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The shade avoidance syndrome (SAS) allows plants to anticipate and avoid shading by neighbouring plants by initiating an elongation growth response. The phytochrome photoreceptors are able to detect a reduction in the red:far red ratio in incident light, the result of selective absorption of red and blue wavelengths by proximal vegetation. A shade-responsive luciferase reporter line (PHYB::LUC) was used to carry out a high-throughput screen to identify novel SAS mutants. The dracula 1 (dra1) mutant, that showed no avoidance of shade for the PHYB::LUC response, was the result of a mutation in the PHYA gene. Like previously characterized phyA mutants, dra1 showed a long hypocotyl in far red light and an enhanced hypocotyl elongation response to shade. However, dra1 additionally showed a long hypocotyl in red light. Since phyB levels are relatively unaffected in dra1, this gain-of-function red light phenotype strongly suggests a disruption of phyB signalling. The dra1 mutation, G773E within the phyA PAS2 domain, occurs at a residue absolutely conserved among phyA sequences. The equivalent residue in phyB is absolutely conserved as a threonine. PAS domains are structurally conserved domains involved in molecular interaction. Structural modelling of the dra1 mutation within the phyA PAS2 domain shows some similarity with the structure of the phyB PAS2 domain, suggesting that the interference with phyB signalling may be the result of non-functional mimicry. Hence, it was hypothesized that this PAS2 residue forms a key distinction between the phyA and phyB phytochrome species.

Detecting sources of bias in transcriptomic data is essential to determine signals of Biological significance. We outline a novel method to detect sequence specific bias in short read Next Generation Sequencing data. This is based on determining intra-exon correlations between specific motifs. This requires a mild assumption that short reads sampled from specific regions from the same exon will be correlated with each other. This has been implemented on Apache Spark and used to analyse two D. melanogaster eye-antennal disc data sets generated at the same laboratory. The wild type data set in drosophila indicates a variation due to motif GC content that is more significant than that found due to exon GC content. The software is available online and could be applied for cross-experiment transcriptome data analysis in eukaryotes.

The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein–ligand docking, clustering of protein–ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up. [ABSTRACT FROM AUTHOR]