Michael Watson

CoXpress: differential co-expression in gene expression data

BackgroundTraditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed.ResultsWe have developed coXpress as a means of identifying groups of genes that are differentially co-expressed. The utility of coXpress is demonstrated using two publicly available microarray datasets. Our software identifies several groups of genes that are highly correlated under one set of biologically related experiments, but which show little or no correlation in a second set of experiments. The software uses a re-sampling method to calculate a p-value for each group, and provides several methods for the visualisation of differentially co-expressed genes.ConclusioncoXpress can be used to find groups of genes that display differential co-expression patterns in microarray datasets.

DetectiV: visualization, normalization and significance testing for pathogen-detection microarray data

DNA microarrays offer the possibility of testing for the presence of thousands of micro-organisms in a single experiment. However, there is a lack of reliable bioinformatics tools for the analysis of such data. We have developed DetectiV, a package for the statistical software R. DetectiV offers powerful yet simple visualization, normalization and significance testing tools. We show that DetectiV performs better than previously published software on a large, publicly available dataset.

Correlation between lymph node pathology and chemokine expression during bovine tuberculosis.

Bovine tuberculosis is a disease of worldwide importance yet comparatively little is known about chemokine responses to infection. We report on the levels of chemokine expression within lymph nodes of cattle infected with Mycobacterium bovis when infection would be well established. Expression levels of a number of chemokines were increased in infected cattle and could be correlated to levels of respective chemokine receptors. Several chemokines were significantly correlated to pathology within the lymph node, indicating a direct relationship between chemokine expression and disease. Vaccinated animals challenged with M. bovis had lower levels of chemokine expression than unvaccinated, challenged animals, correlating with lower levels of disease in vaccinated animals. The chemokine expression profile correlated with previous evidence for a pro-inflammatory bias within the lymph node. At this stage of infection we suggest there is on-going chemokine expression by cells associated with the granuloma and continual recruitment of cells to control infection.

Early response of bovine alveolar macrophages to infection with live and heat-killed Mycobacterium bovis.

Bovine tuberculosis (TB) is a disease of economic importance and a significant animal health and welfare issue. The alveolar macrophage (AlvMϕ) plays a vital role in the immune response to TB and recent studies provide insights into the interactions between Mϕ and Mycobacterium bovis. Here we reveal the early transcriptional response of bovine AlvMϕ to M. bovis infection. We demonstrate up-regulation of immune response genes, including chemokines, members of the NF-κB pathway which may be involved in their transcription and also pro- and anti-apoptotic genes. M. bovis may therefore induce multiple mechanisms to manipulate the host immune response. We compared the response of AlvMϕ to infection with live and heat-killed M. bovis to determine transcriptional differences dependent on the viable pathogen. Several chemokines up-regulated following live M. bovis infection were not up-regulated after heat-killed M. bovis stimulation; hence the Mϕ seems to differentiate between the two stimuli.

Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes (Open Access publication)

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

The bovine chemokine receptors and their mRNA abundance in mononuclear phagocytes

BackgroundThe chemokine and chemokine receptor families play critical roles in both the healthy and diseased organism mediating the migration of cells. The chemokine system is complex in that multiple chemokines can bind to one chemokine receptor and vice versa. Although chemokine receptors have been well characterised in humans, the chemokine receptor repertoire of cattle is not well characterised and many sequences are yet to be experimentally validated.ResultsWe have identified and sequenced bovine homologs to all identified functional human chemokine receptors. The bovine chemokine receptors show high levels of similarity to their human counterparts and similar genome arrangements. We have also characterised an additional bovine chemokine receptor, not present in the available genome sequence of humans or the more closely related pigs or horses. This receptor shows the highest level of similarity to CCR1 but shows significant differences in regions of the protein that are likely to be involved in ligand binding and signalling. We have also examined the mRNA abundance levels of all identified bovine chemokine receptors in mononuclear phagocytic cells. Considerable differences were observed in the mRNA abundance levels of the receptors, and interestingly the identified novel chemokine receptor showed differing levels of mRNA abundance to its closest homolog CCR1. The chemokine receptor repertoire was shown to differ between monocytes, macrophages and dendritic cells. This may reflect the differing roles of these cells in the immune response and may have functional consequences for the trafficking of these cells in vivo.ConclusionsIn summary, we have provided the first characterisation of the complete bovine chemokine receptor gene repertoire including a gene that is potentially unique to cattle. Further study of this receptor and its ligands may reveal a specific role of this receptor in cattle. The availability of the bovine chemokine receptor sequences will allow further characterisation of the function of these genes and will confer wide-reaching benefits to the study of this important aspect of the bovine immune response.

Comparison of three microarray probe annotation pipelines: differences in strategies and their effect on downstream analysis

BackgroundReliable annotation linking oligonucleotide probes to target genes is essential for functional biological analysis of microarray experiments. We used the IMAD, OligoRAP and sigReannot pipelines to update the annotation for the ARK-Genomics Chicken 20 K array as part of a joined EADGENE/SABRE workshop. In this manuscript we compare their annotation strategies and results. Furthermore, we analyse the effect of differences in updated annotation on functional analysis for an experiment involving Eimeria infected chickens and finally we propose guidelines for optimal annotation strategies.ResultsIMAD, OligoRAP and sigReannot update both annotation and estimated target specificity. The 3 pipelines can assign oligos to target specificity categories although with varying degrees of resolution. Target specificity is judged based on the amount and type of oligo versus target-gene alignments (hits), which are determined by filter thresholds that users can adjust based on their experimental conditions. Linking oligos to annotation on the other hand is based on rigid rules, which differ between pipelines.For 52.7% of the oligos from a subset selected for in depth comparison all pipelines linked to one or more Ensembl genes with consensus on 44.0%. In 31.0% of the cases none of the pipelines could assign an Ensembl gene to an oligo and for the remaining 16.3% the coverage differed between pipelines. Differences in updated annotation were mainly due to different thresholds for hybridisation potential filtering of oligo versus target-gene alignments and different policies for expanding annotation using indirect links. The differences in updated annotation packages had a significant effect on GO term enrichment analysis with consensus on only 67.2% of the enriched terms.ConclusionIn addition to flexible thresholds to determine target specificity, annotation tools should provide metadata describing the relationships between oligos and the annotation assigned to them. These relationships can then be used to judge the varying degrees of reliability allowing users to fine-tune the balance between reliability and coverage. This is important as it can have a significant effect on functional microarray analysis as exemplified by the lack of consensus on almost one third of the terms found with GO term enrichment analysis based on updated IMAD, OligoRAP or sigReannot annotation.

IMAD: flexible annotation of microarray sequences

BackgroundAccurate and current functional annotation of microarray probes is essential for the analysis and interpretation of the biological processes involved. As gene structures and functional annotation are updated in genome databases, the annotation attached to microarray probes must be updated so that scientists have access to the latest information with which to analyse their data.ResultsWe have designed a pipeline and database for the annotation of microarray probes using publically available databases. The pipeline is based on NCBI BLAST, Perl and MySQL. The pipeline was used to annotate a subset of 791 differentially expressed ArkGenomics chicken probes from an experiment involving chickens infected with the protozoan parasite Eimeria. Using our pipeline, 770 of the probes were assigned at least one entry in either the Ensembl, UniGene or the DFCI gene indices databases.ConclusionThe pipeline described here provides a simple and robust way of maintaining up-to-date and accurate annotation for microarray probes. The pipeline is designed in such a way as to be flexible and easy to update with new information.

Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication)

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.

The EADGENE Microarray Data Analysis Workshop (Open Access publication)

Microarray analyses have become an important tool in animal genomics. While their use is becoming widespread, there is still a lot of ongoing research regarding the analysis of microarray data. In the context of a European Network of Excellence, 31 researchers representing 14 research groups from 10 countries performed and discussed the statistical analyses of real and simulated 2-colour microarray data that were distributed among participants. The real data consisted of 48 microarrays from a disease challenge experiment in dairy cattle, while the simulated data consisted of 10 microarrays from a direct comparison of two treatments (dye-balanced). While there was broader agreement with regards to methods of microarray normalisation and significance testing, there were major differences with regards to quality control. The quality control approaches varied from none, through using statistical weights, to omitting a large number of spots or omitting entire slides. Surprisingly, these very different approaches gave quite similar results when applied to the simulated data, although not all participating groups analysed both real and simulated data. The workshop was very successful in facilitating interaction between scientists with a diverse background but a common interest in microarray analyses.