Nucleic Acids Research 2004 32(Web Server Issue):W257-W260; doi:10.1093/nar/gkh396
© 2004, the authors
Nucleic Acids Research, Vol. 32, Web Server issue © Oxford University Press 2004; all rights reserved
BEARR: Batch Extraction and Analysis of cis-Regulatory Regions
Vinsensius B. Vega*,
Dhinoth Kumar Bangarusamy,
Lance D. Miller,
Edison T. Liu and
Chin-Yo Lin
Genome Institute of Singapore, 60 Biopolis Street, #02-01, Singapore 138672
* To whom correspondence should be addressed. Tel: +65 6478 8041; Fax: +65 6478 9058; Email: vegav{at}gis.a-star.edu.sg
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
Received February 9, 2004; Revised and Accepted March 24, 2004
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ABSTRACT
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Transcription factors play important roles in regulating biological
and disease processes. Microarray technology has enabled researchers
to simultaneously monitor changes in the expression of thousands
of transcripts. By identifying specific transcription factor
binding sites in the
cis-regulatory regions of differentially
expressed genes, it is then possible to identify direct targets
of transcription factors, model transcriptional regulatory networks
and mine the dataset for relevant targets for experimental and
clinical manipulation. We have developed web-based software
to assist biologists in efficiently carrying out the analysis
of microarray data from studies of specific transcription factors.
Batch Extraction and Analysis of
cis-Regulatory Regions, or
BEARR, accepts gene identifier lists from microarray data analysis
tools and facilitates identification, extraction and analysis
of regulatory regions from the large amount of data that is
typically generated in these types of studies. The software
is publicly available at
http://giscompute.gis.a-star.edu.sg/~vega/BEARR1.0/.
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INTRODUCTION
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Microarray technology has been applied extensively to interrogate
changes in gene expression patterns that underlie biological
and disease processes. The observed alterations in transcript
levels are mediated by intracellular signalling pathways and
downstream transcription factors that interact with specific
regulatory regions of the genome and the transcription machinery.
Therefore, DNA microarrays are especially well suited for experiments
where specific transcription factors are known to play important
roles. Transcription factors that are involved in cancers, such
as p53 and Myc, have been studied using this new technology.
By combining expression data and computational analysis of regulatory
regions, it is then possible to begin identifying the molecular
mechanisms of transcriptional regulation, to map the network
of transcription factors and their target genes which affect
the phenotypes being studied, and to generate additional hypothesis
for testing in the laboratory [see comments in (
1)].
Some online computational tools for mammalian gene regulatory region extraction and analysis are available to researchers, although with varying degrees of accessibility, capability and focus. The most comprehensive tools, including TOUCAN (2), EZRetrieve (3), the Genomatix suite (www.genomtix.de) and TRANSPLORER (www.biobase.de) from BIOBASE (the former two are from academic sources and the latter two are only available commercially for batch analysis), usually employ annotation-based sequence retrieval and position weight matrices, typically from the TRANSFAC database (4) or specialized databases, for sequence extraction (e.g. the upstream region extraction tool developed by the Harvard-Lipper Center, http://arep.med.harvard.edu/labgc/adnan/hsmmupstream) and binding site identification, respectively. There are also standalone [e.g. MEME (5), BioProspector (6), MDScan and REDUCE] and integrated [e.g. MotifSampler and phylogenetic footprinting modules in TOUCAN] motif discovery tools designed to uncover conserved sequences in extracted regulatory regions (7,8). While the sophisticated approaches in these programs represent innovations in comprehensive binding site prediction and discovery and are useful for detailed analysis and mining of refined or idealized datasets—for example members of the same gene family or known targets of specific transcription factors—nevertheless, based on our experience, they may be of limited use against the large amount of ‘noisy’ data generated in microarray studies that often confound the interpretation of the analytical results or fail to yield conserved regulatory sequences. Whereas these tools favour comprehensive approaches for identifying all known binding sites or conserved sequence discovery in the dataset, we propose that a critical and complementary step prior to a more comprehensive analysis and experimental validation is a hypothesis-driven and focused computational strategy based on the experimental design. Furthermore, although batch options are available for some of the tools noted, they have not been optimized for batch operations and are not suitable for the large amount of data that can be generated from genome-scale studies. Therefore, we created Batch Extraction and Analysis of cis-Regulatory Regions, or BEARR, to assist biologists in performing batch extraction and analysis of cis-Regulatory regions of hundreds or even thousands of differentially expressed genes identified in microarray studies. Here, we describe the system design and functionalities.
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SYSTEM DESIGN
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The system is divided into two parts: (i) the regulatory region
sequence extraction module and (ii) the sequence analysis module.
Essentially, the sequence extraction module generates the nucleotide
sequences, using annotation and genomic sequence databases,
to be queried by the analysis module, based on transcription
factor binding sites or any conserved regions defined by the
user. The modular system design allows for maximum component
reusability and extensibility. To ensure platform independence
and ease of installation, no specialized library or programs
were employed. An interactive web-based user interface was developed
for ease of use, interoperability and accessibility.
Figure 1 shows a screenshot of the web interface. The system schema
of BEARR is also available in the FAQ section of website.
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REGULATORY REGION EXTRACTION
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RefSeq accession numbers, recognizable by the NM_ and XM_prefixes,
are the primary gene identifiers employed in the modules, although
the system is capable of translating other identifiers from
microarray analysis tools, including the UniGene cluster ID
and the IMAGE clone ID. The list of gene identifiers can be
easily copied from the output files of most expression analysis
tools and pasted into the input window of the extraction module.
This input feature allows users the flexibility of utilizing
the optimal microarray data analysis tools to identify differentially
expressed genes and then efficiently moving into the extraction
and analysis of their respective regulatory regions. Currently,
BEARR accepts identifiers for human and mouse genes.
The sequence extraction module utilizes NCBI's LocusLink and RefSeq (9) databases to identify the genes and pinpoint their loci within the genome. These annotations were chosen for their comprehensiveness, in terms of number of annotated genes, and their consistency with the current state of the NCBI contig databases, the underlying genomic sequence database used in BEARR. Using the transcription start site (TSS), defined as the 5'-most nucleotide in the reference transcript, and the 3' terminus of the transcript as reference points, the module can extract user-defined regions both upstream and downstream of the references. Extraction speed has been enhanced by utilizing downloaded databases on the BEARR server rather than accessing the information on the NCBI servers via the internet. The resulting sequences are saved in the FASTA format and are downloadable by users. It is important to note that the TSS locations annotated in LocusLink and RefSeq may only approximate true start sites due to incomplete information at the 5' ends of some reference sequences. The large number of annotated genes in LocusLink and RefSeq, however, provides the advantage of maximizing the number of regulatory regions we are able to extract for further analysis. To better annotate human start sites, we have incorporated the information from DBTSS (10), a database of experimentally extended 5' sequences in human transcripts, to assist users in orienting the locations of predicted sites with reference to the more accurate TSS. It is also expected that as the sequence and annotation information is updated regularly in LocusLink and RefSeq, the accuracy of the current extraction method will improve.
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SEQUENCE ANALYSIS
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Once the desired sequences are obtained by the extraction module,
the sequence analysis subsystem queries the input for putative
transcription factor binding sites. Although it may be possible
to map all known binding sites by incorporating information
from TFD (
11), TRANSFAC (
12) and TRRD (
13), BEARR is specifically
designed to efficiently detect a focused group, as determined
by experimental design or the microarray data, of transcription
factor binding sites. We have, however, provided links to the
information in TFD and TRANSFAC to enable users to extract consensus
sites or matrices of their choice for use in BEARR. Users also
have the option of inputting their own binding sites derived
from the latest experimental data rather than relying on the
potentially dated information in the databases. Extracted sequences
can be queried using single or multiple consensus binding sites,
including tandem sites, or position weight matrices of binding
sites [PWMs; (
14)]. Stringency is adjusted by defining the number
of mismatches or spacer sequences allowed in the consensus searches
or the similarity score threshold in the PWM output. The PWM
score signifies the goodness of the match, where higher scores
correspond to better matches. An optional empirical
P-value
calculation is also available for users who wish to assess the
statistical significance of the motifs being identified against
the null hypothesis of finding the motifs in randomly generated
sequences. The analysis results are displayed in a tab-delimited
text file and include the detected putative binding sites, number
of hits and their positions relative to either 5' end or 3'
terminus. Additional columns of PWM score, empirical
P-value,
and DBTSS 5'-extension are also included under the appropriate
settings (see
Figure 2). Further information of the analysis
module can be found in the Help sections of the website.
View larger version (18K):
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Figure 2. Sample output from BEARR when used to identify potential functional Estrogen Response Element, based on TRANSFAC ERE position weight matrix. Output of consensus pattern searches is similar, with the exception of PWM score and empirical P-value columns.
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In some cases, the user may find it necessary to redefine the
published consensus sequences or matrices for binding sites
because the reported derivations may be based on outdated or
incomplete information. It is also highly recommended that the
users optimize the stringency thresholds, using a test set of
known target genes of the transcription factors of interest,
prior to the analysis of the extracted sequences. These parameters
will directly impact the sensitivity and the specificity of
the analysis. For further detailed and comprehensive analysis
of the extracted sequences, users can upload the FASTA sequence
files into other available regulatory region analysis tools
[e.g. MEME (
5)] described above from the links on the website.
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ANALYSIS EXAMPLES
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BEARR was originally created to assist the discovery of potential
estrogen receptor binding sites in the regulatory regions of
hormone-responsive genes identified in our microarray experiments.
We have subsequently used BEARR to analyse expression data from
studies of other nuclear receptors and transcription factor
families in order to identify target genes and downstream pathways.
In general, this software is well suited for the analysis of
data generated from study designs where transcription factor
activity is manipulated experimentally. The software has also
been applied in the analysis of the role of transcription factors
identified in microarray studies of clinical samples in mediating
the observed alterations in gene expression profiles of diseased
tissues. In addition to the analysis of regulatory regions of
genes identified in array studies, genome-wide surveys (all
genes annotated in LocusLink and RefSeq) of transcription factor
binding sites of interest, within defined regulatory regions,
have been carried out to determine the frequency and distribution
of putative binding sites. In a typical analysis flow, a user
starts by formulating the underlying hypothesis, followed by
designing and carrying out the appropriate experiments while
at the same time studying the literature for related transcription
factor binding sites to construct an initial binding site model.
The experiments enable the user to group interesting genes,
from which BEARR tries to identify potential functional binding
sites based on the input transcription factor binding site model.
Refinement of the model might also be necessary. Further biological
investigations and experimentations could be performed on the
binding sites found. Examples, tutorials and analysis workflows
can be found in the online supplementary material on the website.
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FUTURE DEVELOPMENTS
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As more genomes are sequenced and annotated in LocusLink and
RefSeq, additional databases will be integrated into BEARR to
enable users to extract and analyse regulatory regions of genes
from other model organisms. These upgrades will enhance the
comparative analysis of conserved transcription factor binding
sites. Other areas of development include motif search algorithms
that will allow users to identify conserved sequences within
the regulatory regions of co-regulated genes without prior knowledge
of binding sites, i.e. consensus sequences or defined matrices.
These additional functions will necessitate the development
of a graphical results output for optimal data visualisation
and analysis.
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Notes
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The online version of this article has been published under
an open access model. Users are entitled to use, reproduce,
disseminate, or display the open access version of this article
provided that: the original authorship is properly and fully
attributed; the Journal and Oxford University Press are attributed
as the original place of publication with the correct citation
details given; if an article is subsequently reproduced or disseminated
not in its entirety but only in part or as a derivative work
this must be clearly indicated.
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