Salmonella Typhimurium-specific bacteriophage ΦSH19 and the origins of species specificity in the Vi01-like phage family.

BACKGROUND: Whole genome sequencing of bacteriophages suitable for biocontrol of pathogens in food products is a pre-requisite to any phage-based intervention procedure. Trials involving the biosanitization of Salmonella Typhimurium in the pig production environment identified one such candidate, ΦSH19. RESULTS: This phage was sequenced and analysis of its 157,785 bp circular dsDNA genome revealed a number of interesting features. ΦSH19 constitutes another member of the recently-proposed Myoviridae Vi01-like family of phages, containing S. Typhi-specific Vi01 and Shigella-specific SboM-AG3. At the nucleotide level ΦSH19 is highly similar to phage Vi01 (80-98% pairwise identity over the length of the genome), with the major differences lying in the region associated with host-range determination. Analyses of the proteins encoded within this region by ΦSH19 revealed a cluster of three putative tail spikes. Of the three tail spikes, two have protein domains associated with the pectate lyase family of proteins (Tsp2) and P22 tail spike family (Tsp3) with the prospect that these enable Salmonella O antigen degradation. Tail spike proteins of Vi01 and SboM-AG3 are predicted to contain conserved right-handed parallel ß-helical structures but the internal protein domains are varied allowing different host specificities. CONCLUSIONS: The addition or exchange of tail spike protein modules is a major contributor to host range determination in the Vi01-like phage family.

A dual platform approach to transcript discovery for the planarian Schmidtea mediterranea to establish RNAseq for stem cell and regeneration biology.

The use of planarians as a model system is expanding and the mechanisms that control planarian regeneration are being elucidated. The planarian Schmidtea mediterranea in particular has become a species of choice. Currently the planarian research community has access to this whole genome sequencing project and over 70,000 expressed sequence tags. However, the establishment of massively parallel sequencing technologies has provided the opportunity to define genetic content, and in particular transcriptomes, in unprecedented detail. Here we apply this approach to the planarian model system. We have sequenced, mapped and assembled 581,365 long and 507,719,814 short reads from RNA of intact and mixed stages of the first 7 days of planarian regeneration. We used an iterative mapping approach to identify and define de novo splice sites with short reads and increase confidence in our transcript predictions. We more than double the number of transcripts currently defined by publicly available ESTs, resulting in a collection of 25,053 transcripts described by combining platforms. We also demonstrate the utility of this collection for an RNAseq approach to identify potential transcripts that are enriched in neoblast stem cells and their progeny by comparing transcriptome wide expression levels between irradiated and intact planarians. Our experiments have defined an extensive planarian transcriptome that can be used as a template for RNAseq and can also help to annotate the S. mediterranea genome. We anticipate that suites of other 'omic approaches will also be facilitated by building on this comprehensive data set including RNAseq across many planarian regenerative stages, scenarios, tissues and phenotypes generated by RNAi.

Uncovering the expression patterns of chimeric transcripts using surveys of affymetrix GeneChips.

BACKGROUND: A chimeric transcript is a single RNA sequence which results from the transcription of two adjacent genes. Recent studies estimate that at least 4% of tandem human gene pairs may form chimeric transcripts. Affymetrix GeneChip data are used to study the expression patterns of tens of thousands of genes and the probe sequences used in these microarrays can potentially map to exotic RNA sequences such as chimeras. RESULTS: We have studied human chimeras and investigated their expression patterns using large surveys of Affymetrix microarray data obtained from the Gene Expression Omnibus. We show that for six probe sets, a unique probe mapping to a transcript produced by one of the adjacent genes can be used to identify the expression patterns of readthrough transcripts. Furthermore, unique probes mapping to an intergenic exon present only in the MASK-BP3 chimera can be used directly to study the expression levels of this transcript. CONCLUSIONS: We have attempted to implement a new method for identifying tandem chimerism. In this analysis unambiguous probes are needed to measure run-off transcription and probes that map to intergenic exons are particularly valuable for identifying the expression of chimeras.

Using surveys of Affymetrix GeneChips to study antisense expression.

We have used large surveys of Affymetrix GeneChip data in the public domain to conduct a study of antisense expression across diverse conditions. We derive correlations between groups of probes which map uniquely to the same exon in the antisense direction. When there are no probes assigned to an exon in the sense direction we find that many of the antisense groups fail to detect a coherent block of transcription. We find that only a minority of these groups contain coherent blocks of antisense expression suggesting transcription. We also derive correlations between groups of probes which map uniquely to the same exon in both sense and antisense direction. In some of these cases the locations of sense probes overlap with the antisense probes, and the sense and antisense probe intensities are correlated with each other. This configuration suggests the existence of a Natural Antisense Transcript (NAT) pair. We find the majority of such NAT pairs detected by GeneChips are formed by a transcript of an established gene and either an EST or an mRNA. In order to determine the exact antisense regulatory mechanism indicated by the correlation of sense probes with antisense probes, a further investigation is necessary for every particular case of interest. However, the analysis of microarray data has proved to be a good method to reconfirm known NATs, discover new ones, as well as to notice possible problems in the annotation of antisense transcripts.

On the causes of outliers in Affymetrix GeneChip data.

We describe various types of outliers seen in Affymetrix GeneChip data. We have been able to utilise the data in the Gene Expression Omnibus to screen GeneChips across a range of scales, from single probes, to spatially adjacent fractions of arrays, to whole arrays, to whole experiments. In this review we describe a number of causes for why some reported intensities might be misleading on GeneChips.

The use of Affymetrix GeneChips as a tool for studying alternative forms of RNA.

We are developing a computational pipeline to use surveys of Affymetrix GeneChips as a discovery tool for unravelling some of the biology associated with post-transcriptional processing of RNA. This work involves the integration of a number of bioinformatics resources, from comparing annotations to processing images to determining the structure of transcripts. The rapidly growing datasets of GeneChips available to the community puts us in a strong position to discover novel biology about post-transcriptional processing, and should enable us to determine the mechanisms by which some groups of genes make co-ordinated changes in their production of isoforms.

Widespread existence of uncorrelated probe intensities from within the same probeset on Affymetrix GeneChips.

We have developed a computational pipeline to analyse large surveys of Affymetrix GeneChips, for example NCBI's Gene Expression Omnibus. GEO samples data for many organisms, tissues and phenotypes. Because of this experimental diversity, any observed correlations between probe intensities can be associated either with biology that is robust, such as common co-expression, or with systematic biases associated with the GeneChip technology. Our bioinformatics pipeline integrates the mapping of probes to exons, quality control checks on each GeneChip which identifies flaws in hybridization quality, and the mining of correlations in intensities between groups of probes. The output from our pipeline has enabled us to identify systematic biases in GeneChip data. We are also able to use the pipeline as a discovery tool for biology. We have discovered that in the majority of cases, Affymetrix probesets on Human GeneChips do not measure one unique block of transcription. Instead we see numerous examples of outlier probes. Our study has also identified that in a number of probesets the mismatch probes are an informative diagnostic of expression, rather than providing a measure of background contamination. We report evidence for systematic biases in GeneChip technology associated with probe-probe interactions. We also see signatures associated with post-transcriptional processing of RNA, such as alternative polyadenylation.