One commonly performed bioinformatics task is to infer functional regulation of transcription factors by observing differential expression under a knockout, and integrating DNA binding information of ...that transcription factor. However, until now, this task has required dedicated bioinformatics support to perform the necessary data integration. GenomeSpace provides a protocol, or "recipe", and a user interface with inter-operating software tools to identify protein occupancies along the genome from a ChIP-seq experiment and associated differentially regulated genes from a RNA-Seq experiment. By integrating RNA-Seq and ChIP-seq analyses, a user is easily able to associate differing expression phenotypes with changing epigenetic landscapes.
Complex biomedical analyses require the use of multiple software tools in concert and remain challenging for much of the biomedical research community. We introduce GenomeSpace ...(http://www.genomespace.org), a cloud-based, cooperative community resource that currently supports the streamlined interaction of 20 bioinformatics tools and data resources. To facilitate integrative analysis by non-programmers, it offers a growing set of 'recipes', short workflows to guide investigators through high-utility analysis tasks.
Abstract
As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific ...insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The electronic analysis notebook has recently emerged as a versatile tool for this purpose, allowing scientists to combine the scientific exposition with the code that runs the analysis, creating a single “research narrative” document. The Jupyter Notebook system has become the de facto standard notebook environment in data science and genomic analysis. However, the Jupyter environment requires familiarity with programming to run analyses, and even text must be formatted using a programming-style language.To extend notebook capabilities to researchers at all levels of programming expertise, we developed the GenePattern Notebook environment, which integrates Jupyter with the hundreds of genomic tools available through the GenePattern platform. This tool allows scientists to develop, share, collaborate on, and publish their notebooks, requiring only a web browser. Investigators can design their in-silico experiments, refine workflows, launch compute-intensive analyses on cloud-based and high-performance compute resources, and publish results that others can adopt to reproduce the original analyses and modify for their own work.GenePattern Notebook provides: (1) Access to a wide range of genomic analyses within a notebook. Hundreds of analyses are available, from machine learning techniques such as clustering, classification, and dimension reduction, to omic-specific methods for gene expression analysis, proteomics, flow cytometry, sequence variation analysis, pathway analysis, and others. (2) A library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. To support the growing role of single-cell analysis, we have recently released single-cell RNA-seq preprocessing, cluster harmonization, pseudotime, and RNA velocity notebooks. (3) Notebook enhancements. A rich text editor allows scientists to format text as they would in a word processor. A user interface-building tool allows notebook developers to wrap code so it is displayed as a web form, with only the necessary inputs exposed. (4) Publication and collaborative editing. Authors can publish their notebooks, where they are then made available on the community section of the notebook workspace, where other scientists may copy, run, and edit their own version.The GenePattern Notebook environment is freely available at http://genepattern-notebook.org.
Citation Format: Michael M. Reich, Thorin Tabor, Edwin Juarez, Alexander Wenzel, Ted Liefeld, Barbara Hill, David Eby, Forrest Kim, Helga Thorvaldsdóttir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebook: An integrative analytical environment for cancer research abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1903.
Abstract Summary Copy number variation (CNV) and alteration (CNA) analysis is a crucial component in many genomic studies and its applications span from basic research to clinic diagnostics and ...personalized medicine. CNVpytor is a tool featuring a read depth-based caller and combined read depth and B-allele frequency (BAF) based 2D caller to find CNVs and CNAs. The tool stores processed intermediate data and CNV/CNA calls in a compact HDF5 file—pytor file. Here, we describe a new track in igv.js that utilizes pytor and whole genome variant files as input for on-the-fly read depth and BAF visualization, CNV/CNA calling and analysis. Embedding into HTML pages and Jupiter Notebooks enables convenient remote data access and visualization simplifying interpretation and analysis of omics data. Availability The CNVpytor track is integrated with igv.js and available at https://github.com/igvteam/igv.js. The documentation is available at https://github.com/igvteam/igv.js/wiki/cnvpytor. Usage can be tested in the IGV-Web app at https://igv.org/app and also on https://github.com/abyzovlab/CNVpytor. Supplementary information Supplementary data are available at Bioinformatics online.
One commonly performed bioinformatics task is to infer functional regulation of transcription factors by observing differential expression under a knockout, and integrating DNA binding information of ...that transcription factor. However, until now, this this task has required dedicated bioinformatics support to perform the necessary data integration. GenomeSpace provides a protocol, or "recipe", and a user interface with inter-operating software tools to identifying protein occupancies along the genome from a ChIP-seq experiment and associated differentially regulated genes from an RNA-Seq experiment. By integrating RNA-Seq and ChIP-seq analyses, a user is easily able to associate differing expression phenotypes with changing epigenetic landscapes.
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, 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, and 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. The online notebook format allows researchers to explain the analytical and scientific considerations of each step in any level of detail, promoting reproducibility and adoption.
GenePattern Notebook features are designed to help nonprogramming users. We have developed additional cell types allowing users to select analyses, specify inputs, navigate results, send result files to new analyses, and create richly formatted text, all without the need for programming.
The GenePattern Notebook Environment is also a platform for open science and reproducible research. Authors can elect to publish a notebook, making it visible to all users, who can then create a copy and use it to reproduce the author’s results or adapt it to their own work. The repository provides links to public notebooks that can be used in a publication as permalinks. Researchers can invite collaborators to work together on a notebook prior to publication.
A free, cloud-based GenePattern Notebook workspace is available at http://www.genepattern-notebook.org, where researchers can develop, share, and publish notebook documents. We have provided a collection of template notebooks that walk users through various genomic and machine learning analyses, and are collaborating with research laboratories to create integrative cancer genomics notebooks.
Note: This abstract was not presented at the meeting.
Citation Format: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov. The GenePattern Notebook environment for reproducible cancer research abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5106.
Abstract
As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential ...scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The electronic analysis notebook has recently emerged as an effective and versatile tool for this purpose, allowing scientists to combine the scientific exposition – text, images, and multimedia – with the actual code that runs the analysis, creating a single “research narrative” document. The Jupyter Notebook system has become the de facto standard notebook environment in data science and genomic analysis. However, the Jupyter environment requires familiarity with a programming language to run analyses, and even text must be formatted using a programming-style language.
To extend notebook capabilities to the needs of researchers at all levels of programming expertise, we developed the GenePattern Notebook environment, which integrates Jupyter's capabilities with the hundreds of genomic tools available through the GenePattern platform. This tool allows scientists to develop, share, collaborate on, and publish their notebooks, requiring only a web browser. In this environment, investigators can design their in-silico experiments, perform and refine analyses, launch compute-intensive analyses on cloud-based and high-performance compute resources, and publish their results as electronic notebooks that other scientists can adopt to reproduce the original analyses and modify for their own work.
GenePattern Notebook provides: (1) Access to a wide range of genomic analyses within a notebook. Hundreds of analyses are available, from machine learning techniques such as clustering, classification, and dimension reduction, to omic-specific methods for gene expression analysis, proteomics, flow cytometry, sequence variation analysis, pathway analysis, and others. (2) A library of featured genomic analysis notebooks is provided. These include templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. (3) Notebook enhancements. A rich text editor allows scientists to enter and format text as they would in a word processor. A user interface-building tool allows notebook developers to wrap their code so it is displayed as a web form, with only the necessary inputs exposed. Users of the notebook are presented with a simplified display that allows them to run the analyses without needing to interact with the code behind them. (4) Publication and collaborative editing. To make a notebook public, an author selects the “publish” feature and adds descriptive information. The notebook is then made available on the community section of the notebook workspace. An author can include a web link to a public notebook in a publication, and users who follow the link will see a read-only version of the notebook, with the option to log in to the workspace, where they can run, copy, and edit their own version.
The GenePattern Notebook environment is freely available at http://genepattern-notebook.org.
Citation Format: Michael M. Reich, Thorin Tabor, Ted Liefeld, Edwin Juarez, Barbara Hill, Helga Thorvaldsdottir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebook: An integrative analytical environment for cancer research abstract. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3207.
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, 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, and 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. The online notebook format allows researchers to explain the analytical and scientific considerations of each step in any level of detail, promoting reproducibility and adoption. Notebooks can also be shared between researchers for collaborative development.
GenePattern Notebook features are designed to help nonprogramming users create and adapt notebooks. We have developed additional cell types allowing users to choose analyses, specify inputs, navigate results, send result files to new analyses, and create richly formatted text, all without the need for programming.
A free online GenePattern Notebook workspace is available at http://www.genepattern-notebook.org, where researchers can develop, share, and publish notebook documents. We have provided a collection of template notebooks that walk users through various genomic and machine learning analyses, and are collaborating with cancer research laboratories to create integrative cancer genomics notebooks.
Citation Format: Michael M. Reich, Thorin T. Tabor, Ted Liefeld, Barbara Hill, Helga Thorvaldsdottir, Jill P. Mesirov. The GenePattern Notebook environment for reproducible cancer research abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2279.
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
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