ALBERT

Description: The Yellowstone–East Snake River Plain hotspot track has been intensely studied since several decades and is widely considered to result from the interaction of a mantle plume with the North American plate. An integrated conclusive geodynamic interpretation of this extensive data set is however presently still lacking, and our knowledge of the dynamical processes beneath Yellowstone is patchy. It has been argued that the Yellowstone plume has delaminated the lower part of the thick Wyoming cratonic lithosphere. We derive an original dynamic model to quantify delamination processes related to mantle plume–lithosphere interactions. We show that fast (~300 ka) lithospheric delamination is consistent with the observed timing of formation of successive volcanic centres along the Yellowstone hotspot track and requires (i) a tensile stress regime within the whole lithosphere exceeding its failure threshold, (ii) a purely plastic rheology in the lithosphere when stresses reach this yield limit, (iii) a dense lower part of the 200 km thick Wyoming lithosphere and (iv) a decoupling melt horizon inside the median part of the lithosphere. We demonstrate that all these conditions are verified and that ~150 km large and ~100 km thick lithospheric blocks delaminate within 300 ka when the Yellowstone plume ponded below the 200 km thick Wyoming cratonic lithosphere. Furthermore, we take advantage of the available extensive regional geophysical and geological observation data sets to design a numerical 3-D upper-mantle convective model. We propose a map of the ascending convective sheets contouring the Yellowstone plume. The model further evidences the development of a counter-flow within the lower part of the lithosphere centred just above the Yellowstone mantle plume axis. This counter-flow controls the local lithospheric stress field, and as a result the trajectories of feeder dykes linking the partial melting source within the core of the mantle plume with the crust by crosscutting the lithospheric mantle. This counter-flow further explains the 50 km NE shift observed between the mantle plume axis and the present-day Yellowstone Caldera as well as the peculiar shaped crustal magma chambers.

Description: An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. However, several factors, including extensive regulation of mRNA translation and the need for three- or six-frame-translation, impede the use of mRNA-seq data for the construction of a protein sequence search database. With that in mind, we developed the PROTEOFORMER tool that automatically processes data of the recently developed ribosome profiling method (sequencing of ribosome-protected mRNA fragments), resulting in genome-wide visualization of ribosome occupancy. Our tool also includes a translation initiation site calling algorithm allowing the delineation of the open reading frames (ORFs) of all translation products. A complete protein synthesis-based sequence database can thus be compiled for mass spectrometry-based identification. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse, respectively, and enables proteome-wide detection of 5'-extended proteoforms, upstream ORF translation and near-cognate translation start sites. The PROTEOFORMER tool is available as a stand-alone pipeline and has been implemented in the galaxy framework for ease of use.

Description: RNA–RNA interactions are fast emerging as a major functional component in many newly discovered non-coding RNAs. Basepairing is believed to be a major contributor to the stability of these intermolecular interactions, much like intramolecular basepairs formed in RNA secondary structure. As such, using algorithms similar to those for predicting RNA secondary structure, computational methods have been recently developed for the prediction of RNA–RNA interactions. We provide the first comprehensive comparison comprising 14 methods that predict general intermolecular basepairs. To evaluate these, we compile an extensive data set of 54 experimentally confirmed fungal snoRNA–rRNA interactions and 102 bacterial sRNA–mRNA interactions. We test the performance accuracy of all methods, evaluating the effects of tool settings, sequence length, and multiple sequence alignment usage and quality. Our results show that—unlike for RNA secondary structure prediction—the overall best performing tools are non-comparative energy-based tools utilizing accessibility information that predict short interactions on this data set. Furthermore, we find that maintaining high accuracy across biologically different data sets and increasing input lengths remains a huge challenge, causing implications for de novo transcriptome-wide searches. Finally, we make our interaction data set publicly available for future development and benchmarking efforts.

Description: We present a new, computationally efficient numerical method to simulate global seismic wave propagation in realistic 3-D Earth models. We characterize the azimuthal dependence of 3-D wavefields in terms of Fourier series, such that the 3-D equations of motion reduce to an algebraic system of coupled 2-D meridian equations, which is then solved by a 2-D spectral element method (SEM). Computational efficiency of such a hybrid method stems from lateral smoothness of 3-D Earth models and axial singularity of seismic point sources, which jointly confine the Fourier modes of wavefields to a few lower orders. We show novel benchmarks for global wave solutions in 3-D structures between our method and an independent, fully discretized 3-D SEM with remarkable agreement. Performance comparisons are carried out on three state-of-the-art tomography models, with seismic period ranging from 34 s down to 11 s. It turns out that our method has run up to two orders of magnitude faster than the 3-D SEM, featured by a computational advantage expanding with seismic frequency.