Using Fluorescent Reporters in Conjunction with Cytometry and Statistics to Assess Nuclear Accumulation of Ribosomal Proteins

Fluorescently labeled ribosomal proteins can be used to detect and monitor the intracellular localization of these proteins. Both Rps2, a subunit of the 40S ribosome, and Rpl25, a subunit of the 60S ribosome, have been fused to the coding sequence of GFP at their C-termini and the fusions have been used to monitor their localization within cells using fluorescent microscopy. Normally these proteins are efficiently incorporated into ribosomes and exported into the cytoplasm where they exhibit a low uniform fluorescence. However, these Rps2- and Rpl25-GFP proteins accumulate in the nucleus of mutants defective in ribosome biogenesis or export. Here, we describe a single-cell quantitative method to assess the nuclear accumulation of ribosomal proteins using cytometry and biostatistics. This assay was developed for use with GFP reporters for ribosome biogenesis in budding yeast but could be adapted for use with any GFP reporter that accumulates into the nucleus under adverse conditions.

Evaluation of the impact of genetic linkage in forensic identity and relationship testing for expanded DNA marker sets

Advances in massively parallel sequencing technology have enabled the combination of a much-expanded number of DNA markers (notably STRs and SNPs in one or combined multiplexes), with the aim of increasing the weight of evidence in forensic casework. However, when data from multiple loci on the same chromosome are used, genetic linkage can affect the final likelihood calculation. In order to study the effect of linkage for different sets of markers we developed the biostatistical tool ILIR, (Impact of Linkage on forensic markers for Identity and Relationship tests). The ILIR tool can be used to study the overall impact of genetic linkage for an arbitrary set of markers used in forensic testing. Application of ILIR can be useful during marker selection and design of new marker panels, as well as being highly relevant for existing marker sets as a way to properly evaluate the effects of linkage on a case-by-case basis. ILIR, implemented via the open source platform R, includes variation and genomic position reference data for over 40 STRs and 140 SNPs, combined with the ability to include additional forensic markers of interest. The use of the software is demonstrated with examples from several different established marker sets (such as the expanded CODIS core loci) including a review of the interpretation of linked genetic data.

P values: from suggestion to superstition

A threshold probability value of 'p≤0.05' is commonly used in clinical investigations to indicate statistical significance. To allow clinicians to better understand evidence generated by research studies, this review defines the p value, summarizes the historical origins of the p value approach to hypothesis testing, describes various applications of p≤0.05 in the context of clinical research and discusses the emergence of p≤5×10(-8) and other values as thresholds for genomic statistical analyses. Corresponding issues include a conceptual approach of evaluating whether data do not conform to a null hypothesis (ie, no exposure-outcome association). Importantly, and in the historical context of when p≤0.05 was first proposed, the 1-in-20 chance of a false-positive inference (ie, falsely concluding the existence of an exposure-outcome association) was offered only as a suggestion. In current usage, however, p≤0.05 is often misunderstood as a rigid threshold, sometimes with a misguided 'win' (p≤0.05) or 'lose' (p>0.05) approach. Also, in contemporary genomic studies, a threshold of p≤10(-8) has been endorsed as a boundary for statistical significance when analyzing numerous genetic comparisons for each participant. A value of p≤0.05, or other thresholds, should not be employed reflexively to determine whether a clinical research investigation is trustworthy from a scientific perspective. Rather, and in parallel with conceptual issues of validity and generalizability, quantitative results should be interpreted using a combined assessment of strength of association, p values, CIs, and sample size.

Insights in Hypothesis Testing and Making Decisions in Biomedical Research

It is a fact that p values are commonly used for inference in biomedical and other social fields of research. Unfortunately, the role of p value is very often misused and misinterpreted; that is why it has been recommended the use of resampling methods, like the bootstrap method, to calculate the confidence interval, which provides more robust results for inference than does p value. In this review a discussion is made about the use of p values through hypothesis testing and its alternatives using resampling methods to develop confidence intervals of the tested statistic or effect measure.

RBioplot: an easy-to-use R pipeline for automated statistical analysis and data visualization in molecular biology and biochemistry

Background

Statistical analysis and data visualization are two crucial aspects in molecular biology and biology. For analyses that compare one dependent variable between standard (e.g., control) and one or multiple independent variables, a comprehensive yet highly streamlined solution is valuable. The computer programming language R is a popular platform for researchers to develop tools that are tailored specifically for their research needs. Here we present an R package RBioplot that takes raw input data for automated statistical analysis and plotting, highly compatible with various molecular biology and biochemistry lab techniques, such as, but not limited to, western blotting, PCR, and enzyme activity assays.

Method

The package is built based on workflows operating on a simple raw data layout, with minimum user input or data manipulation required. The package is distributed through GitHub, which can be easily installed through one single-line R command. A detailed installation guide is available at http://kenstoreylab.com/?page_id=2448. Users can also download demo datasets from the same website.

Results and Discussion

By integrating selected functions from existing statistical and data visualization packages with extensive customization, RBioplot features both statistical analysis and data visualization functionalities. Key properties of RBioplot include: -

Fully automated and comprehensive statistical analysis, including normality test, equal variance test, Student’s t-test and ANOVA (with post-hoc tests);

-

Fully automated histogram, heatmap and joint-point curve plotting modules;

-

Detailed output files for statistical analysis, data manipulation and high quality graphs;

-

Axis range finding and user customizable tick settings;

-

High user-customizability.

P value interpretations and considerations

Application and interpretation of statistical evaluation of relationships is a necessary element in biomedical research. Statistical analyses rely on P value to demonstrate relationships. The traditional level of significance, P<0.05, can be negatively impacted by small sample size, bias, and random error, and has evolved to include interpretation of statistical trends, correction factors for multiple analyses, and acceptance of statistical significance for P>0.05 for complex relationships such as effect modification.

[Coding Causes of Death with IRIS Software. Impact in Navarre Mortality Statistic]

BACKGROUND

There are few studies that analyze changes in mortality statistics derived from the use of IRIS software, an automatic system for coding multiple causes of death and for the selection of the underlying cause of death, compared to manual coding. This study evaluated the impact of the use of IRIS in the Navarre mortality statistic.

METHODS

We proceeded to double coding 5,060 death certificates corresponding to residents in Navarra in 2014. We calculated coincidence between the two encodings for ICD10 chapters and for the list of causes of the Spanish National Statistics Institute (INE-102) and we estimated the change on mortality rates.

RESULTS

IRIS automatically coded 90% of death certificates. The coincidence to 4 characters and in the same chapter of the CIE10 was 79.1% and 92.0%, respectively. Furthermore, coincidence with the short INE-102 list was 88.3%. Higher matches were found in death certificate of people under 65 years. In comparison with manual coding there was an increase in deaths from endocrine diseases (31%), mental disorders (19%) and disease of nervous system (9%), while a decrease of genitourinary system diseases was observed (21%).

CONCLUSIONS

The coincidence at level of ICD10 chapters coding by IRIS in comparison to manual coding was 9 out of 10 deaths, similar to what is observed in other studies. The implementation of IRIS has led to increased of endocrine diseases, especially diabetes and hyperlipidaemia, and mental disorders, especially dementias.

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.

Common pitfalls in statistical analysis: The perils of multiple testing

Multiple testing refers to situations where a dataset is subjected to statistical testing multiple times - either at multiple time-points or through multiple subgroups or for multiple end-points. This amplifies the probability of a false-positive finding. In this article, we look at the consequences of multiple testing and explore various methods to deal with this issue.

Significant results: statistical or clinical?

The null hypothesis significance test method is popular in biological and medical research. Many researchers have used this method for their research without exact knowledge, though it has both merits and shortcomings. Readers will know its shortcomings, as well as several complementary or alternative methods, as such the estimated effect size and the confidence interval.

Research design and statistical methods in Pakistan Journal of Medical Sciences (PJMS)

OBJECTIVE

This article compares the study design and statistical methods used in 2005, 2010 and 2015 of Pakistan Journal of Medical Sciences (PJMS).

METHODS

Only original articles of PJMS were considered for the analysis. The articles were carefully reviewed for statistical methods and designs, and then recorded accordingly. The frequency of each statistical method and research design was estimated and compared with previous years.

RESULTS

A total of 429 articles were evaluated (n=74 in 2005, n=179 in 2010, n=176 in 2015) in which 171 (40%) were cross-sectional and 116 (27%) were prospective study designs. A verity of statistical methods were found in the analysis. The most frequent methods include: descriptive statistics (n=315, 73.4%), chi-square/Fisher's exact tests (n=205, 47.8%) and student t-test (n=186, 43.4%). There was a significant increase in the use of statistical methods over time period: t-test, chi-square/Fisher's exact test, logistic regression, epidemiological statistics, and non-parametric tests.

CONCLUSION

This study shows that a diverse variety of statistical methods have been used in the research articles of PJMS and frequency improved from 2005 to 2015. However, descriptive statistics was the most frequent method of statistical analysis in the published articles while cross-sectional study design was common study design.

Insight into the Conformational Variations in SoxYZ Protein Complex from Two Different Members of the β-Proteobacterial Family Involved in Sulfur Oxidation

Sulfur anions serve as the important environmental pollutants. Microbes use hydrogen sulfide in different redox reactions and thus make the environment pollution-free. The sulfur redox processes are performed by a gene cluster called the sox operon, possessed by a diverse set of microorganisms. However, most of the previous studies were confined to α-proteobacteria. In this work, we tried to elucidate the mechanistic details of sulfur oxidation in β-proteobacteria. We compared the molecular mechanism of sulfur oxidation process using Dechloromonas aromatica and Thiobacillus denitrificans. Dechloromonas aromatica possesses the entire sox operon, whereas T. denitrificans lacks SoxCD. In both the organisms, SoxYZ complex formation is essential for thiosulfate oxidation. This SoxYZ protein complex interacts with SoxCD and SoxAX, respectively, for recycling the thiosulfate-bound SoxY protein. For this purpose, individual proteins were modeled via manifold modeling techniques. Protein-protein docking studies were executed to generate duo- and quadro-protein complexes. Different stability parameters such as free energy of folding, solvent accessibility area (for final complexes), and electrostatic surface potential (for SoxYZ complexes) were calculated and analyzed. Fifteen strengthening ionic interactions were accomplished in the SoxYZAX complex, whereas eight such interactions were observed in SoxYZCD complex. From the result, SoxYZAX complex was found to be more stable and interactive one. This study is the first of its kind that analyzes the comparative aspects of the binding interactions of the proteins involved in redox reactions of sulfur anions. This study may, therefore, be helpful in tailoring the microorganisms to function in a better way to remove the environmental sulfur pollutants.

Predicting the occurrence of major adverse cardiac events within 30 days of a vascular surgery: an empirical comparison of the minimum p value method and ROC curve approach using individual patient data meta-analysis

We aimed to compare the minimum p value method and the area under the receiver operating characteristics (ROC) curve approach to categorize continuous biomarkers for the prediction of postoperative 30-day major adverse cardiac events in noncardiac vascular surgery patients. Individual-patient data from six cohorts reporting B-type natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NTproBNP) were obtained. These biomarkers were dichotomized using the minimum p value method and compared with previously reported ROC curve-derived thresholds using logistic regression analysis. A final prediction model was developed, internally validated, and assessed for its sensitivity to clustering effects. Finally, a preoperative risk score system was proposed. Thresholds identified by the minimum p value method and ROC curve approach were 115.57 pg/ml (p < 0.001) and 116 pg/ml for BNP, and 241.7 pg/ml (p = 0.001) and 277.5 pg/ml for NTproBNP, respectively. The minimum p value thresholds were slightly stronger predictors based on our logistic regression analysis. The final model included a composite predictor of the minimum p value method’s BNP and NTproBNP thresholds [odds ratio (OR) = 8.5, p < 0.001], surgery type (OR = 2.5, p = 0.002), and diabetes (OR = 2.1, p = 0.015). Preoperative risks using the scoring system ranged from 2 to 49 %. The minimum p value method and ROC curve approach identify similar optimal thresholds. We propose to replace the revised cardiac risk index with our risk score system for individual-specific preoperative risk stratification after noncardiac nonvascular surgery.

Common pitfalls in statistical analysis: Absolute risk reduction, relative risk reduction, and number needed to treat

In the previous article in this series on common pitfalls in statistical analysis, we looked at the difference between risk and odds. Risk, which refers to the probability of occurrence of an event or outcome, can be defined in absolute or relative terms. Understanding what these measures represent is essential for the accurate interpretation of study results.

Circadian mRNA expression: insights from modeling and transcriptomics

Circadian clocks synchronize organisms to the 24 h rhythms of the environment. These clocks persist under constant conditions, have their origin at the cellular level, and produce an output of rhythmic mRNA expression affecting thousands of transcripts in many mammalian cell types. Here, we review the charting of circadian output rhythms in mRNA expression, focusing on mammals. We emphasize the challenges in statistics, interpretation, and quantitative descriptions that such investigations have faced and continue to face, and outline remaining outstanding questions.

Common pitfalls in statistical analysis: The use of correlation techniques

Correlation is a statistical technique which shows whether and how strongly two continuous variables are related. In this article, which is the eighth part in a series on ‘Common pitfalls in Statistical Analysis’, we look at the interpretation of the correlation coefficient and examine various situations in which the use of technique of correlation may be inappropriate.

Engaging Future Clinical Oncology Researchers: An Initiative to Integrate Teaching of Biostatistics and Research Methodology into Specialty Training

AIMS

To evaluate the learner's perspectives on a novel workshop programme designed to improve skills in biostatistics, research methodology and critical appraisal in oncology.

MATERIALS AND METHODS

Trainees were surveyed anonymously at the completion of each annual workshop from 2012 to 2015. In total, 103 trainees in years 2-4 of training in radiation oncology responded, giving a 94% survey response rate. A 1 day workshop, designed by biostatisticians and radiation oncologist facilitators, is the central component of a programme teaching skills in biostatistics, research methods and critical appraisal. This links short didactic lectures about statistical concepts to interactive trainee discussions around discipline-related publications.

RESULTS

The workshop was run in conjunction with the major radiation oncology clinical trials group meeting with alternating programmes (A and B). Most of the participants (44-47/47 for A and 48-55/56 for B), reported that their understanding of one or more individual topics improved as a result of teaching. Refinement of the workshop over time led to a more favourable perception of the 'optimal' balance between didactic/interactive teaching: nine of 27 (33%) 'optimal' responses seen in 2013 compared with 23 of 29 (79%) in 2015 (P < 0.001). Commonly reported themes were: clinician facilitators and access to biostatisticians helped contextualise learning and small group, structured discussions provided an environment conducive to learning.

CONCLUSIONS

Overall, radiation oncology trainees reported positive perceptions of the educational value of this programme, with feedback identifying areas where this resource might be improved. This model could readily be adapted to suit other medical disciplines and/or other training environments, using specialty-specific research to illuminate key statistical concepts.

Common pitfalls in statistical analysis: Odds versus risk

In biomedical research, we are often interested in quantifying the relationship between an exposure and an outcome. “Odds” and “Risk” are the most common terms which are used as measures of association between variables. In this article, which is the fourth in the series of common pitfalls in statistical analysis, we explain the meaning of risk and odds and the difference between the two.

Nursing students' attitudes toward statistics: Effect of a biostatistics course and association with examination performance

BACKGROUND

Although statistical knowledge and skills are necessary for promoting evidence-based practice, health sciences students have expressed anxiety about statistics courses, which may hinder their learning of statistical concepts.

OBJECTIVES

To evaluate the effects of a biostatistics course on nursing students' attitudes toward statistics and to explore the association between these attitudes and their performance in the course examination.

DESIGN

One-group quasi-experimental pre-test/post-test design.

SETTING AND PARTICIPANTS

Undergraduate nursing students of the fifth or higher semester of studies, who attended a biostatistics course.

METHODS

Participants were asked to complete the pre-test and post-test forms of The Survey of Attitudes Toward Statistics (SATS)-36 scale at the beginning and end of the course respectively. Pre-test and post-test scale scores were compared, while correlations between post-test scores and participants' examination performance were estimated.

RESULTS

Among 156 participants, post-test scores of the overall SATS-36 scale and of the Affect, Cognitive Competence, Interest and Effort components were significantly higher than pre-test ones, indicating that the course was followed by more positive attitudes toward statistics. Among 104 students who participated in the examination, higher post-test scores of the overall SATS-36 scale and of the Affect, Difficulty, Interest and Effort components were significantly but weakly correlated with higher examination performance.

CONCLUSIONS

Students' attitudes toward statistics can be improved through appropriate biostatistics courses, while positive attitudes contribute to higher course achievements and possibly to improved statistical skills in later professional life.

T test as a parametric statistic

In statistic tests, the probability distribution of the statistics is important. When samples are drawn from population N (µ, σ(2)) with a sample size of n, the distribution of the sample mean X̄ should be a normal distribution N (µ, σ(2)/n). Under the null hypothesis µ = µ0, the distribution of statistics [Formula: see text] should be standardized as a normal distribution. When the variance of the population is not known, replacement with the sample variance s (2) is possible. In this case, the statistics [Formula: see text] follows a t distribution (n-1 degrees of freedom). An independent-group t test can be carried out for a comparison of means between two independent groups, with a paired t test for paired data. As the t test is a parametric test, samples should meet certain preconditions, such as normality, equal variances and independence.

Molecular modeling, mutational analysis and conformational switching in IL27: An in silico structural insight towards AIDS research

The advancement in proteomics and bioinformatics provokes to discern the molecular-level probe for HIV inhibitor; human interleukin-27 (IL27). Documentation documents that tyrosine residues in IL27 play a pivotal role for interacting with HIV, causing apoptosis of the HIV+ cells. Primarily, 3D structure of human wild-type (WT) IL27 was built through manifold molecular modeling techniques after the satisfaction of stereo-chemical properties. Its essential tyrosine residues were identified. Two mutant models for IL27 were prepared following the similar protocol by first substituting the tyrosine residues with glycine (MT_G) and then with alanine (MT_A) in the WT protein. Molecular dynamics (MD) simulation was performed to obtain a stable conformation. Conformational alterations in WT, MT_G and MT_A (before and after MD simulation) disclosed that MT_A was the steadiest one with the best secondary structure conformation supported by statistical significances. Though huge RMSD variations were observed on superimposing the MT structures on WT individually, the MTs were examined to share similar SCOP/CATH fold with TM-score=0.8, indicating that they retained their functionality even after mutation. Electrostatic surface potential again unveiled MT_A to be the most stable one. MT_A was thereby revealed to be the potent peptide inhibitor for HIV. This probe presents a pathway to investigate and compare the bio-molecular interaction of WT IL27 and MT_A IL27 (strongest model) with HIV in the future. This is the first report regarding the structural biology of IL27 accompanied by alteration at its genetic level and delving into the unknown residue-level and functional biochemistry for bringing about an annihilation towards AIDS.

The panacea statistical toolbox of a biomedical peer reviewer

The main role of a peer reviewer is to make judgments on the research articles by asking a number of questions to evaluate the quality of the research article. Statistics is a major part of any biomedical research article, and most reviewers gain their experiences in manuscript reviewing by undertaking it but not through an educational process. Therefore, reviewers of the biomedical journals normally do not have enough knowledge and skills to evaluate the validity of statistical methods used in biomedical research articles submitted for consideration. Hence, inappropriate statistical analysis in medical journals can lead to misleading conclusions and incorrect results. In this paper, the most common basic statistical guidelines are described that might be a road map to the biomedical reviewers. It is not meant for statisticians or medical editors who have special interest and expertise in statistical analysis.

High-throughput concentration-response analysis for omics datasets

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.

Do Subjective Measures Improve the Ability to Identify Limited Health Literacy in a Clinical Setting?

BACKGROUND

Existing health literacy assessments developed for research purposes have constraints that limit their utility for clinical practice, including time requirements and administration protocols. The Brief Health Literacy Screen (BHLS) consists of 3 self-administered Single-Item Literacy Screener (SILS) questions and obviates these clinical barriers. We assessed whether the addition of SILS items or the BHLS to patient demographics readily available in ambulatory clinical settings reaching underserved patients improves the ability to identify limited health literacy.

METHODS

We analyzed data from 2 cross-sectional convenience samples of patients from an urban academic emergency department (n = 425) and a primary care clinic (n = 486) in St. Louis, Missouri. Across samples, health literacy was assessed using the Rapid Estimate of Adult Literacy in Medicine-Revised (REALM-R), Newest Vital Sign (NVS), and the BHLS. Our analytic sample consisted of 911 adult patients, who were primarily female (62%), black (66%), and had at least a high school education (82%); 456 were randomly assigned to the estimation sample and 455 to the validation sample.

RESULTS

The analysis showed that the best REALM-R estimation model contained age, sex, education, race, and 1 SILS item (difficulty understanding written information). In validation analysis this model had a sensitivity of 62%, specificity of 81%, a positive likelihood ratio (LR(+)) of 3.26, and a negative likelihood ratio (LR(-)) of 0.47; there was a 28% misclassification rate. The best NVS estimation model contained the BHLS, age, sex, education and race; this model had a sensitivity of 77%, specificity of 72%, LR(+) of 2.75, LR(-) of 0.32, and a misclassification rate of 25%.

CONCLUSIONS

Findings suggest that the BHLS and SILS items improve the ability to identify patients with limited health literacy compared with demographic predictors alone. However, despite being easier to administer in clinical settings, subjective estimates of health literacy have misclassification rates >20% and do not replace objective measures; universal precautions should be used with all patients.

Common pitfalls in statistical analysis: Clinical versus statistical significance

In clinical research, study results, which are statistically significant are often interpreted as being clinically important. While statistical significance indicates the reliability of the study results, clinical significance reflects its impact on clinical practice. The third article in this series exploring pitfalls in statistical analysis clarifies the importance of differentiating between statistical significance and clinical significance.