Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks

Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback–Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni.     

Concerted and differential actions of two enzymatic domains underlie Rad5 contributions to DNA damage tolerance

Many genome maintenance factors have multiple enzymatic activities. In most cases, how their distinct activities functionally relate with each other is unclear. Here we examined the conserved budding yeast Rad5 protein that has both ubiquitin ligase and DNA helicase activities. The Rad5 ubiquitin ligase activity mediates PCNA poly-ubiquitination and subsequently recombination-based DNA lesion tolerance. Interestingly, the ligase domain is embedded in a larger helicase domain comprising seven consensus motifs. How features of the helicase domain influence ligase function is controversial. To clarify this issue, we use genetic, 2D gel and biochemical analyses and show that a Rad5 helicase motif important for ATP binding is also required for PCNA poly-ubiquitination and recombination-based lesion tolerance. We determine that this requirement is due to a previously unrecognized contribution of the motif to the PCNA and ubiquitination enzyme interaction, and not due to its canonical role in supporting helicase activity. We further show that Rad5′s helicase-mediated contribution to replication stress survival is separable from recombination. These findings delineate how two Rad5 enzymatic domains concertedly influence PCNA modification, and unveil their discrete contributions to stress tolerance.     

Allele-specific copy number profiling by next-generation DNA sequencing

The progression and clonal development of tumors often involve amplifications and deletions of genomic DNA. Estimation of allele-specific copy number, which quantifies the number of copies of each allele at each variant loci rather than the total number of chromosome copies, is an important step in the characterization of tumor genomes and the inference of their clonal history. We describe a new method, falcon, for finding somatic allele-specific copy number changes by next generation sequencing of tumors with matched normals. falcon is based on a change-point model on a bivariate mixed Binomial process, which explicitly models the copy numbers of the two chromosome haplotypes and corrects for local allele-specific coverage biases. By using the Binomial distribution rather than a normal approximation, falcon more effectively pools evidence from sites with low coverage. A modified Bayesian information criterion is used to guide model selection for determining the number of copy number events. Falcon is evaluated on in silico spike-in data and applied to the analysis of a pre-malignant colon tumor sample and late-stage colorectal adenocarcinoma from the same individual. The allele-specific copy number estimates obtained by falcon allows us to draw detailed conclusions regarding the clonal history of the individual''s colon cancer.