CleanEx: new data extraction and merging tools based on MeSH term annotation

Insights into the molecular features of the von Hippel-Lindau-like protein

We present an in silico characterization of the von Hippel-Lindau-like protein (VLP), the only known human paralog of the von Hippel-Lindau tumor suppressor protein (pVHL). Phylogenetic investigation showed VLP to be mostly conserved in upper mammals and specifically expressed in brain and testis. Structural analysis and molecular dynamics simulations show VLP to be very similar to pVHL three-dimensional organization and binding dynamics. In particular, conservation of elements at the protein interfaces suggests VLP to be a functional pVHL homolog potentially possessing multiple functions beyond HIF-1α-dependent binding activity. Our findings show that VLP may share at least seven interactors with pVHL, suggesting novel functional roles for this understudied human protein. These may occur at precise hypoxia levels where functional overlap with pVHL may permit a finer modulation of pVHL functions.

Halophilic Protein Adaptation Results from Synergistic Residue-Ion Interactions in the Folded and Unfolded States

Halophilic organisms thrive in environments with extreme salt concentrations and have adapted by allowing molar quantities of cosolutes, mainly KCl, to accumulate in their cytoplasm. To cope with this high intracellular salinity, halophilic organisms modified the chemical composition of their proteins to enrich their surface with acidic and short polar side chains, while lysines and bulky hydrophobic residues got depleted. We have emulated the evolutionary process of haloadaptation with natural and designed halophilic polypeptides and applied novel nuclear magnetic resonance (NMR) methodology to study the different mechanisms contributing to protein haloadaptation at a per residue level. Our analysis of an extensive set of NMR observables, determined over several proteins, allowed us to disentangle the synergistic contributions of protein haloadaptation: cation exclusion and electrostatic repulsion between negatively charged residues destabilize the denatured state ensemble while cumulative weak cation-protein interactions stabilize the folded conformations.

Microarray meta-analysis database (M(2)DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database

Background

Over the past decade, gene expression microarray studies have greatly expanded our knowledge of genetic mechanisms of human diseases. Meta-analysis of substantial amounts of accumulated data, by integrating valuable information from multiple studies, is becoming more important in microarray research. However, collecting data of special interest from public microarray repositories often present major practical problems. Moreover, including low-quality data may significantly reduce meta-analysis efficiency.

Results

M²DB is a human curated microarray database designed for easy querying, based on clinical information and for interactive retrieval of either raw or uniformly pre-processed data, along with a set of quality-control metrics. The database contains more than 10,000 previously published Affymetrix GeneChip arrays, performed using human clinical specimens. M²DB allows online querying according to a flexible combination of five clinical annotations describing disease state and sampling location. These annotations were manually curated by controlled vocabularies, based on information obtained from GEO, ArrayExpress, and published papers. For array-based assessment control, the online query provides sets of QC metrics, generated using three available QC algorithms. Arrays with poor data quality can easily be excluded from the query interface. The query provides values from two algorithms for gene-based filtering, and raw data and three kinds of pre-processed data for downloading.

Conclusion

M²DB utilizes a user-friendly interface for QC parameters, sample clinical annotations, and data formats to help users obtain clinical metadata. This database provides a lower entry threshold and an integrated process of meta-analysis. We hope that this research will promote further evolution of microarray meta-analysis.

Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data

High-throughput “omics” technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput “omics” data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput “omics” data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied “omics” data from different laboratories to make useful connections that could lead to new biological knowledge.

GEOGLE: context mining tool for the correlation between gene expression and the phenotypic distinction

Background

In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches.

Results

In our work, we integrated gene expression information from Gene Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings (MeSH) and signaling pathway knowledge from sigPathway entries to develop a context mining tool for gene expression analysis – GEOGLE. GEOGLE offers a rapid and convenient way for searching relevant experimental datasets, pathways and biological terms according to multiple types of queries: including biomedical vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, GEOGLE summarizes the signature genes from a subset of GDSes and estimates the correlation between gene expression and the phenotypic distinction with an integrated p value.

Conclusion

This approach performing global searching of expression data may expand the traditional way of collecting heterogeneous gene expression experiment data. GEOGLE is a novel tool that provides researchers a quantitative way to understand the correlation between gene expression and phenotypic distinction through meta-analysis of gene expression datasets from different experiments, as well as the biological meaning behind. The web site and user guide of GEOGLE are available at: