This document summarizes the accomplishments of the National Resource for Network Biology over a reporting period. It lists numerous quantitative metrics of success, including over 100 publications citing their grants, thousands of daily downloads and uses of their software tools, and training over 100 users. It also provides details on improvements and developments made to several of their modeling frameworks, algorithms, and software applications. Finally, it outlines the formation of a new working group on single-cell RNA-seq analysis and visualization, and improvements made to their computing infrastructure.
The document summarizes the accomplishments of the National Resource for Network Biology (NRNB) over the past year, including:
- Over 100 publications citing NRNB funding and high usage of Cytoscape tools
- 18 supported tools, 93 collaborations, and training of over 100 users
- Progress on developing algorithms for differential network analysis, predictive networks, and multi-scale networks
- Launch of two new NRNB workgroups on single cell genomics and patient similarity networks
- 18 new collaboration projects in areas like cancer, neuroinflammation, and drug transporters
This document summarizes the accomplishments of the National Resource for Network Biology over a reporting period. It lists numerous quantitative metrics of success, including over 100 publications citing their grants, thousands of daily downloads and uses of their software tools, and training over 100 users. It also provides details on improvements and developments made to several of their modeling frameworks, algorithms, and software applications. Finally, it outlines the formation of a new working group on single-cell RNA-seq analysis and visualization, and improvements made to their computing infrastructure.
The document summarizes the accomplishments of the National Resource for Network Biology (NRNB) over the past year, including:
- Over 100 publications citing NRNB funding and high usage of Cytoscape tools
- 18 supported tools, 93 collaborations, and training of over 100 users
- Progress on developing algorithms for differential network analysis, predictive networks, and multi-scale networks
- Launch of two new NRNB workgroups on single cell genomics and patient similarity networks
- 18 new collaboration projects in areas like cancer, neuroinflammation, and drug transporters
This document outlines a presentation on biological networks and the software Cytoscape. It begins with an introduction to biological networks and their taxonomy, as well as analytical approaches and visualization techniques. It then provides an overview of Cytoscape, covering core concepts like networks and tables, visual properties, and apps. The document demonstrates how to load networks and data, use visual style managers, and save and export networks. It concludes with tips and tricks for using Cytoscape and a link to a hands-on tutorial.
The National Resource for Network Biology (NRNB) aims to advance network biology science through bioinformatic methods, software, infrastructure, collaboration, and training. In the past year, the NRNB made progress in its specific aims, including developing new network analysis methods, catalyzing changes in network representation, establishing software and databases, engaging in collaborations, and providing training opportunities. Going forward, the NRNB plans to further develop methods for differential and predictive network analysis, multi-scale network representation, and pathway analysis tools.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
油
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex big data. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
This document provides an introduction and outline for a workshop on using Cytoscape 3 to analyze and visualize biological networks. The workshop will cover core Cytoscape concepts like networks and tables, visual properties, and apps. Participants will learn how to import networks and data, use layouts and apps to explore their own data, and get help resources. The document outlines why networks are important in biology and different analytical approaches and visualization techniques in Cytoscape.
This document discusses the application of Network-augmented Genomic Analysis (NAGA) to studies of cystic fibrosis. NAGA integrates functional genomics data from siRNA screens with interactomics data from techniques like AP-MS and MudPIT proteomics to prioritize novel candidate genes. When applied to a cystic fibrosis cell line, NAGA identified 11 novel candidate regulators, of which 8 were validated in follow-up experiments. The approach leverages publicly available protein-protein interaction data to connect functional hits to potential drug targets. While it demonstrated good specificity, the method's coverage of validated targets could be improved.
The document provides information about the Network Biology Special Interest Group (SIG) meeting at the 2014 International Conference on Intelligent Systems for Molecular Biology (ISMB). The SIG meeting will include a flash journal club, lightning talks, two poster sessions with prizes for the top posters, and discussions on community subnetworks and network biology at ISMB. Participants are from research institutions across several countries. Attendees are encouraged to vote for their top three favorite posters presented during the poster sessions.
This document summarizes research on developing a human cancer coessentiality network using data from pooled shRNA screens across 107 cancer cell lines. Key points:
- A network of 866 genes and 1877 edges was constructed based on correlations in essentiality profiles across cell lines.
- Network clustering identified groups of genes essential for similar cell line subtypes (e.g. breast, ovarian, pancreatic cancers).
- One cluster involved in oxidative phosphorylation was particularly essential for luminal/HER2 breast cancers.
- The network provides a functional genomics resource, though opportunities exist to improve coverage and accuracy.
Tijana Milenkovi is an assistant professor who develops algorithms for network alignment and mining of biological networks. Her lab has developed methods like GRAAL, H-GRAAL, and MAGNA for mapping similar nodes between networks to transfer knowledge across species. MAGNA directly optimizes edge conservation during alignment to improve accuracy. The lab has also applied network alignment to study aging networks and predict novel aging genes, and developed tools for dynamic network analysis and de-noising networks via link prediction.
This document summarizes research on mapping protein-protein interaction networks between human and virus proteins at the domain level. The researchers found three main principles in the human-virus interaction network: it is antagonistic, economical, and shaped by convergent evolution. Most signatures of these principles were only apparent at high resolution by mapping interactions to the domain level. Viral proteins tended to target human proteins containing linear motif-binding domains, and targeted these domains more densely than human proteins. Mapping interactions at the domain level provided a higher quality network.
This document summarizes research on how the nematode C. elegans is affected by different bacterial diets, and the role of interspecies systems biology in understanding this. Studies found that a Comamonas bacterial diet caused different effects on C. elegans' development, fertility and lifespan compared to an E. coli diet. Further work identified vitamin B12 as a key metabolite produced at much higher levels by Comamonas that mimics the effects of this diet. Vitamin B12 was found to regulate C. elegans gene expression and accelerate development via the methionine/SAM cycle, while also mitigating toxicity from propionic acid, another metabolite affected by the bacterial diets. This research demonstrates how studying
TimeXNet is a method that identifies active gene sub-networks using time-course gene expression profiles. It partitions differentially expressed genes into three time-based groups and then identifies the most probable paths in an interaction network that connect the three groups of genes. TimeXNet formulates this as a minimum cost flow optimization problem. It sets edge capacities based on gene expression levels and edge costs inversely based on interaction reliability. The method has been shown to accurately and rapidly identify active sub-networks involved in innate immune response, circadian regulation, and yeast osmotic stress response.
This document summarizes a study that integrated pathway and gene expression data from over 13,000 samples across 17 platforms to perform multi-label classification of 48 diseases. Pathway activity scores were calculated for each sample and used as features for classification, along with sample labels determined through manual dataset analysis. Classification was performed using multiple algorithms and validated through cross-validation and comparison to previous studies. Performance was improved over previous work, as shown by increased recall and precision. Relationships between diseases and pathways were also modeled in a network graph.
1) The document presents an approach called Multi-MMSB to identify context-dependent community structure across multiple networks.
2) Multi-MMSB jointly learns modules from all networks, allowing each module to be present in only a subset of networks.
3) When applied to synthetic and real biological data sets, Multi-MMSB identified context-specific modules more accurately than naive methods and discovered biologically plausible modules.
This document proposes a framework for identifying key regulators of complex traits from genome-wide association studies (GWAS). It utilizes cell type-specific regulatory networks constructed from epigenomic maps of 127 tissues to assign GWAS variants to regulatory elements without directly mapping variants to genes. Applying this approach to GWAS of various complex traits revealed candidate regulators for traits like BMI, cholesterol and type 2 diabetes. Experimental validation in model systems provided further support for some candidates, including TBX15 which reduces lipid accumulation in human adipocytes and IRX3 whose adipocyte-specific knockdown increases fat mass. However, identifying target genes and completing cell type-specific regulatory networks remains challenging.
Visualization and Analysis of Dynamic Networks Alexander Pico
油
DynNetwork development was taken up initially by Sabina Sara Pfister back in GSoC 2012. She laid out a strong foundation for dynamic network visualization in Cytoscape and my job was to extend the plugins functionality to help users analyse time changing networks. The two of us were mentored by Jason Montojo. We had developed a decent tool over the course of two GSoC programs to aid dynamic network analysis and our efforts culminated in DynNetwork getting accepted for an oral presentation at the International Network for Social Network Analysis (INSNA), Sunbelt 2014 which was held in St. Petersburg, FL in February.