PrecisionFDA
Truth Challenge

Engage and improve DNA test results with our community challenges

The precisionFDA Truth Challenge ran from April 26, 2016 to May 26, 2016, giving participants the unique and exciting opportunity to test their NGS pipelines on an uncharacterized sample (HG002) and publish results on precisionFDA for subsequent evaluation against newly-revealed "truth" data.
This web page discusses the results and lists the awards and recognitions handed out by the precisionFDA team. Due to novelties related to both the truth data and the comparison methodology, these results are only a first cut at conducting an evaluation. In the sections below, we present some lessons learned during the process of setting up the challenge, determining the truth, and using a new comparison methodology; and we finally invite the community to further explore these results and provide insight for the future.
Community Challenge Awards
Highest
SNP Performance
in the precisionFDA Truth Challenge
Awarded to
Verily Life Sciences
  • Ryan Poplin
  • Mark DePristo
  • Verily Life Sciences Team
Highest
SNP Recall
in the precisionFDA Truth Challenge
Awarded to
Sentieon
  • Rafael Aldana
  • Hanying Feng
  • Brendan Gallagher
  • Jun Ye
Highest
SNP Precision
in the precisionFDA Truth Challenge
Awarded to
Kinghorn Center
for Clinical Genomics
  • Aaron Statham
  • Mark Cowley
  • Joseph Copty
  • Mark Pinese
Highest
INDEL Performance
in the precisionFDA Truth Challenge
Awarded to
Sanofi-Genzyme
  • Deepak Grover
Highest
INDEL Recall
in the precisionFDA Truth Challenge
Awarded to
Sanofi-Genzyme
  • Deepak Grover
Highest
INDEL Precision
in the precisionFDA Truth Challenge
Awarded to
Sentieon
  • Rafael Aldana
  • Hanying Feng
  • Brendan Gallagher
  • Jun Ye
Must-read Introductory Remarks

We are very excited to see growing numbers of challenge participants! For this challenge we received 35 entries, not only from many who participated in the previous challenge, but also from several first time participants. Some participants showcased their developing new methods, while others decided to use existing methods and see how they perform in this particular environment. We are extremely grateful to all participants for their willingness to share their results and to contribute to this effort.

This second precisionFDA challenge was conducted in collaboration with the Genome in a Bottle (GiaB) consortium, which provided the "truth" data, as well as with the Global Alliance for Genomics and Health (GA4GH), which provided best practices and software for conducting the comparison of participants' entries to the truth data. In fact, this effort represents the first time that this new truth data has been investigated (and it's only an approximation of the truth, hence sometimes we say "truth" instead of truth), and the first time that this new comparison methodology has been applied at scale across the vast number of submitted entries.

Our evaluation against the HG002 truth data has produced several metrics (such as f-score, recall and precision) across different variant types (such as SNP and indels), subtypes (such as insertions or deletions of specific size ranges) and genomic contexts (such as whole genome, coding regions, etc.), leading to thousands of computed numbers per challenge entry. We've chosen six of them (f-score, recall and precision, across SNPs and across indels, in the whole genome) to use as the basis for handing out awards and recognitions.

These results are by no means the final word. Given the originality of the truth data and the comparison methodology, we expect the community to further conduct analyses and contribute to improvements in the benchmarking methodology, the correctness of the truth data, and the definition of comparison metrics.

We would like to acknowledge and thank all of those who participated in the precisionFDA Truth Challenge. As with the previous challenge, we hope that everyone will feel like a winner.

Overview of results

The following table summarizes the challenge entries, and the results of the comparison against the HG002 truth data.

We've given each entry a unique label, comprised of the name of the submitting user as well as a short mnemonic keyword representing the pipeline. (As with the previous challenge, these keywords are merely indicative of each pipeline's main component, hence somewhat subjective; for a more faithful description of each pipeline, refer to the full text that accompanied each submission by following the label links). Each submitted entry consisted of two VCFs, corresponding to the variants called on the HG001/NA12878 and HG002/NA24385 datasets respectively. Links to these files can be found in the "Datasets" tab of the table.

The entries are sorted alphabetically. You can click on any column header to re-sort the table according to that column.

Label Submitter Organization SNP-Fscore SNP-recall SNP-precision INDEL-Fscore INDEL-recall INDEL-precision
raldana-dualsentieon Rafael Aldana   et al. Sentieon 99.9260 99.9131 99.9389 99.1095 98.7566 99.4648
bgallagher-sentieon Brendan Gallagher   et al. Sentieon 99.9296 99.9673 99.8919 99.2678 99.2143 99.3213
mlin-fermikit Mike Lin DNAnexus Science 98.8629 98.2311 99.5029 95.5997 94.8918 96.3183
jmaeng-gatk Ju Heon Maeng Yonsei University 99.6144 99.4608 99.7686 99.1098 99.0216 99.1981
ckim-dragen Changhoon Kim Macrogen 99.8268 99.9524 99.7015 99.1359 99.1574 99.1143
ckim-gatk Changhoon Kim Macrogen 99.6466 99.4788 99.8150 99.2271 99.1551 99.2992
ckim-isaac Changhoon Kim Macrogen 98.5357 97.1616 99.9494 95.8099 93.7006 98.0163
ckim-vqsr Changhoon Kim Macrogen 99.2866 98.6511 99.9303 99.2541 99.0614 99.4476
ltrigg-rtg2 Len Trigg RTG 99.8749 99.8935 99.8562 99.2539 98.8759 99.6347
ltrigg-rtg1 Len Trigg RTG 99.8754 99.8921 99.8587 99.0160 98.3355 99.7061
hfeng-pmm1 Hanying Feng   et al. Sentieon 99.9496 99.9227 99.9766 99.3397 99.0289 99.6526
hfeng-pmm2 Hanying Feng   et al. Sentieon 99.9416 99.9254 99.9579 99.3119 99.0152 99.6103
hfeng-pmm3 Hanying Feng   et al. Sentieon 99.9548 99.9339 99.9756 99.3628 99.0161 99.7120
jlack-gatk Justin Lack NIH 99.7200 99.9393 99.5016 98.6899 98.8138 98.5664
astatham-gatk Aaron Statham   et al.