Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources

Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms.

AVAILABILITY AND IMPLEMENTATION

Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.

Antioxidant Activity of Leaf Extracts from Stevia rebaudiana Bertoni Exerts Attenuating Effect on Diseased Experimental Rats: A Systematic Review and Meta-Analysis

Stevia (Stevia rebaudiana Bertoni) is an aromatic plant known for its high sweetening power ascribed to its glycosides. Stevia also contains several bioactive compounds showing antioxidant, antiproliferative, antimicrobial, and anti-inflammatory activities. Since inflammation and oxidative stress play critical roles in the pathogenesis of many diseases, stevia emerges as a promising natural product that could support human health. In this study we set out to investigate the way stevia affects oxidative stress markers (e.g., SOD, CAT, GPx, GSH, MDA) in diseased rats administered stevia leaf extracts or glycosides. To this end, we performed an inclusive literature search, following PRISMA guidelines, and recruited multivariate meta-analysis and meta-regression to synthesize all available data on experimental animal models encountering (a) healthy, (b) diseased, and (c) stevia-treated diseased rats. From the 184 articles initially retrieved, 24 satisfied the eligibility criteria, containing 104 studies. Our results demonstrate that regardless of the assay employed, stevia leaf extracts restored all oxidative stress markers to a higher extent compared to pure glycosides. Meta-regression analysis revealed that results from SOD, CAT, GSH, and TAC assays are not statistically significantly different (p = 0.184) and can be combined in meta-analysis. Organic extracts from stevia leaves showed more robust antioxidant properties compared to aqueous or hydroalcoholic ones. The restoration of oxidative markers ranged from 65% to 85% and was exhibited in all tested tissues. Rats with diabetes mellitus were found to have the highest restorative response to stevia leaf extract administration. Our results suggest that stevia leaf extract can act protectively against various diseases through its antioxidant properties. However, which of each of the multitude of stevia compounds contribute to this effect, and to what extent, awaits further investigation.

In Silico Identification and Analysis of Proteins Containing the Phox Homology Phosphoinositide-Binding Domain in Kinetoplastea Protists: Evolutionary Conservation and Uniqueness of Phox-Homology-Domain-Containing Protein Architectures

Kinetoplastea are free living and parasitic protists with unique features among Eukaryota. Pathogenic Kinetoplastea parasites (i.e., Trypanosoma and Leishmania spp.) undergo several developmental transitions essential for survival in their hosts. These transitions require membrane and cytoskeleton reorganizations that involve phosphoinositides (PIs). Phospholipids like PIs are key regulators of vital functions in all eukaryotes including signal transduction, protein transport and sorting, membrane trafficking, and cytoskeleton and membrane remodeling. A large repertoire of PI-metabolizing enzymes and PI-binding proteins/effectors carrying distinct PI-binding modules like the PX (phox homology) module could play significant roles in the life and virulence of pathogenic Kinetoplastea. The aim of this study was to retrieve the entire spectrum of Kinetoplastea protein sequences containing the PX module (PX-proteins), predict their structures, and identify in them evolutionary conserved and unique traits. Using a large array of bioinformatics tools, protein IDs from two searches (based on PFam's pHMM for PX domain (PF00787)) were combined, aligned, and utilized for the construction of a new Kinetoplastea_PX pHMM. This three-step search retrieved 170 PX-protein sequences. Structural domain configuration analysis identified PX, Pkinase, Lipocalin_5, and Vps5/BAR3-WASP domains and clustered them into five distinct subfamilies. Phylogenetic tree and domain architecture analysis showed that some domain architectures exist in proteomes of all Kinetoplastea spp., while others are genus-specific. Finally, amino acid conservation logos of the Kinetoplastea spp. and Homo sapiens PX domains revealed high evolutionary conservation in residues forming the critical structural motifs for PtdIns3P recognition. This study highlights the PX-Pkinase domain architecture as unique within Trypanosoma spp. and forms the basis for a targeted functional analysis of Kinetoplastea PX-proteins as putative targets for a rational design of anti-parasitic drugs.

School Health Services and Health Education Curricula in Greece: Scoping Review and Policy Plan

The new generation's health and wellbeing is of paramount importance: it constitutes United Nations' priority, complies with Children's Rights and responds to the Sustainable Development Goals of the United Nations. In this perspective, school health and health education, as facets of the public health domain targeted at young people, deserve further attention after the unprecedented COVID-19 pandemic crisis in order to revise policies. The key objectives of this article are (a) to review the evidence generated over a span of two decades (2003-2023), identifying the main policy gaps by taking Greece as a case study, and (b) to provide a concrete and integrated policy plan. Following the qualitative research paradigm, a scoping review is used to identify policy gaps in school health services (SHS) and school health education curricula (SHEC). Data are extracted from four databases: Scopus, PubMed, Web of Science and Google Scholar, while the findings are categorized into the following themes following specific inclusion and exclusion criteria: school health services, school health education curricula, school nursing, all with reference to Greece. A corpus of 162 out 282 documents in English and Greek initially accumulated, is finally used. The 162 documents consisted of seven doctoral theses, four legislative texts, 27 conference proceedings, 117 publications in journals and seven syllabuses. Out of the 162 documents, only 17 correspond to the set of research questions. The findings suggest that school health services are not school-based but a function of the primary health care system, whereas health education retains a constantly changing position in school curricula, and several deficiencies in schoolteachers' training, coordination and leadership impede the implementation. Regarding the second objective of this article, a set of policy measures is provided in terms of a problem-solving perspective, towards the reform and integration of school health with health education.

Influenza and Pneumococcal Vaccination and the Risk of COVID-19: A Systematic Review and Meta-Analysis

A number of studies have investigated the potential on-specific effects of some routinely administered vaccines (e.g., influenza, pneumococcal) on COVID-19 related outcomes, with contrasting results. In order to elucidate this discrepancy, we conducted a systematic review and meta-analysis to assess the association between seasonal influenza vaccination and pneumococcal vaccination with SARS-CoV-2 infection and its clinical outcomes. PubMed and medRxiv databases were searched up to April 2022. A random effects model was used in the meta-analysis to pool odds ratio (OR) and adjusted estimates with 95% confidence intervals (CIs). Heterogeneity was quantitatively assessed using the Cochran's Q and the I2 index. Subgroup analysis, sensitivity analysis and assessment of publication bias were performed for all outcomes. In total, 38 observational studies were included in the meta-analysis and there was substantial heterogeneity. Influenza and pneumococcal vaccination were associated with lower risk of SARS-CoV-2 infection (OR: 0.80, 95% CI: 0.75-0.86 and OR: 0.70, 95% CI: 0.57-0.88, respectively). Regarding influenza vaccination, it seems that the majority of studies did not properly adjust for all potential confounders, so when the analysis was limited to studies that adjusted for age, gender, comorbidities and socioeconomic indices, the association diminished. This is not the case regarding pneumococcal vaccination, for which even after adjustment for such factors the association persisted. Regarding harder endpoints such as ICU admission and death, current data do not support the association. Possible explanations are discussed, including trained immunity, inadequate matching for socioeconomic indices and possible coinfection.

Immune-based treatment re-challenge in renal cell carcinoma: A systematic review and meta-analysis

Introduction

The use of immune checkpoint inhibitors (ICIs) as a front-line treatment for metastatic renal cell carcinoma (RCC) has significantly improved patient’ outcome. However, little is known about the efficacy or lack thereof of immunotherapy after prior use of anti-PD1/PD-L1 or/and anti-CTLA monoclonal antibodies.

Methods

Electronic databases, including PubMed, EMBASE, Medline, Web of Science, and Cochrane Library, were comprehensively searched from inception to July 2022. Objective response rates (ORR), progression-free survival (PFS), and ≥ grade 3 adverse events (AEs) were assessed in the meta-analysis, along with corresponding 95% confidence intervals (CIs) and publication bias.

Results

Ten studies which contained a total of 500 patients were included. The pooled ORR was 19% (95% CI: 10, 31), and PFS was 5.6 months (95% CI: 4.1, 7.8). There were ≥ grade 3 AEs noted in 25% of patients (95% CI: 14, 37).

Conclusion

This meta-analysis on different second-line ICI-containing therapies in ICI-pretreated mRCC patients supports a modest efficacy and tolerable toxicity.

Drug genetic associations with COVID-19 manifestations: a data mining and network biology approach

Available drugs have been used as an urgent attempt through clinical trials to minimize severe cases of hospitalizations with Coronavirus disease (COVID-19), however, there are limited data on common pharmacogenomics affecting concomitant medications response in patients with comorbidities. To identify the genomic determinants that influence COVID-19 susceptibility, we use a computational, statistical, and network biology approach to analyze relationships of ineffective concomitant medication with an adverse effect on patients. We statistically construct a pharmacogenetic/biomarker network with significant drug-gene interactions originating from gene-disease associations. Investigation of the predicted pharmacogenes encompassing the gene-disease-gene pharmacogenomics (PGx) network suggests that these genes could play a significant role in COVID-19 clinical manifestation due to their association with autoimmune, metabolic, neurological, cardiovascular, and degenerative disorders, some of which have been reported to be crucial comorbidities in a COVID-19 patient.

Identifying and Predicting Healthcare Waste Management Costs for an Optimal Sustainable Management System: Evidence from the Greek Public Sector

The healthcare sector is an ever-growing industry which produces a vast amount of waste each year, and it is crucial for healthcare systems to have an effective and sustainable medical waste management system in order to protect public health. Greek public hospitals in 2018 produced 9500 tons of hazardous healthcare wastes, and it is expected to reach 18,200 tons in 2025 and exceed 18,800 tons in 2030. In this paper, we investigated the factors that affect healthcare wastes. We obtained data from all Greek public hospitals and conducted a regression analysis, with the management cost of waste and the kilos of waste as the dependent variables, and a number of variables reflecting the characteristics of each hospital and its output as the independent variables. We applied and compared several models. Our study shows that healthcare wastes are affected by several individual-hospital characteristics, such as the number of beds, the type of the hospital, the services the hospital provides, the number of annual inpatients, the days of stay, the total number of surgeries, the existence of special units, and the total number of employees. Finally, our study presents two prediction models concerning the management costs and quantities of infectious waste for Greece's public hospitals and proposes specific actions to reduce healthcare wastes and the respective costs, as well as to implement and adopt certain tools, in terms of sustainability.

Genetic Analysis and Status of Brown Bear Sub-Populations in Three National Parks of Greece Functioning as Strongholds for the Species’ Conservation

In order to optimize the appropriate conservation actions for the brown bear (Ursus arctos L.) population in Greece, we estimated the census (Nc) and effective (Ne) population size as well as the genetic status of brown bear sub-populations in three National Parks (NP): Prespa (MBPNP), Pindos (PINDNP), and Rhodopi (RMNP). The Prespa and Pindos sub-populations are located in western Greece and the Rhodopi population is located in eastern Greece. We extracted DNA from 472 hair samples and amplified through PCR 10 microsatellite loci. In total, 257 of 472 samples (54.5%) were genotyped for 6–10 microsatellite loci. Genetic analysis revealed that the Ne was 35, 118, and 61 individuals in MBPNP, PINDNP, and RMNP, respectively, while high levels of inbreeding were found in Prespa and Rhodopi but not in Pindos. Moreover, analysis of genetic structure showed that the Pindos population is genetically distinct, whereas Prespa and Rhodopi show mutual overlaps. Finally, we found a notable gene flow from Prespa to Rhodopi (10.19%) and from Rhodopi to Prespa (14.96%). Therefore, targeted actions for the conservation of the bears that live in the abovementioned areas must be undertaken, in order to ensure the species’ viability and to preserve the corridors that allow connectivity between the bear sub-populations in Greece.

MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies

MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package.

Performance of Antigen Detection Tests for SARS-CoV-2: A Systematic Review and Meta-Analysis

Coronavirus disease 2019 (COVID-19) initiated global health care challenges such as the necessity for new diagnostic tests. Diagnosis by real-time PCR remains the gold-standard method, yet economical and technical issues prohibit its use in points of care (POC) or for repetitive tests in populations. A lot of effort has been exerted in developing, using, and validating antigen-based tests (ATs). Since individual studies focus on few methodological aspects of ATs, a comparison of different tests is needed. Herein, we perform a systematic review and meta-analysis of data from articles in PubMed, medRxiv and bioRxiv. The bivariate method for meta-analysis of diagnostic tests pooling sensitivities and specificities was used. Most of the AT types for SARS-CoV-2 were lateral flow immunoassays (LFIA), fluorescence immunoassays (FIA), and chemiluminescence enzyme immunoassays (CLEIA). We identified 235 articles containing data from 220,049 individuals. All ATs using nasopharyngeal samples show better performance than those with throat saliva (72% compared to 40%). Moreover, the rapid methods LFIA and FIA show about 10% lower sensitivity compared to the laboratory-based CLEIA method (72% compared to 82%). In addition, rapid ATs show higher sensitivity in symptomatic patients compared to asymptomatic patients, suggesting that viral load is a crucial parameter for ATs performed in POCs. Finally, all methods perform with very high specificity, reaching around 99%. LFIA tests, though with moderate sensitivity, appear as the most attractive method for use in POCs and for performing seroprevalence studies.

Hidden neural networks for transmembrane protein topology prediction

Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.

VICTOR: A visual analytics web application for comparing cluster sets

Clustering is the process of grouping different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate clustering results. However, different algorithms or parameterizations may produce quite dissimilar cluster sets. In this way, the user is often forced to manually filter and compare these results in order to decide which of them generate the ideal clusters. To automate this process, in this study, we present VICTOR, the first fully interactive and dependency-free visual analytics web application which allows the visual comparison of the results of various clustering algorithms. VICTOR can handle multiple cluster set results simultaneously and compare them using ten different metrics. Clustering results can be filtered and compared to each other with the use of data tables or interactive heatmaps, bar plots, correlation networks, sankey and circos plots. We demonstrate VICTOR's functionality using three examples. In the first case, we compare five different network clustering algorithms on a Yeast protein-protein interaction dataset whereas in the second example, we test four different parameters of the MCL clustering algorithm on the same dataset. Finally, as a third example, we compare four different meta-analyses with hierarchically clustered differentially expressed genes found to be involved in myocardial infarction. VICTOR is available at http://victor.pavlopouloslab.info or http://bib.fleming.gr:3838/VICTOR.

OMPdb: A Global Hub of Beta-Barrel Outer Membrane Proteins

OMPdb (www.ompdb.org) was introduced as a database for β-barrel outer membrane proteins from Gram-negative bacteria in 2011 and then included 69,354 entries classified into 85 families. The database has been updated continuously using a collection of characteristic profile Hidden Markov Models able to discriminate between the different families of prokaryotic transmembrane β-barrels. The number of families has increased ultimately to a total of 129 families in the current, second major version of OMPdb. New additions have been made in parallel with efforts to update existing families and add novel families. Here, we present the upgrade of OMPdb, which from now on aims to become a global repository for all transmembrane β-barrel proteins, both eukaryotic and bacterial.

Smoking prevalence among hospitalized COVID-19 patients and its association with disease severity and mortality: an expanded re-analysis of a recent publication

BACKGROUND

There is a lot of debate about the effects of smoking on COVID-19. A recent fixed-effects meta-analysis found smoking to be associated with disease severity among hospitalized patients, but other studies report an unusually low prevalence of smoking among hospitalized patients. The purpose of this study was to expand the analysis by calculating the prevalence odds ratio (POR) of smoking among hospitalized COVID-19 patients, while the association between smoking and disease severity and mortality was examined by random-effects meta-analyses considering the highly heterogeneous study populations.

METHODS

The same studies as examined in the previous meta-analysis were analyzed (N = 22, 20 studies from China and 2 from USA). The POR relative to the expected smoking prevalence was calculated using gender and age-adjusted population smoking rates. Random-effects meta-analyses were used for all other associations.

RESULTS

A total of 7162 patients were included, with 482 being smokers. The POR was 0.24 (95%CI 0.19-0.30). Unlike the original study, the association between smoking and disease severity was not statistically significant using random-effects meta-analysis (OR 1.40, 95%CI 0.98-1.98). In agreement with the original study, no statistically significant association was found between smoking and mortality (OR 1.86, 95%CI 0.88-3.94).

CONCLUSION

An unusually low prevalence of smoking, approximately 1/4th the expected prevalence, was observed among hospitalized COVID-19 patients. Any association between smoking and COVID-19 severity cannot be generalized but should refer to the seemingly low proportion of smokers who develop severe COVID-19 that requires hospitalization. Smokers should be advised to quit due to long-term health risks, but pharmaceutical nicotine or other nicotinic cholinergic agonists should be explored as potential therapeutic options, based on a recently presented hypothesis.

Staphylococcus aureus Carriage in Hidradenitis Suppurativa: Impact on Response to Adalimumab

BACKGROUND

Several patients with hidradenitis suppurativa (HS) present flare-ups during treatment with adalimumab (ADA), the cause of which is not clear. ADA is the only FDA-approved biologic for the therapy of moderate-to-severe HS. A previous study of our group has shown that Staphylococcus aureus stimulation of whole blood affects the production of human β-defensin 2 and modulates HS severity. It is, therefore, hypothesized, that carriage of S. aureus may drive HS flare-ups.

OBJECTIVE

To explore the association between carriage of S. aureus and loss of response to ADA.

PATIENTS AND METHODS

Among patients with moderate-to-severe HS without carriage of S. aureus at start of treatment with ADA, we investigated for carriage of S. aureus from the nares when flare-ups occurred. Flare-ups were pre-defined as at least 25% increase of inflammatory lesions (sum of inflammatory nodules and abscesses) from baseline. Samplings were also done after completion of 12 weeks of ADA treatment from all patients who did not present flare-ups. Clinical response to ADA was assessed by the HS Clinical Response score (HiSCR).

RESULTS

Thirty-nine patients were studied; 24 with Hurley II stage HS and 15 with Hurley III stage HS. Twenty-nine patients achieved HiSCR after 12 weeks of treatment without any flare-ups; 10 patients had flare-ups and failed HiSCR. Three (10.3%) and 5 (50%) patients, respectively, had nasal carriage of S. aureus (odds ratio 8.67; 95% CI 1.54-48.49; p = 0.014). Among 32 patients reaching follow-up week 48, 20 patients achieved HiSCR and 12 had flare-ups leading to ADA failure; 2 (10%) and 5 (41.7%) patients, respectively, had positive culture for S. aureus (odds ratio 6.42; 95% CI 1.00-41.20; p = 0.05).

CONCLUSION

Nasal carriage of S. aureus may be associated with loss of response to ADA. Findings need confirmation in larger series of patients.

Improved strategies to counter the COVID-19 pandemic: Lockdowns vs. primary and community healthcare

COVID-19 pandemic mitigation strategies are mainly based on social distancing measures and healthcare system reinforcement. However, many countries in Europe and elsewhere implemented strict, horizontal lockdowns because of extensive viral spread in the community which challenges the capacity of the healthcare systems. However, strict lockdowns have various untintended adverse social, economic and health effects, which have yet to be fully elucidated, and have not been considered in models examining the effects of various mitigation measures. Unlike commonly suggested, the dilemma is not about health vs wealth because the economic devastation of long-lasting lockdowns will definitely have adverse health effects in the population. Furthermore, they cannot provide a lasting solution in pandemic containment, potentially resulting in a vicious cycle of consecutive lockdowns with in-between breaks. Hospital preparedness has been the main strategy used by governments. However, a major characteristic of the COVID-19 pandemic is the rapid viral transmission in populations with no immunity. Thus, even the best hospital system could not cope with the demand. Primary, community and home care are the only viable strategies that could achieve the goal of pandemic mitigation. We present the case example of Greece, a country which followed a strategy focused on hospital preparedness but failed to reinforce primary and community care. This, along with strategic mistakes in epidemiological surveillance, resulted in Greece implementing a second strict, horizontal lockdown and having one of the highest COVID-19 death rates in Europe during the second wave. We provide recommendations for measures that will reinstate primary and community care at the forefront in managing the current public health crisis by protecting hospitals from unnecessary admissions, providing primary and secondary prevention services in relation to COVID-19 and maintaining population health through treatment of non-COVID-19 conditions. This, together with more selective social distancing measures (instead of horizontal lockdowns), represents the only viable and realistic long-term strategy for COVID-19 pandemic mitigation.

Diagnostic Performance of Biomarkers Urinary KIM-1 and YKL-40 for Early Diabetic Nephropathy, in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis

There is a lack of prediction markers for early diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM). The aim of this systematic review and meta-analysis was to evaluate the performance of two promising biomarkers, urinary kidney injury molecule 1 (uKIM-1) and Chitinase-3-like protein 1 (YKL-40) in the diagnosis of early diabetic nephropathy in type 2 diabetic patients. A comprehensive search was performed on PubMed by two reviewers until May 2020. For each study, a 2 × 2 contingency table was formulated. Sensitivity, specificity, and other estimates of accuracy were calculated using the bivariate random effects model. The hierarchical summary receiver operating characteristic curve hsROC) was used to pool data and evaluate the area under curve (AUC). The sources of heterogeneity were explored by sensitivity analysis. Publication bias was assessed using Deek’s test. The meta-analysis enrolled 14 studies involving 598 healthy individuals, 765 T2DM patients with normoalbuminuria, 549 T2DM patients with microalbuminuria, and 551 T2DM patients with macroalbuminuria, in total for both biomarkers. The AUC of uKIM-1 and YKL-40 for T2DM patients with normoalbuminuria, was 0.85 (95%CI; 0.82–0.88) and 0.91 (95%CI; 0.88–0.93), respectively. The results of this meta-analysis suggest that both uKIM-1 and YKL-40 can be considered as valuable biomarkers for the early detection of DN in T2DM patients with the latter showing slightly better performance than the former.

JUCHMME: a Java Utility for Class Hidden Markov Models and Extensions for biological sequence analysis

SUMMARY

JUCHMME is an open-source software package designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols. We incorporate a large collection of standard algorithms for HMMs as well as a number of extensions and evaluate the software on various biological problems. Importantly, the JUCHMME toolkit includes several additional features that allow for easy building and evaluation of custom HMMs, which could be a useful resource for the research community.

AVAILABILITY AND IMPLEMENTATION

http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Semi-supervised learning of Hidden Markov Models for biological sequence analysis

MOTIVATION

Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications.

RESULTS

We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antibody Tests in Detecting SARS-CoV-2 Infection: A Meta-Analysis

The emergence of Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 made imperative the need for diagnostic tests that can identify the infection. Although Nucleic Acid Test (NAT) is considered to be the gold standard, serological tests based on antibodies could be very helpful. However, individual studies are usually inconclusive, thus, a comparison of different tests is needed. We performed a systematic review and meta-analysis in PubMed, medRxiv and bioRxiv. We used the bivariate method for meta-analysis of diagnostic tests pooling sensitivities and specificities. We evaluated IgM and IgG tests based on Enzyme-linked immunosorbent assay (ELISA), Chemiluminescence Enzyme Immunoassays (CLIA), Fluorescence Immunoassays (FIA), and the Lateral Flow Immunoassays (LFIA). We identified 38 studies containing data from 7848 individuals. Tests using the S antigen are more sensitive than N antigen-based tests. IgG tests perform better compared to IgM ones and show better sensitivity when the samples were taken longer after the onset of symptoms. Moreover, a combined IgG/IgM test seems to be a better choice in terms of sensitivity than measuring either antibody alone. All methods yield high specificity with some of them (ELISA and LFIA) reaching levels around 99%. ELISA- and CLIA-based methods perform better in terms of sensitivity (90%-94%) followed by LFIA and FIA with sensitivities ranging from 80% to 89%. ELISA tests could be a safer choice at this stage of the pandemic. LFIA tests are more attractive for large seroprevalence studies but show lower sensitivity, and this should be taken into account when designing and performing seroprevalence studies.

Methods for multiple outcome meta-analysis of gene-expression data

Meta-analysis is a valuable tool for the synthesis of evidence across a wide range study types including high-throughput experiments such as genome-wide association studies (GWAS) and gene expression studies. There are situations though, in which we have multiple outcomes or multiple treatments, in which the multivariate meta-analysis framework which performs a joint modeling of the different quantities of interest may offer important advantages, such as increasing statistical power and allowing performing global tests. In this work we adapted the multivariate meta-analysis method and applied it in gene expression data. With this method we can test for pleiotropic effects, that is, for genes that influence both outcomes or discover genes that have a change in expression not detectable in the univariate method. We tested this method on data regarding inflammatory bowel disease (IBD), with its two main forms, Crohn’s disease (CD) and Ulcerative colitis (UC), sharing many clinical manifestations, but differing in the location and extent of inflammation and in complications. The Stata code is given in the Appendix and it is available at: www.compgen.org/tools/multivariate-microarrays.•

Multivariate meta-analysis method for gene expression data.

•

Discover genes with pleiotropic effects.

•

Differentially Expressed Genes (DEGs) identification in complex traits.

Landscape of Eukaryotic Transmembrane Beta Barrel Proteins

Even though in the last few years several families of eukaryotic β-barrel outer membrane proteins have been discovered, their computational characterization and their annotation in public databases are far from complete. The PFAM database includes only very few characteristic profiles for these families, and in most cases, the profile hidden Markov models (pHMMs) have been trained using prokaryotic and eukaryotic proteins together. Here, we present for the first time a comprehensive computational analysis of eukaryotic transmembrane β-barrels. Twelve characteristic pHMMs were built, based on an extensive literature search, which can discriminate eukaryotic β-barrels from other classes of proteins (globular and bacterial β-barrel ones), as well as between mitochondrial and chloroplastic ones. We built eight novel profiles for the chloroplastic β-barrel families that are not present in the PFAM database and also updated the profile for the MDM10 family (PF12519) in the PFAM database and divide the porin family (PF01459) into two separate families, namely, VDAC and TOM40.

Susceptibility to leishmaniasis is affected by host SLC11A1 gene polymorphisms: a systematic review and meta-analysis

Leishmaniases are cutaneous, mucocutaneous, and visceral diseases affecting humans and domesticated animals mostly in the tropical and subtropical areas of the planet. Host genetics have been widely investigated for their role in developing various infectious diseases. The SLC11A1 gene has been reported to play a role in neutrophil function and is associated with susceptibility to infectious and inflammatory diseases such as tuberculosis or rheumatoid arthritis. In the present meta-analysis, we investigate the genetic association of SLC11A1 polymorphisms with susceptibility to leishmaniasis. Genotypes and other risk-related data were collected from 13 case-control and family-based studies (after literature search). Conventional random-effects meta-analysis was performed using STATA 13. To pool case-control and family-based data, the weighted Stouffer's method was also applied. Eight polymorphisms were investigated: rs2276631, rs3731865, rs3731864, rs17221959, rs201565523, rs2279015, rs17235409, and rs17235416. We found that rs17235409 (D543N) and rs17235416 (1729 + 55del4) are significantly associated with a risk for cutaneous leishmaniasis (CL), whereas rs17221959, rs2279015, and rs17235409 are associated with visceral leishmaniasis (VL). Our results suggest that polymorphisms in SLC11A1 affect susceptibility to CL and VL. These findings open new pathways in understanding macrophage response to Leishmania infection and the genetic factors predisposing to symptomatic CL or VL that can lead to the usage of predictive biomarkers in populations at risk.

Diagnostic Accuracy of Neutrophil Gelatinase-Associated Lipocalin for Predicting Early Diabetic Nephropathy in Patients with Type 1 and Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis

BACKGROUND

Currently, there is a lack of prediction markers for diabetic nephropathy (DN) in patients with type 1 and type 2 diabetes mellitus (T1DM/T2DM). The aim of this systematic review and meta-analysis was to evaluate the value of a promising biomarker, neutrophil gelatinase-associated lipocalin (NGAL), in both serum and urine for the diagnosis of early DN in T1DM and T2DM patients with different stages of albuminuria.

METHODS

A comprehensive search was performed on PubMed by 2 reviewers until September 2018. Studies in which (a) the degree of DN was determined according to the urinary albumin/creatinine ratio and (b) NGAL was measured in healthy individuals and in diabetes patients with DN were included in the meta-analysis. For each study, a 2 × 2 contingency table was formulated. Sensitivity, specificity, and other estimates of accuracy were calculated using a bivariate random effects model. The hierarchical summary ROC method was used to pool data and to evaluate the area under the curve (AUC). The sources of heterogeneity were explored by subgroup analysis. Publication bias was assessed using the Deeks test.

RESULTS

The meta-analysis enrolled 22 studies involving 683 healthy individuals and 3249 patients with diabetes, of which 488 were T1DM and 2761 were T2DM patients. Overall, pooled sensitivity and specificity among the different settings analyzed ranged from 0.42 (95% CI, 0.22-0.66) to 1.00 (95% CI, 0.99-1.00) and 0.72 (95% CI, 0.62-0.80) to 0.98 (95% CI, 0.50-1.00) in T2DM patients, respectively. For T1DM patients, the corresponding estimates were 0.71 (95% CI, 0.59-0.81) to 0.89 (95% CI, 0.64-0.97) and 0.72 (95% CI, 0.62-0.80) to 0.79 (95% CI, 0.67-0.87). The AUC of NGAL for T2DM patients ranged from 0.69 (95% CI, 0.65-0.73) to 1.00 (95% CI, 0.99-1.00) in the different settings.

CONCLUSION

The results of this meta-analysis suggest that NGAL in both serum and urine can be considered a valuable biomarker for early detection of DN in diabetes patients.

Multivariate Methods for Meta-Analysis of Genetic Association Studies

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Bipartite graphs in systems biology and medicine: a survey of methods and applications

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

Extending hidden Markov models to allow conditioning on previous observations

Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both. Here, we implement a simple extension of the standard HMM in which the current observed symbol (amino acid residue) depends both on the current state and on a series of observed previous symbols. The major advantage of the method is the simplicity in the implementation, which is achieved by properly transforming the observation sequence, using an extended alphabet. Thus, it can utilize all the available algorithms for the training and decoding of HMMs. We investigated the use of several encoding schemes and performed tests in a number of important biological problems previously studied by our team (prediction of transmembrane proteins and prediction of signal peptides). The evaluation shows that, when enough data are available, the performance increased by 1.8%–8.2% and the existing prediction methods may improve using this approach. The methods, for which the improvement was significant (PRED-TMBB2, PRED-TAT and HMM-TM), are available as web-servers freely accessible to academic users at www.compgen.org/tools/ .

Interleukin gene polymorphisms and susceptibility to HIV-1 infection: a meta-analysis

Some subjects are repeatedly exposed to human immunodeficiency virus (HIV), yet they remain uninfected. This suggests the existence of host-resistance mechanisms. The current study synthesizes the evidence regarding the association between interleukin (IL) gene polymorphisms and HIV susceptibility. Medline, Scopus and the Web of Science databases were systematically searched, and a meta-analysis of case-control studies was conducted. Univariate and bivariate methods were used. The literature search identified 42 eligible studies involving 15,727 subjects. Evidence was obtained on eight single-nucleotide polymorphisms (SNPs): IL1A -889 C>T (rs1800587), IL1B +3953/4 C>T (rs1143634), IL4 -589/90 C>T (rs2243250), IL6 -174 G>C (rs1800795), IL10 -592 C>A (rs1800872), IL10-1082 A>G (rs1800896), IL12B -1188 A>C (rs3212227) and IL28B C>T (rs12979860). The IL1B +3953/4 C>T variant appears to increase the risk of HIV acquisition, under the assumption of a recessive genetic model (odds ratio (OR): 4.47, 95% CI: 2.35-8.52). The AA homozygotes of the IL10 -592 C>A SNP had an increased, marginally nonsignificant, risk (OR: 1.39, 95% CI: 0.97-2.01). It reached, however, significance in sub analyses (OR: 1.49, 95% CI: 1.04-2.12). Finally, the well-studied hepatitis C virus (HCV) infection IL28B (rs12979860) CT/TT genotypes were associated with a 27% decrease in HIV infection risk, especially in populations infected with HCV (OR: 0.73, 95% CI: 0.57-0.95). Interleukin signalling is perhaps important in HIV infection and some interleukin genetic variants may affect the risk of HIV acquisition. Approaches targeting specific genes and genome wide association studies should be conducted to decipher the effect of these polymorphisms.

Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes

Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis.

NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks

OBJECTIVE

Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks.

RESULTS

Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

Bioinformatics Education in Greece: A Survey

<p>Bioinformatics is an interdisciplinary field, placed at the interface of Biology, Mathematics and Computer Science. In this work, we tried for the first time to investigate the current situation of Bioinformatics education in Greece. We searched the online resources of all relevant University Departments for Bioinformatics or relevant courses. We found that all the Departments of Biological Sciences include in their curricula courses dedicated to Bioinformatics, but this is not the case for Departments of Computer Science, Computer Engineering, or Medical Schools. Despite the fact that large Universities played a crucial role in establishing Bioinformatics research and education in Greece, we observe that Universities of the periphery invest in the field, by including more relevant courses in the curricula and appointing faculty members trained in the field. In order for us to “triangulate” we didn’t confine ourselves to online resources and descriptive statistics but we also included interviews so as to have a more spherical view of the subject under discussion. The interviews provided useful insights regarding the teaching methods used by bioinformatics tutors, their attitudes and the difficulties they encounter. The tutors mentioned also the material that they choose, the audience’s attraction techniques and the feedback they receive.</p>

The impact of pneumatic tube system on routine laboratory parameters: a systematic review and meta-analysis

Background:

Pneumatic tube system (PTS) is a widely used method of transporting blood samples in hospitals. The aim of this study was to evaluate the effects of the PTS transport in certain routine laboratory parameters as it has been implicated with hemolysis.

Methods:

A systematic review and a meta-analysis were conducted. PubMed and Scopus databases were searched (up until November 2016) to identify prospective studies evaluating the impact of PTS transport in hematological, biochemical and coagulation measurements. The random-effects model was used in the meta-analysis utilizing the mean difference (MD). Heterogeneity was quantitatively assessed using the Cohran’s

Results:

From a total of 282 studies identified by the searching procedure, 24 were finally included in the meta-analysis. The meta-analysis yielded statistically significant results for potassium (K) [MD=0.04 mmol/L; 95% confidence interval (CI)=0.015–0.065; p=0.002], lactate dehydrogenase (LDH) (MD=10.343 U/L; 95% CI=6.132–14.554; p<10

Conclusions:

This meta-analysis suggests that PTS may be associated with alterations in K, LDH and AST measurements. Although these findings may not have any significant clinical effect on laboratory results, it is wise that each hospital validates their PTS.

Predicting Beta Barrel Transmembrane Proteins Using HMMs

Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.

Chlamydia pneumoniae infection and the risk of multiple sclerosis: a meta-analysis

We conducted a meta-analysis of studies comparing the presence of Chlamydia pneumoniae (Cpn) between multiple sclerosis (MS) patients and other neurological diseases patients or healthy controls. We identified 26 studies with 1332 MS patients and 1464 controls. Using random-effects methods, MS patients were found more likely to have detectable levels of Cpn DNA (OR = 3.216; 95% CI: 1.204, 8.585) in their cerebrospinal fluid, and intrathecally synthesized immunoglobulins (OR = 3.842; 95% CI: 1.317, 11.212), compared to other patients with neurological diseases. There is no evidence for increased levels of serum immunoglobulins (OR = 1.068; 95% CI: 0.745, 1.530), even though this result is confounded by the presence of studies using normal subjects as controls. Similarly, there is no evidence for association of immunoglobulins against Cpn in the cerebrospinal fluid (OR = 3.815; 95% CI: 0.715, 20.369). Up to 59.7% of the between-studies variability could be explained by the inappropriate matching of cases and controls for gender. In random-effects meta-regressions, adjusting for the confounding effect of gender differences results in stronger and statistically significant associations of MS with detectable levels of Cpn DNA, intrathecally synthesized immunoglobulins and immunoglobulins in the cerebrospinal fluid. Even though the presence of Cpn is clearly more likely in MS patients, these findings are insufficient to establish an etiologic relation.

Network analysis of genes and their association with diseases

A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms.

Creating a specialist protein resource network: a meeting report for the protein bioinformatics and community resources retreat

During 11–12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.

Key challenges for the creation and maintenance of specialist protein resources

As the volume of data relating to proteins increases, researchers rely more and more on the analysis of published data, thus increasing the importance of good access to these data that vary from the supplemental material of individual articles, all the way to major reference databases with professional staff and long-term funding. Specialist protein resources fill an important middle ground, providing interactive web interfaces to their databases for a focused topic or family of proteins, using specialized approaches that are not feasible in the major reference databases. Many are labors of love, run by a single lab with little or no dedicated funding and there are many challenges to building and maintaining them. This perspective arose from a meeting of several specialist protein resources and major reference databases held at the Wellcome Trust Genome Campus (Cambridge, UK) on August 11 and 12, 2014. During this meeting some common key challenges involved in creating and maintaining such resources were discussed, along with various approaches to address them. In laying out these challenges, we aim to inform users about how these issues impact our resources and illustrate ways in which our working together could enhance their accuracy, currency, and overall value.

The association between plasminogen activator inhibitor type 1 (PAI-1) levels, PAI-1 4G/5G polymorphism, and myocardial infarction: a Mendelian randomization meta-analysis

BACKGROUND

The circulating levels of plasminogen activator inhibitor type 1 (PAI-1) are increased in individuals carrying the 4G allele at position -675 of the PAI-1 gene. In turn, overexpression of PAI-1 has been found to affect both atheroma and thrombosis. However, the association between PAI-1 levels and the incidence of myocardial infarction (MI) is complicated by the potentially confounding effects of well-known cardiovascular risk factors. The current study tried to investigate in parallel the association of PAI-1 activity with the PAI-1 4G/5G polymorphism, with MI, and some components of metabolic syndrome (MetS).

METHODS

Using meta-analytical Mendelian randomization approaches, genotype-disease and genotype-phenotype associations were modeled simultaneously.

RESULTS

According to an additive model of inheritance and the Mendelian randomization approach, the MI-related odd ratio for individuals carrying the 4G allele was 1.088 with 95% confidence interval (CI) 1.007, 1.175. Moreover, the 4G carriers had, on average, higher PAI-1 activity than 5G carriers by 1.136 units (95% CI 0.738, 1.533). The meta-regression analyses showed that the levels of triglycerides (p=0.005), cholesterol (p=0.037) and PAI-1 (p=0.021) in controls were associated with the MI risk conferred by the 4G carriers.

CONCLUSIONS

The Mendelian randomization meta-analysis confirmed previous knowledge that the PAI-1 4G allele slightly increases the risk for MI. In addition, it supports the notion that PAI-1 activity and established cardiovascular determinants, such as cholesterol and triglyceride levels, could lie in the etiological pathway from PAI-1 4G allele to the occurrence of MI. Further research is warranted to elucidate these interactions.

Genetic model selection in genome-wide association studies: robust methods and the use of meta-analysis

In genetic association studies (GAS) as well as in genome-wide association studies (GWAS), the mode of inheritance (dominant, additive and recessive) is usually not known a priori. Assuming an incorrect mode of inheritance may lead to substantial loss of power, whereas on the other hand, testing all possible models may result in an increased type I error rate. The situation is even more complicated in the meta-analysis of GAS or GWAS, in which individual studies are synthesized to derive an overall estimate. Meta-analysis increases the power to detect weak genotype effects, but heterogeneity and incompatibility between the included studies complicate things further. In this review, we present a comprehensive summary of the statistical methods used for robust analysis and genetic model selection in GAS and GWAS. We then discuss the application of such methods in the context of meta-analysis. We describe the theoretical properties of the various methods and the foundations on which they are based. We also present the available software implementations of the described methods. Finally, since only few of the available robust methods have been applied in the meta-analysis setting, we present some simple extensions that allow robust meta-analysis of GAS and GWAS. Possible extensions and proposals for future work are also discussed.

Are sample sizes clear and justified in RCTs published in dental journals?

Sample size calculations are advocated by the CONSORT group to justify sample sizes in randomized controlled trials (RCTs). The aim of this study was primarily to evaluate the reporting of sample size calculations, to establish the accuracy of these calculations in dental RCTs and to explore potential predictors associated with adequate reporting. Electronic searching was undertaken in eight leading specific and general dental journals. Replication of sample size calculations was undertaken where possible. Assumed variances or odds for control and intervention groups were also compared against those observed. The relationship between parameters including journal type, number of authors, trial design, involvement of methodologist, single-/multi-center study and region and year of publication, and the accuracy of sample size reporting was assessed using univariable and multivariable logistic regression. Of 413 RCTs identified, sufficient information to allow replication of sample size calculations was provided in only 121 studies (29.3%). Recalculations demonstrated an overall median overestimation of sample size of 15.2% after provisions for losses to follow-up. There was evidence that journal, methodologist involvement (OR = 1.97, CI: 1.10, 3.53), multi-center settings (OR = 1.86, CI: 1.01, 3.43) and time since publication (OR = 1.24, CI: 1.12, 1.38) were significant predictors of adequate description of sample size assumptions. Among journals JCP had the highest odds of adequately reporting sufficient data to permit sample size recalculation, followed by AJODO and JDR, with 61% (OR = 0.39, CI: 0.19, 0.80) and 66% (OR = 0.34, CI: 0.15, 0.75) lower odds, respectively. Both assumed variances and odds were found to underestimate the observed values. Presentation of sample size calculations in the dental literature is suboptimal; incorrect assumptions may have a bearing on the power of RCTs.