Evidence map›Paper›PMID 36824759›Full record

ArticlebioRxiv : the preprint server for biology2023

Olivar: automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens.

Michael X Wang, Esther G Lou, Nicolae Sapoval, Eddie Kim, Prashant Kalvapalle, Bryce Kille, R A Leo Elworth, Yunxi Liu, Yilei Fu, Lauren B Stadler and 1 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Michael X WangDepartment of Bioengineering, Rice University, Houston, 77030, USA.ORCID 0000-0001-7009-6958
Esther G LouDepartment of Civil and Environmental Engineering, Rice University, Houston, 77005, USA.ORCID 0000-0001-7230-6517
Nicolae SapovalDepartment of Computer Science, Rice University, Houston, 77005, USA.ORCID 0000-0002-0736-5075
Eddie KimDepartment of Computer Science, Rice University, Houston, 77005, USA.ORCID 0000-0002-3770-6838
Prashant KalvapalleDepartment of Civil and Environmental Engineering, Rice University, Houston, 77005, USA.ORCID 0000-0002-8255-3623
Bryce KilleDepartment of Computer Science, Rice University, Houston, 77005, USA.ORCID 0000-0003-2946-6915
R A Leo ElworthDepartment of Computer Science, Rice University, Houston, 77005, USA.ORCID 0000-0002-3945-0661
Yunxi LiuDepartment of Computer Science, Rice University, Houston, 77005, USA.ORCID 0000-0003-2657-1816
Yilei FuDepartment of Computer Science, Rice University, Houston, 77005, USA.ORCID 0000-0002-7721-7027
Lauren B StadlerDepartment of Civil and Environmental Engineering, Rice University, Houston, 77005, USA.ORCID 0000-0001-7469-1981
Todd J TreangenDepartment of Computer Science, Rice University, Houston, 77005, USA.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
NLM NIH HHS T15 LM007093
6 · The paper itself

Abstract

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 2 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer SNPs overlapping with primers and predicted PCR byproducts. We also compared Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluated Olivar on real wastewater samples and found that Olivar had up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available as a web application at https://olivar.rice.edu. Olivar can also be installed locally as a command line tool with Bioconda. Source code, installation guide and usage are available at https://github.com/treangenlab/Olivar.

Identifiers

PMID36824759
PMCPMC9948974

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LicenceCC BY-NC-ND
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Registered trials

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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.