The suboptimal structures find the optimal RNAs: homology search for bacterial non-coding RNAs using suboptimal RNA structures

Non-coding RNAs (ncRNAs) are regulatory molecules encoded in the intergenic or intragenic regions of the genome. In prokaryotes, biocomputational identification of homologs of known ncRNAs in other species often fails due to weakly evolutionarily conserved sequences, structures, synteny and genome localization, except in the case of evolutionarily closely related species. To eliminate results from weak conservation, we focused on RNA structure, which is the most conserved ncRNA property. Analysis of the structure of one of the few well-studied bacterial ncRNAs, 6S RNA, demonstrated that unlike optimal and consensus structures, suboptimal structures are capable of capturing RNA homology even in divergent bacterial species. A computational procedure for the identification of homologous ncRNAs using suboptimal structures was created. The suggested procedure was applied to strongly divergent bacterial species and was capable of identifying homologous ncRNAs

Choosing a genome browser for a Model Organism Database: surveying the Maize community

As the B73 maize genome sequencing project neared completion, MaizeGDB began to integrate a graphical genome browser with its existing web interface and database. To ensure that maize researchers would optimally benefit from the potential addition of a genome browser to the existing MaizeGDB resource, personnel at MaizeGDB surveyed researchers’ needs. Collected data indicate that existing genome browsers for maize were inadequate and suggest implementation of a browser with quick interface and intuitive tools would meet most researchers’ needs. Here, we document the survey’s outcomes, review functionalities of available genome browser software platforms and offer our rationale for choosing the GBrowse software suite for MaizeGDB. Because the genome as represented within the MaizeGDB Genome Browser is tied to detailed phenotypic data, molecular marker information, available stocks, etc., the MaizeGDB Genome Browser represents a novel mechanism by which the researchers can leverage maize sequence information toward crop improvement directly

Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex

Lewis Base Mediated β-Elimination and Lewis Acid Mediated Insertion Reactions of Disilazido Zirconium Compounds

The reactivity of a series of disilazido zirconocene complexes is dominated by the migration of anionic groups (hydrogen, alkyl, halide, OTf) between the zirconium and silicon centers. The direction of these migrations is controlled by the addition of two-electron donors (Lewis bases) or two-electron acceptors (Lewis acids). The cationic nonclassical [Cp2ZrN(SiHMe2)2]+ ([2]+) is prepared from Cp2Zr{N(SiHMe2)2}H (1) and B(C6F5)3 or [Ph3C][B(C6F5)4], while reactions of B(C6F5)3 and Cp2Zr{N(SiHMe2)2}R (R = Me (3), Et (5), n-C3H7 (7), CH═CHSiMe3 (9)) provide a mixture of [2]+ and [Cp2ZrN(SiHMe2)(SiRMe2)]+. The latter products are formed through B(C6F5)3 abstraction of a β-H and R group migration from Zr to the β-Si center. Related β-hydrogen abstraction and X group migration reactions are observed for Cp2Zr{N(SiHMe2)2}X (X = OTf (11), Cl (13), OMe (15), O-i-C3H7 (16)). Alternatively, addition of DMAP (DMAP = 4-(dimethylamino)pyridine) to [2]+ results in coordination to a Si center and hydrogen migration to zirconium, giving the cationic complex [Cp2Zr{N(SiHMe2)(SiMe2DMAP)}H]+ ([19]+). Related hydrogen migration occurs from [Cp2ZrN(SiHMe2)(SiMe2OCHMe2)]+ ([18]+) to give [Cp2Zr{N(SiMe2DMAP)(SiMe2OCHMe2)}H]+ ([22]+), whereas X group migration is observed upon addition of DMAP to [Cp2ZrN(SiHMe2)(SiMe2X)]+ (X = OTf ([12]+), Cl ([14]+)) to give [Cp2Zr{N(SiHMe2)(SiMe2DMAP)}X]+ (X = OTf ([26]+), Cl ([20]+)). The species involved in these transformations are described by resonance structures that suggest β-elimination. Notably, such pathways are previously unknown in early metal amide chemistry. Finally, these migrations facilitate direct Si–H addition to carbonyls, which is proposed to occur through a pathway that previously had been reserved for later transition metal compounds