Comparison of computational methods for identifying translation initiation sites in EST data

Background

Expressed Sequence Tag (EST) sequences are generally single-strand, single-pass sequences, only 200–600 nucleotides long, contain errors resulting in frame shifts, and represent different parts of their parent cDNA. If the cDNAs contain translation initiation sites, they may be suitable for functional genomics studies. We have compared five methods to predict translation initiation sites in EST data: first-ATG, ESTScan, Diogenes, Netstart, and ATGpr.

Results

A dataset of 100 EST sequences, 50 with and 50 without, translation initiation sites, was created. Based on analysis of this dataset, ATGpr is found to be the most accurate for predicting the presence versus absence of translation initiation sites. With a maximum accuracy of 76%, ATGpr more accurately predicts the position or absence of translation initiation sites than NetStart (57%) or Diogenes (50%). ATGpr similarly excels when start sites are known to be present (90%), whereas NetStart achieves only 60% overall accuracy. As a baseline for comparison, choosing the first ATG correctly identifies the translation initiation site in 74% of the sequences. ESTScan and Diogenes, consistent with their intended use, are able to identify open reading frames, but are unable to determine the precise position of translation initiation sites.

Conclusions

ATGpr demonstrates high sensitivity, specificity, and overall accuracy in identifying start sites while also rejecting incomplete sequences. A database of EST sequences suitable for validating programs for translation initiation site prediction is now available. These tools and materials may open an avenue for future improvements in start site prediction and EST analysis.

     

Identifying and quantifying metabolites by scoring peaks of GC-MS data

Background

Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS.

Results

Here we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice.

Conclusions

Identification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.     

Application of stains in clinical microbiology

Stains have been used for diagnosing infectious diseases since the late 1800s. The Gram stain remains the most commonly used stain because it detects and differentiates a wide range of pathogens. The next most commonly used diagnostic technique is acid-fast staining that is used primarily to detect Mycobacterium tuberculosis and other severe infections. Many infectious agents grow slowly on culture media or may not grow at all; stains may be the only method to detect these organisms in clinical specimens. In the hands of experienced clinical microscopists, stains provide rapid and cost-effective information for preliminary diagnosis of infectious diseases. A review of the most common staining methods used in the clinical microbiology laboratory is presented here.     

A novel membrane stirrer system enables foam-free biosurfactant production

Bioreactors are the operative backbone, for example, for the production of biopharmaceuticals, biomaterials in tissue engineering, and sustainable substitutes for chemicals. Still, the Achilles' heel of bioreactors nowadays is the aeration which is based on intense stirring and gas sparging, yielding inherent drawbacks such as shear stress, foaming, and sterility concerns. We present the synergistic combination of simulations and experiments toward a membrane stirrer for the efficient bubble-free aeration of bioreactors. A digital twin of the bioreactor with an integrated membrane-module stirrer (MemStir) was developed with computational fluid dynamics (CFD) studies addressing the determination of fluid mixing, shear rates, and local oxygen concentration. Usability of the MemStir is shown in a foam-free recombinant production process of biosurfactants (rhamnolipids) from glucose with different strains of Pseudomonas putida KT2440 in a 3-L vessel and benchmarked against a regular aerated process. The MemStir delivered a maximal oxygen transfer rate (OTR_max) of 175 mmol L⁻¹ h⁻¹ in completely foam-free cultivations. With a high space-time yield (STY) of 118 mg_RL L⁻¹ h⁻¹ during a fed-batch fermentation, the effectiveness of the novel MemStir is demonstrated. Simulations show the generic value of the MemStir beyond biosurfactant production, for example, for animal cell cultivation.