UvA-DARE ( Digital Academic Repository ) SigWin-detector : A Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences

Background: 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 eScience approach, hence generic, modular, and reusable. A virtual laboratory environment with workflows, workflow management systems, and Grid computation are therefore essential. Findings: 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. Conclusion: 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 proofof-principle for the modular e-Science based concept of integrative bioinformatics experimentation. Published: 8 August 2008 BMC Research Notes 2008, 1:63 doi:10.1186/1756-0500-1-63 Received: 25 April 2008 Accepted: 8 August 2008 This article is available from: http://www.biomedcentral. com/1756-0500/1/63 © 2008 Inda et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Page 1 of 10 (page number not for citation purposes) BMC Research Notes 2008, 1:63 http://www.biomedcentral. com/1756-0500/1/63 Findings 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 [4] 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. [4] 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): Page 2 of 10 (page number not for citation purposes) BMC Research Notes 2008, 1:63 http://www.biomedcentral. com/1756-0500/1/63 Page 3 of 10 (page number not for citation purposes) Figure 1 Using a mmFDR method to detect RIDGEs in a human transcriptome map. Schematic representation of the moving median false discovery rate (mmFDR) procedure identifying regions of high and low density of gene expression (RIDGEs and anti-RIDGEs, respectively) [4]. (A) Input sequence, a human transcriptome map (HTM), i.e., expression values of genes ordered by their chromosome location (cyan; chromosome 6). (B) mm(w), moving medians of the HTM for a given window size S. (C) Determination of the high and low mmFDR thresholds at a given level α: The high threshold mk is the smallest gene expression value for which the , here f(m) is the theoretical probability distribution of mm(w), and g(m) is the observed distribution of mm(w). (In [4], f(m) is estimated by simple sampling). Similarly, the low threshold mj is the largest gene expression value for which . (D) Selection of significant windows in chromosome 6: RIDGEs (in red) all windows for which the median gene expression is higher than or equal to mk; anti-RIDGEs (in blue) all windows for which the median gene expression is lower than or equal to mj. (E) Output RIDGEOGRAM of chromosome 6. Each row (y-axis) in the RIDGEOGRAM represents a window size, ranging from S = 3 to S = M (the number of genes on the chromosome). Each column (x-axis) represents a sliding window number, ranging from w = S/2 to w = M-S/2 (hence the triangular form). Color is used to mark window medians significantly above (red) or below (blue) the genome-wide median. The scheme shows median expression data for w