Evidence map›Paper›PMID 33515232›Full record

ArticleBioinformatics (Oxford, England)2021

Fur: Find unique genomic regions for diagnostic PCR.

Bernhard Haubold, Fabian Klötzl, Lars Hellberg, Daniel Thompson, Markus Cavalar

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Rapid detection of cultivated truffle fungi (Applied and environmental microbiology · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Fast detection of unique genomic regions.Computational and structural biotechnology journal · 2025
    Review
  9. Article
  10. Marker discovery in the large.Bioinformatics advances · 2024
    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

5 authors.

Bernhard HauboldDepartment of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.ORCID 0000-0001-9442-4012
Fabian KlötzlDepartment of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Biology, Plön, Germany.
Lars HellbergMolecular Infection Diagnostics, Euroimmun Medizinische Labordiagnostika, Lübeck, Germany.
Daniel ThompsonMolecular Infection Diagnostics, Euroimmun Medizinische Labordiagnostika, Lübeck, Germany.
Markus CavalarMolecular Infection Diagnostics, Euroimmun Medizinische Labordiagnostika, Lübeck, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationUnique marker sequences are highly sought after in molecular diagnostics. Nevertheless, there are only few programs available to search for marker sequences, compared to the many programs for similarity search. We therefore wrote the program Fur for Finding Unique genomic Regions.

resultsFur takes as input a sample of target sequences and a sample of closely related neighbors. It returns the regions present in all targets and absent from all neighbors. The recently published program genmap can also be used for this purpose and we compared it to fur. When analyzing a sample of 33 genomes representing the major phylogroups of E.coli, fur was 40 times faster than genmap but used three times more memory. On the other hand, genmap yielded three times more markers, but they were less accurate when tested in silico on a sample of 237 E.coli genomes. We also designed phylogroup-specific PCR primers based on the markers proposed by genmap and fur, and tested them by analyzing their virtual amplicons in GenBank. Finally, we used fur to design primers specific to a Lactobacillus species, and found excellent sensitivity and specificity in vitro. AVAILABILITY AND IMPLEMENTATION: Fur sources and documentation are available from https://github.com/evolbioinf/fur. The compiled software is posted as a docker container at https://hub.docker.com/r/haubold/fox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifiers

PMID33515232
PMCPMC8352509

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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.