PrecisionFDA
BioCompute Object (BCO) App-a-thon

PrecisionFDA is partnering with George Washington University and FDA/CBER HIVE to launch a BioCompute Object (BCO) App-a-thon. Participants will be given the opportunity to enhance standards around reproducibility and documentation of biomedical high-throughput sequencing through BCO creation and conformance. Beginner and advanced tracks will be available for all participant levels.

• Starts

  2019-05-14 14:55:14 UTC

• Ends

  2019-07-12 14:55:26 UTC

about 2 months remaining

PrecisionFDA
CDRH Biothreat Challenge

Provide challenge data sets and reference standards for performance comparison of bioinformatics tools used in the biothreat and infectious disease NGS diagnostics community. The focus of this challenge is to enable tool developers to test their algorithms on blinded mock-clinical and in silico metagenomics samples using provided regulatory-grade reference genomes from the FDA-ARGOS database. This will enable the community to look at bioinformatics pipeline performance using a fixed reference genome data standard. The challenge will help familiarize precisionFDA users with the agency’s innovative FDA-ARGOS database resource (www.fda.gov/argos).

• Starts

  2018-08-04 00:00:00 UTC

• Ends

  2018-10-19 03:00:00 UTC

PrecisionFDA
NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge - Subchallenge 1

Sample mislabeling (accidental swapping of patient samples) or data mislabeling (accidental swapping of patient omics data) is known to be one of the obstacles in basic and translational research because this accidental swapping contributes to irreproducible results and invalid conclusions. The objective of this challenge is to encourage development and evaluation of computational algorithms that can accurately detect and correct mislabeled samples using rich multi-omics datasets.

• Starts

  2018-09-24 19:00:00 UTC

• Ends

  2018-11-05 04:59:59 UTC

PrecisionFDA
NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge - Subchallenge 2

Sample mislabeling (accidental swapping of patient samples) or data mislabeling (accidental swapping of patient omics data) is known to be one of the obstacles in basic and translational research because this accidental swapping contributes to irreproducible results and invalid conclusions. The objective of this challenge is to encourage development and evaluation of computational algorithms that can accurately detect and correct mislabeled samples using rich multi-omics datasets.

• Starts

  2018-11-06 00:00:00 UTC

• Ends

  2018-12-19 07:59:59 UTC