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We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data.The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI’s Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized.The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

Developments in high-throughput sequencing (HTS) result in an exponential increase in the amount of data generated by sequencing experiments, an increase in the complexity of bioinformatics analysis reporting and an increase in the types of data generated. These increases in volume, diversity and complexity of the data generated and their analysis expose the necessity of a structured and standardized reporting template. BioCompute Objects (BCOs) provide the requisite support for communication of HTS data analysis that includes support for workflow, as well as data, curation, accessibility and reproducibility of communication. BCOs standardize how researchers report provenance and the established verification and validation protocols used in workflows while also being robust enough to convey content integration or curation in knowledge bases. BCOs that encapsulate tools, platforms, datasets and workflows are FAIR (findable, accessible, interoperable and reusable) compliant. Providing operational workflow and data information facilitates interoperability between platforms and incorporation of future dataset within an HTS analysis for use within industrial, academic and regulatory settings. Cloud-based platforms, including High-performance Integrated Virtual Environment (HIVE), Cancer Genomics Cloud (CGC) and Galaxy, support BCO generation for users. Given the 100K+ userbase between these platforms, BioCompute can be leveraged for workflow documentation. In this paper, we report the availability of platform-dependent and platform-independent BCO tools: HIVE BCO App, CGC BCO App, Galaxy BCO API Extension and BCO Portal. Community engagement was utilized to evaluate tool efficacy. We demonstrate that these tools further advance BCO creation from text editing approaches used in earlier releases of the standard. Moreover, we demonstrate that integrating BCO generation within existing analysis platforms greatly streamlines BCO creation while capturing granular workflow details. We also demonstrate that the BCO tools described in the paper provide an approach to solve the long-standing challenge of standardizing workflow descriptions that are both human and machine readable while accommodating manual and automated curation with evidence tagging. Database URL: https://www.biocomputeobject.org/resources

BackgroundThe field of bioinformatics has grown at such a rapid pace that a gap in standardization exists when reporting an analysis. In response, the BioCompute project was created to standardize the type and method of information communicated when describing a bioinformatic analysis. Once the project became established, its goals shifted to broadening awareness and usage of BioCompute, and soliciting feedback from a larger audience. To address these goals, the BioCompute project collaborated with precisionFDA on a crowdsourced challenge that ran from May 2019 to October 2019. This challenge had a beginner track where participants submitted BCOs based on a pipeline of their choosing, and an advanced track where participants submitted applications supporting the creation of a BCO and verification of BCO conformance to specifications.ResultsIn total, there were 28 submissions to the beginner track (including submissions from a bioinformatics master’s class at George Washington University) and three submissions to the advanced track. Three top performers were selected from the beginner track, while a single top performer was selected for the advanced track. In the beginner track, top performers differentiated themselves by submitting BCOs that included more than the minimally compliant content. Advanced track submissions were very impressive. They included a complete web application, a command line tool that produced a static result, and a dockerized container that automatically created the BCO as the tool was run. The ability to harmonize the correct function, a simple user experience, and the aesthetics of the tool interface differentiated the tools.ConclusionsDespite being new to the concept, most beginner track scores were high, indicating that most users understood the fundamental concepts of the BCO specification. Novice bioinformatics students were an ideal cohort for this Challenge because of their lack of familiarity with BioCompute, broad diversity of research interests, and motivation to submit high-quality work. This challenge was successful in introducing the BCO to a wider audience, obtaining feedback from that audience, and resulting in a tool novices may use for BCO creation and conformance. In addition, the BCO specification itself was improved based on feedback illustrating the utility of a “wisdom of the crowd” approach to standards development.