Evidence map›Paper›PMID 31675989›Full record

ArticleGenome biology2019

Scaling computational genomics to millions of individuals with GPUs.

Amaro Taylor-Weiner, François Aguet, Nicholas J Haradhvala, Sager Gosai, Shankara Anand, Jaegil Kim, Kristin Ardlie, Eliezer M Van Allen, Gad Getz

Abstract readEvaluation Study
In one paragraph

Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 213 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
213citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

213 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Long-term Survival and Molecular Biomarker Evaluation of a Phase II Cetuximab and Nivolumab Clinical Trial in Recurrent/Metastatic Head and Neck Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
    Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Atlas of glomerular disease-specific genetic effects on blood transcriptome.medRxiv : the preprint server for health sciences · 2026
    Article
  20. Article

153 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Amaro Taylor-WeinerBroad Institute of MIT and Harvard, Cambridge, MA, USA.
François AguetBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Nicholas J HaradhvalaBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Sager GosaiBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Shankara AnandBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Jaegil KimBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Kristin ArdlieBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Eliezer M Van AllenBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Gad GetzBroad Institute of MIT and Harvard, Cambridge, MA, USA. gadgetz@broadinstitute.org.ORCID 0000-0002-0936-0753

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
MOLECULAR BIOPHYSICS TRAINING GRANTT32GM008313 · NIGMS · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI MURTHY, VENKATESH N · 1989 to 2023
$14.6M
NHGRI NIH HHS T32 HG002295NIGMS NIH HHS T32 GM008313
6 · The paper itself

Abstract

Current genomics methods are designed to handle tens to thousands of samples but will need to scale to millions to match the pace of data and hypothesis generation in biomedical science. Here, we show that high efficiency at low cost can be achieved by leveraging general-purpose libraries for computing using graphics processing units (GPUs), such as PyTorch and TensorFlow. We demonstrate > 200-fold decreases in runtime and ~ 5-10-fold reductions in cost relative to CPUs. We anticipate that the accessibility of these libraries will lead to a widespread adoption of GPUs in computational genomics.

Indexed as

Computer GraphicsGenomicsMachine LearningQuantitative Trait LociSoftware

Identifiers

PMID31675989
PMCPMC6823959

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.