The C-SHIFT Algorithm for Normalizing Covariances.

Omics technologies are powerful tools for analyzing patterns in gene expression data for thousands of genes. Due to a number of systematic variations in experiments, the raw gene expression data is often obfuscated by undesirable technical noises. Various normalization techniques were designed in an attempt to remove these non-biological errors prior to any statistical analysis.

Noninvasive prenatal paternity determination using microhaplotypes: a pilot study.

Background: The use of noninvasive techniques to determine paternity prenatally is increasing because it reduces the risks associated with invasive procedures. Current methods, based on SNPs, use the analysis of at least 148 markers, on average.

Methods: To reduce the number of regions, we used microhaplotypes, which are chromosomal segments smaller than 200 bp containing two or more SNPs.

Differentially correlated genes in co-expression networks control phenotype transitions.

Background: Co-expression networks are a tool widely used for analysis of "Big Data" in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another.

Unexpected links reflect the noise in networks.

Background: Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis.

Results: We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches.

Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists.

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform these data into biological knowledge, for example, how to use these data to answer questions such as: Which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction, and network interrogation. Here we provide an overview of network analysis including a step-by-step guide on how to perform and use this approach to investigate a biological question.

Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer.

Although human papillomavirus was identified as an aetiological factor in cervical cancer, the key human gene drivers of this disease remain unknown. Here we apply an unbiased approach integrating gene expression and chromosomal aberration data. In an independent group of patients, we reconstruct and validate a gene regulatory meta-network, and identify cell cycle and antiviral genes that constitute two major subnetworks upregulated in tumour samples.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview.

Background: DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes.

Results: Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments.

New approach reveals CD28 and IFNG gene interaction in the susceptibility to cervical cancer.

Cervical cancer is a complex disease with multiple environmental and genetic determinants. In this study, we sought an association between polymorphisms in immune response genes and cervical cancer using both single-locus and multi-locus analysis approaches. A total of 14 single nucleotide polymorphisms (SNPs) distributed in CD28, CTLA4, ICOS, PDCD1, FAS, TNFA, IL6, IFNG, TGFB1 and IL10 genes were determined in patients and healthy individuals from three independent case/control sets.

Selection of control genes for quantitative RT-PCR based on microarray data.

Use of internal reference gene(s) is necessary for adequate quantification of target gene expression by RT-PCR. Herein, we elaborated a strategy of control gene selection based on microarray data and illustrated it by analyzing endomyocardial biopsies with acute cardiac rejection and infection. Using order statistics and binomial distribution we evaluated the probability of finding low-varying genes by chance.