ExonSurfer: a web-tool to design primers at exon-exon junctions.
Alternative splicing
Exon-exon junction
Gene expression
Primer design
RT-qPCR
Web tool
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
12 Jun 2024
12 Jun 2024
Historique:
received:
23
01
2024
accepted:
24
05
2024
medline:
13
6
2024
pubmed:
13
6
2024
entrez:
12
6
2024
Statut:
epublish
Résumé
Reverse transcription quantitative PCR (RT-qPCR) with intercalating dyes is one of the main techniques to assess gene expression levels used in basic and applied research as well as in diagnostics. However, primer design for RT-qPCR can be complex due to the high demands on primer quality. Primers are best placed on exon junctions, should avoid polymorphic regions, be specific to the target transcripts and also prevent genomic amplification accurately, among others. Current software tools manage to meet all the necessary criteria only insufficiently. Here, we present ExonSurfer, a novel, user-friendly web-tool for qPCR primer design. ExonSurfer combines the different steps of the primer design process, encompassing target selection, specificity and self-complementarity assessment, and the avoidance of issues arising from polymorphisms. Amplification of potentially contaminating genomic DNA is avoided by designing primers on exon-exon junctions, moreover, a genomic alignment is performed to filter the primers accordingly and inform the user of any predicted interaction. In order to test the whole performance of the application, we designed primer pairs for 26 targets and checked both primer efficiency, amplicon melting temperature and length and confirmed the targeted amplicon by Sanger sequencing. Most of the tested primers accurately and selectively amplified the corresponding targets. ExonSurfer offers a comprehensive end-to-end primer design, guaranteeing transcript-specific amplification. The user interface is intuitive, providing essential specificity and amplicon details. The tool can also be used by command line and the source code is available. Overall, we expect ExonSurfer to facilitate RT-qPCR set-up for researchers in many fields.
Sections du résumé
BACKGROUND
BACKGROUND
Reverse transcription quantitative PCR (RT-qPCR) with intercalating dyes is one of the main techniques to assess gene expression levels used in basic and applied research as well as in diagnostics. However, primer design for RT-qPCR can be complex due to the high demands on primer quality. Primers are best placed on exon junctions, should avoid polymorphic regions, be specific to the target transcripts and also prevent genomic amplification accurately, among others. Current software tools manage to meet all the necessary criteria only insufficiently. Here, we present ExonSurfer, a novel, user-friendly web-tool for qPCR primer design.
RESULTS
RESULTS
ExonSurfer combines the different steps of the primer design process, encompassing target selection, specificity and self-complementarity assessment, and the avoidance of issues arising from polymorphisms. Amplification of potentially contaminating genomic DNA is avoided by designing primers on exon-exon junctions, moreover, a genomic alignment is performed to filter the primers accordingly and inform the user of any predicted interaction. In order to test the whole performance of the application, we designed primer pairs for 26 targets and checked both primer efficiency, amplicon melting temperature and length and confirmed the targeted amplicon by Sanger sequencing. Most of the tested primers accurately and selectively amplified the corresponding targets.
CONCLUSION
CONCLUSIONS
ExonSurfer offers a comprehensive end-to-end primer design, guaranteeing transcript-specific amplification. The user interface is intuitive, providing essential specificity and amplicon details. The tool can also be used by command line and the source code is available. Overall, we expect ExonSurfer to facilitate RT-qPCR set-up for researchers in many fields.
Identifiants
pubmed: 38867172
doi: 10.1186/s12864-024-10456-2
pii: 10.1186/s12864-024-10456-2
doi:
Substances chimiques
DNA Primers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
594Informations de copyright
© 2024. The Author(s).
Références
VanGuilder HD, Vrana KE, Freeman WM. Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques. 2008;44(5):619–26.
doi: 10.2144/000112776
pubmed: 18474036
Bustin S, Huggett J. qPCR primer design revisited. Biomol Detect Quantif. 2017;14:19–28.
doi: 10.1016/j.bdq.2017.11.001
pubmed: 29201647
pmcid: 5702850
Guo J, Starr D, Guo H. Classification and review of free PCR primer design software. Jonathan W, editor. Bioinformatics. 2021;36(22–23):5263–8.
doi: 10.1093/bioinformatics/btaa910
pubmed: 33104196
Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 2012;40(15):e115–e115.
doi: 10.1093/nar/gks596
pubmed: 22730293
pmcid: 3424584
Koressaar T, Remm M. Enhancements and modifications of primer design program Primer3. Bioinformatics. 2007;23(10):1289–91.
doi: 10.1093/bioinformatics/btm091
pubmed: 17379693
Kõressaar T, Lepamets M, Kaplinski L, Raime K, Andreson R, Remm M. Primer3_masker: integrating masking of template sequence with primer design software. Hancock J, editor. Bioinformatics. 2018;34(11):1937–8.
doi: 10.1093/bioinformatics/bty036
pubmed: 29360956
Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 2012;13(1):134.
doi: 10.1186/1471-2105-13-134
pubmed: 22708584
pmcid: 3412702
Johnston AD, Lu J, Ru KL, Korbie D, Trau M. PrimerROC: accurate condition-independent dimer prediction using ROC analysis. Sci Rep. 2019;9(1):209.
doi: 10.1038/s41598-018-36612-9
pubmed: 30659212
pmcid: 6338771
Govindkumar B, Kavyashree B, Patel K, Sasidharan K, Siva Arumugam T, Thomas L, et al. Ex-Ex primer: an experimentally validated tool for designing oligonucleotides spanning spliced nucleic acid regions from multiple species. J Biotechnol. 2022;343:1–6.
doi: 10.1016/j.jbiotec.2021.10.009
pubmed: 34756973
Arvidsson S, Kwasniewski M, Riaño-Pachón DM, Mueller-Roeber B. QuantPrime – a flexible tool for reliable high-throughput primer design for quantitative PCR. BMC Bioinformatics. 2008;9(1):465.
doi: 10.1186/1471-2105-9-465
pubmed: 18976492
pmcid: 2612009
Kalendar R, Khassenov B, Ramankulov Y, Samuilova O, Ivanov KI. FastPCR: an in silico tool for fast primer and probe design and advanced sequence analysis. Genomics. 2017;109(3–4):312–9.
doi: 10.1016/j.ygeno.2017.05.005
pubmed: 28502701
Martin FJ, Amode MR, Aneja A, Austine-Orimoloye O, Azov AG, Barnes I, et al. Ensembl 2023. Nucleic Acids Res. 2023;51(D1):D933–41.
doi: 10.1093/nar/gkac958
pubmed: 36318249
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10(1):421.
doi: 10.1186/1471-2105-10-421
pubmed: 20003500
pmcid: 2803857
Sherry ST. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–11.
doi: 10.1093/nar/29.1.308
pubmed: 11125122
pmcid: 29783
O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44(D1):D733–45.
doi: 10.1093/nar/gkv1189
pubmed: 26553804
Schmedt T, Chen Y, Nguyen TT, Li S, Bonanno JA, Jurkunas UV. Telomerase immortalization of human corneal endothelial cells yields functional hexagonal monolayers. Lewin A, editor. PLoS One. 2012;7(12):e51427.
doi: 10.1371/journal.pone.0051427
pubmed: 23284695
pmcid: 3528758
Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611–22.
doi: 10.1373/clinchem.2008.112797
pubmed: 19246619
Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001;29(9):45e–45.
doi: 10.1093/nar/29.9.e45
Malucelli A, Sauerwein H, Pfaffl MW, Meyer HHD. Quantification of androgen receptor mRNA in tissues by competitive co-amplification of a template in reverse transcription—polymerase chain reaction. J Steroid Biochem Mol Biol. 1996;58(5–6):563–8.
doi: 10.1016/0960-0760(96)00077-5
pubmed: 8918983
Pabinger S, Rödiger S, Kriegner A, Vierlinger K, Weinhäusel A. A survey of tools for the analysis of quantitative PCR (qPCR) data. Biomol Detect Quantif. 2014;1(1):23–33.
doi: 10.1016/j.bdq.2014.08.002
pubmed: 27920994
pmcid: 5129434
Khan-Malek R, Wang Y. Statistical analysis of quantitative RT-PCR results. In: Gautier JC, editor. Drug safety evaluation. New York: Springer New York; 2017. p. 281–96. (Methods in Molecular Biology; vol. 1641). Available from: http://link.springer.com/10.1007/978-1-4939-7172-5_15 .
doi: 10.1007/978-1-4939-7172-5_15
Dagnall CL, Hicks B, Teshome K, Hutchinson AA, Gadalla SM, Khincha PP, et al. Effect of pre-analytic variables on the reproducibility of qPCR relative telomere length measurement. Criscuolo F, editor. PLOS One. 2017;12(9):e0184098.
doi: 10.1371/journal.pone.0184098
pubmed: 28886139
pmcid: 5590866
Ruijter JM, Ruiz Villalba A, Hellemans J, Untergasser A, Van Den Hoff MJB. Removal of between-run variation in a multi-plate qPCR experiment. Biomol Detect Quantif. 2015;5:10–4.
doi: 10.1016/j.bdq.2015.07.001
pubmed: 27077038
pmcid: 4822202
Sherina V, McMurray HR, Powers W, Land H, Love TMT, McCall MN. Statistical approaches to decreasing the discrepancy of non-detects in qPCR Data. 2017. Available from: http://biorxiv.org/lookup/doi/10.1101/231621 .
Barker M, Chue Hong NP, Katz DS, Lamprecht AL, Martinez-Ortiz C, Psomopoulos F, et al. Introducing the FAIR principles for research software. Sci Data. 2022;9(1):622.
doi: 10.1038/s41597-022-01710-x
pubmed: 36241754
pmcid: 9562067
Andreson R, Möls T, Remm M. Predicting failure rate of PCR in large genomes. Nucleic Acids Res. 2008J;36(11):e66.
doi: 10.1093/nar/gkn290
pubmed: 18492719
pmcid: 2441781
Cordaro NJ, Kavran AJ, Smallegan M, Palacio M, Lammer N, Brant TS, et al. Optimizing polymerase chain reaction (PCR) using machine learning. 2021 . Available from: http://biorxiv.org/lookup/doi/10.1101/2021.08.12.455589 .