MetaMine – A tool to detect and analyse gene patterns in their environmental context

Background Modern sequencing technologies allow rapid sequencing and bioinformatic analysis of genomes and metagenomes. With every new sequencing project a vast number of new proteins become available with many genes remaining functionally unclassified based on evidences from sequence similarities alone. Extending similarity searches with gene pattern approaches, defined as genes sharing a distinct genomic neighbourhood, have shown to significantly improve the number of functional assignments. Further functional evidences can be gained by correlating these gene patterns with prevailing environmental parameters. MetaMine was developed to approach the large pool of unclassified proteins by searching for recurrent gene patterns across habitats based on key genes. Results MetaMine is an interactive data mining tool which enables the detection of gene patterns in an environmental context. The gene pattern search starts with a user defined environmentally interesting key gene. With this gene a BLAST search is carried out against the Microbial Ecological Genomics DataBase (MEGDB) containing marine genomic and metagenomic sequences. This is followed by the determination of all neighbouring genes within a given distance and a search for functionally equivalent genes. In the final step a set of common genes present in a defined number of distinct genomes is determined. The gene patterns found are associated with their individual pattern instances describing gene order and directions. They are presented together with information about the sample and the habitat. MetaMine is implemented in Java and provided as a client/server application with a user-friendly graphical user interface. The system was evaluated with environmentally relevant genes related to the methane-cycle and carbon monoxide oxidation. Conclusion MetaMine offers a targeted, semi-automatic search for gene patterns based on expert input. The graphical user interface of MetaMine provides a user-friendly overview of the computed gene patterns for further inspection in an ecological context. Prevailing biological processes associated with a key gene can be used to infer new annotations and shape hypotheses to guide further analyses. The use-cases demonstrate that meaningful gene patterns can be quickly detected using MetaMine

Psychological distress in newly diagnosed colorectal cancer patients following microsatellite instability testing for Lynch syndrome on the pathologist’s initiative

According to the Dutch Guideline on Hereditary Colorectal Cancer published in 2008, patients with recently diagnosed colorectal cancer (CRC) should undergo microsatellite instability (MSI) testing by a pathologist immediately after tumour resection if they are younger than 50 years, or if a second CRC has been diagnosed before the age of 70 years, owing to the high risk of Lynch syndrome (MIPA). The aim of the present MIPAPS study was to investigate general distress and cancer-specific distress following MSI testing. From March 2007 to September 2009, 400 patients who had been tested for MSI after newly diagnosed CRC were recruited from 30 Dutch hospitals. Levels of general distress (SCL-90) and cancer-specific distress (IES) were assessed immediately after MSI result disclosure (T1) and 6 months later (T2). Response rates were 23/77 (30%) in the MSI-positive patients and 58/323 (18%) in the MSI-negative patients. Levels of general distress and cancer-specific distress were moderate. In the MSI-positive group, 27% of the patients had high general distress at T1 versus 18% at T2 (p = 0.5), whereas in the MSI-negative group, these percentage were 14 and 18% (p = 0.6), respectively. At T1 and T2, cancer-specific distress rates in the MSI-positive group and MSI-negative group were 39 versus 27% (p = 0.3) and 38 versus 36% (p = 1.0), respectively. High levels of general distress were correlated with female gender, low social support and high perceived cancer risk. Moderate levels of distress were observed after MSI testing, similar to those found in other patients diagnosed with CRC. Immediately after result disclosure, high cancer-specific distress was observed in 40% of the MSI-positive patients

Predicting protein linkages in bacteria: Which method is best depends on task

<p>Abstract</p> <p>Background</p> <p>Applications of computational methods for predicting protein functional linkages are increasing. In recent years, several bacteria-specific methods for predicting linkages have been developed. The four major genomic context methods are: Gene cluster, Gene neighbor, Rosetta Stone, and Phylogenetic profiles. These methods have been shown to be powerful tools and this paper provides guidelines for when each method is appropriate by exploring different features of each method and potential improvements offered by their combination. We also review many previous treatments of these prediction methods, use the latest available annotations, and offer a number of new observations.</p> <p>Results</p> <p>Using <it>Escherichia coli </it>K12 and <it>Bacillus subtilis</it>, linkage predictions made by each of these methods were evaluated against three benchmarks: functional categories defined by COG and KEGG, known pathways listed in EcoCyc, and known operons listed in RegulonDB. Each evaluated method had strengths and weaknesses, with no one method dominating all aspects of predictive ability studied. For functional categories, as previous studies have shown, the Rosetta Stone method was individually best at detecting linkages and predicting functions among proteins with shared KEGG categories while the Phylogenetic profile method was best for linkage detection and function prediction among proteins with common COG functions. Differences in performance under COG versus KEGG may be attributable to the presence of paralogs. Better function prediction was observed when using a weighted combination of linkages based on reliability versus using a simple unweighted union of the linkage sets. For pathway reconstruction, 99 complete metabolic pathways in <it>E. coli </it>K12 (out of the 209 known, non-trivial pathways) and 193 pathways with 50% of their proteins were covered by linkages from at least one method. Gene neighbor was most effective individually on pathway reconstruction, with 48 complete pathways reconstructed. For operon prediction, Gene cluster predicted completely 59% of the known operons in <it>E. coli </it>K12 and 88% (333/418)in <it>B. subtilis</it>. Comparing two versions of the <it>E. coli </it>K12 operon database, many of the unannotated predictions in the earlier version were updated to true predictions in the later version. Using only linkages found by both Gene Cluster and Gene Neighbor improved the precision of operon predictions. Additionally, as previous studies have shown, combining features based on intergenic region and protein function improved the specificity of operon prediction.</p> <p>Conclusion</p> <p>A common problem for computational methods is the generation of a large number of false positives that might be caused by an incomplete source of validation. By comparing two versions of a database, we demonstrated the dramatic differences on reported results. We used several benchmarks on which we have shown the comparative effectiveness of each prediction method, as well as provided guidelines as to which method is most appropriate for a given prediction task.</p

Methods for comparative metagenomics

<p>Abstract</p> <p>Background</p> <p>Metagenomics is a rapidly growing field of research that aims at studying uncultured organisms to understand the true diversity of microbes, their functions, cooperation and evolution, in environments such as soil, water, ancient remains of animals, or the digestive system of animals and humans. The recent development of ultra-high throughput sequencing technologies, which do not require cloning or PCR amplification, and can produce huge numbers of DNA reads at an affordable cost, has boosted the number and scope of metagenomic sequencing projects. Increasingly, there is a need for new ways of comparing multiple metagenomics datasets, and for fast and user-friendly implementations of such approaches.</p> <p>Results</p> <p>This paper introduces a number of new methods for interactively exploring, analyzing and comparing multiple metagenomic datasets, which will be made freely available in a new, comparative version 2.0 of the stand-alone metagenome analysis tool MEGAN.</p> <p>Conclusion</p> <p>There is a great need for powerful and user-friendly tools for comparative analysis of metagenomic data and MEGAN 2.0 will help to fill this gap.</p

Tolerance and rebound with zafirlukast in patients with persistent asthma

<p>Abstract</p> <p>Background</p> <p>The potential for tolerance to develop to zafirlukast, a cysteinyl leukotriene (CysLT) receptor antagonist (LRA) in persistent asthma, has not been specifically examined.</p> <p>Objective</p> <p>To look for any evidence of tolerance and potential for short-term clinical worsening on LRA withdrawal. Outcome measures included changes in; airway hyperresponsiveness to inhaled methacholine (PD₂₀FEV₁), daily symptoms and peak expiratory flows (PEF), sputum and blood cell profiles, sputum CysLT and prostaglandin (PG)E₂and exhaled nitric oxide (eNO) levels.</p> <p>Methods</p> <p>A double blind, placebo-controlled study of zafirlukast, 20 mg twice daily over 12 weeks in 21 asthmatics taking β₂-agonists only (Group I), and 24 subjects treated with ICS (Group II).</p> <p>Results</p> <p>In Group I, zafirlukast significantly improved morning PEF and FEV₁compared to placebo (p < 0.01), and reduced morning waking with asthma from baseline after two weeks (p < 0.05). Similarly in Group II, FEV₁improved compared to placebo (p < 0.05), and there were early within-treatment group improvements in morning PEF, β₂-agonist use and asthma severity scores (p < 0.05). However, most improvements with zafirlukast in Group I and to a lesser extent in Group II deteriorated toward baseline values over 12 weeks. In both groups, one week following zafirlukast withdrawal there were significant deteriorations in morning and evening PEFs and FEV₁compared with placebo (p ≤ 0.05) and increased nocturnal awakenings in Group II (p < 0.05). There were no changes in PD₂₀FEV₁, sputum CysLT concentrations or exhaled nitric oxide (eNO) levels. However, blood neutrophils significantly increased in both groups following zafirlukast withdrawal compared to placebo (p = 0.007).</p> <p>Conclusion</p> <p>Tolerance appears to develop to zafirlukast and there is rebound clinical deterioration on drug withdrawal, accompanied by a blood neutrophilia.</p

Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset

We present an approach in which the semantics of an XML language is defined by means of a transformation from an XML document model (an XML schema) to an application specific model. The application specific model implements the intended behavior of documents written in the language. A transformation is specified in a model transformation language used in the Model Driven Architecture (MDA) approach for software development. Our approach provides a better separation of three concerns found in XML applications: syntax, syntax processing logic and intended meaning of the syntax. It frees the developer of low-level syntactical details and improves the adaptability and reusability of XML applications. Declarative transformation rules and the explicit application model provide a finer control over the application parts affected by adaptations. Transformation rules and the application model for an XML language may be composed with the corresponding rules and application models defined for other XML languages. In that way we achieve reuse and composition of XML applications

A fluorescence approach to investigate repartitioning of coalescing agents in acrylic polymer emulsions

Repartitioning of co-solvents between particles of latex emulsions was investigated by means of a fluorescence method based on the detection of the amount of co-solvent via the solvatochromic shift of the emission maximum of a fluorescent probe, copolymerized at a low concentration. Complete repartitioning of co-solvents between particles of latex materials with a low Tg (ca. 25 °C) occurred within minutes. For a hydrophilic latex with a Tg of 68 °C, equilibration was achieved within an hour. Repartitioning was faster for more hydrophobic co-solvents. For a hydrophobic latex of similar Tg, co-solvent repartitioning took place on the same time scale, but complete equilibration was not reached. Possibly, there is an additional slow component in the repartitioning, or the prolonged presence of co-solvent causes a structural change in the latex particles that affects the outcome of the experiment

TACOA – Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach

Diaz NN, Krause L, Goesmann A, Niehaus K, Nattkemper TW. TACOA - Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach. BMC Bioinformatics. 2009;10(1):56.Background: Metagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning. Results Our novel strategy was extensively evaluated using the leave-one-out cross validation strategy on fragments of variable length (800 bp – 50 Kbp) from 373 completely sequenced genomes. TACOA is able to classify genomic fragments of length 800 bp and 1 Kbp with high accuracy until rank class. For longer fragments ≥ 3 Kbp accurate predictions are made at even deeper taxonomic ranks (order and genus). Remarkably, TACOA also produces reliable results when the taxonomic origin of a fragment is not represented in the reference set, thus classifying such fragments to its known broader taxonomic class or simply as "unknown". We compared the classification accuracy of TACOA with the latest intrinsic classifier PhyloPythia using 63 recently published complete genomes. For fragments of length 800 bp and 1 Kbp the overall accuracy of TACOA is higher than that obtained by PhyloPythia at all taxonomic ranks. For all fragment lengths, both methods achieved comparable high specificity results up to rank class and low false negative rates are also obtained. Conclusion: An accurate multi-class taxonomic classifier was developed for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp. The proposed method is transparent, fast, accurate and the reference set can be easily updated as newly sequenced genomes become available. Moreover, the method demonstrated to be competitive when compared to the most current classifier PhyloPythia and has the advantage that it can be locally installed and the reference set can be kept up-to-date. Background

Understanding Communication Signals during Mycobacterial Latency through Predicted Genome-Wide Protein Interactions and Boolean Modeling

About 90% of the people infected with Mycobacterium tuberculosis carry latent bacteria that are believed to get activated upon immune suppression. One of the fundamental challenges in the control of tuberculosis is therefore to understand molecular mechanisms involved in the onset of latency and/or reactivation. We have attempted to address this problem at the systems level by a combination of predicted functional protein∶protein interactions, integration of functional interactions with large scale gene expression studies, predicted transcription regulatory network and finally simulations with a Boolean model of the network. Initially a prediction for genome-wide protein functional linkages was obtained based on genome-context methods using a Support Vector Machine. This set of protein functional linkages along with gene expression data of the available models of latency was employed to identify proteins involved in mediating switch signals during dormancy. We show that genes that are up and down regulated during dormancy are not only coordinately regulated under dormancy-like conditions but also under a variety of other experimental conditions. Their synchronized regulation indicates that they form a tightly regulated gene cluster and might form a latency-regulon. Conservation of these genes across bacterial species suggests a unique evolutionary history that might be associated with M. tuberculosis dormancy. Finally, simulations with a Boolean model based on the regulatory network with logical relationships derived from gene expression data reveals a bistable switch suggesting alternating latent and actively growing states. Our analysis based on the interaction network therefore reveals a potential model of M. tuberculosis latency