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.

Association of ITPA gene polymorphisms with adverse effects of AZA/6-MP administration: a systematic review and meta-analysis.

Azathioprine (AZA) and its metabolite, mercaptopurine (6-MP), are widely used immunosuppressant drugs. Polymorphisms in genes implicated in AZA/6-MP metabolism, reportedly, could account in part for their potential toxicity. In the present study we performed a systematic review and a meta-analysis, comprising 30 studies and 3582 individuals, to investigate the putative genetic association of two inosine triphosphatase (ITPA) polymorphisms with adverse effects in patients treated with AZA/6-MP. We found that rs1127354 is associated with neutropenia in general populations and in children (OR: 2.39, 95%CI: 1.97-2.90, and OR: 2.43, 95%CI: 2.12-2.79, respectively), and with all adverse effects tested herein in adult populations (OR: 2.12, 95%CI: 1.22-3.69). We also found that rs7270101 is associated with neutropenia and leucopenia in all-ages populations (OR: 2.93, 95%CI: 2.36-3.63, and OR: 2.82, 95%CI: 1.76-4.50, respectively) and with all adverse effects tested herein in children (OR: 1.74, 95%CI: 1.06-2.87). Stratification according to background disease, in combination with multiple comparisons corrections, verified neutropenia to be associated with both polymorphisms, in acute lymphoblastic leukemia (ALL) patients. These findings suggest that ITPA polymorphisms could be used as predictive biomarkers for adverse effects of thiopurine drugs to eliminate intolerance in ALL patients and clarify dosing in patients with different ITPA variants.

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.

Multiple outcome meta-analysis of gene-expression data in inflammatory bowel disease.

We performed a multivariate meta-analysis of microarray data in Crohn's disease (CD) and Ulcerative colitis (UC), which are the main forms of inflammatory bowel disease (IBD). They share similar symptoms but differ in the location and extent of inflammation and in complications. We identified 249 differentially expressed genes (DEGs) in CD and 38 in UC at a false discovery rate of 1%. 20 of the DEGs were common to both diseases. A multivariate test identified 260 DEGs associated with IBD, 53 of which were not found in any of the disorders. We identified important molecular pathways implicated in the pathogenesis of IBD, such as the JAK/STAT and interferon-gamma signaling pathways, genes involved in cell adhesion, apoptosis and carcinogenesis. Among others, BCAT1 and GZMB are interesting novel DEGs that deserve further investigation in experimental models. The method could also be useful to other cases of meta-analysis of gene expression data.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

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.

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.

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.

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 Q and the I2 index. Subgroup analysis, meta-regression analysis, sensitivity analysis, cumulative meta-analysis and assessment of publication bias were performed for all outcomes. 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<10-4) and aspartate aminotransferase (AST) (MD=1.023 IU/L; 95% CI=0.344-1.702; p=0.003). Subgroup analysis and random-effects meta-regression analysis according to the speed and distance of the samples traveled via the PTS revealed that there is relation between the rate and the distance of PTS with the measurements of K, LDH, white blood cells and red blood cells. 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.

Polymorphisms of the CD24 Gene Are Associated with Risk of Multiple Sclerosis: A Meta-Analysis.

CD24 is a cell-surface protein mainly expressed in cells of the immune and central nervous system (CNS), cells that play a critical role in the development of multiple sclerosis (MS). In the current study, we investigated four polymorphisms of the CD24 gene regarding their associations with MS. To this end, univariate and multivariate meta-analysis were applied along with modifications to include data from family-trios so as to increase the robustness of the meta-analysis. We found that the polymorphism 226 C>T (Ala57Val) of the CD24 gene is associated with MS according to the recessive mode of inheritance (odds ratio = 1.75; 95% CI: 1.09, 2.81). Moreover, the 1527-1528 TG>del polymorphism is inversely associated with MS according to the dominant mode of inheritance (odds ratio = 0.57; 95% CI 0.39, 0.83). Conversely, the 1056 A>G and 1626 A>G polymorphisms were not found to be associated with MS. We conclude that the CD24 226 C>T polymorphism increases the risk of MS, while the 1527-1528 TG>del polymorphism seems to have a protective role against MS, suggesting that these two polymorphisms can be used as predictive biomarkers for MS development.

Integration of Antioxidant Activity Assays Data of Stevia Leaf Extracts: A Systematic Review and Meta-Analysis.

Stevia rebaudiana Bertoni, a no-calorie natural sweetener, contains a plethora of polyphenols that exert antioxidant properties with potential medicinal significance. Due to the variety of functional groups, polyphenols exhibit varying solubility depending on the nature of the extraction solvents (water, organic, or their mixtures, defined further on as hydroalcoholic extracts). In the present study, we performed a systematic review, following PRISMA guidelines, and meta-analysis, synthesizing all available data from 45 articles encompassing 250 different studies. Our results showed that the total phenolic content (TPC) of hydroalcoholic and aqueous extracts presents higher values (64.77 and 63.73 mg GAE/g) compared to organic extracts (33.39). Total flavonoid content (TFC) was also higher in aqueous and hydroalcoholic extracts; meta-regression analysis revealed that outcomes in different measuring units (mg QE/g, mg CE/g, and mg RUE/g) do not present statistically significant differences and can be synthesized in meta-analysis. Using meta-regression analysis, we showed that outcomes from the chemical-based ABTS, FRAP, and ORAC antioxidant assays for the same extract type can be combined in meta-analysis because they do not differ statistically significantly. Meta-analysis of ABTS, FRAP, and ORAC assays outcomes revealed that the antioxidant activity profile of various extract types follows that of their phenolic and flavonoid content. Using regression meta-analysis, we also presented that outcomes from SOD, CAT, and POX enzymatic antioxidant assays are independent of the assay type (p-value = 0.905) and can be combined. Our study constitutes the first effort to quantitatively and statistically synthesize the research results of individual studies using all methods measuring the antioxidant activity of stevia leaf extracts. Our results, in light of evidence-based practice, uncover the need for a broadly accepted, unified, methodological strategy to perform antioxidant tests, and offer documentation that the use of ethanol:water 1:1 mixtures or pure water can more efficiently extract stevia antioxidant compounds.

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.

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.

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.

A Counterintuitive Neutrophil-Mediated Pattern in COVID-19 Patients Revealed through Transcriptomics Analysis.

The COVID-19 pandemic has persisted for almost three years. However, the mechanisms linked to the SARS-CoV-2 effect on tissues and disease severity have not been fully elucidated. Since the onset of the pandemic, a plethora of high-throughput data related to the host transcriptional response to SARS-CoV-2 infections has been generated. To this end, the aim of this study was to assess the effect of SARS-CoV-2 infections on circulating and organ tissue immune responses. We profited from the publicly accessible gene expression data of the blood and soft tissues by employing an integrated computational methodology, including bioinformatics, machine learning, and natural language processing in the relevant transcriptomics data. COVID-19 pathophysiology and severity have mainly been associated with macrophage-elicited responses and a characteristic "cytokine storm". Our counterintuitive findings suggested that the COVID-19 pathogenesis could also be mediated through neutrophil abundance and an exacerbated suppression of the immune system, leading eventually to uncontrolled viral dissemination and host cytotoxicity. The findings of this study elucidated new physiological functions of neutrophils, as well as tentative pathways to be explored in asymptomatic-, ethnicity- and locality-, or staging-associated studies.

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.

Isolation of Listeria ivanovii from Bulk-Tank Milk of Sheep and Goat Farms-From Clinical Work to Bioinformatics Studies: Prevalence, Association with Milk Quality, Antibiotic Susceptibility, Predictors, Whole Genome Sequence and Phylogenetic Relationships.

A cross-sectional study was performed in 325 sheep and 119 goat dairy farms in Greece. Samples of bulk-tank milk were examined by standard microbiological techniques for Listeria spp. Listeria monocytogenes was isolated from one (0.3%) and Listeria ivanovii from three (0.9%) sheep farms. No associations between the isolation of L. monocytogenes or L. ivanovii and milk quality were found. No resistance to antibiotics was identified. Three variables emerged as significant predictors of isolation of the organism: the presence of pigs, low average relative humidity and a high number of ewes on the farm. The three L. ivanovii isolates were assessed in silico for identification of plasmids, prophages, antibiotic resistance genes, virulence factors, CRISPRs and CAS genes. Phylogenetic analysis using the core genome revealed that the three strains belonged to the L. ivanovii subsp. ivanovii branch and were especially close to the PAM 55 strain. All strains of the branch appeared to be very similar, with the distance between them being small.

Urine-Based Molecular Diagnostic Tests for Leishmaniasis Infection in Human and Canine Populations: A Meta-Analysis.

Leishmaniasis is a neglected tropical disease affecting humans and domesticated animals with high mortality in endemic countries. The pleiotropy of symptoms and the complicated gold-standard methods make the need for non-invasive, highly sensitive diagnostic tests imperative. Individual studies on molecular-based Leishmania diagnosis in urine show high discrepancy; thus, a data-evidenced comparison of various techniques is necessary. We performed a systematic review and meta-analysis using the bivariate method of diagnostic methods to pool sensitivities and specificities. We investigated the impact of DNA-extraction method, PCR type, amplified locus, host species, leishmaniasis form, and geographical region. The pooled sensitivity was 69.2%. Tests performed with the kit-based DNA extraction method and qPCR outweighed in sensitivity the phenol-chloroform-based and PCR methods, while their combination showed a sensitivity of 79.3%. Amplified locus, human or canine as host and cutaneous or visceral leishmaniasis revealed similar sensitivities. Tests in European and Middle Eastern countries performed better than tests in other regions (sensitivity 81.7% vs. 43.7%). A combination of kit-based DNA extraction and qPCR could be a safer choice for molecular diagnosis for Leishmania infection in urine samples in European-Middle Eastern countries. For the rest of the world, more studies are needed to better characterize the endemic parasite species.

Investigating Molecular Determinants of Cancer Cell Resistance to Ionizing Radiation Through an Integrative Bioinformatics Approach.

Eradication of cancer cells through exposure to high doses of ionizing radiation (IR) is a widely used therapeutic strategy in the clinical setting. However, in many cases, cancer cells can develop remarkable resistance to radiation. Radioresistance represents a prominent obstacle in the effective treatment of cancer. Therefore, elucidation of the molecular mechanisms and pathways related to radioresistance in cancer cells is of paramount importance. In the present study, an integrative bioinformatics approach was applied to three publicly available RNA sequencing and microarray transcriptome datasets of human cancer cells of different tissue origins treated with ionizing radiation. These data were investigated in order to identify genes with a significantly altered expression between radioresistant and corresponding radiosensitive cancer cells. Through rigorous statistical and biological analyses, 36 genes were identified as potential biomarkers of radioresistance. These genes, which are primarily implicated in DNA damage repair, oxidative stress, cell pro-survival, and apoptotic pathways, could serve as potential diagnostic/prognostic markers cancer cell resistance to radiation treatment, as well as for therapy outcome and cancer patient survival. In addition, our findings could be potentially utilized in the laboratory and clinical setting for enhancing cancer cell susceptibility to radiation therapy protocols.

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.