Although the Ebbinghaus illusion is commonly used as an example of a simple size-contrast effect, previous studies have emphasised its complexity by identifying many factors that potentially influence the magnitude of the illusion. Here, in a series of three experiments, we attempt to simplify this complexity. In each trial, subjects saw a display comprising, on one side, a target stimulus surrounded by inducers and, on the other, an isolated probe stimulus. Their task was to indicate whether the probe appeared larger or smaller than the target. Probe size was adjusted with a one-up, one-down staircase procedure to find the point of subjective equality between probe and target. From these experiments, we argue that the apparent effects of inducer size are often confounded by the relative completeness of the inducing surround and that factors such as the similarity of the inducers and target are secondary. We suggest a simple model that can explain most of the data in terms of just two primary and independent factors: the relative size of the inducers and target, and the distance between the inducers and the target. The balance between these two factors determines whether the size of the target is underestimated or overestimated.

BackgroundRNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci.ResultsWe found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to t<1 suggesting that the background error rate is extremely low in RNA-Seq. We also found RNA-Seq more sensitive to detecting differentially expressed exons than the Exon array, reflecting the wider dynamic range achievable on the SOLiD platform. In addition, we find significant evidence of novel protein coding regions outside known exons, 93% of which map to Exon array probesets, and are able to infer the presence of thousands of novel transcripts through the detection of previously unreported exon-exon junctions.ConclusionsBy focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.

Strand-specific RNA sequencing of S. pombe reveals a highly structured programme of ncRNA expression at over 600 loci. Functional investigations show that this extensive ncRNA landscape controls the complex programme of sexual differentiation in S. pombe.

Affymetrix exon arrays aim to target every known and predicted exon in the human, mouse or rat genomes, and have reporters that extend beyond protein coding regions to other areas of the transcribed genome. This combination of increased coverage and precision is important because a substantial proportion of protein coding genes are predicted to be alternatively spliced, and because many non-coding genes are known also to be of biological significance. In order to fully exploit these arrays, it is necessary to associate each reporter on the array with the features of the genome it is targeting, and to relate these to gene and genome structure. X:Map is a genome annotation database that provides this information. Data can be browsed using a novel Google-maps based interface, and analysed and further visualized through an associated BioConductor package. The database can be found at http://xmap.picr.man.ac.uk.