Integration of “omics” Data and Phenotypic Data Within a Unified Extensible Multimodal Framework

Analysis of “omics” data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data. In 2015, the Ludmer Centre for Neuroinformatics and Mental Health at McGill University brought together expertise from the Douglas Mental Health University Institute, the Lady Davis Institute and the Montreal Neurological Institute (MNI) to form a genetics/epigenetics working group. The objectives of this working group are to: (i) design an automated and seamless process for (epi)genetic data that consolidates heterogeneous datasets into the LORIS open-source data platform; (ii) streamline data analysis; (iii) integrate results with provenance information; and (iv) facilitate structured and versioned sharing of pipelines for optimized reproducibility using high-performance computing (HPC) environments via the CBRAIN processing portal. This article outlines the resulting generalizable “omics” framework and its benefits, specifically, the ability to: (i) integrate multiple types of biological and multi-modal datasets (imaging, clinical, demographics and behavioral); (ii) automate the process of launching analysis pipelines on HPC platforms; (iii) remove the bioinformatic barriers that are inherent to this process; (iv) ensure standardization and transparent sharing of processing pipelines to improve computational consistency; (v) store results in a queryable web interface; (vi) offer visualization tools to better view the data; and (vii) provide the mechanisms to ensure usability and reproducibility. This framework for workflows facilitates brain research discovery by reducing human error through automation of analysis pipelines and seamless linking of multimodal data, allowing investigators to focus on research instead of data handling

Advancements in the CBRAIN Platform through Integration of Community-Based Tools and Standards

Introduction Since 2009, CBRAIN, a collaborative, web-based research platform, has served a broad international community of researchers in performing large-scale data and computational neuroscience (Sherif, 2014). With over 800 users at 193 sites in 32 different countries and hosting over 60 software pipelines, CBRAIN has provided over 35 million CPU hours on computing resources around the world, including Stampede2 at TACC, the world&#39;s largest supercomputer dedicated to academic research. CBRAIN is a central component in the Healthy Brains for Healthy Lives infrastructure (HBHL, 2018) and the Canadian Open Neuroscience Platform (CONP, 2018), which require new features connecting existing platforms (e.g. LORIS (Das, 2016), OpenNeuro (Gorgolewski, 2017), and BrainCode (Vaccarino, 2018), utilizing community-based standards (e.g. BIDS (Gorgolewski, 2016), Boutiques (Glatard, 2018), and CARMIN (Glatard, 2015)), and offering new usage modalities and interfaces. Methods CBRAIN&#39;s primary purpose is to provide an ecosystem that abstracts away the low-level details of data movement and computational execution on advanced research computing resources. CBRAIN provides an orchestration system consisting of a central control instrument, termed a Portal, which communicates and submit tasks to remote compute servers, called Bourreaux. Portals and Bourreaux access remote data resources through passive DataProviders.The CBRAIN platform provides a unifying service layer for access to remote computing resources around the world (e.g. Compute Canada, XSEDE, and the CCC-Axis). CBRAIN is a Ruby on Rails application, is completely open source (https://github.com/aces/cbrain) and provided as a service free of charge (https://portal.cbrain.mcgill.ca). Requirements from new national and international initiatives lead to developments increasing interoperability, functionality and usability supporting a wider community and integrating with a broader set of community-driven tools and standards. Results New features developed in the CBRAIN platform are: RESTful API: A fully documented and functional CBRAIN RESTful API is now available at https://app.swaggerhub.com/apis/prioux/CBRAIN/5.1.0 and allows projects to utilize CBRAIN as a backend technology. To promote a community standard, CBRAIN will support CARMIN, a common web API for remote pipeline execution, such that any CARMIN compliant tool can use CBRAIN as a backend without rewriting their package. Datalad and S3 Integration: A Datalad DataProvider provides an interface to move data from Datalad (Datalad, 2018) versioned resources into the CBRAIN ecosystem. A new S3 DataProvider provides data movement from cloud-based resources. BIDS Compatibility: Capitalizing on the BIDS standard, an automatic parallelization capability to ensure BIDSApps run efficiently. Provenance and error-handling are also available, and the user only needs to specify the BIDS-formatted input dataset and the pipeline to execute. Boutiques Integration: To enable computational pipelines to be discoverable and shareable, and ease the burden of integration and deployment, we have adopted the Boutiques JSON standard to define&nbsp;our computational pipelines and to pull new pipelines from the Boutiques Repository. New Interactive Interface: A user-focused, interactive and dynamic CBRAIN user-interface has been designed, built with React.js and GraphQL, interacting through the CBRAIN API to launch tasks and manage data, The UI is highly modular and can quickly be adapted to new user and visualization requirements over time. New modular visualizations have also been developed using React.js. Conclusions CBRAIN has for nearly a decade served as a platform for accomplishing large-scale, large-data neuroinformatics. The new implementations provide a connection to a larger community of neuroinformatics and scientific&nbsp;research and promote FAIR standards in computational science.</p

Proceedings of the OHBM Brainhack 2021

The global pandemic presented new challenges and op-portunities for organizing conferences, and OHBM 2021was no exception. The OHBM Brainhack is an event thatoccurs just prior to the OHBM meeting, typically in-per-son, where scientists of all levels of expertise and interestgather to work and learn together for a few days in a col-laborative hacking-style environment on projects of com-mon interest (1). Building off the success of the OHBM2020 Hackathon (2), the 2021 Open Science SpecialInterest Group came together online to organize a largecoordinated Brainhack event that would take place overthe course of 4 days. The OHBM 2021 Brainhack eventwas organized along two guiding principles, providinga highly inclusive collaborative environment for inter-action between scientists across disciplines and levelsof expertise to push forward important projects thatneed support, also known as the “Hack-Track” of theBrainhack. The second aim of the OHBM Brainhack is toempower scientists to improve the quality of their sci-entific endeavors by providing high-quality hands-ontraining on best practices in open-science approaches.This is best exemplified by the training events providedby the “Train-Track” at the OHBM 2021 Brainhack. Here,we briefly explain both of these elements of the OHBM2021 Brainhack, before continuing on to the Brainhackproceedings