PrecisionFDA
Truth Challenge
Engage and improve DNA test results with our community challenges
- Ryan Poplin
- Mark DePristo
- Verily Life Sciences Team
- Rafael Aldana
- Hanying Feng
- Brendan Gallagher
- Jun Ye
for Clinical Genomics
- Aaron Statham
- Mark Cowley
- Joseph Copty
- Mark Pinese
- Deepak Grover
- Deepak Grover
- Rafael Aldana
- Hanying Feng
- Brendan Gallagher
- Jun Ye
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.
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. |