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A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clustering that artificially generates data with expression patterns similar to those of DEGs to identify genes that are highly likely to be classified into the same cluster as the initial cluster data. The proposed method is advantageous in that it does not perform any test. Furthermore, a certain level of accuracy can be maintained even when the sample size is biased, and we show that such a situation may improve the accuracy of the proposed method. We compared the proposed method with the conventional method using simulations. In the simulations, we changed the sample size and difference between the expression levels of group 1 and group 2 in the DEGs to obtain the desired accuracy of the proposed method. The results show that the proposed method is superior in all cases under the conditions simulated. We also show that the effect of the difference between group 1 and group 2 on the accuracy is more prominent when the sample size is biased. [ABSTRACT FROM AUTHOR]

The analysis of plasma microRNAs (miRNAs) has been widely used as a method for finding potential biomarkers for human diseases, especially those with a link to cancer. Methods of analyzing plasma miRNA have been thoroughly discussed from sample extraction to data modeling. However, some issues exist within the process that have rarely been talked about. Rice et al. discussed some issues in plasma miRNA studies, such as the lack of standard methodology including the use of different cycle threshold, time to plasma extraction, among others. These issues can lead to inconsistent data, and thus impact the result and assay reproducibility. Other external issues, such as batch effect and operator effect, may also indirectly impact the statistical analysis. Here, we discuss issues in plasma miRNA studies from a statistical point of view. The interaction effect of different ways of calculating fold-change, the choice of housekeeping genes, and methods of normalization are among the issues we discuss, with data demonstrations. P values are calculated and compared to determine the effect of those issues on statistical conclusions. Statistical methods such as analysis of variance and analysis of covariance are crucial in the analysis of miRNA but investigators are often confused about them; therefore, a brief explanation of these statistical methods is also included. In addition, 3-group classification is discussed, as it is often challenging, compared with 2-group classification. [ABSTRACT FROM AUTHOR]

Nowadays, due to the development of potent Direct-Acting Antiviral Agents (DAAs) that specifically target NS3, NS5A and NS5B viral proteins, several new and highly efficacious options to treat chronic Hepatitis C virus (HCV) infection are available. The natural presence of resistance associated substitutions (RASs), as well as their rapid emergence during incomplete drug-pressure, are intrinsic characteristics of HCV that greatly affect treatment outcome and the chances to achieve a virolgical cure. To date, a high number of RASs in NS3, NS5A, and NS5B have been associated in vivo and/or in vitro with reduced susceptibility to DAAs, but no comprehensive RASs list is available. This review thus provides an updated, systematic overview of the role of RASs to currently approved DAAs or in phase II/III of clinical development against HCV-infection, discriminating their impact in different HCV-genotypes and DAAs, providing assistance for a fruitful use of HCV resistance testing in clinical practice. [ABSTRACT FROM AUTHOR]

The canonical nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway regulates central processes in mammalian cells and plays a fundamental role in the regulation of inflammation and immunity. Aberrant regulation of the activation of the transcription factor NF-κB is associated with severe diseases such as inflammatory bowel disease and arthritis. In the canonical pathway, the inhibitor IκB suppresses NF-κB’s transcriptional activity. NF-κB becomes active upon the degradation of IκB, a process that is, in turn, regulated by the β-transducin repeat-containing protein (β-TrCP). β-TrCP has therefore been proposed as a promising pharmacological target in the development of novel therapeutic approaches to control NF-κB’s activity in diseases. This study explores the extent to which β-TrCP affects the dynamics of nuclear NF-κB using a computational model of canonical NF-κB signaling. The analysis predicts that β-TrCP influences the steady-state concentration of nuclear NF-κB, as well as changes characteristic dynamic properties of nuclear NF-κB, such as fold-change and the duration of its response to pathway stimulation. The results suggest that the modulation of β-TrCP has a high potential to regulate the transcriptional activity of NF-κB.

Background In a randomized, double-blind, placebo-controlled phase 2 clinical trial of Vacc-4x, a peptide-based therapeutic HIV-1 p24 Gag vaccine candidate, 135 HIV-infected participants (vaccine:placebo = 92:43) received a series of six immunizations while on combination antiretroviral therapy (cART). At week 28, all participants underwent an analytical treatment interruption (ATI) for up to 24 weeks. preART VL appeared to be higher among Vacc-4x recipients. Based on a previous analysis, during ATI viral load (VL) appeared to be lower in Vacc-4x recipients, but no difference in CD4 level was observed between Vacc-4x and placebo groups. We propose fold-change-based endpoints and report comparative analyses accounting for imbalanced preART VL and missing data. Methods All analyses included per-protocol (PP) participants who received the full immunization and underwent ATI. Linear regression models were used to identify predictors of study endpoints and to estimate the vaccine effect based on fold changes in CD4 counts or VL over preART values at week 40 or at set-point (geometric mean over weeks 48 and 52 values). We adjusted for potential baseline factors and used a multiple imputation approach to account for missing endpoints due to cART resumption or dropout. P -values were adjusted for multiple comparisons using q -values. Results preART VL and CD4 count were significant predictors of study endpoints. The vaccine recipients had a higher fold change in week 40 CD4 counts (vaccine vs. placebo mean fold-change difference = 0.08; p = 0.02; q = 0.03), a higher fold change in CD4 count set-point (0.06; p = 0.06; q = 0.07), a lower fold change in week 40 VL (−0.47; p = 0.03; q = 0.05), and a lower fold change in VL set-point (−0.50; p = 0.02; q = 0.03). Conclusions These exploratory analyses consistently suggested that Vacc-4x provided positive effects on both CD4 counts and VL. Future HIV therapeutic vaccine studies may adopt similar preART-adjusted endpoints and missing data imputation methods in vaccine effect evaluations. [ABSTRACT FROM AUTHOR]

Background: The arrival of RNA-seq as a high-throughput method competitive to the established microarray technologies has necessarily driven a need for comparative evaluation. To date, cross-platform comparisons of these technologies have been relatively few in number of platforms analyzed and were typically gene name annotation oriented. Here, we present a more extensive and yet precise assessment to elucidate differences and similarities in performance of numerous aspects including dynamic range, fidelity of raw signal and fold-change with sample titration, and concordance with qRT-PCR (TaqMan). To ensure that these results were not confounded by incompatible comparisons, we introduce the concept of probe mapping directed "transcript pattern". A transcript pattern identifies probe(set)s across platforms that target a common set of transcripts for a specific gene. Thus, three levels of data were examined: entire data sets, data derived from a subset of 15,442 RefSeq genes common across platforms, and data derived from the transcript pattern defined subset of 7,034 RefSeq genes. Results: In general, there were substantial core similarities between all 6 platforms evaluated; but, to varying degrees, the two RNA-seq protocols outperformed three of the four microarray platforms in most categories. Notably, a fourth microarray platform, Agilent with a modified protocol, was comparable, or marginally superior, to the RNA-seq protocols within these same assessments, especially in regards to fold-change evaluation. Furthermore, these 3 platforms (Agilent and two RNA-seq methods) demonstrated over 80 % fold-change concordance with the gold standard qRT-PCR (TaqMan). Conclusions: This study suggests that microarrays can perform on nearly equal footing with RNA-seq, in certain key features, specifically when the dynamic range is comparable. Furthermore, the concept of a transcript pattern has been introduced that may minimize potential confounding factors of multi-platform comparison and may be useful for similar evaluations. [ABSTRACT FROM AUTHOR]

The use of fold-change (FC) to prioritize differentially expressed genes (DEGs) for post-hoc characterization is a common technique in the analysis of RNA sequencing datasets. However, the use of FC can overlook certain population of DEGs, such as high copy number transcripts which undergo metabolically expensive changes in expression yet fail to exceed the ratiometric FC cut-off, thereby missing potential important biological information. Here we evaluate an alternative approach to prioritizing RNAseq data based on absolute changes in normalized transcript counts (ΔT) between control and treatment conditions. In five pairwise comparisons with a wide range of effect sizes, rank-ordering of DEGs based on the magnitude of ΔT produced a power curve-like distribution, in which 4.7-5.0% of transcripts were responsible for 36-50% of the cumulative change. Thus, differential gene expression is characterized by the high production-cost expression of a small number of genes (large ΔT genes), while the differential expression of the majority of genes involves a much smaller metabolic investment by the cell. To determine whether the large ΔT datasets are representative of coordinated changes in the transcriptional program, we evaluated large ΔT genes for enrichment of gene ontologies (GOs) and predicted protein interactions. In comparison to randomly selected DEGs, the large ΔT transcripts were significantly enriched for both GOs and predicted protein interactions. Furthermore, enrichments were were consistent with the biological context of each comparison yet distinct from those produced using equal-sized populations of large FC genes, indicating that the large ΔT genes represent an orthagonal transcriptional response. Finally, the composition of the large ΔT gene sets were unique to each pairwise comparison, indicating that they represent coherent and context-specific responses to biological conditions rather than the non-specific upregulation of a family of genes. These findings suggest that the large ΔT genes are not a product of random or stochastic phenomenon, but rather represent biologically meaningful changes in the transcriptional program. They furthermore imply that high abundance transcripts are associated with particularly cellular states, and as cells change in response to internal or external conditions, the relative distribution of the abundant transcripts changes accordingly. Thus, prioritization of DEGs based on the concept of metabolic cost is a simple yet powerful method to identify biologically important transcriptional changes and provide novel insights into cellular behaviors. [ABSTRACT FROM AUTHOR]

Highlights: [•] An application of FCLARANS for attribute clustering and dimensionality reduction in gene expression data has been demonstrated. [•] Domain knowledge based on gene ontology and differential gene expressions are employed in the process. [•] Important representative features are extracted from each enriched gene partition to form the reduced gene space. [•] External validation using various well-known classifiers, establishes the effectiveness of the proposed methodology. [Copyright &y& Elsevier]

Normalization of spectral counts (SpCs) in label-free shotgun proteomic approaches is important to achieve reliable relative quantification. Three different SpC normalization methods, total spectral count (TSpC) normalization, normalized spectral abundance factor (NSAF) normalization, and normalization to selected proteins (NSP) were evaluated based on their ability to correct for day-to-day variation between gel-based sample preparation and chromatographic performance. Three spectral counting data sets obtained from the same biological conidia sample of the rice blast fungus Magnaporthe oryzae were analyzed by 1D gel and liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). Equine myoglobin and chicken ovalbumin were spiked into the protein extracts prior to 1D-SDS- PAGE as internal protein standards for NSP. The correlation between SpCs of the same proteins across the different data sets was investigated. We report that TSpC normalization and NSAF normalization yielded almost ideal slopes of unity for normalized SpC versus average normalized SpC plots, while NSP did not afford effective corrections of the unnormalized data. Furthermore, when utilizing TSpC normalization prior to relative protein quantification, t-testing and fold-change revealed the cutoff limits for determining real biological change to be a function of the absolute number of SpCs. For instance, we observed the variance decreased as the number of SpCs increased, which resulted in a higher propensity for detecting statistically significant, yet artificial, change for highly abundant proteins. Thus, we suggest applying higher confidence level and lower fold-change cutoffs for proteins with higher SpCs, rather than using a single criterion for the entire data set. By choosing appropriate cutoff values to maintain a constant false positive rate across different protein levels (i.e., SpC levels), it is expected this will reduce the overall false negative rate, particularly for proteins with higher SpCs. [ABSTRACT FROM AUTHOR]

One common objective in microarray experiments is to identify a subset of genes that express differentially among different experimental conditions, for example, between drug treatment and no drug treatment. Often, the goal is to determine the underlying relationship between poor versus good gene signatures for identifying biological functions or predicting specific therapeutic outcomes. Because of the complexity in studying hundreds or thousands of genes in an experiment, selection of a subset of genes to enhance relationships among the underlying biological structures or to improve prediction accuracy of clinical outcomes has been an important issue in microarray data analysis. Selection of differentially expressed genes is a two-step process. The first step is to select an appropriate test statistic and compute the P-value. The genes are ranked according to their P-values as evidence of differential expression. The second step is to assign a significance level, that is, to determine a cutoff threshold from the P-values in accordance with the study objective. In this paper, we consider four commonly used statistics, t-, S- (SAM), U-(Mann–Whitney) and M-statistics to compute the P-values for gene ranking. We consider the family-wise error and false discovery rate false-positive error-controlled procedures to select a limited number of genes, and a receiver-operating characteristic (ROC) approach to select a larger number of genes for assigning the significance level. The ROC approach is particularly useful in genomic/genetic profiling studies. The well-known colon cancer data containing 22 normal and 40 tumor tissues are used to illustrate different gene ranking and significance level assignment methods for applications to genomic/genetic profiling studies. The P-values computed from the t-, U- and M-statistics are very similar. We discuss the common practice that uses the P-value, false-positive error probability, as the primary criterion, and then uses the fold-change as a surrogate measure of biological significance for gene selection. The P-value and the fold-change can be pictorially shown simultaneously in a volcano plot. We also address several issues on gene selection.The Pharmacogenomics Journal (2007) 7, 212–220. doi:10.1038/sj.tpj.6500412; published online 29 August 2006 [ABSTRACT FROM AUTHOR]

The canonical nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway regulates central processes in mammalian cells and plays a fundamental role in the regulation of inflammation and immunity. Aberrant regulation of the activation of the transcription factor NF-κB is associated with severe diseases such as inflammatory bowel disease and arthritis. In the canonical pathway, the inhibitor IκB suppresses NF-κB's transcriptional activity. NF-κB becomes active upon the degradation of IκB, a process that is, in turn, regulated by the β-transducin repeat-containing protein (β-TrCP). β-TrCP has therefore been proposed as a promising pharmacological target in the development of novel therapeutic approaches to control NF-κB's activity in diseases. This study explores the extent to which β-TrCP affects the dynamics of nuclear NF-κB using a computational model of canonical NF-κB signaling. The analysis predicts that β-TrCP influences the steady-state concentration of nuclear NF-κB, as well as changes characteristic dynamic properties of nuclear NF-κB, such as fold-change and the duration of its response to pathway stimulation. The results suggest that the modulation of β-TrCP has a high potential to regulate the transcriptional activity of NF-κB. [ABSTRACT FROM AUTHOR]

Protein quantification, identification, and abundance determination are important aspects of proteome characterization and are crucial in understanding biological mechanisms and human diseases. Different strategies are available to quantify proteins using mass spectrometric detection, and most are performed at the peptide level and include both targeted and untargeted methodologies. Discovery-based or untargeted approaches oftentimes use covalent tagging strategies (i.e., iTRAQ, TMT), where reporter ion signals collected in the tandem MS experiment are used for quantification. Herein we investigate the behavior of the iTRAQ 8-plex chemistry using MALDI-TOF/TOF instrumentation. The experimental design and data analysis approach described is simple and straightforward, which allows researchers to optimize data collection and proper analysis within a laboratory. iTRAQ reporter ion signals were normalized within each spectrum to remove peptide biases. An advantage of this approach is that missing reporter ion values can be accepted for purposes of protein identification and quantification without the need for ANOVA analysis. We investigate the distribution of reporter ion peak areas in an equimolar system and a mock biological system and provide recommendations for establishing fold-change cutoff values at the peptide level for iTRAQ data sets. These data provide a unique data set available to the community for informatics training and analysis.

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne virus responsible for hemorrhagic manifestations and multiple organ failure, with a high mortality rate. In infected humans, damage to endothelial cells and vascular leakage may be a direct result of virus infection or an immune response-mediated indirect effect. The main target cells are mononuclear phagocytes, endothelial cells and hepatocytes; the liver being a key target for the virus, which was described as susceptible to interferon host response and to induce apoptosis. To better understand the early liver cell alterations due to virus infection, the protein profile of in vitro CCHFV-infected HepG2 cells was analyzed using two quantitative proteomic approaches, 2D-DIGE and iTRAQ. A set of 243 differentially expressed proteins was identified. Bioinformatics analysis (Ingenuity Pathways Analysis) revealed multiple host cell pathways and functions altered after CCHFV infection, with notably 106 proteins related to cell death, including 79 associated with apoptosis. Different protein networks emerged with associated pathways involved in inflammation, oxidative stress and apoptosis, ubiquitination/sumoylation, regulation of the nucleo-cytoplasmic transport, and virus entry. Collectively, this study revealed host liver protein abundances that were modified at the early stages of CCHFV infection, offering an unparalleled opportunity of the description of the potential pathogenesis processes and of possible targets for antiviral research., (Copyright © 2013 Elsevier B.V. All rights reserved.)

We present new statistical approaches for identification of proteins with expression levels that are significantly changed when applying meta-analysis to two or more independent experiments. We showed that the Euclidean distance measure has reduced risk of false positives compared to the rank product method. Our Ψ-ranking method has advantages over the traditional fold-change approach by incorporating both the fold-change direction as well as the p-value. In addition, the second novel method, Π-ranking, considers the ratio of the fold-change and thus integrates all three parameters. We further improved the latter by introducing our third technique, Σ-ranking, which combines all three parameters in a balanced nonparametric approach., (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)