Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs

TEC Response and Subsequent GPS Error Caused by the Most severe Geomagnetic Storm of Solar Cycle 24 at India

339-348This paper presents the response of low-latitude and mid-latitude ionosphere to a severe geomagnetic storm that occurred on 17 March 2015 at 0445 UT, and the subsequent effect of this storm on GPS error in the East-West (E-W) and North-South (N-S) directions. The Vertical Total Electron Content (VTEC) data has been analysed from three dual frequency GPS receivers, which were installed under the framework of the International GNSS Service (IGS). For each day of the year, the data is downloadable as a single file in the Receiver Independent Exchange Format (RINEX) from the IGS data portal. The VTEC values from the IGS are obtained at one minute intervals. Results show the variations in GPS derived VTEC during the severe geomagnetic storm. Negative ionospheric storms caused by composition changes are observed at mid-latitude region of Lucknow, while positive ionospheric storms caused by magnetospheric convection and Equatorial Ionospheric Anomaly (EIA) are prominent at low-latitude regions of Bangalore and Hyderabad. The maximum depletion in VTEC peak at mid-latitude region of Lucknow when compared to the quiet day mean VTEC was 61 percent during a negative ionospheric storm that occurred on 18 March 2015, and maximum enhancement in VTEC peak at low-latitude region of Bangalore and Hyderabad when compared to the quiet day mean VTEC was 26 percent and 21 percent respectively during an early positive ionospheric storm on 18 March 2015. Positive ionospheric storms caused by enhanced EIA and Prompt Penetration Electric Fields (PPEF) are prominent at low-latitudes. The highest GPS error during storm time was +7.2m and +11.3m in E-W and N-S directions respectively at Lucknow. The average GPS error in E-W and N-S directions during storm time was higher at the mid-latitude station of Lucknow

TEC Response and Subsequent GPS Error Caused by the Most severe Geomagnetic Storm of Solar Cycle 24 at India

This paper presents the response of low-latitude and mid-latitude ionosphere to a severe geomagnetic storm that occurred on 17 March 2015 at 0445 UT, and the subsequent effect of this storm on GPS error in the East-West (E-W) and North-South (N-S) directions. The Vertical Total Electron Content (VTEC) data has been analysed from three dual frequency GPS receivers, which were installed under the framework of the International GNSS Service (IGS). For each day of the year, the data is downloadable as a single file in the Receiver Independent Exchange Format (RINEX) from the IGS data portal. The VTEC values from the IGS are obtained at one minute intervals. Results   show the variations in GPS derived VTEC during the severe geomagnetic storm. Negative ionospheric storms caused by composition changes are observed at mid-latitude region of Lucknow, while positive ionospheric storms caused by magnetospheric convection and Equatorial Ionospheric Anomaly (EIA) are prominent at low-latitude regions of Bangalore and Hyderabad. The maximum depletion in VTEC peak at mid-latitude region of Lucknow when compared to the quiet day mean VTEC was 61 percent during a negative ionospheric storm that occurred on 18 March 2015, and maximum enhancement in VTEC peak at low-latitude region of Bangalore and Hyderabad when compared to the quiet day mean VTEC was 26 percent and 21 percent respectively during an early positive ionospheric storm on 18 March 2015. Positive ionospheric storms caused by enhanced EIA and Prompt Penetration Electric Fields (PPEF) are prominent at low-latitudes. The highest GPS error during storm time was +7.2 m and +11.3 m in E-W and N-S directions respectively at Lucknow. The average GPS error in E-W and N-S directions during storm time was higher at the mid-latitude station of Lucknow

Genome-wide analysis of regions similar to promoters of histone genes

Background: The purpose of this study is to: i) develop a computational model of promoters of human histone-encoding genes (shortly histone genes), an important class of genes that participate in various critical cellular processes, ii) use the model so developed to identify regions across the human genome that have similar structure as promoters of histone genes; such regions could represent potential genomic regulatory regions, e.g. promoters, of genes that may be coregulated with histone genes, and iii/ identify in this way genes that have high likelihood of being coregulated with the histone genes. Results: We successfully developed a histone promoter model using a comprehensive collection of histone genes. Based on leave-one-out cross-validation test, the model produced good prediction accuracy (94.1% sensitivity, 92.6% specificity, and 92.8% positive predictive value). We used this model to predict across the genome a number of genes that shared similar promoter structures with the histone gene promoters. We thus hypothesize that these predicted genes could be coregulated with histone genes. This hypothesis matches well with the available gene expression, gene ontology, and pathways data. Jointly with promoters of the above-mentioned genes, we found a large number of intergenic regions with similar structure as histone promoters. Conclusions: This study represents one of the most comprehensive computational analyses conducted thus far on a genome-wide scale of promoters of human histone genes. Our analysis suggests a number of other human genes that share a high similarity of promoter structure with the histone genes and thus are highly likely to be coregulated, and consequently coexpressed, with the histone genes. We also found that there are a large number of intergenic regions across the genome with their structures similar to promoters of histone genes. These regions may be promoters of yet unidentified genes, or may represent remote control regions that participate in regulation of histone and histone-coregulated gene transcription initiation. While these hypotheses still remain to be verified, we believe that these form a useful resource for researchers to further explore regulation of human histone genes and human genome. It is worthwhile to note that the regulatory regions of the human genome remain largely un-annotated even today and this study is an attempt to supplement our understanding of histone regulatory regions.Statistic

Context-Specific Protein Network Miner – An Online System for Exploring Context-Specific Protein Interaction Networks from the Literature

Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/.Statistic

Computational promoter analysis of mouse, rat, and human antimicrobial peptide-coding genes

Mammalian antimicrobial peptides (AMPs) are effectors of the innate immune response. A multitude of signals coming from pathways of mammalian pathogen/pattern recognition receptors and other proteins affect the expression of AMP-coding genes (AMPcgs). For many AMPcgs the promoter elements and transcription factors that control their tissue cell-specific expression have yet to be fully identified and characterized. Results- Based upon the RIKEN full-length cDNA and public sequence data derived from human, mouse and rat, we identified 178 candidate AMP transcripts derived from 61 genes belonging to 29 AMP families. However, only for 31 mouse genes belonging to 22 AMP families we were able to determine true orthologous relationships with 30 human and 15 rat sequences. We screened the promoter regions of AMPcgs in the three species for motifs by an ab initio motif finding method and analyzed the derived promoter characteristics. Promoter models were developed for alpha-defensins, penk and zap AMP families. The results suggest a core set of transcription factors (TFs) that regulate the transcription of AMPcg families in mouse, rat and human. The three most frequent core TFs groups include liver-, nervous system-specific and nuclear hormone receptors (NHRs). Out of 440 motifs analyzed, we found that three represent potentially novel TF-binding motifs enriched in promoters of AMPcgs, while the other four motifs appear to be species-specific. Conclusion- Our large-scale computational analysis of promoters of 22 families of AMPcgs across three mammalian species suggests that their key transcriptional regulators are likely to be TFs of the liver-, nervous system-specific and NHR groups. The computationally inferred promoter elements and potential TF binding motifs provide a rich resource for targeted experimental validation of TF binding and signaling studies that aim at the regulation of mouse, rat or human AMPcgs

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs

Routine Cerebral Embolic Protection during Transcatheter Aortic-Valve Implantation.

BACKGROUND: Transcatheter aortic-valve implantation (TAVI) is associated with procedure-related stroke. Cerebral embolic protection (CEP) devices may reduce embolization to the cerebral circulation and hence the incidence of stroke. METHODS: We conducted a randomized, controlled trial across 33 centers in the United Kingdom. We randomly assigned 7635 participants with aortic stenosis in a 1:1 ratio to undergo TAVI with a CEP device (CEP group) or TAVI without a CEP device (control group). The primary outcome was stroke within 72 hours after TAVI or before discharge from the hospital (if discharge occurred sooner). RESULTS: A total of 3815 participants were assigned to the CEP group and 3820 to the control group. A primary-outcome event occurred in 81 of 3795 participants (2.1%) in the CEP group and in 82 of 3799 participants (2.2%) in the control group (difference, -0.02 percentage points; 95% confidence interval, -0.68 to 0.63; P = 0.94). Disabling stroke occurred in 47 participants (1.2%) in the CEP group and in 53 (1.4%) in the control group. Death occurred in 29 participants (0.8%) in the CEP group and in 26 (0.7%) in the control group. Overall access-site complications appeared to be similar in the two groups (8.1% in the CEP group and 7.7% in the control group). A total of 24 serious adverse events occurred in 22 of 3798 participants (0.6%) in the CEP group, and 13 serious adverse events occurred in 13 of 3803 participants (0.3%) in the control group. CONCLUSIONS: Among participants undergoing TAVI, routine use of CEP did not decrease the incidence of stroke within 72 hours. (Funded by the British Heart Foundation and Boston Scientific; BHF PROTECT-TAVI ISRCTN Registry number, ISRCTN16665769.)