Kahn Rhrissorrakrai

MINE: Module Identification in Networks.

BackgroundGraphical models of network associations are useful for both visualizing and integrating multiple types of association data. Identifying modules, or groups of functionally related gene products, is an important challenge in analyzing biological networks. However, existing tools to identify modules are insufficient when applied to dense networks of experimentally derived interaction data. To address this problem, we have developed an agglomerative clustering method that is able to identify highly modular sets of gene products within highly interconnected molecular interaction networks.ResultsMINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties.ConclusionsMINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans.

Mating induces an immune response and developmental switch in the Drosophila oviduct

Mating triggers physiological and behavioral changes in females. To understand how females effect these changes, we used microarray, proteomic, and comparative analyses to characterize gene expression in oviducts of mated and unmated Drosophila females. The transition from non-egg laying to egg laying elicits a distinct molecular profile in the oviduct. Immune-related transcripts and proteins involved in muscle and polarized epithelial function increase, whereas cell growth and differentiation-related genes are down-regulated. Our combined results indicate that mating triggers molecular and biochemical changes that mediate progression from a “poised” state to a mature, functional stage.

Benzo[ a ]pyrene diol epoxide stimulates an inflammatory response in normal human lung fibroblasts through a p53 and JNK mediated pathway

Cellular responses to carcinogens are typically studied in transformed cell lines, which do not reflect the physiological status of normal tissues. To address this question, we have characterized the transcriptional program and cellular responses of human lung WI-38 fibroblasts upon exposure to the ultimate carcinogen benzo[a]pyrene diol epoxide (BPDE). In contrast to observations in cell lines, we find that BPDE treatment induces a strong inflammatory response in these normal fibroblasts. Whole-genome microarrays show induction of numerous inflammatory factors, including genes that encode interleukins (ILs), growth factors and enzymes related to prostaglandin synthesis and signaling. Real-time reverse transcription-polymerase chain reaction and enzyme-linked immunosorbent assay (ELISA) revealed a time- and dose-dependent-induced expression and production of cyclooxygenase 2, prostglandin E2 and IL1B, IL6 and IL8. In parallel, cell cycle progression and DNA repair processes were repressed, but DNA damage signaling was increased via p53-Ser15 phosphorylation and induced expression levels of GADD45A, CDKN1A, BTG2 and SESN1. Network analysis suggested that activator protein 1 transcription factors may link the cell cycle response and DNA damage signaling with the inflammatory stress-response in these cells. We confirmed this hypothesis by showing that p53-dependent signaling through c-jun N-terminal kinase (JNK) led to increased cJun-Ser63 phosphorylation and that inhibition of JNK-mediated cJun activation using p53- or JNK-specific inhibitors significantly reduced IL gene expression and subsequent production of IL8. This is the first demonstration that a strong inflammatory response is triggered in normal fibroblasts by BPDE and that this occurs through coordinated regulation with other cellular processes.

On Crowd-verification of Biological Networks

Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

Motivation: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data. Results: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species. Availability and implementation: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip. Contact: meikelbiehl@gmail.com

sbv IMPROVER Diagnostic Signature Challenge

The sbv IMPROVER (systems biology verification—Industrial Methodology for Process Verification in Research) process aims to help companies verify component steps or tasks in larger research workflows for industrial applications. IMPROVER is built on challenges posed to the community that draws on the wisdom of crowds to assess the most suitable methods for a given research task. The Diagnostic Signature Challenge, open to the public from Mar. 5 to Jun. 21, 2012, was the first instantiation of the IMPROVER methodology and evaluated a fundamental biological question, specifically, if there is sufficient information in gene expression data to diagnose diseases. Fifty-four teams used publically available data to develop prediction models in four disease areas: multiple sclerosis, lung cancer, psoriasis, and chronic obstructive pulmonary disease. The predictions were scored against unpublished, blinded data provided by the organizers, and the results, including methods of the top performers, presented at a conference in Boston on Oct. 2–3, 2012. This paper offers an overview of the Diagnostic Signature Challenge and the accompanying symposium, and is the first article in a special issue of Systems Biomedicine, providing focused reviews of the submitted methods and general conclusions from the challenge. Overall, it was observed that optimal method choice and performance appeared largely dependent on endpoint, and results indicate the psoriasis and lung cancer subtypes sub-challenges were more accurately predicted, while the remaining classification tasks were much more challenging. Though no one approach was superior for every sub-challenge, there were methods, like linear discriminant analysis, that were found to perform consistently well in all.

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma.

Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. Conclusions: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. ClinicalTrials.gov identifier: NCT02725684.

Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge

MOTIVATION Using gene expression to infer changes in protein phosphorylation levels induced in cells by various stimuli is an outstanding problem. The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue. RESULTS Rat lung epithelial cells were treated with 52 stimuli, and gene expression and phosphorylation levels were measured. Competing teams used gene expression data from 26 stimuli to develop protein phosphorylation prediction models and were ranked based on prediction performance for the remaining 26 stimuli. Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation. In spite of the similar performance, the approaches used by these three teams, described in detail in this article, were different, with the average number of predictor genes per phosphoprotein used by the teams ranging from 3 to 124. However, a significant overlap of gene signatures between teams was observed for the majority of the proteins considered, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in the union of the predictor genes of the three teams for multiple proteins. AVAILABILITY AND IMPLEMENTATION Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091). Software implementation of the approach of Teams 49 and 75 are available at http://bioinformaticsprb.med.wayne.edu and http://people.cs.clemson.edu/∼luofeng/sbv.rar, respectively. CONTACT gyanbhanot@gmail.com or luofeng@clemson.edu or atarca@med.wayne.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Enhancing Next‐Generation Sequencing‐Guided Cancer Care Through Cognitive Computing

Next‐generation sequencing (NGS) has emerged as an affordable and reproducible means to query tumors for somatic genetic anomalies. To help interpret somatic NGS data, many institutions have created a molecular tumor board to analyze the results of NGS and make recommendations. This article evaluates the utility of cognitive computing systems to analyze data for clinical decision‐making.

Watson for Genomics: Moving Personalized Medicine Forward

The confluence of genomic technologies and cognitive computing has brought us to the doorstep of widespread usage of personalized medicine. Cognitive systems, such as Watson for Genomics (WG), integrate massive amounts of new omic data with the current body of knowledge to assist physicians in analyzing and acting on patients genomic profiles.