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| abbreviation = BCO
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The '''BioCompute Object''' ('''BCO''') project is a community-driven initiative to build a framework for standardizing and sharing computations and analyses generated from [[High-throughput sequencing]] (
Originally started as a collaborative contract between the [[George Washington University]] and the [[Food and Drug Administration]], the project has grown to include over 20 universities, biotechnology companies, public-private partnerships and pharmaceutical companies including Seven Bridges and [[Harvard Medical School]].<ref>{{Cite web|url=https://www.europeanpharmaceuticalreview.com/news/67524/biocompute-genomic-data/|title=BioCompute Objects specifications to advance genomic data analysis|website=www.europeanpharmaceuticalreview.com|language=en|access-date=2017-12-21}}</ref> The BCO aims to ease the exchange of HTS workflows between various organizations, such as the FDA, pharmaceutical companies, contract research organizations, bioinformatic platform providers, and academic researchers. Due to the sensitive nature of regulatory filings, few direct references to material can be published. However, the project is currently funded to train FDA Reviewers and administrators to read and interpret BCOs, and currently has 4 publications either submitted or nearly submitted.
== Background ==
One of the biggest challenges in bioinformatics is documenting and sharing [[Scientific workflow system#Scientific workflows|scientific workflows]] in such a way that the computation and its results can be peer-reviewed or reliably reproduced.<ref>{{cite journal|title=Ten Simple Rules for Reproducible Computational Research|first1=Geir Kjetil|last1=Sandve|first2=Anton|last2=Nekrutenko|first3=James|last3=Taylor|first4=Eivind|last4=Hovig|date=24 October 2013|journal=PLOS Computational Biology|volume=9|issue=10|pages=e1003285|doi=10.1371/journal.pcbi.1003285|pmid=24204232|pmc=3812051 |bibcode=2013PLSCB...9E3285S |doi-access=free }}</ref> Bioinformatic [[Pipeline (software)|pipelines]] typically use multiple pieces of software, each of which typically has multiple versions available, multiple input parameters, multiple outputs, and possibly platform-specific configurations. As with experimental parameters in a laboratory protocol, small changes in computational parameters may have a large impact on the scientific validity of the results. The BioCompute Framework provides an [[Object-oriented design|object oriented design]] from which a BCO that contains details of a pipeline and how it was used can be constructed, [[digitally signed]], and shared. The BioCompute concept was originally developed to satisfy FDA regulatory research and review needs for evaluation, validation, and verification of genomics data. However, the Biocompute Framework follows FAIR Data Principles<ref>{{Cite journal|
== Utility ==
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== BCO Consortium ==
The BioCompute Object working group
== Implementations ==
The simple R package biocompute<ref name="biocompute-r">{{cite web|url=https://cran.r-project.org/package=biocompute|title=CRAN - Package biocompute|publisher=cran.r-project.org|accessdate=2019-11-28}}</ref> can create, validate, and export BioCompute Objects. The [https://github.com/sbg/gcs Genomics Compliance Suite] is a Shiny app that offers similar features to regular expressions found in all modern text editors. There are several internally developed [[Open-source software|open source]] software packages and web applications that implement the BioCompute specification, three of which have been deployed in a publicly accessible [[
Some bioinformatics platforms have built-in support for Biocompute, which let a user automatically create a BCO from a workflow and edit the descriptive content.
* DNAnexus and PrecisionFDA facilitate the generation of BCOs by importing workflows, allowing users to edit descriptive content. The platform supports metadata import and export of WDL and CWL scripts, and offers the BCOnexus tool, which is a high-level, platform-free tool with a graphical user interface that lets a user merge BCOs.
* Velsera's Seven Bridges Genomics and Cancer Genomics Cloud also have support for BioCompute by enabling direct pre-population of BCO fields from workflows.
* BioCompute has also been integrated into [https://hivelab.biochemistry.gwu.edu/ HIVE] and the main Galaxy instance, both of which similarly enable users to automatically generate BCOs and edit content within these platforms.
* BioCompute has also been implemented in the Common Fund Data Elements Playbook Partnership project. This implementation lets a user save a workflow when they're satisfied with the results, which aids in traceability through the network of independently-versioned resources, allowing users to save queries and annotate them for future use, sharing, or repeatability, aligning with its role in advancing bioinformatics practices.
Integration into platforms is meant to improve data handling and collaboration and provide effective ways for users to execute a workflow, and graphical representations of BCOs are often more intuitive ways of browsing or reading BCOs.
== References ==
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