ABSTRACT
The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples. Cell lines were differentiated and characterized at a central laboratory using standardized cell culture methodologies, protocols, and metadata descriptors. Stem cell and derived differentiated lines were characterized using RNA-seq, miRNA-seq, copy number arrays, DNA methylation arrays, flow cytometry, and molecular histology. All materials, including raw data, metadata, analysis and processing code, and methodological and provenance documentation are publicly available for re-use and interactive exploration at https://www.synapse.org/pcbc. The goal is to provide data that can improve our ability to robustly and reproducibly use human pluripotent stem cells to understand development and disease.
Subject(s)
Induced Pluripotent Stem Cells , Pluripotent Stem Cells , Animals , Cell Culture Techniques , HumansABSTRACT
We propose that data mining and network analysis utilizing public databases can identify and quantify relationships between scientific discoveries and major advances in medicine (cures). Further development of such approaches could help to increase public understanding and governmental support for life science research and could enhance decision making in the quest for cures.
Subject(s)
Biomedical Research/economics , Data Mining , Publications , Animals , Biological Science Disciplines/economics , Clinical Trials as Topic , Decision Making , Drug Discovery , Humans , National Institutes of Health (U.S.)/economics , United States , United States Food and Drug Administration/economicsABSTRACT
In this paper we present the open-source WikiPathways app for Cytoscape ( http://apps.cytoscape.org/apps/wikipathways) that can be used to import biological pathways for data visualization and network analysis. WikiPathways is an open, collaborative biological pathway database that provides fully annotated pathway diagrams for manual download or through web services. The WikiPathways app allows users to load pathways in two different views: as an annotated pathway ideal for data visualization and as a simple network to perform computational analysis. An example pathway and dataset are used to demonstrate the functionality of the WikiPathways app and how they can be combined and used together with other apps. More than 2000 downloads between its first release in August 2013 and the submission of the paper in May 2014 highlight the importance and adoption of the app in the network biology field.
ABSTRACT
setsApp ( http://apps.cytoscape.org/apps/setsapp) is a relatively simple Cytoscape 3 app for users to handle groups of nodes and/or edges. It supports several important biological workflows and enables various set operations. setsApp provides basic tools to create sets of nodes or edges, import or export sets, and perform standard set operations (union, difference, intersection) on those sets. Automatic set partitioning and layout functions are also provided. The sets functionality is also exposed to users and app developers in the form of a set of commands that can be used for scripting purposes or integrated in other Cytoscape apps.
ABSTRACT
As a network visualization and analysis platform, Cytoscape relies on apps to provide domain-specific features and functions. There are many resources available to support Cytoscape app development and distribution, including the Cytoscape App Store and an online "cookbook" for app developers. This article collection is another resource to help researchers find out more about relevant Cytoscape apps and to provide app developers with useful implementation tips. The collection will grow over time as new Cytoscape apps are developed and published.
ABSTRACT
SUMMARY: Cytoscape is an open source software tool for biological network visualization and analysis, which can be extended with independently developed apps. We launched the Cytoscape App Store to highlight the important features that apps add to Cytoscape, enable researchers to find and install apps they need and help developers promote their apps. AVAILABILITY: The App Store is available at http://apps.cytoscape.org. CONTACT: apico@gladstone.ucsf.edu.
Subject(s)
Computational Biology/instrumentation , Metabolic Networks and Pathways , Software , InternetABSTRACT
Cytoscape is open-source software for integration, visualization and analysis of biological networks. It can be extended through Cytoscape plugins, enabling a broad community of scientists to contribute useful features. This growth has occurred organically through the independent efforts of diverse authors, yielding a powerful but heterogeneous set of tools. We present a travel guide to the world of plugins, covering the 152 publicly available plugins for Cytoscape 2.5-2.8. We also describe ongoing efforts to distribute, organize and maintain the quality of the collection.
Subject(s)
Gene Regulatory Networks , Genes/physiology , Genomics/methods , Software , Algorithms , Computational Biology , Computer Simulation , Data Mining , Database Management Systems , Gene Expression Profiling , Models, BiologicalABSTRACT
Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.