Evidence map›Paper›PMID 35725628›Full record

ArticleGenome biology2022

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.

Advait Balaji, Bryce Kille, Anthony D Kappell, Gene D Godbold, Madeline Diep, R A Leo Elworth, Zhiqin Qian, Dreycey Albin, Daniel J Nasko, Nidhi Shah and 4 more

Abstract read
In one paragraph

Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–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

17 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. ADAPT: a programme for the advanced detection of AI-enabled pathogenic threats.Frontiers in bioengineering and biotechnology · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Practical Questions for Securing Nucleic Acid Synthesis.Applied biosafety : journal of the American Biological Safety Association · 2024
    Article
  11. Review
  12. Article
  13. Review
  14. Article
  15. Article
  16. KOMB: K-core based de novo characterization of copy number variation in microbiomes.Computational and structural biotechnology journal · 2022
    Article
  17. Article
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

14 authors.

Advait Balaji *Department of Computer Science, Rice University, Houston, TX, USA.ORCID 0000-0001-9858-9578
Bryce Kille *Department of Computer Science, Rice University, Houston, TX, USA.ORCID 0000-0003-2946-6915
Anthony D KappellSignature Science, LLC, 8329 North Mopac Expressway, Austin, TX, USA.ORCID 0000-0003-3511-9207
Gene D GodboldSignature Science, LLC, 1670 Discovery Drive, Charlottesville, VA, USA.ORCID 0000-0002-5702-4690
Madeline DiepFraunhofer USA Center Mid-Atlantic CMA, Riverdale, MD, USA.ORCID 0000-0002-9908-0367
R A Leo ElworthDepartment of Computer Science, Rice University, Houston, TX, USA.ORCID 0000-0002-3945-0661
Zhiqin QianDepartment of Computer Science, Rice University, Houston, TX, USA.
Dreycey AlbinDepartment of Computer Science, Rice University, Houston, TX, USA.
Daniel J NaskoDepartment of Computer Science, University of Maryland, College Park, MD, USA.ORCID 0000-0002-8359-6975
Nidhi ShahDepartment of Computer Science, University of Maryland, College Park, MD, USA.
Mihai PopDepartment of Computer Science, University of Maryland, College Park, MD, USA.ORCID 0000-0001-9617-5304
Santiago SegarraDepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USA.ORCID 0000-0002-8408-9633
Krista L TernusSignature Science, LLC, 8329 North Mopac Expressway, Austin, TX, USA. kternus@signaturescience.com.ORCID 0000-0003-1138-5308
Todd J TreangenDepartment of Computer Science, Rice University, Houston, TX, USA. treangen@rice.edu.ORCID 0000-0002-3760-564X

Funding

TRAINING PROGRAM IN COMPUTATIONAL BIOLOGY AND MEDICINET15LM007093 · NLM · RICE UNIVERSITY · PI Lydia E. Kavraki · 1992 to 2026
$20.8M
Project 3: Functional Microbiome and Host Signatures in Transition from Commensal to pathogenP01AI152999 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ARIAS, CESAR AUGUSTO, SAVIDGE, TOR C. · 2020 to 2025
$12.0M
NIAID NIH HHS P01 AI152999NLM NIH HHS T15 LM007093
6 · The paper itself

Abstract

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen .

Indexed as

Machine LearningBacteriaCOVID-19HumansLeukocytes, MononuclearOpen Reading Frames

Identifiers

PMID35725628
PMCPMC9208262

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.