Nucleic Acids Research Advance Access originally published online on October 16, 2007
Nucleic Acids Research 2008 36(Database issue):D97-D101; doi:10.1093/nar/gkm901
Nucleic Acids Research, 2008, Vol. 36, Database issue D97-D101
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
DBTSS: database of transcription start sites, progress report 2008
Hiroyuki Wakaguri,
Riu Yamashita,
Yutaka Suzuki,
Sumio Sugano and
Kenta Nakai*
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan and Department of Medical Genome Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
* To whom correspondence should be addressed. Tel: +81 3 5449 5131; Fax: +81 3 5449 5133; Email: knakai{at}ims.u-tokyo.ac.jp
Received September 15, 2007. Accepted October 4, 2007.
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ABSTRACT
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DBTSS is a database of transcriptional start sites, based on
our unique collection of precise, experimentally determined
5'-end sequences of full-length cDNAs. Since its first release
in 2002, several major updates have been made. In this update,
we expanded the human transcriptional start site dataset by
19 million uniquely mapped, and RefSeq-associated, 5'-end sequences,
which were generated by a newly introduced Solexa sequencer.
Moreover, in order to provide means for interpreting those massive
TSS data, we implemented two new analytical tools: one for connecting
expression information with predicted transcription factor binding
sites; the other for examining evolutionary conservation or
species-specificity of promoters and transcripts, which can
be browsed by our own comparative genome viewer. With the expanded
dataset and the enhanced functionalities, DBTSS provides a unique
platform that enables in-depth transcriptome analyses. DBTSS
is accessible at
http://dbtss.hgc.jp/.
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INTRODUCTION
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One of the most challenging subjects of the genome science in
the post-genome-sequencing era is to understand the transcriptional
regulatory networks, by which the timing and quantity of transcriptions
are collectively controlled. To eventually decode the genome
information into a macromolecular system of the cell, in-depth
knowledge of its higher-order regulatory mechanisms should be
indispensable (
1,
2). It is also expected that detailed evolutionary
comparisons of transcriptional networks would explain molecular
mechanisms underlying the phenotypic divergence and speciation
(
3). Therefore, the identification and analyses of promoters,
where most of the binding sites of transcription factors are
contained, are essential. To define promoter regions, positional
information about transcriptional start sites (TSSs) is the
first clue (
4).
In order to provide the precise information of TSSs, we launched DBTSS in 2002 (5). DBTSS contains the TSS information determined with our experiments. The 5'-end sequences of full-length cDNA clones isolated from libraries, which are mainly constructed by the oligo-capping method and are enriched in full-length cDNAs, are mapped on to genome sequences. Each TSS is determined as a genomic position to which the 5'-end of some full-length cDNAs is corresponded (6). Since the initial release of DBTSS, we have made several updates, including the expansion of the data amount as well as the covered species, the addition of the information of so-called alternative promoters (7), and the implementation of an analytical tool, which enables the promoter sequence comparison between human and mouse. In this article, we introduce new updates and additions since DBTSS 2006, the most important one being the addition of TSS information which has been massively produced by a new-generation sequencer.
Newly developed, massively parallel sequencing technologies, such as GS20 and Solexa sequencer systems, have enabled to determine millions of sequences in a single run (8). We recently accommodated our full-length cDNA technique, the oligo-capping method, for the Solexa sequencers (detailed protocol will be published elsewhere). Utilizing the extremely high-throughput Solexa sequencer, we generated 19-million TSS information and incorporated it into DBTSS. At the same time, we also considered that, in order to maximally extract biological information from this size of TSS data, the implementation of powerful analytical tools should be essential. Therefore, in this update, together with the expansion of the TSS data, we put two analytical tools into service; the first for correlating expression information with promoter elements; the second for examining the evolutionary conservation or species-specificity of promoters and transcripts, which can be browsed by a dynamic and flexible comparative genome browser. Both of these new functionalities are closely related with the Solexa sequence browser and enable the enhanced interpretation of the TSS data.
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NEW FEATURES
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Incorporation of the TSS data produced by the Solexa sequencer
A major update of the current version of DBTSS, version 6, since
the previous report, is that the amount of data for human TSSs
has been significantly increased. We recently started to determine
the 5'-end sequences of the oligo-capped cDNAs using the Solexa
sequencer. Briefly, 5'- and 3'-adaptor sequences necessary for
the Solexa sequencing were introduced as a 5'-end-oligo at the
RNA ligation and as a random hexamer primer at the first strand
cDNA synthesis, respectively. Because of this straightforward
procedure, thereby collected data should be improved by its
size without degrading its quality. Obtained data were processed
as previously described for the classical Sanger sequences with
some minor modifications. The genomic positions to which the
5'-ends of the Solexa sequences were mapped were defined as
putative TSSs. Clustering by 500 bp bins was also applied to
separate putative alternative promoters (
7). As a result, 10
000 349 and 8 633 345 sequences were obtained from the MCF7
cells (a human cell line of breast cancer origin; ATCC#HTB-22)
and the human embryonic kidney 293 cells (HEK293; ATCC#CRL-1573),
respectively, which were uniquely mapped to the RefSeq mRNA
regions of the human genome (hg18). Those sequences collectively
represent 29 210 and 41 238 putative (alternative) promoters
of 12 133 and 11 598 RefSeq genes in the two cell lines, respectively
(
Table 1). This is one of the largest collections of human TSS
information collected from a single cell type (
9).
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Table 1. Statistics of the datasets produced by the Solexa sequencer (upper two rows) and by the original Sanger sequencer (bottom row) in humans. TSSs which were supported by equal or greater than five sequences were counted for the Solexa datasets
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The Solexa data can be retrieved in parallel with the original
Sanger data (
Figure 1). In other words, we did not mix the new
TSS information from the Solexa data with the original dataset
so that the in-depth transcriptome analysis focusing on a single
cell type is possible, while the previous data representing
general features of promoters taken from the data of various
cell types and tissues, could be preserved. Otherwise, the clustering
and the representations of the promoters in the original dataset
could be severely biased because of the size of the new data.
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Figure 1. Screenshot from the Solexa sequence viewer. Its basic utility is similar to that of the previous version (5). Users can choose the database to search, either Sanger or Solexa dataset, in the left panel and then retrieve the results (red circle). Users can also switch the browsers between the Sanger and Solexa results (blue circle).
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Connecting expression information with promoter elements
In order to further investigate the biological significance
or the underlying regulatory mechanisms of the observed TSSs,
the information on the changes of expression levels invoked
by various environmental stimulations would provide important
clues. For this purpose, at least before the expression information
produced by the Solexa and the GS20 sequencers [where millions
of collected sequences are subjected to the SAGE-like analysis;
see (
10)] will be accumulated to some sufficient level, hitherto
compiled microarray data are conveniently available. In particular,
for MCF 7, there is a series of microarray data, which was recently
produced by the Connectivity Map project, representing the expression
profiles of this particular cell on the administrations of over
200 kinds of drugs in several time courses (
11).
In addition to the incorporation of the MCF7 expression data, we implemented an analytical tool. This analytical tool looks for putative transcription factor binding sites commonly occurring in the promoters, which show similar behaviors on various drug perturbations. It is assumed that those promoters should be under the regulations of similar transcription factors, thus, containing particular transcription factor binding sites in common. As shown in Figure 2, as for the user-specified group of promoters or promoters which satisfy some specified search conditions on the expression profiles, transcription factor binding sites are predicted by the matrix search of the TRANFAC database (12) under any cut-off values. The degree of enrichment of the predicted transcription factor binding sites is evaluated assuming the hypergeometric distributions. Thereby, the enriched binding sites are listed and users can further retrieve the information regarding in which promoters and in which part of them these sites are located. Although the MCF7 dataset is currently the only resource of the expression data, we will further expand the dataset to cover expression profiles in various cell types and conditions. Moreover, further increases in the amount of the Solexa data for TSS determination themselves will enrich the source of expression information. Such data will directly couple the positional information within promoters with the changes of expression levels, as the latter should be represented by the number of allocated sequences.
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Figure 2. Screenshot from the search engine for enrichment of the putative transcription factor binding sites (A). The figure exemplifies the search for common TF binding sites appearing in the promoters of genes with which more than 10 Solexa sequences are associated and the relative expression levels are more than 2-fold elevated both by the 1 µm trichostatin A treatment and by the 1 µm wortmannin treatment (B). From the resultant list of the enriched sites, the link can be followed to the main viewer to retrieve further detailed information (C).
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Evolutionary conservation of promoters and their downstream transcripts
Another approach to infer the biological relevance of the TSSs
is to examine the details of their evolutionary conservation
or turnovers in both their upstream promoters and downstream
transcribed regions. Recently, it has become clear that some
of the (alternative) promoters are well conserved, while others
are rapidly evolving (
13). Whether the non-conserved (alternative)
promoters are the noise of transcription or actually playing
species-specific roles still remains elusive. However, further
ways for investigating those (alternative) promoters should
be different from the first step, depending on whether they
are associated with conserved, possibly principal, biological
roles or species-specific phenotypic features.
As shown in Figure 3, our new comparative browser enables users to examine evolutionary conservation of the surrounding regions of TSSs, based on the genomic sequences of various kinds of mammals, such as mice, rats and monkeys, as well as their mutual base-pair alignments from the UCSC Genome Browser (14). Furthermore, users can search for the promoters or transcripts according to the degree of evolutionary conservation, using variable parameters, including the coverage of alignable regions and the base substitution rate therein. Further detailed searches focusing on limited regions of the transcripts, such as UTRs, CDSs or the ‘coding exons’ are also supported. These results are browsed with our new comparative browser, which provides the dynamic magnification from the sequence level to the overview level. This viewer currently supports up to four-way comparisons between four genomes of user's choice.
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Figure 3. Screenshot from the search engine for the evolutionary conservation of the promoters and transcripts (A). The figure exemplifies the results of the search for promoters for which more than 10 Solexa sequences are associated, the alignable region in the promoters in human–mouse comparison is <300 bp and the overall base substitution of the downstream transcript region is <20% (B). The regions specified between red (one selection) and green (second selection) vertical lines (or one left click) can be magnified up to the sequence level.
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FUTURE PERSPECTIVE
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We will continue Solexa sequencing in various tissues with various
environmental conditions, by which not only TSS data but also
expression data will be increased further. We will also take
a similar approach for model organisms other than humans. Their
relevant data will be incorporated into DBTSS and made publicly
available. Especially, in many organisms, their cDNA resources
still remain scarce, while the genome sequences are being hastily
released. Our massive transcriptome data will be most helpful
not only for determining TSSs and upstream promoters but also
for putting accurate annotations for the compiled genome sequences.
DBTSS, with expanded data and supported by several new analytical
tools, provides a unique platform for enabling in-depth analyses
of transcriptomes. We believe DBTSS will serve as a firm foundation
leading us into deeper insights on how the transcriptional regulatory
network is realized by the code of genomic DNA sequences.
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ACKNOWLEDGEMENTS
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We thank Katsuyuki Tsuritani, Kazumi Abe and Kiyomi Imamura
for their excellent works in sequencing generation and processing.
We are also thankful to Nicolas Sierro and Fah Sathirapongsasuti
for critically reading the article. This work was supported
by a Grant-in-Aid for Scientific Research on Priority Areas
and by special coordination funds for promoting science and
technology (SCF), both from the Ministry of Education, Culture,
Sports, Science and Technology in Japan (MEXT). Computation
time was provided by the Super Computer System, Human Genome
Center, Institute of Medical Science, University of Tokyo. Funding
to pay the Open Access publication charges for this article
was provided by MEXT.
Conflict of interest statement. None declared.
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