Vesicle Dynamics during Plant Cell Cytokinesis Reveals Distinct Developmental Phases

Cell division in plant cells requires the deposition of a new cell wall between the two daughter cells. The assembly of this plate requires the coordinated movement of cargo vesicles whose size is below the diffraction-limited resolution of the optical microscope. We combined high spatial and temporal resolution confocal laser scanning microscopy with advanced image-processing tools and fluorescence fluctuation methods and distinguished three distinct phases during cell plate expansion in tobacco (Nicotiana tabacum) 'Bright Yellow-2' cells: massive delivery of preexisting vesicles to a disk-shaped region at the equatorial plane precedes a primary rapid expansion phase followed by a secondary, slow expansion phase during which the extremity of the circular plate seeks contact with the mother wall and brings about the separation of the two portions of cytoplasm. Different effects of pharmacological inhibition emphasize the distinct nature of the assembly and expansion mechanisms characterizing these phases.

A review of genomic data warehousing systems

To facilitate the integration and querying of genomics data, a number of generic data warehousing frameworks have been developed. They differ in their design and capabilities, as well as their intended audience. We provide a comprehensive and quantitative review of those genomic data warehousing frameworks in the context of large-scale systems biology. We reviewed in detail four genomic data warehouses (BioMart, BioXRT, InterMine and PathwayTools) freely available to the academic community. We quantified 20 aspects of the warehouses, covering the accuracy of their responses, their computational requirements and development efforts. Performance of the warehouses was evaluated under various hardware configurations to help laboratories optimize hardware expenses. Each aspect of the benchmark may be dynamically weighted by scientists using our online tool BenchDW (http://warehousebenchmark.fungalgenomics.ca/benchmark/) to build custom warehouse profiles and tailor our results to their specific needs.

The EnzymeTracker: an open-source laboratory information management system for sample tracking

BACKGROUND

In many laboratories, researchers store experimental data on their own workstation using spreadsheets. However, this approach poses a number of problems, ranging from sharing issues to inefficient data-mining. Standard spreadsheets are also error-prone, as data do not undergo any validation process. To overcome spreadsheets inherent limitations, a number of proprietary systems have been developed, which laboratories need to pay expensive license fees for. Those costs are usually prohibitive for most laboratories and prevent scientists from benefiting from more sophisticated data management systems.

RESULTS

In this paper, we propose the EnzymeTracker, a web-based laboratory information management system for sample tracking, as an open-source and flexible alternative that aims at facilitating entry, mining and sharing of experimental biological data. The EnzymeTracker features online spreadsheets and tools for monitoring numerous experiments conducted by several collaborators to identify and characterize samples. It also provides libraries of shared data such as protocols, and administration tools for data access control using OpenID and user/team management. Our system relies on a database management system for efficient data indexing and management and a user-friendly AJAX interface that can be accessed over the Internet. The EnzymeTracker facilitates data entry by dynamically suggesting entries and providing smart data-mining tools to effectively retrieve data. Our system features a number of tools to visualize and annotate experimental data, and export highly customizable reports. It also supports QR matrix barcoding to facilitate sample tracking.

CONCLUSIONS

The EnzymeTracker was designed to be easy to use and offers many benefits over spreadsheets, thus presenting the characteristics required to facilitate acceptance by the scientific community. It has been successfully used for 20 months on a daily basis by over 50 scientists. The EnzymeTracker is freely available online at http://cubique.fungalgenomics.ca/enzymedb/index.html under the GNU GPLv3 license.

Bacterial protein structures reveal phylum dependent divergence

Protein sequence space is vast compared to protein fold space. This raises important questions about how structures adapt to evolutionary changes in protein sequences. A growing trend is to regard protein fold space as a continuum rather than a series of discrete structures. From this perspective, homologous protein structures within the same functional classification should reveal a constant rate of structural drift relative to sequence changes. The clusters of orthologous groups (COG) classification system was used to annotate homologous bacterial protein structures in the Protein Data Bank (PDB). The structures and sequences of proteins within each COG were compared against each other to establish their relatedness. As expected, the analysis demonstrates a sharp structural divergence between the bacterial phyla Firmicutes and Proteobacteria. Additionally, each COG had a distinct sequence/structure relationship, indicating that different evolutionary pressures affect the degree of structural divergence. However, our analysis also shows the relative drift rate between sequence identity and structure divergence remains constant.

PROFESS: a PROtein function, evolution, structure and sequence database

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are approximately 1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of protein-protein interaction networks. Database URL: http://cse.unl.edu/~profess/

Classification integration and reclassification using constraint databases

OBJECTIVE

We propose classification integration as a new method for data integration from different sources. We also propose reclassification as a new method of combining existing medical classifications for different classes.

BACKGROUND

In many problems the raw data are already classified according to a set of features but need to be reclassified. Data reclassification is usually achieved using data integration methods that require the raw data, which may not be available or sharable because of privacy and legal concerns.

METHODOLOGY

We introduce general classification integration and reclassification methods that create new classes by combining in a flexible way the existing classes without requiring access to the raw data. The flexibility is achieved by representing any linear classification in a constraint database.

RESULTS

The experiments using support vector machines and decision trees on heart disease diagnosis and primary biliary cirrhosis data show that our classification integration method is more accurate than current data integration methods when there are many missing values in the data. The reclassification problem also can be solved using constraint databases without requiring access to the raw data.

CONCLUSIONS

The classification integration and the reclassification methods are applied to two particular data sets. Beside these particular cases, our general method is also appropriate for many other application areas and may yield similar accuracy improvements. These methods may be also extended to non-linear classifiers.