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Fur: Find unique genomic regions for diagnostic PCR
MOTIVATION: Unique 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. RESULT...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352509/ https://www.ncbi.nlm.nih.gov/pubmed/33515232 http://dx.doi.org/10.1093/bioinformatics/btab059 |
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author | Haubold, Bernhard Klötzl, Fabian Hellberg, Lars Thompson, Daniel Cavalar, Markus |
author_facet | Haubold, Bernhard Klötzl, Fabian Hellberg, Lars Thompson, Daniel Cavalar, Markus |
author_sort | Haubold, Bernhard |
collection | PubMed |
description | MOTIVATION: Unique 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. RESULTS: Fur 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. |
format | Online Article Text |
id | pubmed-8352509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-83525092021-08-10 Fur: Find unique genomic regions for diagnostic PCR Haubold, Bernhard Klötzl, Fabian Hellberg, Lars Thompson, Daniel Cavalar, Markus Bioinformatics Original Papers MOTIVATION: Unique 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. RESULTS: Fur 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. Oxford University Press 2021-01-30 /pmc/articles/PMC8352509/ /pubmed/33515232 http://dx.doi.org/10.1093/bioinformatics/btab059 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Haubold, Bernhard Klötzl, Fabian Hellberg, Lars Thompson, Daniel Cavalar, Markus Fur: Find unique genomic regions for diagnostic PCR |
title |
Fur: Find unique genomic regions for diagnostic PCR |
title_full |
Fur: Find unique genomic regions for diagnostic PCR |
title_fullStr |
Fur: Find unique genomic regions for diagnostic PCR |
title_full_unstemmed |
Fur: Find unique genomic regions for diagnostic PCR |
title_short |
Fur: Find unique genomic regions for diagnostic PCR |
title_sort | fur: find unique genomic regions for diagnostic pcr |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352509/ https://www.ncbi.nlm.nih.gov/pubmed/33515232 http://dx.doi.org/10.1093/bioinformatics/btab059 |
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