The genome of the jellyfish Clytia hemisphaerica and the evolution of the cnidarian life-cycle.

Jellyfish (medusae) are a distinctive life-cycle stage of medusozoan cnidarians. They are major marine predators, with integrated neurosensory, muscular and organ systems. The genetic foundations of this complex form are largely unknown. We report the draft genome of the hydrozoan jellyfish Clytia hemisphaerica and use multiple transcriptomes to determine gene use across life-cycle stages. Medusa, planula larva and polyp are each characterized by distinct transcriptome signatures reflecting abrupt life-cycle transitions and all deploy a mixture of phylogenetically old and new genes. Medusa-specific transcription factors, including many with bilaterian orthologues, associate with diverse neurosensory structures. Compared to Clytia, the polyp-only hydrozoan Hydra has lost many of the medusa-expressed transcription factors, despite similar overall rates of gene content evolution and sequence evolution. Absence of expression and gene loss among Clytia orthologues of genes patterning the anthozoan aboral pole, secondary axis and endomesoderm support simplification of planulae and polyps in Hydrozoa, including loss of bilateral symmetry. Consequently, although the polyp and planula are generally considered the ancestral cnidarian forms, in Clytia the medusa maximally deploys the ancestral cnidarian-bilaterian transcription factor gene complement.

Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence.

Identifying kinase substrates and the specific phosphorylation sites they regulate is an important factor in understanding protein function regulation and signalling pathways. Computational prediction of kinase targets - assigning kinases to putative substrates, and selecting from protein sequence the sites that kinases can phosphorylate - requires the consideration of both the cellular context that kinases operate in, as well as their binding affinity. This consideration enables investigation of how phosphorylation influences a range of biological processes. We report here a novel probabilistic model for classifying kinase-specific phosphorylation sites from sequence across three model organisms: human, mouse and yeast. The model incorporates position-specific amino acid frequencies, and counts of co-occurring amino acids from kinase binding sites. We show how this model can be seamlessly integrated with protein interactions and cell-cycle abundance profiles. When evaluating the prediction accuracy of our method, PhosphoPICK, on an independent hold-out set of kinase-specific phosphorylation sites, it achieved an average specificity of 97%, with 32% sensitivity. We compared PhosphoPICK's ability, through cross-validation, to predict kinase-specific phosphorylation sites with alternative methods, and show that at high levels of specificity PhosphoPICK obtains greater sensitivity for most comparisons made. We investigated the relationship between kinase-specific phosphorylation sites and nuclear localisation signals. We show that kinases PKA, Akt1 and AurB have an over-representation of predicted binding sites at particular positions downstream from predicted nuclear localisation signals, demonstrating an important role for these kinases in regulating the nuclear import of proteins. PhosphoPICK is freely available as a web-service at http://bioinf.scmb.uq.edu.au/phosphopick.