A comparison of diversity estimators applied to a database of host–parasite associations

Understanding the drivers of biodiversity is important for forecasting changes in the distribution of life on earth. However, most studies of biodiversity are limited by uneven sampling effort, with some regions or taxa better sampled than others. Numerous methods have been developed to account for differences in sampling effort, but most methods were developed for systematic surveys in which all study units are sampled using the same design and assemblages are sampled randomly. Databases compiled from multiple sources, such as from the literature, often violate these assumptions because they are composed of studies that vary widely in their goals and methods. Here, we compared the performance of several popular methods for estimating parasite diversity based on a large and widely used parasite database, the Global Mammal Parasite Database (GMPD). We created artificial datasets of host–parasite interactions based on the structure of the GMPD, then used these datasets to evaluate which methods best control for differential sampling effort. We evaluated the precision and bias of seven methods, including species accumulation and nonparametric diversity estimators, compared to analyzing the raw data without controlling for sampling variation. We find that nonparametric estimators, and particularly the Chao2 and second-order jackknife estimators, perform better than other methods. However, these estimators still perform poorly relative to systematic sampling, and effect sizes should be interpreted with caution because they tend to be lower than actual effect sizes. Overall, these estimators are more effective in comparative studies than for producing true estimates of diversity. We make recommendations for future sampling strategies and statistical methods that would improve estimates of global parasite diversity.     

In silico identification of novel lncRNAs with a potential role in diagnosis of gastric cancer

Abstract

Gastric cancer (GC) is the second leading cause of cancer-related deaths in the world. Due to the shortage of adequate symptoms in the early stages, it is diagnosed when the tumor has spread to distant organs. Early recognition of GC enhances the chance of successful treatment. Molecular mechanisms of GC are still poorly understood. LncRNAs are emerging as new players in cancer in both oncogene and tumor suppressor roles. High-throughput technologies such as RNA-Seq, have revealed thousands of lncRNAs which are dysregulated in GC. In this study, we retrieved lncRNAs obtained by High-throughput technologies from OncoLnc database. Consequently, retrieved lncRNAs were compared in literature-based databases including PubMed. As a result, two lists, including experimentally validated lncRNAs and predicted lncRNAs were provided. We found 43 predicted lncRNAs that had not been experimentally validated in GC, so far. Further Bioinformatics analyses were performed to obtain the expression profile of predicted lncRNAs in tumor and normal tissues. Also, the roles and targets of predicted lncRNAs in GC were identified by related databases. Finally, using the GEPIA database was reviewed the significant relationship of predicted lncRNAs with the survival of GC patients. By recognizing the lncRNAs involved in initiation and progression of GC, they may be considered as potential biomarkers in the GC early diagnosis or targeted treatment and lead to novel therapeutic strategies.

Communicated by Ramaswamy H. Sarma     

Development of a risk scoring system for patients with papillary thyroid cancer

As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network‐Regularized high‐dimensional Cox‐regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event‐free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.     

Automated system for gene annotation and metabolic pathway reconstruction using general sequence databases

Despite the growing number of genomes published or currently being sequenced, there is a relative paucity of software for functional classification of newly discovered genes and their assignment to metabolic pathways. Available software for such analyses has a very steep learning curve and requires the installation, configuration, and maintenance of large amounts of complex infrastructure, including complementary software and databases. Many such tools are restricted to one or a few data sources and classification schemes. In this work, we report an automated system for gene annotation and metabolic pathway reconstruction (ASGARD), which was designed to be powerful and generalizable, yet simple for the biologist to install and run on centralized, commonly available computers. It avoids the requirement for complex resources such as relational databases and web servers, as well as the need for administrator access to the operating system. Our methodology contributes to a more rapid investigation of the potential biochemical capabilities of genes and genomes by the biological researcher, and is useful in biochemical as well as comparative and evolutionary studies of pathways and networks.     

Community structure of ectomycorrhizal fungi in a Pinus muricata forest: minimal overlap between the mature forest and resistant propagule communities

We have investigated colonization strategies by comparing the abundance and frequency of ectomycorrhizal fungal species on roots in a mature Pinus muricata forest with those present as resistant propagules colonizing potted seedlings grown in the same soil samples. Thirty-seven fungal species were distinguished by internal transcribed spacer (ITS) restriction fragment length polymorphisms (RFLPs); most were identified to species level by sporocarp RFLP matches or to genus/family level by using sequence databases for the mitochondrial and nuclear large-subunit rRNA genes. The below-ground fungal community found in the mature forest contrasted markedly with the resistant propagule community, as only four species were found in both communities. The dominant species in the mature forest were members of the Russulaceae, Thelephorales and Amanitaceae. In contrast, the resistant propagule community was dominated by Rhizopogon species and by species of the Ascomycota. Only one species, Tomentella sublilacina (Thelephorales), was common in both communities. The spatial distribution of mycorrhizae on mature roots and propagules in the soil differed among the dominant species. For example, T. sublilacina mycorrhizae exhibited a unique bias toward the organic horizons, Russula brevipes mycorrhizae were denser and more clumped than those of other species and Cenococcum propagules were localized, whereas R. subcaerulescens propagules were evenly distributed. We suggest that species differences in resource preferences and colonization strategies, such as those documented here, contribute to the maintenance of species richness in the ectomycorrhizal community.     

Characterization of SCAR markers of Eimeria spp. of domestic fowl and construction of a public relational database (The Eimeria SCARdb)

This study reports the development and characterization of 151 sequence characterized amplified region (SCAR) markers for the seven Eimeria species that infect the domestic fowl. From this set, 84 markers are species-specific and 67 present partial specificity. The complete nucleotide sequence was derived for all markers, revealing the presence of micro- and minisatellite repetitive units in 22 SCARs, with up to five distinct repeat units being observed per marker. Only 15 markers showed significant hits in similarity searches against public sequence databases, thus confirming their anonymous and non-coding character. Finally, a relational database of the markers (the Eimeria SCARdb) was developed and made available on the Internet, providing a valuable resource of SCAR markers that can be useful for molecular diagnosis, and also for epizootiological, genetic variability and genome mapping studies.     

NMRShiftDB -- compound identification and structure elucidation support through a free community-built web database

Compound identification and support for computer-assisted structure elucidation via a free community-built web database for organic structures and their NMR data is described. The new database NMRShiftDB is available on . As the first NMR database, NMRShiftDB allows not only open access to the database but also open and peer reviewed submission of datasets, enabling the natural products community to build its first free repository of assigned 1H and 13C NMR spectra. In addition to the open access, the underlying database software is built solely from free software and is available under an open source license. This allows collaborating laboratories to fully replicate the database and to create a highly available network of NMRShiftDB mirrors. The database contains about 10,000 structures and assigned spectra, with new datasets constantly added. Its functionality includes (sub-) spectra and (sub-) structure searches as well as shift prediction of 13C spectra based on the current database material.     

The progress of membrane protein structure determination

The rate of membrane protein (MP) structure determination has been examined for the 18-year period following the publication of the first high-resolution crystal structure. The growth is solidly exponential, but lags behind the rate for soluble proteins during the equivalent time period.