- Technical Note
- Open Access
SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences
BMC Research Notes volume 1, Article number: 63 (2008)
Chromosome location is often used as a scaffold to organize genomic information in both the living cell and molecular biological research. Thus, ever-increasing amounts of data about genomic features are stored in public databases and can be readily visualized by genome browsers. To perform in silico experimentation conveniently with this genomics data, biologists need tools to process and compare datasets routinely and explore the obtained results interactively. The complexity of such experimentation requires these tools to be based on an e-Science approach, hence generic, modular, and reusable. A virtual laboratory environment with workflows, workflow management systems, and Grid computation are therefore essential.
Here we apply an e-Science approach to develop SigWin-detector, a workflow-based tool that can detect significantly enriched windows of (genomic) features in a (DNA) sequence in a fast and reproducible way. For proof-of-principle, we utilize a biological use case to detect regions of increased and decreased gene expression (RIDGEs and anti-RIDGEs) in human transcriptome maps. We improved the original method for RIDGE detection by replacing the costly step of estimation by random sampling with a faster analytical formula for computing the distribution of the null hypothesis being tested and by developing a new algorithm for computing moving medians. SigWin-detector was developed using the WS-VLAM workflow management system and consists of several reusable modules that are linked together in a basic workflow. The configuration of this basic workflow can be adapted to satisfy the requirements of the specific in silico experiment.
As we show with the results from analyses in the biological use case on RIDGEs, SigWin-detector is an efficient and reusable Grid-based tool for discovering windows enriched for features of a particular type in any sequence of values. Thus, SigWin-detector provides the proof-of-principle for the modular e-Science based concept of integrative bioinformatics experimentation.
Genomic information is encoded in DNA and as such retained in a fairly steady configuration. In contrast to RNA, proteins, and metabolites, DNA is organized by a limited number of large chromosomes with relatively stable DNA sequences. Therefore, position in the DNA sequence, i.e., chromosome location, provides a convenient and essential scaffold for both the living cell and molecular biological research. In cells, for example, chromosomal organization is important for gene-transcription processes. Expression-profiling studies showed that gene expression is not only controlled at the level of individual genes, but also via autonomous regulation of chromosomal domains [1–5]. This suggests the existence of higher-order transcriptional regulatory mechanisms related to DNA organization or structures. The use of chromosomal organization in the life sciences is exemplified by the popularity of genome browsers that use chromosome location to map many genomic features, such as genes and their products, regulatory elements, gene expression, and epigenetic markers. The search for connections between genomic features is important in unraveling cellular mechanisms.
The pace at which omics experiments continuously keep producing large amounts of data about genomic features for an increasing number of sequenced genomes, creates a need for new high-throughput methods for identification of correlations between DNA related features [6–12]. Therefore, biologists would benefit from tools that could quickly identify enriched regions of genomic features. This would allow extensive, yet convenient in silico experimentation based on routinely processing and comparing multiple datasets. However, this requires these tools to be implemented in such a way that they deal with the many steps involved in this kind of experimentation. These include: acquiring the data from local or remote data repositories, converting it to the desired format, using it with the actual application that searches for the desired enrichment (possibly using Grid computation), visualizing the results, and comparing and/or integrating multiple datasets. Therefore, such a tool should be developed applying an e-Science approach [13–17]: it should be generic with respect to which data it can analyze, easy to adapt, and its parts should be reusable.
In an e-Science approach, a computational environment that provides transparent access to distributed data, adequate computational resources, as well as the necessary interfacing tools, is called a virtual laboratory (VL). Workflow management systems (WMSs, [18–21]) are an example of interfacing tooling that takes care of scheduling, keeps track of task executions, and provides the management framework necessary to develop applications inside a VL. WMSs can be used to design scientific workflows that automate in silico experimentation by providing a pipeline for streaming large quantities of data through various algorithms, applications and services.
This paper describes an e-Science based data integration and analysis tool: SigWin-detector. This application can detect clusters with increased (or decreased) density of a genomic feature in a DNA-related sequence in a fast and reproducible way. In the context of the development of a VL, our tool was implemented as a workflow running under WS-VLAM[20, 21], a Grid-enabled WMS. A biological use case shows its relevance for biological research. SigWin-detector is based on a method previously used by Versteeg and coworkers  to detect regions of increased and decreased gene expression (RIDGEs and anti-RIDGES) in human transcriptome maps (HTM). We improved the original method by i) deriving an analytical formula for computing the new hypothesis probability distribution, which replaces the costly step of estimation by random sampling and ii) developing a new algorithm for computing moving medians. While these improvements radically increase the intrinsic efficiency of the method, implementing SigWin-detector using a generic e-Science approach with access to Grid resources broadens its applicability and makes it amenable to a wide spectrum of experiments on genomic features or in fact on any sequence of values.
Significant windows and the mmFDR procedure
Versteeg et al.  identified clusters where the median expression level of the genes involved is significantly higher than expected (RIDGEs), using a moving median false discovery rate (mmFDR) procedure (Figure 1). The mmFDR procedure identifies RIDGEs by testing the input gene-expression against the null hypothesis that the position of the genes on the chromosomes does not affect their expression levels. This same procedure can be used to identify significant windows (i.e., windows in the input sequence that have a median value that deviates significantly from expected, if assumed that the ordering of the numbers in the input sequence is random) related to any genomic feature mapped to DNA sequences. In an even wider scope, it can also be used to identify significant windows in any sequence of numbers.
Avoiding permutations in the mmFDR procedure
Computationally, the most expensive step in the original mmFDR procedure is the repeated determination of medians over sliding windows of permutations of the input data to estimate the probability function corresponding to the null hypothesis. Our first improvement to the original method was to derive an exact formula for this distribution (see definitions and derivation in Additional file 1):
This exact formula reduces the number of cycles of computing moving medians of an input sequence of approximately 25,000 entries from at least 5,000 to 1, giving SigWin-detector the efficiency it needs to be used routinely and for processing and comparing multiple datasets within minutes to hours, instead of days. This efficiency could not be if f(m) was estimated by sampling the permutation space E π , and counting the number of times m was the median value in any sliding window of size S.
Speeding up the computation of moving medians
Although we removed the need for computing moving medians over permutations of the input sequence, we still need to compute medians of windows sliding over the input sequence. We developed a new algorithm to compute those moving medians efficiently by exploiting the fact that moving medians for many window sizes must be computed simultaneously (Figure 2). This new algorithm is also suitable for computing any other order-statistics.
Additional Figure A1 (Additional file 2) shows a graph comparing our moving medians algorithm with the commonly used Hardle and Steiger's algorithm . While the execution time of their algorithm increases with window size (for a fixed sequence size), the execution time of our algorithm decreases with window size (Figure A1, upper panel). Because SigWin-detector needs to compute moving medians for many window sizes, our algorithm has a clear advantage over Hardle and Steiger's algorithm. In Figure A1, the break-even point of the cumulative computation is for S max around 400. The efficiency of our method can be further improved by using a mixed algorithm that uses Hardle and Steiger's algorithm for small window sizes and our algorithm for large window sizes, or by employing a divide-and-conquer approach. For example, a two-phase algorithm would start by dividing the input sequence into chunks of size 2M, with M ≥ 2S max , and applying the original algorithm to each chunk separately. Similarly, the second phase computes the medians for the missing sliding windows by dividing the sequence into chunks of the same size, but now using an offset M. This two-phase algorithm is also suitable for parallelization.
Designing a Grid-enabled generic workflow
To broaden the applicability of the mmFDR procedure, we implemented SigWin-detector using an e-Science approach by implementing a general, reusable, and adaptable tool with access to Grid resources using the WS-VLAM workflow management system[20, 21].
First we split the procedure into a collection of workflow components (called modules), each module performing a specific task that may be fine-tuned using parameters. The modules exchange data with each other by means of input and output ports. We then can choose the appropriate modules and compose a workflow suited to our specific needs . Figure 3 describes a basic workflow configuration of SigWin-detector.
The SigWin-detector Config-Basic1 workflow was tested on a Grid computer cluster composed of geographically distributed computational nodes: Distributed ASCI Supercomputer 3 (DAS-3, ). Additional Figure A2 (Additional file 2) presents wall clock execution times of the SigWin-detector Config-Basic1 workflow (Figure 3) for input sequences of various sizes.
The basic workflow can be altered by substituting, deleting, or adding modules. For example, we can extend the workflow to get the input sequence from a remote uniform resource identifier (URI)and then put the resulting SigWin-map back into it. We can modify the workflow to generate one SigWin-map per logical subsequence of the input sequence, instead of a single SigWin-map for the complete sequence . We can also expand our workflow by computing significant windows for high median values (e.g., RIDGEs) and significant windows for low median values (e.g., anti-RIDGEs) simultaneously. The SigWin-detector workflow itself can be made into a "composite module" for more complex workflows. Furthermore, interconnection of WS-VLAM with the TAVERNA workbench  will permit the use of the existing TAVERNA components in connection with SigWin-detector. At the moment, Grid authentication prevents WS-VLAM workflows being used outside the Grid without the extra step of Grid certification. However, we are working on a Taverna workflow that encapsulates the SigWin detector, to be made available through the myExperiment webpage .
Biological application: finding RIDGES in a human transcriptome map
Once we finished our basic SigWin-detector, we modified it (Additional file 3) for application in our biological use case that aims to find (anti-)RIDGES in transcriptome maps. Figures 4 and 5 show a series of RIDGEOGRAMS for gene expression data for a recent version of the human transcriptome map (HTM) based on the UCSC release hg18 , and Table 1 summarizes some RIDGE statistics. Each RIDGEOGRAM displays both RIDGEs (red-shades) and anti-RIDGEs (blue-shades), the different color shades representing different mmFDR threshold levels. The size of the resulting RIDGEOGRAMS is proportional to the number of genes on a chromosome. We determined i) genome-wide (anti-)RIDGEs, i.e., windows for which the median expression is significantly higher (lower) than expected by considering the whole genome gene expression profile in the mmFDR procedure (Figure 4), and ii) chromosome specific (anti-)RIDGEs, i.e., the same analysis, but considering only the specific chromosome gene expression profile (Figure 5). This distinction has a major effect on the outcome. If the expression values of the genes on a certain chromosome are typically significantly higher than the genome-wide values, then there are less chromosome specific than genome-wide RIDGEs (e.g., chromosome 19 in Figures 4 and 5 and Table 1). Conversely, if the expression values of the genes on a chromosome are typically significantly smaller than the genome-wide values, then there are more chromosome specific RIDGEs (e.g., chromosome 6 in Table 1 and Figures 4 and 5). In the case of anti-RIDGEs the opposite holds (e.g., chromosomes 17 in Table 1 and Figures 4 and 5). This example shows the importance of choosing the right sequence to compute the null hypothesis distribution. Based on the fact that chromosomes are separate molecules in a cell, one may favor the results from the individual chromosome SigWin-detector analysis to investigate potential higher-order gene expression regulatory mechanisms.
The RIDGEOGRAMS shown in Figures 4 and 5 only take the ordering of the genes into account, and not their actual physical position in the chromosome. However, from a biological perspective it is likely that the higher order gene-expression mechanisms that underlie RIDGEs relate to an actual section of the chromosome rather than a cluster of genes just ordered by their chromosome location. So we used our SigWin-detector to take the physical gene position into account by subdividing the chromosomes in stretches of constant value (250 kb). If a stretch contains the beginning of one or more genes, their average expression value is assigned to that stretch of DNA. For this analysis we used the SigWin-detector Config-Sub2 with preprocessed HTM data and adapted parameters. The resulting RIDGEOGRAMS are proportional to the chromosome's size (Additional Figure A3, Additional file 2). The anti-RIDGEs show a lower cut-off caused by the many 0 values in the HTM. The results from the SigWin-detector analysis using chromosome position are substantially different to those using chromosome ordering. This application demonstrated that SigWin-detector is an e-Science tool that allows convenient in-silico experimentation. To prove that this tool is generic, we used our workflow to examine a simple sequential data set: an extended time series of hourly ground level ozone concentration measurements (Additional file 4).
Availability and requirements
Project name: SigWin-detector
Project home page: http://mad-db.science.uva.nl/projects/sigwin/
Programming language: C++
Other requirements: SigWin-detector needs the WS-VLAM workflow management system. WS-VLAM has a client distribution and site distribution.
i. WS-VLAM client distribution: The WS-VLAM composer, a graphical interface used for creating, modifying, and submitting workflows. Needs Java virtual machine (version1.5 or higher).
ii. WS-VLAM site distribution: The WS-VLAM engine, which is needed for running the workflows in a Grid. The WS-VLAM engine needs a GLOBUS GT4 (4.0.3) installation.
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We thank R. Monajemi for assistance with the HTM data sets, R. H. Bisseling for checking the mathematics, L. O. Hertzberger for his constant support, and J. Batson for proofreading the paper. This work was carried out in the context of the Virtual Laboratory e-Science project http://www.vl-e.nl and BioRange program of the Netherlands Bioinformatics Centre (NBIC). VL-e is supported by a BSIK grant from the Dutch Ministry of Education, Culture and Science (OC&W) and the ICT innovation program of the Ministry of Economic Affairs (EZ). BioRange is supported by a BSIK grant through the Netherlands Genomics Initiative (NGI).
The authors declare that they have no competing interests.
MAI carried out the entire research project and wrote the manuscript. MFvB participated in development of the statistical methods. MR was involved in the conceptualization of the analytical formula, in the e-Science approach, and in the coordination of the project. ASZB, DV, and WA worked on the development and support of WS-VLAM. AHvK developed the methods for the genomic mapping of expression data and was involved in the development of the statistical methods. TMB conceived the study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
Electronic supplementary material
Additional file 1: Derivation of the exact formula for the probability function f(m), and detailed description of the mmFDR-procedure. (PDF 72 KB)
Additional file 5: This tar file contains the source files of the WS-VLAM modules needed to run the SigWin-detector workflow, and some examples. To uncompress use. ▪ tar -xvzf SigWin-VLAM.v1.1.tar.gz (Linux users). ▪ WinZip or a similar tool. (GZ 377 KB)
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Cite this article
Inda, M.A., van Batenburg, M.F., Roos, M. et al. SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences. BMC Res Notes 1, 63 (2008). https://doi.org/10.1186/1756-0500-1-63
- Input Sequence
- Genomic Feature
- Grid Resource
- Virtual Laboratory
- Uniform Resource Identifier