The Genome Sequence DataBase: towards an integrated functional genomics resource.

During 1998 the primary focus of the Genome Sequence DataBase (GSDB; http://www.ncgr.org/gsdb ) located at the National Center for Genome Resources (NCGR) has been to improve data quality, improve data collections, and provide new methods and tools to access and analyze data. Data quality has been improved by extensive curation of certain data fields necessary for maintaining data collections and for using certain tools. Data quality has also been increased by improvements to the suite of programs that import data from the International Nucleotide Sequence Database Collaboration (IC). The Sequence Tag Alignment and Consensus Knowledgebase (STACK), a database of human expressed gene sequences developed by the South African National Bioinformatics Institute (SANBI), became available within the last year, allowing public access to this valuable resource of expressed sequences. Data access was improved by the addition of the Sequence Viewer, a platform-independent graphical viewer for GSDB sequence data. This tool has also been integrated with other searching and data retrieval tools. A BLAST homology search service was also made available, allowing researchers to search all of the data, including the unique data, that are available from GSDB. These improvements are designed to make GSDB more accessible to users, extend the rich searching capability already present in GSDB, and to facilitate the transition to an integrated system containing many different types of biological data.     

Determination of fluorescent probe orientations on biomolecules by conformational searching: algorithm testing and applications to the atomic model of myosin.

The ability of a localized conformational searching method to predict probe orientation was tested on model nucleic acid and protein structures and applied to the prediction of skeletal myosin integrity upon chemical modification of its reactive thiols. Double-stranded oligonucleotides were chemically labeled with donor and acceptor resonance energy transfer probes at each end for distance determinations. These measurements were made independently using a terbium chelate as a donor to each of four chemically and spectroscopically distinct acceptor probes from the xanthene and cyanine dye groups. The choice of acceptor significantly affected the separation distance measured. Conformational searching algorithms on the atomic model corrected for the differences to within 0.2 nm on average. Verifying its usefulness on proteins, the localized conformational searching method determined the orientation of a fluorescent probe on RNase A that corresponds closely to available crystallographic models of the labeled protein (RMS deviation = 0.1 nm). Also, analysis of the symmetry of the fluorophores' structures suggests why FRET orientation factors are often closer to their dynamic average value than might normally be expected. Furthermore, the computational method provides insights about FRET data that are important for assessing the stability of the alpha-helix separating the SH1 and SH2 reactive thiols in skeletal myosin.     

Prediction of Arctic plant phenological sensitivity to climate change from historical records

The pace of climate change in the Arctic is dramatic, with temperatures rising at a rate double the global average. The timing of flowering and fruiting (phenology) is often temperature dependent and tends to advance as the climate warms. Herbarium specimens, photographs, and field observations can provide historical phenology records and have been used, on a localised scale, to predict species’ phenological sensitivity to climate change. Conducting similar localised studies in the Canadian Arctic, however, poses a challenge where the collection of herbarium specimens, photographs, and field observations have been temporally and spatially sporadic. We used flowering and seed dispersal times of 23 Arctic species from herbarium specimens, photographs, and field observations collected from across the 2.1 million km² area of Nunavut, Canada, to determine (1) which monthly temperatures influence flowering and seed dispersal times; (2) species’ phenological sensitivity to temperature; and (3) whether flowering or seed dispersal times have advanced over the past 120 years. We tested this at different spatial scales and compared the sensitivity in different regions of Nunavut. Broadly speaking, this research serves as a proof of concept to assess whether phenology–climate change studies using historic data can be conducted at large spatial scales. Flowering times and seed dispersal time were most strongly correlated with June and July temperatures, respectively. Seed dispersal times have advanced at double the rate of flowering times over the past 120 years, reflecting greater late‐summer temperature rises in Nunavut. There is great diversity in the flowering time sensitivity to temperature of Arctic plant species, suggesting climate change implications for Arctic ecological communities, including altered community composition, competition, and pollinator interactions. Intraspecific temperature sensitivity and warming trends varied markedly across Nunavut and could result in greater changes in some parts of Nunavut than in others.     

Measurement error in racial and ethnic statistics

In the United States, the racial and ethnic statistics published by the National Center for Health Statistics (NCHS) assume that each member of the U.S. population has a race and ethnicity and that if a member is black or white with respect to his risk of one disease, he is the same race with respect to his risk of another. Such an assumption is mistaken. Race and ethnicity are taken by the NCHS to be an intrinsic property of members of a population, when they should be taken to depend on interest. The actual or underlying race or ethnicity of members of a population depends on the risk whose variation within the population we wish to describe or explain.

Commentary: Do we have a consistent terminology for species diversity? The fallacy of true diversity

There is no single best index that can be used to answer all questions about species diversity. Entropy-based diversity indices, including Hill’s indices, cannot account for geographical and phylogenetic structure. While a single diversity index arises if we impose several constraints—most notably that gamma diversity be completely decomposed into alpha and beta diversity—there are many ecological questions regarding species diversity for which it is counterproductive, requiring decomposability. Non-decomposable components of gamma diversity may quantify important intrinsic ecological properties, such as resilience or nestedness.     

A whole-genome single nucleotide polymorphism-based approach to trace and identify outbreaks linked to a common Salmonella enterica subsp. enterica serovar Montevideo pulsed-field gel electrophoresis type

In this study, we report a whole-genome single nucleotide polymorphism (SNP)-based evolutionary approach to study the epidemiology of a multistate outbreak of Salmonella enterica subsp. enterica serovar Montevideo. This outbreak included 272 cases that occurred in 44 states between July 2009 and April 2010. A case-control study linked the consumption of salami made with contaminated black and red pepper to the outbreak. We sequenced, on the SOLiD System, 47 isolates with XbaI PFGE pattern JIXX01.0011, a common pulsed-field gel electrophoresis (PFGE) pattern associated with isolates from the outbreak. These isolates represented 20 isolates collected from human sources during the period of the outbreak and 27 control isolates collected from human, food, animal, and environmental sources before the outbreak. Based on 253 high-confidence SNPs, we were able to reconstruct a tip-dated molecular clock phylogeny of the isolates and to assign four human isolates to the actual outbreak. We developed an SNP typing assay to rapidly discriminate between outbreak-related cases and non-outbreak-related cases and tested this assay on an extended panel of 112 isolates. These results suggest that only a very small percentage of the human isolates with the outbreak PFGE pattern and obtained during the outbreak period could be attributed to the actual pepper-related outbreak (20%), while the majority (80%) of the putative cases represented background cases. This study demonstrates that next-generation-based SNP typing provides the resolution and accuracy needed for outbreak investigations of food-borne pathogens that cannot be distinguished by currently used subtyping methods.