Loizon E, Poitou C, Barsh GS, Riou JP, Laville M, Vidal H: Microarray profiling of human skeletal muscle reveals that insulin regulates approximately 800 genes during a hyperinsulinemic clamp. J Biol Chem 2003, 278: 18063–18068. 10.1074/jbc.M300293200

    Article  CAS  PubMed  Google Scholar 

  41. Mink M, Fogelgren B, Olszewski K, Maroy P, Csiszar K: A novel human gene (SARM) at chromosome 17q11 encodes a protein with a SAM motif and structural similarity to Armadillo/beta-catenin that is conserved in mouse, Drosophila, and Caenorhabditis elegans. Genomics 2001, 74: 234–244. 10.1006/geno.2001.6548

    Article  CAS  PubMed  Google Scholar 

  42. Wasmuth JJ, Carlock LR: Chromosomal localization of human gene for histidyl-tRNA synthetase: clustering of genes encoding aminoacyl-tRNA synthetases on human chromosome 5. Somat Cell Mol Genet 1986, 12(5):513–517. 10.1007/BF01539922

    Article  CAS  PubMed  Google Scholar 

  43. Strausberg RL, Feingold EA, Grouse LH, Derge JG, Klausner RD, Collins FS, Wagner L, Shenmen CM, Schuler GD, Altschul SF, et al.: Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences. Proc Natl Acad Sci USA. 2002, 16899–16903.

    Google Scholar 

  44. Genome database at NCBI[]

  45. Jurka J: Repbase update: a database and an electronic journal of repetitive elements. Trends Genet 2000, 16(9):418–420. 10.1016/S0168-9525(00)02093-X

    Article  CAS  PubMed  Google Scholar 

Download references


The authors would like to thank Dr. Jiu-Zhou Wang and miss Hui Yu for their guidance of statistical analysis. This work was supported in part by grants from the National "973" Basic Research Program (2003CB715901) and the National Key Technologies R&D Programme (2005BA711A03).

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Wei-Zhong He or Yi-Xue Li.

Additional information

Authors' contributions

YY Li and L Qin designed the research pipeline, performed the statistical analysis and drafted the manuscript. ZM Guo, H Xu and J Su carried out the programming. L Liu, P Hao and YX Shi participated in the design of the study. WZ He and L Liu helped to draft the manuscript. YX Li conceived and oversaw the research. All the authors read and approved the final manuscript.

Yuan-Yuan Li, Lei Qin, Zong-Ming Guo contributed equally to this work.

Electronic supplementary material

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Li, YY., Qin, L., Guo, ZM. et al. In silico discovery of human natural antisense transcripts. BMC Bioinformatics 7, 18 (2006).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: