Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data.

Motivation: Protein phosphorylation is a post-translational modification that underlines various aspects of cellular signaling. A key step to reconstructing signaling networks involves identification of the set of all kinases and their substrates. Experimental characterization of kinase substrates is both expensive and time-consuming.

Network-based biomarkers enhance classical approaches to prognostic gene expression signatures.

Background: Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling.

MicroRNA and mRNA expression profiling in metastatic melanoma reveal associations with BRAF mutation and patient prognosis.

The role of microRNAs (miRNAs) in melanoma is unclear. We examined global miRNA expression profiles in fresh-frozen metastatic melanomas in relation to clinical outcome and BRAF mutation, with validation in independent cohorts of tumours and sera. We integrated miRNA and mRNA information from the same samples and elucidated networks associated with outcome and mutation.

Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway.

Motivation: With the advancement of high-throughput techniques, large-scale profiling of biological systems with multiple experimental perturbations is becoming more prevalent. Pathway analysis incorporates prior biological knowledge to analyze genes/proteins in groups in a biological context. However, the hypotheses under investigation are often confined to a 1D space (i.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations.

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations.

VAN: an R package for identifying biologically perturbed networks via differential variability analysis.

Background: Large-scale molecular interaction networks are dynamic in nature and are of special interest in the analysis of complex diseases, which are characterized by network-level perturbations rather than changes in individual genes/proteins. The methods developed for the identification of differentially expressed genes or gene sets are not suitable for network-level analyses. Consequently, bioinformatics approaches that enable a joint analysis of high-throughput transcriptomics datasets and large-scale molecular interaction networks for identifying perturbed networks are gaining popularity.

Disturbed protein-protein interaction networks in metastatic melanoma are associated with worse prognosis and increased functional mutation burden.

For disseminated melanoma, new prognostic biomarkers and therapeutic targets are urgently needed. The organization of protein-protein interaction networks was assessed via the transcriptomes of four independent studies of metastatic melanoma and related to clinical outcome and MAP-kinase pathway mutations (BRAF/NRAS). We also examined patient outcome-related differences in a predicted network of microRNAs and their targets.

Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information.

Background: The cost of RNA-Seq has been decreasing over the last few years. Despite this, experiments with four or less biological replicates are still quite common. Estimating the variances of gene expression estimates becomes both a challenging and interesting problem in these situations of low replication.

Estimation of data-specific constitutive exons with RNA-Seq data.

Background: RNA-Seq has the potential to answer many diverse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene.

BRAF mutation, NRAS mutation, and the absence of an immune-related expressed gene profile predict poor outcome in patients with stage III melanoma.

Prediction of outcome for melanoma patients with surgically resected macroscopic nodal metastases is very imprecise. We performed a comprehensive clinico-pathologic assessment of fresh-frozen macroscopic nodal metastases and the preceding primary melanoma, somatic mutation profiling, and gene expression profiling to identify determinants of outcome in 79 melanoma patients. In addition to disease stage 4 years, 90% confidence interval): the presence of a nodular component in the primary melanoma (6.

Measures of association for identifying microRNA-mRNA pairs of biological interest.

MicroRNAs are a class of small non-protein coding RNAs that play an important role in the regulation of gene expression. Most studies on the identification of microRNA-mRNA pairs utilize the correlation coefficient as a measure of association. The use of correlation coefficient is appropriate if the expression data are available for several conditions and, for a given condition, both microRNA and mRNA expression profiles are obtained from the same set of individuals.

Review and cross-validation of gene expression signatures and melanoma prognosis.

In melanoma, there is an urgent need to identify novel biomarkers with prognostic performance superior to traditional clinical and histological parameters. Gene expression-based prognostic signatures offer promise, but studies have been challenged by sample scarcity, cohort heterogeneity, and doubts about the efficacy of such signatures relative to current clinical practices. Motivated by new studies that have begun to address these challenges, we reviewed prognostic signatures derived from gene expression microarray analysis of human melanoma tissue.

Genome-wide analysis of primary CD4+ and CD8+ T cell transcriptomes shows evidence for a network of enriched pathways associated with HIV disease.

Background: HIV preferentially infects CD4+ T cells, and the functional impairment and numerical decline of CD4+ and CD8+ T cells characterize HIV disease. The numerical decline of CD4+ and CD8+ T cells affects the optimal ratio between the two cell types necessary for immune regulation. Therefore, this work aimed to define the genomic basis of HIV interactions with the cellular transcriptome of both CD4+ and CD8+ T cells.

Gene-gene interaction filtering with ensemble of filters.

Background: Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step.

The yeast homolog of heme oxygenase-1 affords cellular antioxidant protection via the transcriptional regulation of known antioxidant genes.

Heme oxygenase-1 (HO-1) degrades heme and protects cells from oxidative challenge. This antioxidant activity is thought to result from the HO-1 enzymatic activity, manifested by a decrease in the concentration of the pro-oxidant substrate heme, and an increase in the antioxidant product bilirubin. Using a global transcriptional approach, and yeast as a model, we show that HO-1 affords cellular protection via up-regulation of transcripts encoding enzymes involved in cellular antioxidant defense, rather than via its oxygenase activity.

Expression profiling of cytogenetically normal acute myeloid leukemia identifies microRNAs that target genes involved in monocytic differentiation.

MicroRNAs are short ribonucleic acids (RNAs) that play an important role in many aspects of cellular biology such as differentiation and apoptosis, due to their role in the regulation of gene expression. Using microRNA microarrays, we characterized the microRNA gene expression of 27 patients with acute myeloid leukemia (AML) with normal cytogenetics, focusing on the microRNAs differentially expressed between the M1 and M5 French-American-British (FAB) subtypes. An accurate delineation of these two AML entities was observed based on the expression of 12 microRNAs.

Comparison study of microarray meta-analysis methods.

Background: Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods.

Results: We compare eight meta-analysis methods, five existing methods, two naive methods and a novel approach (mDEDS).

Analysis of the prototypical Staphylococcus aureus multiresistance plasmid pSK1.

The Staphylococcus aureus multiresistance plasmid pSK1 is the prototype of a family of structurally related plasmids that were first identified in epidemic S. aureus strains isolated in Australia during the 1980s and subsequently in Europe. Here we present the complete 28.

Reduced p16 and increased cyclin D1 and pRb expression are correlated with progression in cutaneous melanocytic tumors.

The immunohistochemical expression of cell cycle proteins p16, cyclin D1, and pRb was assessed in 112 benign and malignant melanocytic tumors and correlated with tumor progression, prognosis, and outcome. Comparing benign and malignant tumors, there were significant differences in the median score for all 3 proteins, with decreased p16 (P = .000001), increased cyclin D1 (P = .

Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data.

Over the past decade, a class of small RNA molecules called microRNAs (miRNAs) has been shown to regulate gene expression at the post-transcription stage. While early work focused on the identification of miRNAs using a combination of experimental and computational techniques, subsequent studies have focused on identification of miRNA-target mRNA pairs as each miRNA can have hundreds of mRNA targets. The experimental validation of some miRNAs as oncogenic has provided further motivation for research in this area.

Prediction of alternatively skipped exons and splicing enhancers from exon junction arrays.

Background: Alternative splicing of exons in a pre-mRNA transcript is an important mechanism which contributes to protein diversity in human. Arrays for detecting alternative splicing are available using several different probe designs, including those based on exon-junctions. In this work, we introduce a new method for predicting alternatively skipped exons from exon-junction arrays.

Transcriptional profiles in CD8+ T cells from HIV+ progressors on HAART are characterized by coordinated up-regulation of oxidative phosphorylation enzymes and interferon responses.

The functional impairment and numerical decline of CD8+ T cells during HIV infection has a profound effect on disease progression, but only limited microarray studies have used CD8+ T cells. To understand the interactions of HIV and host CD8+ T cells at different disease status, we used the Illumina Human-6 BeadChips to evaluate the transcriptional profile (>48,000 transcripts) in primary CD8+ T cells from HIV+ therapy-naive non-progressors and therapy-experienced progressors. 68 differentially expressed genes were identified, of which 6 have been reported in HIV context, while others are associated with biological functions relevant to HIV pathogenesis.

Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids.

Airway inflammation and epithelial remodeling are two key features of asthma. IL-13 and other cytokines produced during T helper type 2 cell-driven allergic inflammation contribute to airway epithelial goblet cell metaplasia and may alter epithelial-mesenchymal signaling, leading to increased subepithelial fibrosis or hyperplasia of smooth muscle. The beneficial effects of corticosteroids in asthma could relate to their ability to directly or indirectly decrease epithelial cell activation by inflammatory cells and cytokines.

Novel stepwise normalization method for two-channel cDNA microarrays.

Microarray experiments contain many sources of systematic errors. In order to extract biologically relevant information from microarray data, normalization needs to be applied to remove such variations. Although a number of normalization models have been proposed, it has not been well researched on how to select the most appropriate model with respect to the observed data.

Stepwise normalization of two-channel spotted microarrays.

Intensities measurements of spotted microarrays embody many undesirable systematic variations. Very commonly, varying amounts and types of such variations are observed in different arrays. Although various normalization methods have been proposed to remove such systematic effects, it has not been well studied how to assess or select the most appropriate method for different arrays and data sets.