Babel's tower revisited: a universal resource for cross-referencing across annotation databases

MOTIVATION: Annotation databases are widely used as public repositories of biological knowledge. However, most of these resources have been developed by independent groups which used different designs and different identifiers for the same biological entities. As we show in this article, incoherent name spaces between various databases represent a serious impediment to using the existing annotations at their full potential. Navigating between various such name spaces by mapping IDs from one database to another is a very important issue which is not properly addressed at the moment. RESULTS: We have developed a web-based resource, Onto-Translate (OT), which effectively addresses this problem. OT is able to map onto each other different types of biological entities from the following annotation databases: Swiss-Prot, TrEMBL, NREF, PIR, Gene Ontology, KEGG, Entrez Gene, GenBank, GenPept, IMAGE, RefSeq, UniGene, OMIM, PDB, Eukaryotic Promoter Database, HUGO Gene Nomenclature Committee and NetAffx. Currently, OT is able to perform 462 types of mappings between 29 different types of IDs from 17 databases concerning 53 organisms. Among these, over 300 types of translations and 15 types of IDs are not currently supported by any other tool or resource. On average, OT is able to correctly map between 96 and 99% of the biological entities provided as input. In terms of speed, sets of approximately 20 000 IDs can be translated in <30 s, in most cases. AVAILABILITY: OT is a part of Onto-Tools, which is freely available at http://vortex.cs.wayne.edu/Projects.html     

Global functional profiling of gene expression

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.     

Solid state NMR sequential resonance assignments and conformational analysis of the 2×10.4 kDa dimeric form of the Bacillus subtilis protein Crh

Solid state NMR sample preparation and resonance assignments of the U-[¹³C,¹⁵N] 2×10.4 kDa dimeric form of the regulatory protein Crh in microcrystalline, PEG precipitated form are presented. Intra– and interresidue correlations using dipolar polarization transfer methods led to nearly complete sequential assignments of the protein, and to 88% of all ¹⁵N, ¹³C chemical shifts. For several residues, the resonance assignments differ significantly from those reported for the monomeric form analyzed by solution state NMR. Dihedral angles obtained from a TALOS-based statistical analysis suggest that the microcrystalline arrangement of Crh must be similar to the domain-swapped dimeric structure of a single crystal form recently solved using X-ray crystallography. For a limited number of protein residues, a remarkable doubling of the observed NMR resonances is observed indicative of local static or dynamic conformational disorder. Our study reports resonance assignments for the largest protein investigated by solid state NMR so far and describes the conformational dimeric variant of Crh with previously unknown chemical shifts.     

Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays

MOTIVATION: A crucial step in microarray data analysis is the selection of subsets of interesting genes from the initial set of genes. In many cases, especially when comparing a specific condition to a reference, the genes of interest are those which are differentially expressed. Two common methods for gene selection are: (a) selection by fold difference (at least n fold variation) and (b) selection by altered ratio (at least n standard deviations away from the mean ratio). RESULTS: The novel method proposed here is based on ANOVA and uses replicate spots to estimate an empirical distribution of the noise. The measured intensity range is divided in a number of intervals. A noise distribution is constructed for each such interval. Bootstrapping is used to map the desired confidence levels from the noise distribution corresponding to a given interval to the measured log ratios in that interval. If the method is applied on individual arrays having replicate spots, the method can calculate an overall width of the noise distribution which can be used as an indicator of the array quality. We compared this method with the fold change and unusual ratio method. We also discuss the relationship with an ANOVA model proposed by Churchill et al. In silico experiments were performed while controlling the degree of regulation as well as the amount of noise. Such experiments show the performance of the classical methods can be very unsatisfactory. We also compared the results of the 2-fold method with the results of the noise sampling method using pre and post immortalization cell lines derived from the MDAH041 fibroblasts hybridized on Affymetrix GeneChip arrays. The 2-fold method reported 198 genes as upregulated and 493 genes as downregulated. The noise sampling method reported 98 gene upregulated and 240 genes downregulated at the 99.99% confidence level. The methods agreed on 221 genes downregulated and 66 genes upregulated. Fourteen genes from the subset of genes reported by both methods were all confirmed by Q-RT-PCR. Alternative assays on various subsets of genes on which the two methods disagreed suggested that the noise sampling method is likely to provide fewer false positives.     

Predicting HIV drug resistance with neural networks

MOTIVATION: Drug resistance is a very important factor influencing the failure of current HIV therapies. The ability to predict the drug resistance of HIV protease mutants may be useful in developing more effective and longer lasting treatment regimens. METHODS: The HIV resistance is predicted to two current protease inhibitors, Indinavir and Saquinavir. The problem was approached from two perspectives. First, a predictor was constructed based on the structural features of the HIV protease-drug inhibitor complex. A particular structure was represented by its list of contacts between the inhibitor and the protease. Next, a classifier was constructed based on the sequence data of various drug resistant mutants. In both cases, self-organizing maps were first used to extract the important features and cluster the patterns in an unsupervised manner. This was followed by subsequent labelling based on the known patterns in the training set. RESULTS: The prediction performance of the classifiers was measured by cross-validation. The classifier using the structure information correctly classified previously unseen mutants with an accuracy of between 60 and 70%. Several architectures were tested on the more abundant sequence data. The best single classifier provided an accuracy of 68% and a coverage of 69%. Multiple networks were then combined into various majority voting schemes. The best combination yielded an average of 85% coverage and 78% accuracy on previously unseen data. This is more than two times better than the 33% accuracy expected from a random classifier.     

Inhibition of allergic inflammation in the airways using aerosolized antisense to Syk kinase

Activation of the protein tyrosine kinase Syk is an early event that follows cross-linking of Fc gamma R and Fc epsilon R, leading to the release of biologically active molecules in inflammation. We reported previously that aerosolized Syk antisense oligodeoxynucleotides (ASO) depresses Syk expression in inflammatory cells, the release of mediators from alveolar macrophages, and pulmonary inflammation. To study the effect of Syk ASO in allergic inflammation and airway hyperresponsiveness, we used the Brown Norway rat model of OVA-induced allergic asthma. Syk ASO, delivered in a liposome, carrier/lipid complex by aerosol to rats, significantly inhibited the Ag-induced inflammatory cell infiltrate in the bronchoalveolar space, decreasing both neutrophilia and eosinophilia. The number of eosinophils in the lung parenchyma was also diminished. Syk ASO also depressed up-regulation of the expression of beta(2) integrins, alpha(4) integrin, and ICAM-1 in bronchoalveolar lavage leukocytes and reversed the Ag-induced decrease in CD62L expression on neutrophils. Furthermore, the increase in TNF levels in bronchoalveolar lavage following Ag challenge was significantly inhibited. Syk ASO also suppressed Ag-mediated contraction of the trachea in a complementary model. Thus, aerosolized Syk ASO suppresses many of the central components of allergic asthma and inflammation and may provide a new therapeutic approach.     

Algorithmic strategies for the single nucleotide polymorphism haplotype assembly problem

With the consensus human genome sequenced and many other sequencing projects at varying stages of completion, greater attention is being paid to the genetic differences among individuals and the abilities of those differences to predict phenotypes. A significant obstacle to such work is the difficulty and expense of determining haplotypes--sets of variants genetically linked because of their proximity on the genome--for large numbers of individuals for use in association studies. This paper presents some algorithmic considerations in a new approach for haplotype determination: inferring haplotypes from localised polymorphism data gathered from short genome 'fragments.' Formalised models of the biological system under consideration are examined, given a variety of assumptions about the goal of the problem and the character of optimal solutions. Some theoretical results and algorithms for handling haplotype assembly given the different models are then sketched. The primary conclusion is that some important simplified variants of the problem yield tractable problems while more general variants tend to be intractable in the worst case.     

Above‐ and belowground linkages in Sphagnum peatland: climate warming affects plant‐microbial interactions

Peatlands contain approximately one third of all soil organic carbon (SOC). Warming can alter above‐ and belowground linkages that regulate soil organic carbon dynamics and C‐balance in peatlands. Here we examine the multiyear impact of in situ experimental warming on the microbial food web, vegetation, and their feedbacks with soil chemistry. We provide evidence of both positive and negative impacts of warming on specific microbial functional groups, leading to destabilization of the microbial food web. We observed a strong reduction (70%) in the biomass of top‐predators (testate amoebae) in warmed plots. Such a loss caused a shortening of microbial food chains, which in turn stimulated microbial activity, leading to slight increases in levels of nutrients and labile C in water. We further show that warming altered the regulatory role of Sphagnum‐polyphenols on microbial community structure with a potential inhibition of top predators. In addition, warming caused a decrease in Sphagnum cover and an increase in vascular plant cover. Using structural equation modelling, we show that changes in the microbial food web affected the relationships between plants, soil water chemistry, and microbial communities. These results suggest that warming will destabilize C and nutrient recycling of peatlands via changes in above‐ and belowground linkages, and therefore, the microbial food web associated with mosses will feedback positively to global warming by destabilizing the carbon cycle. This study confirms that microbial food webs thus constitute a key element in the functioning of peatland ecosystems. Their study can help understand how mosses, as ecosystem engineers, tightly regulate biogeochemical cycling and climate feedback in peatlands