Dinucleotide controlled null models for comparative RNA gene prediction

Background  

Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babaket al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available.     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye

Background

Genomic prediction is becoming a daily tool for plant breeders. It makes use of genotypic information to make predictions used for selection decisions. The accuracy of the predictions depends on the number of genotypes used in the calibration; hence, there is a need of combining data across years. A proper phenotypic analysis is a crucial prerequisite for accurate calibration of genomic prediction procedures. We compared stage-wise approaches to analyse a real dataset of a multi-environment trial (MET) in rye, which was connected between years only through one check, and used different spatial models to obtain better estimates, and thus, improved predictive abilities for genomic prediction. The aims of this study were to assess the advantage of using spatial models for the predictive abilities of genomic prediction, to identify suitable procedures to analyse a MET weakly connected across years using different stage-wise approaches, and to explore genomic prediction as a tool for selection of models for phenotypic data analysis.

Results

Using complex spatial models did not significantly improve the predictive ability of genomic prediction, but using row and column effects yielded the highest predictive abilities of all models. In the case of MET poorly connected between years, analysing each year separately and fitting year as a fixed effect in the genomic prediction stage yielded the most realistic predictive abilities. Predictive abilities can also be used to select models for phenotypic data analysis. The trend of the predictive abilities was not the same as the traditionally used Akaike information criterion, but favoured in the end the same models.

Conclusions

Making predictions using weakly linked datasets is of utmost interest for plant breeders. We provide an example with suggestions on how to handle such cases. Rather than relying on checks we show how to use year means across all entries for integrating data across years. It is further shown that fitting of row and column effects captures most of the heterogeneity in the field trials analysed.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-646) contains supplementary material, which is available to authorized users.     

A comparative study of S/MAR prediction tools

Background  

S/MARs are regions of the DNA that are attached to the nuclear matrix. These regions are known to affect substantially the expression of genes. The computer prediction of S/MARs is a highly significant task which could contribute to our understanding of chromatin organisation in eukaryotic cells, the number and distribution of boundary elements, and the understanding of gene regulation in eukaryotic cells. However, while a number of S/MAR predictors have been proposed, their accuracy has so far not come under scrutiny.     

A comparative study on filtering protein secondary structure prediction

Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.     

Methods for prediction of peptide binding to MHC molecules: a comparative study

BACKGROUND: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. MATERIALS AND METHODS: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. RESULTS: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. CONCLUSION: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.     

Survival of anaerobic fungus Caecomyces sp. in various preservation methods: a comparative study

The present investigation was designed to observe the survival of the anaerobic fungus Caecomyces sp. in various routine preservation methods. Among all the treatments, cryopreservation of fungi at ?70°C with glycerol was found to be most effective for long-term maintenance (more than 90 days) of rumen fungi, followed by dimethyl sulfoxide (DMSO) and ethylene glycol (up to 60 days). In contrast, at ?196°C, DMSO showed maximum survival (more than 90 days), followed by glycerol (up to 90 days) and ethylene glycol (up to 30 days). At 39°C, maximum survival (up to 30 days) was observed with soft agar and wheat straw; at refrigeration temperature, preservation with Orpin's media containing straw showed maximum survival (up to 30 days).     

The nitrilase family of CN hydrolysing enzymes - a comparative study

The enzymes nitrilase, cyanide dihydratase and cyanide hydratase are a group of closely related proteins. The proteins show significant similarities at the amino acid and protein structure level but the enzymes show many differences in catalytic capability. Nitrilases, while catalysing the hydration of nitrile to the corresponding acid, vary widely in substrate specificity. Cyanide dihydratase and cyanide hydratase use HCN as the only efficient substrate but produce acid and amide products, respectively. The similarities of all these enzymes at the amino acid level but the functional differences between them provide a rich source of material for the study of structure/function relationships in this biotechnologically important group of enzymes. This review provides an overview of current understanding of the genetics and biochemistry of this interesting group of enzymes.     

Proteomic analysis and protein structure prediction of Shigella phage Sfk20 based on a comparative study using structure prediction approaches

Bacteriophages are the natural predators of bacteria and are available abundantly everywhere in nature. Lytic phages can specifically infect their bacterial host (through attachment to the receptor) and use their host replication machinery to replicate rapidly, a feature that enables them to kill a disease-causing bacteria. Hence, phage attachment to the host bacteria is the first important step of the infection process. It is reported in this study that the receptor could be an LPS which is responsible for the attachment of the Sfk20 phage to its host (Shigella flexneri 2a). Phage Sfk20 bacteriolytic activity was examined for preliminary optimization of phage titer. The phage Sfk20 viability at different saline conditions was conducted. The LC–MS/MS technique used here for detecting and identifying 40 Sfk20 phage proteins helped us to get an initial understanding of the structural landscape of phage Sfk20. From the identified proteins, six structurally significant proteins were selected for structure prediction using two neural network systems: AlphaFold2 and ESMFold, and one homology modeling software: Phyre2. Later the performance of these modeling systems was compared using various metrics. We conclude from the available and generated information that AlphaFold2 and Phyre2 perform better than ESMFold for predicting Sfk20 phage protein structures.     

Radiosensitivity of murine and human melanoma cells: a comparative study with different models

The in vitro radiosensitivity of one murine melanoma cell line (Cloudman S91 CCL 53.1) and three human melanoma cell strains (C8146C, C8161, and R83-4) were studied. Cells were irradiated by single dose X-rays and plated either in agar or on plastic. The survival curves were fitted by the single-hit multitarget, two-hit multitarget, and quadratic models. Multiple comparisons of the residual sum of squares suggested that the two-hit model was clearly inferior to the single-hit and quadratic models. No statistically significant difference was suggested for either the single-hit or quadratic models. Furthermore, on examination of the differences in correlations between the observed and predicted values, the residual plots (observed minus predicted over dose) failed to suggest a clear advantage of either the single-hit multitarget or the quadratic models. Either model could be recommended for analysis of in vitro radiation data.     

A comparative study of two community-based conservation models in Nepal

Conservation areas (CAs) and community forests (CFs) are generally considered to be two successful community-based conservation (CBC) models in Nepal. Nepal’s two CAs are administered by a nongovernmental organization (NGO), and all of its CFs are administered by a government agency (GA). The goal of this research is to compare and contrast these two models using quantitative and qualitative data collected through field research in the Annapurna Conservation Area in the summer of 2007 and adjacent CFs in the fall of 2008. The Conservation Area Management Committee (CAMC) and the Community Forest User Committee (CFUC) are the functional decision-making entities at the local level in CAs and CFs, respectively. We conducted one-on-one semi-structured interviews with 66 executive members of 10 CAMCs and 67 members of 9 CFUCs. While both models appear to have performed well overall, the CA under the direction of the NGO appears to have fared better with regard to (i) developing trust of local constituencies, (ii) garnering favorable attitudes among villagers, (iii) building capacity of executive members, and (iv) improving standards of living. We suggest that the particular accountabilities associated with NGOs may situate them in a better position to cultivate local governance than state entities on their own.     

Survival time prediction in marine environments

One of the most challenging questions regarding aquatic search and rescue or recovery operations is the physiological status of the victims. A Probability of Survival Decision Aid (PSDA) was developed to predict survival time for hypothermia and dehydration during prolonged exposure in marine environments for a wide range of environmental conditions. PSDA calculates the survival time of a victim in the water or floating in an emergency craft as a function of environmental conditions, clothing and human anthropometric parameters. PSDA consists of a human thermoregulatory model and a Graphic User Interface, which manages inputs, run the model and display outputs. PSDA was validated using historical survival data and reports of accidental water immersions. For eight immersion victims of known height and weight, the victims’ predicted survival time was either in close agreement or greater than the observed survival time. Additional physiological data from case histories and/or controlled studies are needed to validate the PSDA and to expand its application to other hazardous situations. PSDA is intended to assist the search and rescue personnel in operation optimal planning.     

Calcium paradox from cyclostome to man: a comparative study

In seven poikilotherm species, i.e. cyclostome, Myxine glutinosa; teleosts, Gadus morrhua; Platichtys flesus; amphibia, R. pipiens; R. temporaria; Xenopus laevis; reptiles, Vipera berus. Ca paradox could not be obtained in ventricular strips at 8-12 degrees C. In similar preparations from human atria at 30 degrees C and 37 degrees, Ca paradox was obtained similarly as in other homoiotherms. In homoiotherms, lowered temperature (below 30 degrees C) has a protective effect. Viper myocardium did not display Ca paradox at 12, 22 or 32 degrees C, whereas human myocardium displayed Ca paradox even at 30 C. It is postulated that the absence of Ca paradox in poikilotherms is not due to the low temperature. In hagfish (Myxine glutinosa) the absence of Ca paradox seems to be due to the specific conditions concerning Ca exchange in the myocardial cell (large glycocalyx).