- Open Access
Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources
© Blom et al; licensee BioMed Central Ltd. 2008
- Received: 18 June 2008
- Accepted: 21 October 2008
- Published: 21 October 2008
Despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. The increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar expression patterns are more likely to participate in shared biological processes.
We developed Prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. Our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. Furthermore, Prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. We analyzed publicly available transcriptome data sets and used literature sources to validate putative functions suggested by Prosecutor. We supply the complete results of our analysis for 11 prokaryotic organisms on a dedicated website.
The Prosecutor software and supplementary datasets available at http://www.prosecutor.nl allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest. A de novo analysis allows new organisms to be studied.
- Functional Category
- Functional Prediction
- Transcriptional Module
- Annotation Source
- Stanford Microarray Database
One of the central challenges in computational biology is the prediction of gene function . The inference of gene function typically starts with DNA sequence analysis based on ortholog information [2–5]. Although this method has proven to be successful in many cases, considerable numbers of genes (20–50%) in current genome annotations still are of unknown function. Complementary approaches are therefore required to characterize the function of these genes.
Since the start of the DNA microarray era, the "guilt-by-association" (GBA) methodology has been used to infer gene function [6–9]. This concept is based on the assumption that genes involved in similar cellular functions are likely to display correlated expression behavior [10–12]. In addition, this correlated behavior might identify common regulatory mechanisms.
Ultimately, to understand the function of a new gene, one should exploit all available experimental data sources (e.g., transcriptomics, proteomics, protein-protein interactions and metabolomics) [13, 14] or even by the joint efforts of many scientists in a community annotation . Previous work on gene function prediction has mainly been focused on higher organisms using multiple high-throughput data sources [16–18]. On the other hand, genome organizational principles that are unique for prokaryotes supply valuable additional information about gene function.
However, it is expected that the GBA method is particularly powerful for prokaryotes, due to their tight coupling of transcription and translation . In addition, for many prokaryotes, the available gene expression datasets greatly outnumber other experimental data sources.
To improve the analysis of the predictions, Prosecutor provides additional information for each annotated gene, most notably in its genomic context, which is particularly useful for operons. The occurrence of adjacent divergent co-expressed genes is also highlighted since these are expected to be co-regulated . Finally, putative new members of transcriptional modules are examined for the presence of the same regulatory motif that is already known for the module.
Our Prosecutor software imposes no constraints on the biological annotations used; it generates hypotheses based on large variety of annotation sources e.g., Gene Ontology, metabolic pathways, UniProt keywords, etc. This is in contrast to most other methods [11, 12, 16–18, 21–24] which, with few exceptions [8, 10], are focused on coupling genes to Gene Ontology sources only.
We discuss some of the functional assignments obtained by Prosecutor, as well as a number of mining capabilities provided by the software. We find that the increasing variety of experimental conditions used in DNA microarray experiments has greatly improved the ability to identify the function of unknown genes using GBA principles.
Prosecutor is a standalone application developed in Java and shares its functional database structure with the FIVA software . It features an iterative implementation of the GBA method which is based on iterative Group Analysis algorithm (iGA) . Several characteristics of the software analysis modules are described below.
The Iterative Guilt-By-Association (iGBA) method
Performance of functional categories
Receiver Operating Characteristic curves
The performance on well-annotated genes was assessed to evaluate the sensitivity of the iGBA method. This evaluation has to be specific for each functional category, because for some of them we expect that all members show close correlation, while others are so general that their members will not correlate and iGBA is expected to fail. The category specific evaluation of expression coherence is done as follows: Our iGBA algorithm yields a p-value for every pair of gene-functional category pair (Fig. 1C). This p-value is indicative of the confidence of the assignment of a gene to a functional category. For each category we sort the gene list by p-values and examine the positions of the p-values of its known members in this sorted list. We are then able to calculate an "expression coherence value" for each functional category by plotting the true and false positive rates on Receiver Operating Characteristic (ROC) curves (Fig. 1D) . The corresponding Area Under the ROC Curve (AUC) is a quantitative measure of the expression coherence of the genes of a functional category. A functional category in which all known members show strong co-expression will have an AUC close to 1.0, whereas a randomly predicting functional category (i.e., a category that does not show coexpression of its members) would yield AUC values around 0.5. Using the AUC measure, we are now able to select the most promising functional categories for further analysis.
Parameter free approach
Various methods have been developed that specifically employ data from microarrays studies [21–24]. Some of these methods are designed for temporal gene expression profiles [23, 24] or calculate a functional enrichment for each dataset . Other approaches require preprocessing of the annotation data, e.g., generating a set of validated and highly unlikely associations (see  for more information) used for training of the prediction model . Our Prosecutor application improves on previous methods by providing a parameter free approach for the inferral of gene function. No trusted set of functional associations between proteins is required since Prosecutor treats every functional category individually, thereby circumventing preselection toward particular processes.
Additional layers of information
The strength of Prosecutor comes also from its additional prokaryote-specific layers of information combined with a convenient visualization of the functional predictions. This prioritizing of the results allows for the rapid identification of the most promising function predictions.
Genomic context analysis
Regulatory mechanism analysis
Transcriptional modules represent genes that are regulated by a common regulator. The regulatory mechanisms underlying the co-expression of members of a transcriptional module are used as additional evidence to prioritize the Prosecutor results. For some organisms, functional annotations based on curated knowledge of transcriptional modules are available [29, 30]. Motif instances from all members of a transcriptional module are used to create a position specific scoring matrix. This matrix is used to search for additional hits in the upstream and coding regions from the first gene of the operon as well as the gene of interest (in case of residing in an operon). Using this approach, we are able to predict putative new targets for transcriptional modules that exhibit significant co-expression with known members of the transcriptional module and a putative regulatory motif in their upstream regions (Fig. 2C).
Functional predictions are represented by Prosecutor as graphs using the Prefuse toolkit  to visualize the gene redundancy and overlap between the functional categories of different functional predictions. This method allows to visually determine the uniqueness of each of the function predictions. A force-directed layout from the Prefuse visualization framework is used to position the different nodes (genes) in the network (Fig. 2D).
Performance compared to random microarray data
Most genome annotations deposited to GenBank are rarely if ever updated . As research progresses, knowledge of many previously uncharacterized genes improves. This annotation gap enables us to analyze results obtained by Prosecutor by manual literature mining of genes for which no function was available in the original genome annotation. For this validation, only functional categories exhibiting strong predictive properties, with AUC values higher than 0.7, were taken into account.
First test-case: validating results of Prosecutor
Confirmed results from Prosecutor
Pathway flagellar assembly
GO:0003774 motor activity
GO:0015343 siderophore-iron transmembrane transporter activity
GO:0009432 SOS response
GO:0051082 unfolded protein binding
GO:0019852 L-ascorbic acid metabolic process
GO:0006534 cysteine metabolic process
transcriptional module SigM
transcriptional module Fur
Pathway Flagellar assembly
transcriptional module GerE
PW:Biosynthesis of type II polyketide back- bone
PW:Biosynthesis of type II polyketide products
GO:0006826 iron ion transport
GO:0019290 siderophore biosynthetic process
GO:0019290 siderophore biosynthetic process
Second test-case: extending transcriptional modules in E. coli
Extending transcriptional modules of E. coli
motif sequence in the intergenic region of either the gene or its operon
ArgR Amino acid biosynthesis: Arginine. AUC 0.92
CysB Amino acid biosynthesis: Cysteine AUC 0.91
Fur iron regulatory gene AUC 0.84
LexA major regulator of DNA repair AUC 0.87
MetJ Amino acid biosynthesis: Methionine AUC 0.88
Third test-case: performance of annotation sources for Saccharomyces cerevisiae
The complete results of the annotation efforts from our software for twelve organisms are available on the supplemental website . On this dedicated web-site functional couplings can be mined in three ways: 1) through a list of the best functional couplings for each functional category; this allows "browsing" through the most promising associations, 2) a sorted list of functional categories and their predictive power (AUC); in case that one is interested in the genes that are associated with a specific functional category, and 3) a sorted list of genes; allows to identify to which functional categories a gene of interest is associated. All data sources used for analysis are available, allowing researchers studying any of the analyzed organisms to perform a functional analysis for their expression dataset and/or functional categories.
Prosecutor uses DNA microarray data combined with functional annotations to infer putative gene functions. We show that multiple annotation sources are informative and non-redundant and allow maximizing the use of all available DNA microarray data. For B. subtilis, we were able to confirm 40 out of the 160 best functional predictions generated by Prosecutor, using published literature. We therefore believe that the other functional assignments based on our analysis are also likely to be informative and reliable. Combined with regulatory motif information for the species B. subtilis and E. coli, Prosecutor allows the identification of new transcriptional module members. Prosecutor can thus serve as a generic tool for a genome-wide (re)annotation of gene functions in prokaryotes. The results of such a re-annotation effort, for 11 widely studied bacterial species, is supplied as a community resource at the associated website .
Implementation & Availability
Prosecutor was programmed as a multithreaded standalone application in Java using the Eclipse framework http://www.eclipse.org/ as a Rich Client Platform (source code is available upon request). Prosecutor runs on all Java-supporting operating systems (MS Windows, Linux and Mac OS). The Prosecutor was developed from a bacterial perspective and therefore supports the two major prokaryotic genome annotation formats (Genbank and EMBL). A simplified tabulated genome annotation format can also be used, enabling organisms for which no Genbank or EMBL file is available to be studied.
The basic requirements of an analysis consist of a genome annotation (i.e., Genbank or EMBL) and a collection of microarray data. Currently, six different annotation sources are implemented: (i) transcriptional modules, (ii) gene ontologies (GO) , (iii) metabolic pathways from the KEGG database  (iv) UniProt keywords , (v) InterPro domains  and (vi) user-defined categories.
DNA microarray datasets
DNA microarray data used in this study consisted of dual dye arrays for 11 prokaryotic organisms and yeast from the KEGG expression database  and the Stanford microarray database . For E. coli, an additional 305 Affymetrix expression arrays were obtained from the M3D Database .
Multiple testing correction
A typical problem in genome-wide statistical analysis is the occurrence of many false positives (i.e., a functional prediction that is mistakenly found significant due to multiple testing). The incidence of false positives is roughly proportional to the number of tests performed. Since a typical search in Prosecutor may consist of thousands of tests, the chance of obtaining false positive predictions is large. We have used a strict Bonferroni multiple testing correction method to correct the raw p-values from the iGA results to minimize this problem.
This study was supported by a grant from the Netherlands Organisation for Scientific Research and industrial partners in the NWO-BMI project number 050.50.206 on Computational Genomics of Prokaryotes and by the Center IOP Genomics. Work of SvH was in part supported by the BMBF within the framework of the transnational SysMO initiative in the project BaCell-SysMO. This work was in part supported by an EU program in FW6: Bacell Health, European Union Grant LSHG-CT-2004-503468. We thank Grayson H. Kleine C.B. de Miranda for creating a multi-threaded implementation of the iGBA algorithm.
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