Optimising the analysis of transcript data using high density oligonucleotide arrays and genomic DNA-based probe selection

Background: Affymetrix GeneChip arrays are widely used for transcriptomic studies in a diverse range of species. Each gene is represented on a GeneChip array by a probe-set, consisting of up to 16 probe-pairs. Signal intensities across probe-pairs within a probe-set vary in part due to different physical hybridisation characteristics of individual probes with their target labelled transcripts. We have previously developed a technique to study the transcriptomes of heterologous species based on hybridising genomic DNA (gDNA) to a GeneChip array designed for a different species, and subsequently using only those probes with good homology. Results: Here we have investigated the effects of hybridising homologous species gDNA to study the transcriptomes of species for which the arrays have been designed. Genomic DNA from Arabidopsis thaliana and rice (Oryza sativa) were hybridised to the Affymetrix Arabidopsis ATH1 and Rice Genome GeneChip arrays respectively. Probe selection based on gDNA hybridisation intensity increased the number of genes identified as significantly differentially expressed in two published studies of Arabidopsis development, and optimised the analysis of technical replicates obtained from pooled samples of RNA from rice. Conclusion: This mixed physical and bioinformatics approach can be used to optimise estimates of gene expression when using GeneChip arrays

A genomic analysis and transcriptomic atlas of gene expression in Psoroptes ovis reveals feeding- and stage-specific patterns of allergen expression

Background: Psoroptic mange, caused by infestation with the ectoparasitic mite, Psoroptes ovis, is highly contagious, resulting in intense pruritus and represents a major welfare and economic concern for the livestock industry Worldwide. Control relies on injectable endectocides and organophosphate dips, but concerns over residues, environmental contamination, and the development of resistance threaten the sustainability of this approach, highlighting interest in alternative control methods. However, development of vaccines and identification of chemotherapeutic targets is hampered by the lack of P. ovis transcriptomic and genomic resources. Results: Building on the recent publication of the P. ovis draft genome, here we present a genomic analysis and transcriptomic atlas of gene expression in P. ovis revealing feeding- and stage-specific patterns of gene expression, including novel multigene families and allergens. Network-based clustering revealed 14 gene clusters demonstrating either single- or multi-stage specific gene expression patterns, with 3075 female-specific, 890 male-specific and 112, 217 and 526 transcripts showing larval, protonymph and tritonymph specific-expression, respectively. Detailed analysis of P. ovis allergens revealed stage-specific patterns of allergen gene expression, many of which were also enriched in "fed" mites and tritonymphs, highlighting an important feeding-related allergenicity in this developmental stage. Pair-wise analysis of differential expression between life-cycle stages identified patterns of sex-biased gene expression and also identified novel P. ovis multigene families including known allergens and novel genes with high levels of stage-specific expression. Conclusions: The genomic and transcriptomic atlas described here represents a unique resource for the acarid-research community, whilst the OrcAE platform makes this freely available, facilitating further community-led curation of the draft P. ovis genome

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

Microarray experiments: Considerations for experimental design

Microarrays are useful tools to investigate the expression of thousands of genes rapidly. Some researchers remain reluctant to use the technology, however, largely because of its expense. Careful design of a microarray experiment is key to generating cost-effective results. This article explores issues that researchers face when embarking on a microarray experiment for the first time. These include decisions about which microarray platform is available for the organism of interest, the degree of replication (biological and technical) needed and which design (direct or indirect, loop or balanced block) is suitable

Gene body methylation patterns in Daphnia are associated with gene family size

The relation between gene body methylation and gene function remains elusive. Yet, our understanding of this relationship can contribute significant knowledge on how and why organisms target specific gene bodies for methylation. Here, we studied gene body methylation patterns in two Daphnia species. We observed both highly methylated genes and genes devoid of methylation in a background of low global methylation levels. A small but highly significant number of genes was highly methylated in both species. Remarkably, functional analyses indicate that variation in methylation within and between Daphnia species is primarily targeted to small gene families whereas large gene families tend to lack variation. The degree of sequence similarity could not explain the observed pattern. Furthermore, a significant negative correlation between gene family size and the degree of methylation suggests that gene body methylation may help regulate gene family expansion and functional diversification of gene families leading to phenotypic variation

Computational search for UV radiation resistance strategies in Deinococcus swuensis isolated from Paramo ecosystems

Ultraviolet radiation (UVR) is widely known as deleterious for many organisms since it can cause damage to biomolecules either directly or indirectly via the formation of reactive oxygen species. The goal of this study was to analyze the capacity of high-mountain Espeletia hartwegiana plant phyllosphere microorganisms to survive UVR and to identify genes related to resistance strategies. A strain of Deinococcus swuensis showed a high survival rate of up to 60% after UVR treatment at 800J/m2 and was used for differential expression analysis using RNA-seq after exposing cells to 400J/m2 of UVR (with \u3e95% survival rate). Differentially expressed genes were identified using the R-Bioconductor package NOISeq and compared with other reported resistance strategies reported for this genus. Genes identified as being overexpressed included transcriptional regulators and genes involved in protection against damage by UVR. Non-coding (nc)RNAs were also differentially expressed, some of which have not been previously implicated. This study characterized the immediate radiation response of D. swuensis and indicates the involvement of ncRNAs in the adaptation to extreme environmental conditions