Abstract 2588: GenePattern Notebook: an environment for reproducible cancer research
Authors: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov
Journal: Cancer Research (2017)

Abstract As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, the need has become more urgent for a software environment that supports the entire “idea to dissemination” cycle of an integrative cancer genomics analysis. Such a system would need to provide access to a large number of analysis tools without the need for programming, be sufficiently flexible to accommodate the practices of non-programming biologists as well as experienced bioinformaticians, and would provide a way for researchers to encapsulate their work into a single “executable document” including not only the analytical workflow but also the associated descriptive text, graphics, and supporting research. To address these needs, we have developed GenePattern Notebook, based on the GenePattern environment for integrative genomics and the Jupyter Notebook system. GenePattern Notebook unites the phases of in silico research – experiment design, collaborative analysis, and publication – into a single interface. GenePattern Notebook presents a familiar lab-notebook format that allows researchers to build a record of their work by creating “cells” containing text, graphics, or executable analyses. Researchers add, delete, and modify cells as the research evolves, supporting the initial research phases of prototyping and collaborative analysis. When an analysis is ready for publication, the same document that was used in the design and analysis phases becomes a research narrative that interleaves text, graphics, data, and executable analyses, serving as the complete, reproducible, in silico methods section for a publication. GenePattern Notebook features are designed to make it easy for nonprogramming users to create and adapt notebooks. We have developed new cell types that allow users to choose analyses, specify input parameters and datasets, navigate results, send result files to new analyses, and create richly formatted text, all without the need for programming. We have released a freely available online GenePattern Notebook workspace, http://notebook.genepattern.org, where researchers can develop and publish notebook documents. We have provided a collection of template notebooks that walk users through various machine learning analyses, and are collaborating with cancer research laboratories to create integrative cancer genomics notebooks as well. Notebook topics in development include characterization of intratumoral heterogeneity from single cell RNA-Seq data, effective clinical interpretation of comprehensive genomic profiling from whole exome sequencing of a patient’s tumor and germ line samples, and identification of master regulators/transcription factors associated with the downstream transcriptional effects associated with the activation of an oncogene. Citation Format: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov. GenePattern Notebook: an environment for reproducible cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2588. doi:10.1158/1538-7445.AM2017-2588