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BIOINFORMATICS APPLICATIONS NOTE Vol. 21 no. 8 2005, pages 1699–1700
HESAS: HERVs Expression and Structure Analysis System
Tae-Hyung Kim1, Yeo-Jin Jeon1, Woo-Yeon Kim1 and Heui-Soo Kim1,2,∗1PBBRC, Interdisciplinary Research Program of Bioinformatics and 2Division of Biological Sciences,College of Natural Sciences, Pusan National University, Busan 609–735, Korea Received on October 21, 2004; revised on November 11, 2004; accepted on November 26, 2004 Advance Access publication December 10, 2004 ABSTRACT
Summary: HESAS (HERVs Expression and Structure Analysis Sys-
tem) database was developed to understand the human endogenous Data sources
retroviruses (HERVs) that have an effect on the expression of humanfunctional genes. The database products are generated by the exon- Intron and both of the 5 kb regions, upstream- and downstream- based expressed sequence tag clustering and reconstructing of partial containing genes for the HESAS (HERVs Expression and Structure HERV structures that result from various mutations during primate Analysis System), were obtained from GoldenPath, which is based evolution. The expression types were classified according to the exist- on gene information of NCBI Build 34.3. Intron/exon structures ence of splicing, transcriptional start and polyadenylation signal sites.
forming various HERV-related transcripts in genes are obtained The database currently contains HERV information on 26 981 human by alignment of RefSeq mRNAs as counterparts of the human genes of exon–intron structure. The HERV elements were inserted genes (Pruitt et al., 2003). HERV elements in genes were identified into 17 317 of these genes and linked to expression with 898 genes.
embedded MaskerAid (Bedell et al., 2000) with 352 ERV con- Contact:
sensus sequences from the Repbase Update (Jurka, 2000). A HERV’slocus on the genome, its position in the consensus sequence, dir-ection, subfamily name and Smith–Waterman score were derived INTRODUCTION
from RepeatMasker’s outputs. The expressed sequence tag (EST)sequences were derived from NCBI’s dbEST database that con- The human genome contains various endogenous retroviruses tains 8209 cDNA libraries (Boguski et al., 1993). The useful EST (HERVs) that represent the footprints of ancient germ-cell infec- information for tissues and pathology types was obtained from the tions (Lower et al., 1996). These HERVs and other long terminal eVOC ontology, a set of controlled vocabularies for unifying gene repeat (LTR)-like elements account for ∼8% of the human genome.
expression data (Kelso et al., 2003).
Most of the HERVs are no longer able to code for functional protein,owing to multiple stop codons, insertions, deletions and frame shifts.
EST clustering
However, the presence of LTR-promoters from some HERV familiesare increasing their expression in human placenta and several can- For EST clustering with strict supervisor to detect HERV-related cer cell lines (Mi et al., 2000). Recently, these retroelements have expression patterns from a given set of ESTs, intron/exon structures gained the evolutionary potential role to enhance the coding capacity resulting from the mapping of RefSeq mRNA by sim4 (Florea et al., and regulatory versatility of the genome without compromising its 1998) were collected. We set the criterion that at least one side of the integrity (Sorek et al., 2002). Database for HERV elements was used two-end boundary of an exon region had to overlap with an aligned for searches of individual HERV families (Paces et al., 2002). Over EST with an identity >97% in order to eliminate EST data con- the past decade, a considerable number of studies have been conduc- taminated by genomic sequences. To obtain expressed transcripts ted on the capacity to modify the expression of neighboring genes with HERVs only, non-ERV repeat sequences such as LINE, SINE, (Jordan et al., 2003). These reports are focused mainly on strong MIR and simple repeat elements within all genes were masked by LTR-derived promoters in the specific tissues, including data for RepeatMasker before performing EST clustering.
alternative splicing and primary polyadenylation signal (Mager et al.,1999). However, there has been no study concerning systematic ana- Identification of HERV genomic structure
lyses of the creation of various transcripts caused by HERV elements Consensus domain libraries for scanning HERVs were newly con- within genomic sequences of human genes. Here, we characterized structed by comparing highly conserved residues as potential coding HERV positions, LTR-truncated constructs and HERV ORFs within regions (gag, pro, RT, RNaseH, IN and env) within internal-HERV the human genes. In addition, we present a large number of HERVs with traditional viral genes of Pfam (Bateman et al., 2004) using linked to genes that are expressed in normal tissues and pathology HMMER. Thus, both libraries, of the consensus domain and the tissues using an electronic mapping method.
original Repbase, were simultaneously used to identify the HERVelements within whole gene region. We found that 17 317 of the26 981 human genes were inserted by HERVs in the inner or neigh- ∗To whom correspondence should be addressed.
borhood part of their gene region. The genomic structure of HERV The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: T.-H.Kim et al.
elements was defined as LTR-HERV-LTR including the boundary zoom in and out of any region of a gene to show a sophisticated image.
of the coding region. These were divided into four types (com- The 2 bp flanking region of the exon boundary is compared with the plete, 5 truncate, 3 truncate and 5 –3 truncate) according to LTR given canonical splicing site (AG-GT), and then it is represented by truncations, and solitary LTRs that were detected by calculating the RepeatMasker output in reconstructing the HERV structure ofthe non-fragmental state before accumulating mutation (Kim et al., REFERENCES
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uses provide the primary polyadenylation signal for two new human genes (HHLA2 Using Java applet, we also developed a viewer of sufficient power and HHLA3). Genomics, 59, 255–263.
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