The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.
A longstanding challenge is to understand how ribosomes parse mRNA open reading frames (ORFs). Significantly, GCN codons are over-represented in the initial codons of ORFs of prokaryote and eukaryote mRNAs. We describe a ribosome rRNA-protein surface that interacts with an mRNA GCN codon when next in line for the ribosome A-site. The interaction surface is comprised of the edges of two stacked rRNA bases: the Watson–Crick edge of 16S/18S rRNA C1054 and the adjacent Hoogsteen edge of A1196 (Escherichia coli 16S rRNA numbering). Also part of the interaction surface, the planar guanidinium group of a conserved Arginine (R146 of yeast ribosomal protein Rps3) is stacked adjacent to A1196. On its other side, the interaction surface is anchored to the ribosome A-site through base stacking of C1054 with the wobble anticodon base of the A-site tRNA. Using molecular dynamics simulations of a 495-residue subsystem of translocating ribosomes, we observed base pairing of C1054 to nucleotide G at position 1 of the next-in-line codon, consistent with previous cryo-EM observations, and hydrogen bonding of A1196 and R146 to C at position 2. Hydrogen bonding to both of these codon positions is significantly weakened when C at position 2 is changed to G, A or U. These sequence-sensitive mRNA-ribosome interactions at the C1054-A1196-R146 (CAR) surface potentially contribute to the GCN-mediated regulation of protein translation.
GCN codons are over-represented in initial codons of ORFs of prokaryote and eukaryote mRNAs. We describe a ribosome rRNA-protein surface that interacts with an mRNA GCN codon when next-in-line for the ribosome A site. The interaction surface is comprised of the edges of two stacked rRNA bases: the Watson-Crick edge of 16S/18S rRNA C1054 and adjacent Hoogsteen edge of A1196 (Escherichia coli 16S rRNA numbering). Also part of the interaction surface, the planar guanidinium group of a conserved Arginine (R146 of yeast ribosomal protein Rps3) is stacked adjacent to A1196. On its other side, the interaction surface is anchored to the ribosome A site through base stacking of C1054 with the wobble anticodon base of the A-site tRNA. Using Molecular Dynamics simulations of a 495-residue subsystem of translocating ribosomes, we observe base pairing of C1054 to nucleotide G at position 1 of the next-in-line codon, consistent with previous cryo-EM observations, and hydrogen bonding of A1196 and R146 to C at position 2. Hydrogen bonding to both of these codon positions is significantly weakened when C at position 2 is changed to G, A or U. These sequence-sensitive mRNAribosome interactions at the C1054-A1196-R146 (CAR) surface potentially contribute to GCNmediated regulation of protein translation.Video S1. Example of neutral dynamics for translocation stage II; +1 codon GCU (mp4) Video S2. Example of neutral dynamics for translocation stage II; +1 codon GGU (mp4) AUTHOR INFORMATION
Abstract-Digital data that come from different applications such as, wireless sensor, bioinformatics next generation sequencing, and high throughput instruments are growing in high rate. Dealing with demands of analysis of ever-growing data requires new techniques in software, hardware, and algorithms. MapReduce is a programming model initiated by Google's Team for processing huge datasets in distributed systems; it helps programmers to write programs that process big data. The aim of this paper is to investigate MapReduce research trends, and current research efforts for enhancing MapReduce performance and capabilities. This Study concluded that the research directions of MapReduce concerned with either enhancing MapReduce programming model or adopting MapReduce for deploying existing algorithm to run with MapReduce programming model.
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