Investigation on the Synthesis, Application and Structural Features of Heteroaryl 1,2-Diketones

A set of unsymmetrical heteroaryl 1,2-diketones were synthesized by a heteroarylation/oxidation sequence with up to 65% isolated yields. Palladium catalyst XPhos Pd G4 and SeO2 were the key reagents used in this methodology, and microwave irradiation was utilized to facilitate an efficient and ecofriendly process. The application of heteroaryl 1,2-diketones is demonstrated through the synthesis of an unsymmetrical 2-phenyl-3-(pyridin-3-yl)quinoxaline (5a) from 1-phenyl-2-(pyridin-3-yl)ethane-1,2-dione (4a). The lowest energy conformations of 4a and 5a were located using Density Functional Theory (DFT) at the M06-2X/def2-TZVP level of theory. Two lowest energy conformations of 4a differ with respect to the position of the N atom in the pyridyl ring and 0.27 kcal/mol energy difference between them corresponds to 60.4 and 39.6% at 50 °C in toluene. Four lowest energy conformations for 5a have the energy differences of 0.01, 0.03 and 0.07 kcal/mol that corresponds to 26.0, 25.7, 24.9 and 23.4%, respectively. A comparison of 4a and 5a to the less hindered analogs (oxalyl chloride and oxalic acid) is used to investigate the structural features and bonding using Natural Bond Orbital (NBO) analysis

RNA Captor: A Tool for RNA Characterization

Background: In the genome era, characterizing the structure and the function of RNA molecules remains a major challenge. Alternative transcripts and non-protein-coding genes are poorly recognized by the current genome-annotation algorithms and efficient tools are needed to isolate the less-abundant or stable RNAs. Results: A universal RNA-tagging method using the T4 RNA ligase 2 and special adapters is reported. Based on this system, protocols for RACE PCR and full-length cDNA library construction have been developed. The RNA tagging conditions were thoroughly optimized and compared to previous methods by using a biochemical oligonucleotide tagging assay and RACE PCRs on a range of transcripts. In addition, two large-scale full-length cDNA inventories relying on this method are presented. Conclusion: The RNA Captor is a straightforward and accessible protocol. The sensitivity of this approach was shown to be higher compared to previous methods, and applicable on messenger RNAs, non-protein-coding RNAs, transcription-start sites and microRNA-directed cleavage sites of transcripts. This strategy could also be used to study other classes of RNA and in deep sequencing experiments

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

<p>Abstract</p> <p>Background</p> <p>High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.</p> <p>Results</p> <p>We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.</p> <p>Conclusions</p> <p>Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.</p

Constraints on Nucleon Decay via "Invisible" Modes from the Sudbury Neutrino Observatory

Data from the Sudbury Neutrino Observatory have been used to constrain the lifetime for nucleon decay to ``invisible'' modes, such as n -> 3 nu. The analysis was based on a search for gamma-rays from the de-excitation of the residual nucleus that would result from the disappearance of either a proton or neutron from O16. A limit of tau_inv > 2 x 10^{29} years is obtained at 90% confidence for either neutron or proton decay modes. This is about an order of magnitude more stringent than previous constraints on invisible proton decay modes and 400 times more stringent than similar neutron modes.Comment: Update includes missing efficiency factor (limits change by factor of 2) Submitted to Physical Review Letter

Investigation on the Synthesis, Application and Structural Features of Heteroaryl 1,2-Diketones

A set of unsymmetrical heteroaryl 1,2-diketones were synthesized by a heteroarylation/oxidation sequence with up to 65% isolated yields. Palladium catalyst XPhos Pd G4 and SeO2 were the key reagents used in this methodology, and microwave irradiation was utilized to facilitate an efficient and ecofriendly process. The application of heteroaryl 1,2-diketones is demonstrated through the synthesis of an unsymmetrical 2-phenyl-3-(pyridin-3-yl)quinoxaline (5a) from 1-phenyl-2-(pyridin-3-yl)ethane-1,2-dione (4a). The lowest energy conformations of 4a and 5a were located using Density Functional Theory (DFT) at the M06-2X/def2-TZVP level of theory. Two lowest energy conformations of 4a differ with respect to the position of the N atom in the pyridyl ring and 0.27 kcal/mol energy difference between them corresponds to 60.4 and 39.6% at 50 °C in toluene. Four lowest energy conformations for 5a have the energy differences of 0.01, 0.03 and 0.07 kcal/mol that corresponds to 26.0, 25.7, 24.9 and 23.4%, respectively. A comparison of 4a and 5a to the less hindered analogs (oxalyl chloride and oxalic acid) is used to investigate the structural features and bonding using Natural Bond Orbital (NBO) analysis

Improving gene-set enrichment analysis of RNA-Seq data with small replicates

Deregulated pathways identified from transcriptome data of two sample groups have played a key role in many genomic studies. Gene-set enrichment analysis (GSEA) has been commonly used for pathway or functional analysis of microarray data, and it is also being applied to RNA-seq data. However, most RNA-seq data so far have only small replicates. This enforces to apply the gene-permuting GSEA method (or preranked GSEA) which results in a great number of false positives due to the inter-gene correlation in each gene-set. We demonstrate that incorporating the absolute gene statistic in one-tailed GSEA considerably improves the false-positive control and the overall discriminatory ability of the gene-permuting GSEA methods for RNA-seq data. To test the performance, a simulation method to generate correlated read counts within a gene-set was newly developed, and a dozen of currently available RNA-seq enrichment analysis methods were compared, where the proposed methods outperformed others that do not account for the inter-gene correlation. Analysis of real RNA-seq data also supported the proposed methods in terms of false positive control, ranks of true positives and biological relevance. An efficient R package (AbsFilterG- SEA) coded with C++ (Rcpp) is available from CRAN.open

A physical supply-use table framework for energy analysis on the energy conversion chain

In response to the oil crises of the 1970s, energy accounting experienced a revolution and became the much broader field of energy analysis, in part by expanding along the energy conversion chain from primary and final energy to useful energy and energy services, which satisfy human needs. After evolution and specialization, the field of energy analysis today addresses topics along the entire energy conversion chain, including energy conversion systems, energy resources, carbon emissions, and the role of energy services in promoting human well-being and development. And the expanded field would benefit from a common analysis framework that provides data structure uniformity and methodological consistency. Building upon recent advances in related fields, we propose a physical supply-use table energy analysis framework consisting of four matrices from which the input-output structure of an energy conversion chain can be determined and the effects of changes in final demand can be estimated. Real-world examples demonstrate the physical supply-use table framework via investigation of energy analysis questions for a United Kingdom energy conversion chain. The physical supply use table framework has two key methodological advances over the building blocks that precede it, namely extending a common energy analysis framework through to energy services and application of physical supply-use tables to both energy and exergy analysis. The methodological advances enable the following first-time contributions to the literature: (1) performing energy and exergy analyses on an energy conversion chain using physical supply-use table matrices comprised of disaggregated products in physical units when the last stage is any of final energy, useful energy, or energy services; (2) performing structural path analysis on an energy conversion chain; and (3) developing and utilizing a matrix approach to inhomogeneous units. The framework spans the entire energy conversion chain and is suitable for many sub-fields of energy analysis, including net energy analysis, societal energy analysis, human needs and well-being, and structural path analysis, all of which are explored in this paper

Gene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data

Background: In differential expression analysis of RNA-sequencing (RNA-seq) read count data for two sample groups, it is known that highly expressed genes (or longer genes) are more likely to be differentially expressed which is called read count bias (or gene length bias). This bias had great effect on the downstream Gene Ontology over-representation analysis. However, such a bias has not been systematically analyzed for different replicate types of RNA-seq data. Results: We show that the dispersion coefficient of a gene in the negative binomial modeling of read counts is the critical determinant of the read count bias (and gene length bias) by mathematical inference and tests for a number of simulated and real RNA-seq datasets. We demonstrate that the read count bias is mostly confined to data with small gene dispersions (e.g., technical replicates and some of genetically identical replicates such as cell lines or inbred animals), and many biological replicate data from unrelated samples do not suffer from such a bias except for genes with some small counts. It is also shown that the sample-permuting GSEA method yields a considerable number of false positives caused by the read count bias, while the preranked method does not. Conclusion: We showed the small gene variance (similarly, dispersion) is the main cause of read count bias (and gene length bias) for the first time and analyzed the read count bias for different replicate types of RNA-seq data and its effect on gene-set enrichment analysis

Gene Expression Divergence is Coupled to Evolution of DNA Structure in Coding Regions

Sequence changes in coding region and regulatory region of the gene itself (cis) determine most of gene expression divergence between closely related species. But gene expression divergence between yeast species is not correlated with evolution of primary nucleotide sequence. This indicates that other factors in cis direct gene expression divergence. Here, we studied the contribution of DNA three-dimensional structural evolution as cis to gene expression divergence. We found that the evolution of DNA structure in coding regions and gene expression divergence are correlated in yeast. Similar result was also observed between Drosophila species. DNA structure is associated with the binding of chromatin remodelers and histone modifiers to DNA sequences in coding regions, which influence RNA polymerase II occupancy that controls gene expression level. We also found that genes with similar DNA structures are involved in the same biological process and function. These results reveal the previously unappreciated roles of DNA structure as cis-effects in gene expression