DNA microarray is a powerful technology that can simultaneously determine the levels of thousands of transcripts (generated, for example, from genes/miRNAs) across different experimental conditions or tissue samples. The motto of differential expression analysis is to identify the transcripts whose expressions change significantly across different types of samples or experimental conditions. A number of statistical testing methods are available for this purpose. In this paper, we provide a comprehensive survey on different parametric and non-parametric testing methodologies for identifying differential expression from microarray data sets. The performances of the different testing methods have been compared based on some real-life miRNA and mRNA expression data sets. For validating the resulting differentially expressed miRNAs, the outcomes of each test are checked with the information available for miRNA in the standard miRNA database PhenomiR 2.0. Subsequently, we have prepared different simulated data sets of different sample sizes (from 10 to 100 per group/population) and thereafter the power of each test have been calculated individually. The comparative simulated study might lead to formulate robust and comprehensive judgements about the performance of each test in the basis of assumption of data distribution. Finally, a list of advantages and limitations of the different statistical tests has been provided, along with indications of some areas where further studies are required.

IntroductionThe HIV Nef protein is a multifunctional virulence factor that perturbs intracellular membranes and signalling and is secreted into exosomes. While Nef-containing exosomes have a distinct proteomic profile, no comprehensive analysis of their miRNA cargo has been carried out. Since Nef functions as a viral suppressor of RNA interference and disturbs the distribution of RNA-induced silencing complex proteins between cells and exosomes, we hypothesized that it might also affect the export of miRNAs into exosomes.MethodExosomes were purified from human monocytic U937 cells that stably expressed HIV-1 Nef. The RNA from cells and exosomes was profiled for 667 miRNAs using a Taqman Low Density Array. Selected miRNAs and their mRNA targets were validated by quantitative RT-PCR. Bioinformatics analyses were used to identify targets and predict pathways.ResultsNef expression affected a significant fraction of miRNAs in U937 cells. Our analysis showed 47 miRNAs to be selectively secreted into Nef exosomes and 2 miRNAs to be selectively retained in Nef-expressing cells. The exosomal miRNAs were predicted to target several cellular genes in inflammatory cytokine and other pathways important for HIV pathogenesis, and an overwhelming majority had targets within the HIV genome.ConclusionsThis is the first study to report miRnome analysis of HIV Nef expressing monocytes and exosomes. Our results demonstrate that Nef causes large-scale dysregulation of cellular miRNAs, including their secretion through exosomes. We suggest this to be a novel viral strategy to affect pathogenesis and to limit the effects of RNA interference on viral replication and persistence.

Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate.

Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported.

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Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TFgene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes. Testicular germ cell tumors (TGCT) occur most frequently in men between ages of 20 and 40 1,2. Accordingly to histology, TGCT can be separated into two major types: seminoma (SE) and non-seminoma (NSE) 1-4 , and NSE has several subtypes. While the etiology of the two TGCT subtypes is well studied, their molecular profiles, signature genetic markers, and regulatory mechanisms have not been systematically investigated, unlike other common cancers. Such an investigation is much needed now to identify molecular signatures either common in two subtypes, or unique in subtype. The molecular signatures may be further useful for clinical implications, such as patient stratification and subtype-based or personalized treatment. Currently, there are several challenges in TGCT treatment. First, TGCT patients have a high risk of relapse with poor prognosis. Second, there are severe side effects for current chemotherapy and radiotherapy that lead to development of other pathologies. Third, since most of the patients are adolescent or young men, there is a heavy burden for the patients and families in the long run 2,3. During the last decade, a number of studies have been conducted to explore insights into the genetic, epigenetic, and molecular mechanisms of TGCT. For example, after collecting TGCT related genes from previous

Long non-coding RNAs (lncRNAs) have recently been found to be important in gene regulation. lncRNA H19 has been reported to play an oncogenic role in many human cancers. Its specific regulatory role is still elusive. In this study, we developed a novel analytic approach by integrating the synergistic regulation among lncRNAs (e.g., H19), transcription factors (TFs), target genes, and microRNAs (miRNAs) and then applied it to the pan-cancer expression datasets from The Cancer Genome Atlas (TCGA). Using linear regression models, we identified 88 H19-TF-gene co-regulatory triplets, in which 93% of the TF-gene pairs were related to cancer, indicating that our approach was effective to identify disease-related lncRNA-TFgene co-regulation mechanisms. lncRNAs can function as miRNA sponges. Our further experiments found that H19 might regulate SP1-TGFBR2 through let-7b and miR-200b, ETS1-TGFBR2 through miR-29a and miR-200b, and STAT3-KLF11 through miR-17 in breast cancer cell lines. Our work suggests that miRNA-mediated lncRNA-TF-gene co-regulation is complicated yet important in cancer.

Cleft palate (CP) is the second most common congenital birth defect. The etiology of CP is complicated, with involvement of various genetic and environmental factors. To investigate the gene regulatory mechanisms, we designed a powerful regulatory analytical approach to identify the conserved regulatory networks in humans and mice, from which we identified critical microRNAs (miRNAs), target genes and regulatory motifs (miRNA–TF–gene) related to CP. Using our manually curated genes and miRNAs with evidence in CP in humans and mice, we constructed miRNA and transcription factor (TF) co-regulation networks for both humans and mice. A consensus regulatory loop (miR17/miR20a–FOXE1–PDGFRA) and eight miRNAs (miR-140, miR-17, miR-18a, miR-19a, miR-19b, miR-20a, miR-451a and miR-92a) were discovered in both humans and mice. The role of miR-140, which had the strongest association with CP, was investigated in both human and mouse palate cells. The overexpression of miR-140-5p, but not miR-140-3p, significantly inhibited cell proliferation. We further examined whether miR-140 overexpression could suppress the expression of its predicted target genes (BMP2, FGF9, PAX9 and PDGFRA). Our results indicated that miR-140-5p overexpression suppressed the expression of BMP2 and FGF9 in cultured human palate cells and Fgf9 and Pdgfra in cultured mouse palate cells. In summary, our conserved miRNA–TF–gene regulatory network approach is effective in detecting consensus miRNAs, motifs, and regulatory mechanisms in human and mouse CP.