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.

     

Quantifying the relationship between disease severity and the amount of Aphanomyces euteiches detected in roots of alfalfa and pea with a real-time PCR assay

A real-time PCR assay was used to quantify the relationship in alfalfa and pea between disease severity and the amount of Aphanomyces euteiches detected in roots. The study included isolates of race 1 and race 2 of the alfalfa pathovar of A. euteiches and an isolate obtained from diseased pea. Spearman rank correlations between pathogen DNA content and disease severity index (DSI) ratings were positive ( ? 0.57) and significant (P  0.0007) for individual alfalfa plants, bulked alfalfa plant samples, and individual pea plants. In all experiments, significantly more pathogen was detected in susceptible populations than in resistant populations. The results clearly demonstrate that resistance to A. euteiches in both alfalfa and pea is characterized by a reduction in pathogen colonization relative to levels observed for susceptible reactions. The assay was very specific for A. euteiches, producing very linear assays with DNA extracted from pathogen isolates obtained from alfalfa, pea, and bean. Possible applications of the assay in conjunction with other real-time PCR assays specific to other legume pathogens are discussed in relation to simultaneous disease screening for multiple plant pathogens and the study of microbial population dynamics in mixed plant infections.     

Using quantum dots to visualize clathrin associations

Our current understanding of clathrin-mediated endocytosis proposes that the process is initiated at a specialized anatomical structure called a coated pit. Electron microscopy has been required for elucidation of the morphology of coated pits and the vesicles produced therein, and the presence of a bristle coat has been taken as suggestive of clathrin surrounding these vesicles. More recently, immunocytochemical methods have confirmed that endocytic vesicles are surrounded by clathrin and its adaptor proteins, but there is a need to identify precisely and to follow the fate of the cellular organelles seen by fluorescence microscopy. We used quantum immune-electron microscopy to localize clathrin in a human adrenal cortical cell line (SW-13). Clathrin was shown to associate with a variety of vesicle types including the classic clathrin-coated vesicles and pits used in receptor internalization, pentilaminar annular gap junction vesicles, and multivesicular bodies. The images obtained with quantum dot technology allow accurate and specific localization of clathrin and the clathrin adaptor protein, AP-2, with cellular organelles and suggest that some of the structures classified as typical coated vesicles by immunocytochemical light microscopic techniques actually may be membrane bound pits.     

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.     

Efficacy assessed in follow-ups of clinical trials: methodological conundrum

Increasingly, we see papers describing the long-term follow-up results of randomised clinical trials. Sometimes, like the article by Rantalaiho and colleagues in the previous issue of Arthritis Research & Therapy, the follow-up extends to more than 10 years. It is not uncommon that authors of such articles describe their results as a comparison of the original treatment groups in the original randomised clinical trial. Methodologically, such a comparison is fallible for several reasons. In this editorial, two important sources of bias that may jeopardise the results of such follow-up studies are discussed: confounding by indication and confounding by trial completion.     

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.