The GenePattern Notebook Environment

Transcription

1 Tool The GenePattern Notebook Environment Graphical Abstract Authors Michael Reich, Thorin Tabor, Te Liefel, Helga Thorvalsóttir, Barbara Hill, Pablo Tamayo, Jill P. Mesirov Corresponence In Brief Reich et al. have evelope software that integrates the capabilities of electronic analysis notebooks an bioinformatics analysis portals. GenePattern Notebook uses the popular Jupyter Notebook platform that interleaves text, graphics, an coe, an brings these tools for reproucible research, as well as access to hunres of bioinformatics analyses, to non-programmers. Highlights We integrate the GenePattern genomics platform with the Jupyter Notebook environment Notebooks interleave text, graphics, an analyses into complete research narratives Users can embe genomic analyses into notebooks without the nee to write coe GenePattern Notebook is freely available at genepattern-notebook.org Reich et al., 2017, Cell Systems 5, August 23, 2017 ª 2017 Elsevier Inc.

2 Cell Systems Tool The GenePattern Notebook Environment Michael Reich, 1,4, * Thorin Tabor, 1 Te Liefel, 1 Helga Thorvalsóttir, 2 Barbara Hill, 2 Pablo Tamayo, 1,3 an Jill P. Mesirov 1,2,3 1 School of Meicine, University of California, San Diego, La Jolla, CA, USA 2 The Broa Institute of MIT an Harvar, Cambrige, MA, USA 3 Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA 4 Lea Contact *Corresponence: mmreich@clou.ucs.eu SUMMARY Interactive analysis notebook environments promise to streamline genomics research through interleaving text, multimeia, an executable coe into unifie, sharable, reproucible research narratives. However, current notebook systems require programming knowlege, limiting their wier aoption by the research community. We have evelope the GenePattern Notebook environment ( genepattern-notebook.org), to our knowlege the first system to integrate the ynamic capabilities of notebook systems with an investigator-focuse, easy-to-use interface that provies access to hunres of genomic tools without the nee to write coe. The ongoing explosion of omics atasets an the promise of scientific iscovery arising from their analysis have given rise to software systems that aim to provie easy access to avance methos for nonprogramming scientists. These bioinformatics tool aggregation portals, e.g., Galaxy (Afgan et al., 2016), GenePattern (Reich et al., 2006), an KNIME (Berthol et al., 2009), also provie for the creation an encapsulation of analytic workflows, transparent access to scalable compute resources, an removal of software installation an implementation concerns from the scientific user. Alternatively, analysis notebook environments, inspire by the literate programming philosophy (Knuth, 1984), integrate the exposition of a scientific project with the associate coe. They aim to create an executable ocument that ieally serves as a complete escription of a research project an which coul also be run to reprouce the author s results. Examples inclue SWEAVE (Leisch, 2002), Jupyter Notebook (Ragan-Kelley et al., 2014), Beaker (beakernotebook.com), an Zeppelin (zeppelin. apache.org). Each of these two types of system brings significant value to its targete user base yet has limitations that prevent wier aoption. Notebook environments moel their interface aroun the annotation of sections of coe, an therefore assume that the user is fluent in a programming language such as Python or R. Bioinformatics tool aggregation portals successfully remove the requirement for coing expertise but to ate have ha limite ability to incorporate the variety of rich text an meia formats require to represent the full scientific narrative surrouning each analysis step. We have evelope GenePattern Notebook (Figure 1), an environment that integrates the capabilities of both types of system, allowing users to incorporate encapsulate analysis tools, complete with their user-frienly interface, from a bioinformatics aggregation portal into an interactive analysis notebook. The environment is base on two long-staning software projects: the GenePattern platform for integrative genomics an the Jupyter Notebook environment for interactive computing. GenePattern ( first release in 2004, consists of a repository of hunres of bioinformatics analysis an visualization methos ( moules ), as well as utilities for ata formatting, preprocessing, an other auxiliary functions that provie important glue between analysis steps. The user interface is point an click with no programming require. The public GenePattern server, hoste at org since 2008, has over 40,000 registere users an runs 2,000 5,000 analysis jobs per week. Aitional public servers are available at Iniana University (gp.iniana.eu/gp) an the Garvan Institute (pwbc.garvan.org.au/gp). The software has also been ownloae for local installation by over 17,000 bioinformatics core facilities, research laboratories, an iniviual scientists. The Jupyter Notebook environment ( provies a laboratory notebook metaphor in which researchers buil a step-by-step scientific narrative out of cells that interleaves coe, formatte text, mathematical formulae, plots, an multimeia. The resulting notebooks can be share, eite, execute, an publishe as complete encapsulations of in silico research. The GenePattern Notebook functionality takes the Jupyter Notebook interface one step further, aing analysis, login, an rich text input components that present the GenePattern interface to provie coe-free analysis an visualization (Figure S1). All cell types interact seamlessly with existing Jupyter cell types. Within a Python coe cell, programming users can easily reference analysis results from a previous GenePattern analysis cell, an in a GenePattern analysis cell, programmers can use Python variables as inputs. We integrate GenePattern with Jupyter through the use of Jupyter s ipywigets package, which provies a framework for the creation of new user interface objects within Jupyter Notebooks, an GenePattern s Web services interface, which exposes all of the functionality of GenePattern (e.g., searching for an obtaining moule information or querying for the execution status of an analysis) to programmatic access. This combination is a esign pattern that has general applicability to the class of Cell Systems 5, , August 23, 2017 ª 2017 Elsevier Inc. 149

3 Figure 1. GenePattern Notebook Environment Components The GenePattern Notebook environment consists of (A) an online environment, powere by JupyterHub, where users can create, share, an publish GenePattern Notebooks; (B) a GenePattern server that provies hunres of pre-package genomic an machine-learning analyses, all accessible through (C) a Web browser. Web service-base tools, an the Jupyter evelopment team is incorporating our approach into the currently evolving esign of the Jupyter interfaces for graphical input (Dr. Fernano Perez, personal communication, September 26, 2016). To promote the evelopment an issemination of GenePattern Notebooks with minimal installation requirements, we have release an online GenePattern Notebook repository an workspace where researchers can collaboratively evelop an publish notebook ocuments. It provies a complete Jupyter environment, connections to several GenePattern servers, an for programmers, the common Python packages use in bioinformatics analysis (numpy, panas, matplotlib, scikit, etc.). We seee the repository with notebooks that provie commonly use machine-learning methos: clustering, classification, an preiction, as well as imension reuction an ifferential expression analysis. Those who wish to run the GenePattern Notebook environment on their own compute resources have two options. (1) Non-programmers can install the Kitematic Docker (kitematic. com) application an use it to run the GenePattern Notebook Docker image, available on the stanar Docker Hub repository (hub.ocker.com). This provies a complete, reay-to-run notebook environment with all epenencies preinstalle. (2) Programmers may install the GenePattern Notebook an its epenencies through the pip or cona package manager interfaces. To our knowlege GenePattern Notebook is the first integration of a bioinformatics tool aggregation portal with an analysis notebook environment. This approach benefits both nonprogramming an programming investigators alike. For the nonprogrammer, GenePattern Notebook provies the user-frienly GenePattern genomic analysis capabilities within a publishable notebook format. For the programmer alreay using the Jupyter environment, it affors easy access to the entire GenePattern library of analysis an visualization moules that can be supplemente with the investigator s own coe routines. The GenePattern Notebook environment, along with an introuctory emonstration vieo, ocumentation, an tutorials, is available at The software is freely available uner a BSD-style open source license. STAR+METHODS Detaile methos are provie in the online version of this paper an inclue the following: KEY RESOURCES TABLE CONTACT FOR REAGENT AND RESOURCE SHARING DATA AND SOFTWARE AVAILABILITY ADDITIONAL RESOURCES SUPPLEMENTAL INFORMATION Supplemental Information inclues one figure an can be foun with this article online at AUTHOR CONTRIBUTIONS Conceptualization: M.R., T.T., P.T., J.P.M.; Software, T.T., T.L., Writing Original Draft: M.R., T.T., T.L., H.T., J.P.M.; Writing Review & Eiting: M.R., J.P.M.; Valiation: B.H.; Project Aministration: M.R., H.T.; Funing Acquisition: J.P.M. ACKNOWLEDGMENTS This work was fune by NIH grants R01-GM an U24-CA We thank Fernano Perez an Brian Granger for their technical avice. Receive: November 4, 2016 Revise: February 23, 2017 Accepte: July 13, 2017 Publishe: August 16, Cell Systems 5, , August 23, 2017

4 REFERENCES Afgan, E., Baker, D., Van en Beek, M., Blankenberg, D., Bouvier, D., Cech, M., Chilton, J., Clements, D., Coraor, N., Eberhar, C., an Gr uning, B. (2016). The Galaxy platform for accessible, reproucible an collaborative biomeical analyses: 2016 upate. Nucleic Acis Res. 44, W3 W10. Berthol, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Thiel, K., an Wisweel, B. (2009). KNIME-the Konstanz information miner: version 2.0 an beyon. ACM SIGKDD Explorations Newsletter 11, Knuth, D.E. (1984). Literate programming. Computer J. 27, Leisch, F. (2002). Sweave: ynamic generation of statistical reports using literate ata analysis. In Compstat, W. H arle an B. Rönz, es. (Physica), pp Ragan-Kelley, M., Perez, F., Granger, B., Kluyver, T., Ivanov, P., Freeric, J., an Bussonnier, M. (2014). The Jupyter/IPython architecture: a unifie view of computational research, from interactive exploration to communication an publication. In AGU Fall Meeting Abstracts, Vol. 1 (American Geophysical Union), H44D-07. Reich, M., Liefel, T., Goul, J., Lerner, J., Tamayo, P., an Mesirov, J.P. (2006). GenePattern 2.0. Nat. Genet. 38, Cell Systems 5, , August 23,

5 STAR+METHODS KEY RESOURCES TABLE REAGENT or RESOURCE SOURCE IDENTIFIER Software an Algorithms GenePattern Reich et al., Jupyter Notebook environment Ragan-Kelley et al., Kitematic Docker application N/A kitematic.com GenePattern Notebook web site an workspace This paper CONTACT FOR REAGENT AND RESOURCE SHARING Further information an requests for resources shoul be irecte to an will be fulfille by the Lea Contact, ucs.eu. DATA AND SOFTWARE AVAILABILITY GenePattern Notebook web site an online repository: ADDITIONAL RESOURCES GenePattern web site: Jupyter Notebook environment: Kitematic web site: e1 Cell Systems 5, e1 e1, August 23, 2017