Impact of RNA structure on the prediction of donor and acceptor splice sites

BACKGROUND: gene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms. RESULTS: we sought the role of mRNA secondary structures and their information contents for five vertebrate and plant splice site datasets. We selected 900-nucleotide sequences centered at each (real or decoy) donor and acceptor sites, and predicted their corresponding RNA structures by Vienna software. Then, based on whether the nucleotide is in a stem or not, the conventional four-letter nucleotide alphabet was translated into an eight-letter alphabet. Zero-, first- and second-order Markov models were selected as the signal detection methods. It is shown that applying the eight-letter alphabet compared to the four-letter alphabet considerably increases the accuracy of both donor and acceptor site predictions in case of higher order Markov models. CONCLUSION: Our results imply that RNA structure contains important data and future gene prediction programs can take advantage of such information

FFCA: a feasibility-based method for flux coupling analysis of metabolic networks

<p>Abstract</p> <p>Background</p> <p>Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature.</p> <p>Results</p> <p>We introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods.</p> <p>Conclusions</p> <p>We present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at <url>http://www.bioinformatics.org/ffca/</url>.</p

A tale of two symmetrical tails: Structural and functional characteristics of palindromes in proteins

<p>Abstract</p> <p>Background</p> <p>It has been previously shown that palindromic sequences are frequently observed in proteins. However, our knowledge about their evolutionary origin and their possible importance is incomplete.</p> <p>Results</p> <p>In this work, we tried to revisit this relatively neglected phenomenon. Several questions are addressed in this work. (1) It is known that there is a large chance of finding a palindrome in low complexity sequences (i.e. sequences with extreme amino acid usage bias). What is the role of sequence complexity in the evolution of palindromic sequences in proteins? (2) Do palindromes coincide with conserved protein sequences? If yes, what are the functions of these conserved segments? (3) In case of conserved palindromes, is it always the case that the whole conserved pattern is also symmetrical? (4) Do palindromic protein sequences form regular secondary structures? (5) Does sequence similarity of the two "sides" of a palindrome imply structural similarity? For the first question, we showed that the complexity of palindromic peptides is significantly lower than randomly generated palindromes. Therefore, one can say that palindromes occur frequently in low complexity protein segments, without necessarily having a defined function or forming a special structure. Nevertheless, this does not rule out the possibility of finding palindromes which play some roles in protein structure and function. In fact, we found several palindromes that overlap with conserved protein Blocks of different functions. However, in many cases we failed to find any symmetry in the conserved regions of corresponding Blocks. Furthermore, to answer the last two questions, the structural characteristics of palindromes were studied. It is shown that palindromes may have a great propensity to form α-helical structures. Finally, we demonstrated that the two sides of a palindrome generally do not show significant structural similarities.</p> <p>Conclusion</p> <p>We suggest that the puzzling abundance of palindromic sequences in proteins is mainly due to their frequent concurrence with low-complexity protein regions, rather than a global role in the protein function. In addition, palindromic sequences show a relatively high tendency to form helices, which might play an important role in the evolution of proteins that contain palindromes. Moreover, reverse similarity in peptides does not necessarily imply significant structural similarity. This observation rules out the importance of palindromes for forming symmetrical structures. Although palindromes frequently overlap with conserved Blocks, we suggest that palindromes overlap with Blocks only by coincidence, rather than being involved with a certain structural fold or protein domain.</p

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

Background: Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient’s adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. Methods: We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making. Results: Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%. Conclusions: The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk

Application of bacteriophage cocktails for reducing the bacterial load of nosocomial pathogens in hospital wastewater

Background and Objectives: In the third world and developing countries, hospital sewage is mixed with municipal wastewater. The treated effluent contains dangerous bacteria released into the environment and used in the irrigation of agricultural products, and eventually these bacteria may endanger the human health through foods. Antibiotic-resistant bacteria are mostly found in hospital wastewater. In water and wastewater treatment plants, large amounts of toxic and polluting substances are removed and destroyed, but this process does not eliminate bacteria. Materials and Methods: Wastewater samples from 22 hospitals in Iran were collected and in the meantime specific phages (against drug-resistant pathogenic bacteria) extracted using the bilayer agar technique. Phage amplification was performed by employing a fermenter after phage identification. Amplified phages were added to the primary sedimentation pond using New-Brunwick biofermenter BioFlo/Celligen®115 and the bacterial count was evaluated for the desired bacteria. Results: Our phage cocktail was able to reduce 99.8%, 99.4%, 99.5%, 99.8%, 99.7%, 99.8%, 99.6% and 99.9% of E. coli, E. faecium, E. faecalis, K. pneumoniae, A. baumannii, P. aeruginosa, S. maltophilia and S. aureus counts respectively. Conclusion: The application of phage cocktails can remarkably help improve personal hygiene, the environment, and the optimization of surface wate

Phenotype and genetic determination of resistance to common disinfectants among bioflm-producing and non-producing Pseudomonas aeruginosa strains from clinical specimens in Iran

Background: Pseudomonas aeruginosa is a common pathogen in Hospitalized patients, and its various resistance mechanisms contribute to patient morbidity and mortality. The main aims of the present study were to assess the susceptibility of bioflm-producing and non-producing P. aeruginosa isolates to the fve commonly used Hospital disinfectants, to evaluate the synergistic efect of selected disinfectants and Ethylene-diamine-tetra acetic acid (EDTA), and the efect of exposure to sub-inhibitory concentrations of Sodium hypochlorite on antimicrobial susceptibility test. Results: The results showed that sodium hypochlorite 5% and Ethanol 70% were the most and least efective disinfectants against P. aeruginosa, respectively. The addition of EDTA signifcantly increased the efectiveness of the selected disinfectants. The changes in the antibiotic-resistance profles after exposure to sub-inhibitory concentrations of disinfectants were observed for diferent classes of antibiotics (Carbapenems, Aminoglycosides, Cephalosporins, Fluoroquinolones). As well as near the all isolates harbored efux pump genes and 117 (97.5%) of isolates produced bioflm. Conclusion: In the current study, the mixture of disinfectant and EDTA were the most suitable selection to disinfect Hospital surfaces and instruments. Also, it was clear that exposure to sub-inhibitory concentrations of Sodium hypochlorite results in resistance to some antibiotics in P. aeruginosa species. Strong and intermediate bioflm formers belonged to MDR/XDR strains. Future studies should include more complex microbial communities residing in the Hospitals, and more disinfectants use in Hospitals. Keywords: Nosocomial infection, Disinfectant-resistance, Bioflm, Hospital disinfectants, Pseudomonas aeruginosa, Clinical isolate