TreeDomViewer: a tool for the visualization of phylogeny and protein domain structure

Phylogenetic analysis and examination of protein domains allow accurate genome annotation and are invaluable to study proteins and protein complex evolution. However, two sequences can be homologous without sharing statistically significant amino acid or nucleotide identity, presenting a challenging bioinformatics problem. We present TreeDomViewer, a visualization tool available as a web-based interface that combines phylogenetic tree description, multiple sequence alignment and InterProScan data of sequences and generates a phylogenetic tree projecting the corresponding protein domain information onto the multiple sequence alignment. Thereby it makes use of existing domain prediction tools such as InterProScan. TreeDomViewer adopts an evolutionary perspective on how domain structure of two or more sequences can be aligned and compared, to subsequently infer the function of an unknown homolog. This provides insight into the function assignment of, in terms of amino acid substitution, very divergent but yet closely related family members. Our tool produces an interactive scalar vector graphics image that provides orthological relationship and domain content of proteins of interest at one glance. In addition, PDF, JPEG or PNG formatted output is also provided. These features make TreeDomViewer a valuable addition to the annotation pipeline of unknown genes or gene products. TreeDomViewer is available at

Rickettsia Phylogenomics: Unwinding the Intricacies of Obligate Intracellular Life

BACKGROUND: Completed genome sequences are rapidly increasing for Rickettsia, obligate intracellular alpha-proteobacteria responsible for various human diseases, including epidemic typhus and Rocky Mountain spotted fever. In light of phylogeny, the establishment of orthologous groups (OGs) of open reading frames (ORFs) will distinguish the core rickettsial genes and other group specific genes (class 1 OGs or C1OGs) from those distributed indiscriminately throughout the rickettsial tree (class 2 OG or C2OGs). METHODOLOGY/PRINCIPAL FINDINGS: We present 1823 representative (no gene duplications) and 259 non-representative (at least one gene duplication) rickettsial OGs. While the highly reductive (approximately 1.2 MB) Rickettsia genomes range in predicted ORFs from 872 to 1512, a core of 752 OGs was identified, depicting the essential Rickettsia genes. Unsurprisingly, this core lacks many metabolic genes, reflecting the dependence on host resources for growth and survival. Additionally, we bolster our recent reclassification of Rickettsia by identifying OGs that define the AG (ancestral group), TG (typhus group), TRG (transitional group), and SFG (spotted fever group) rickettsiae. OGs for insect-associated species, tick-associated species and species that harbor plasmids were also predicted. Through superimposition of all OGs over robust phylogeny estimation, we discern between C1OGs and C2OGs, the latter depicting genes either decaying from the conserved C1OGs or acquired laterally. Finally, scrutiny of non-representative OGs revealed high levels of split genes versus gene duplications, with both phenomena confounding gene orthology assignment. Interestingly, non-representative OGs, as well as OGs comprised of several gene families typically involved in microbial pathogenicity and/or the acquisition of virulence factors, fall predominantly within C2OG distributions. CONCLUSION/SIGNIFICANCE: Collectively, we determined the relative conservation and distribution of 14354 predicted ORFs from 10 rickettsial genomes across robust phylogeny estimation. The data, available at PATRIC (PathoSystems Resource Integration Center), provide novel information for unwinding the intricacies associated with Rickettsia pathogenesis, expanding the range of potential diagnostic, vaccine and therapeutic targets

A Project-Centric Course on Cyberinfrastructure to Support High School STEM Education

Recent rapid advances in information technology pose new challenges for teachers in the Science, Technology, Engineering, and Mathematics (STEM) fields to incorporate the latest knowledge and technical expertise into courses in a way that will be applicable to students as future scientists. A demonstration project was designed, developed, and deployed by university faculty and high school teachers for their students to explore the use of the components of cyberinfrastructure. The project explored the introduction of cyberinfrastructure through the use of bioinformatics and the use of team science. This paper describes the high school course that was deployed at Galileo Magnet High School (GMHS) in collaboration with the scientists at Virginia Tech University, and details its overall assessment. Implementation of a project-centric teaching paradigm to engage students in applying the concepts of cyberinfrastructure by integrating the disciplines of biology, computer science, mathematics, and statistics through bioinformatics was an integral part of this study

Child Abuse Listening and Mediation

This investigation tested the incremental utility of cognitive retraining as a component within a program designed to prevent child maltreatment. High-risk families (N � 96) were randomly assigned to a control condition, home visitation modeled after the Healthy Start program (unenhanced home visitation), or home visitation that included a cognitive component (enhanced home visitation). Mothers were identified late during pregnancy or soon after birth, and their participation continued for 1 year. Lower levels of harsh parenting were found among mothers in the enhanced home visitation condition than among those in the unenhanced home visitation or control conditions. Prevalence of physical abuse (percentage of mothers who were abusive) during the first year was 26 % in the control condition, 23 % in the unenhanced home visitation condition, and 4 % in the enhanced home visitation condition. Benefits were greatest in families that included a medically at-risk child. A linear pattern of benefits was found for child health; as program features were added, benefits for child health increased. In recent years, the issue of child maltreatment has received increasing attention in both the scientific and public domains. Much of the attention it has received follows fro

Why is school closed today? Unplanned K-12 school closures in the United States, 2011-2013.

INTRODUCTION: We describe characteristics of unplanned school closures (USCs) in the United States over two consecutive academic years during a non-pandemic period to provide context for implementation of school closures during a pandemic. METHODS: From August 1, 2011 through June 30, 2013, daily systematic internet searches were conducted for publicly announced USCs lasting ≥ 1 day. The reason for closure and the closure dates were recorded. Information on school characteristics was obtained from the National Center for Education Statistics. RESULTS: During the two-year study period, 20,723 USCs were identified affecting 27,066,426 students. Common causes of closure included weather (79%), natural disasters (14%), and problems with school buildings or utilities (4%). Only 771 (4%) USCs lasted ≥ 4 school days. Illness was the cause of 212 (1%) USCs; of these, 126 (59%) were related to respiratory illnesses and showed seasonal variation with peaks in February 2012 and January 2013. CONCLUSIONS: USCs are common events resulting in missed school days for millions of students. Illness causes few USCs compared with weather and natural disasters. Few communities have experience with prolonged closures for illness

Selected characteristics of school closure events and association with ≥4 unplanned closure days — United States, 2011–2013.

<p>*Accounts for clustering by specific events.</p><p>Selected characteristics of school closure events and association with ≥4 unplanned closure days — United States, 2011–2013.</p

Geographic distribution of public school districts with closure events related to weather, natural disasters, and illness, by year — United States, August 2011–June 2013.

<p>Panels A and B: weather-related closure events; panels C and D: natural disaster-related closure events; panels E and F: illness-related closure events, one event in Alaska not shown.</p