Consensus on Molecular Subtypes of High-grade Serous Ovarian Carcinoma

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown. Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors are unclassifiable based on currently proposed subtypes. Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%, p \u3c 10-5) and are associated with overall survival in a metaanalysis across datasets (p \u3c 10-5). Current subtypes do not meet statistical criteria for robustness to re-clustering across multiple datasets (Prediction Strength \u3c 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration. Conclusion: A new consensus ovarian subtype classifier represents the consensus of methods, and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype

JSBML 1.0: providing a smorgasbord of options to encode systems biology models

JSBML, the official pure Java programming library for the SBML format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active open-source project with contributions from a growing, international team of developers who not only maintain compatibility with SBML, but also drive steady improvements to the Java interface and promote ease-of-use with end users

Social Network: a Cytoscape app for visualizing co-authorship networks [version 3; referees: 1 approved, 2 approved with reservations]

Networks that represent connections between individuals can be valuable analytic tools. The Social Network Cytoscape app is capable of creating a visual summary of connected individuals automatically. It does this by representing relationships as networks where each node denotes an individual and an edge linking two individuals represents a connection. The app focuses on creating visual summaries of individuals connected by co-authorship links in academia, created from bibliographic databases like PubMed, Scopus and InCites. The resulting co-authorship networks can be visualized and analyzed to better understand collaborative research networks or to communicate the extent of collaboration and publication productivity among a group of researchers, like in a grant application or departmental review report. It can also be useful as a research tool to identify important research topics, researchers and papers in a subject area

Consensus on molecular subtypes of high-grade Serous Ovarian Carcinoma

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown. Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors are unclassifiable based on currently proposed subtypes. Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%, p \u3c 10-5) and are associated with overall survival in a metaanalysis across datasets (p \u3c 10-5). Current subtypes do not meet statistical criteria for robustness to re-clustering across multiple datasets (Prediction Strength \u3c 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration. Conclusion: A new consensus ovarian subtype classifier represents the consensus of methods, and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype

PharmacoDB-1.1.1

<p>PharmacoDB allows scientists to search across publicly available datasets to find instances where a drug or cell line of interest has been profiled, and to view and compare the dose-response data for a specific cell line - drug pair from any of the studies included in the database.</p> <p>We are providing full access to the contents of the first stable release of PharmacoDB and are distributing the data as a MySQL dump. Making our research fully reproducible is part of the mission of the Haibe-Kains laboratory.</p> <p>The existing dump can be made <em>PostgreSQL</em> compatible by following the instructions below:</p> <p>Step 1: Install Docker (https://www.docker.com/)</p> <p>Step 2: Run the commands below</p> <p>sudo docker pull bhklab/pharmacodb:1.1.1</p> <p>sudo docker run -d -p 3000:3000 -v ~:/home -it --name pharmacodb_v1.1.1 --entrypoint /bin/bash bhklab/pharmacodb:1.1.1</p> <p>sudo docker exec -it --user root pharmacodb_v1.1.1 /usr/src/app/data/mysql_to_postgres</p> <p>The new PostgreSQL compatible dump will be available in your home directory as 'pharmacodb_development.psql'. </p> <p>For more information, please visit https://pharmacodb.pmgenomics.ca</p

JSBML

JSBML is a community-driven project to create a free, open-source, pure Java™ library for reading, writing, and manipulating SBML files (the Systems Biology Markup Language) and data streams. It is an alternative to the mixed Java/native code-based interface provided in libSBML.Related Publication:&lt;/p&gt; JSBML 1.0: providing a smorgasbord of options to encode systems biology models&lt;/p&gt; Nicolas Rodriguez Babraham Institute, Babraham Research Campus, Cambridge, UK&lt;/p&gt; Alex Thomas University of California, San Diego, La Jolla, CA, USA&lt;/p&gt; Leandro Watanabe The University of Utah, Salt Lake City, UT, USA&lt;/p&gt; Ibrahim Y. Vazirabad Marquette University, Milwaukee, WI, USA&lt;/p&gt; Victor Kofia Princess Margaret Cancer Centre&lt;/p&gt; Harold Gómez Boston University, Boston, MA, USA&lt;/p&gt; Florian Mittag Center for Bioinformatics Tübingen&lt;/p&gt; Jakob Matthes Center for Bioinformatics Tübingen&lt;/p&gt; Jan Rudolph Center for Bioinformatics Tübingen&lt;/p&gt; Finja Wrzodek Center for Bioinformatics Tübingen&lt;/p&gt; Eugen Netz Center for Bioinformatics Tübingen&lt;/p&gt; Alexander Diamantikos Center for Bioinformatics Tübingen&lt;/p&gt; Johannes Eichner Center for Bioinformatics Tübingen&lt;/p&gt; Roland Keller Center for Bioinformatics Tübingen&lt;/p&gt; Clemens Wrzodek Center for Bioinformatics Tübingen&lt;/p&gt; Sebastian Fröhlich Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany&lt;/p&gt; Nathan E. Lewis University of California, San Diego, La Jolla, CA, USA&lt;/p&gt; Chris J. Myers The University of Utah, Salt Lake City, UT, USA&lt;/p&gt; Nicolas Le Novère Babraham Institute, Babraham Research Campus, Cambridge, UK&lt;/p&gt; Bernhard Ø. Pallson University of California, San Diego, La Jolla, CA, USA&lt;/p&gt; Michael Hucka Caltech&lt;/p&gt; Andreas Dräger Center for Bioinformatics Tübingen&lt;/p&gt; Bioinformatics&lt;/p&gt; 2015-10-15&lt;/p&gt; https://doi.org/10.1093/bioinformatics/btv341&lt;/p&gt;en