Academic Bibliography
https://biblio.ugent.be/
Ghent University Academic Bibliography2000-01-01T00:00+00:001monthlyWhole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA
https://biblio.ugent.be/publication/01GST4A17DHB0WACV21RZF6NSA
Vermeirssen, VanessaDeleu, JillMorlion, AnnelienEveraert, CelineDe Wilde, JilkeAnckaert, JasperDurinck, KaatNuytens, JustineRishfi, MuhammadSpeleman, FrankiVan Droogenbroeck, HanneVerniers, KimberlyBaietti, Maria FrancescaAlbersen, MaartenLeucci, EleonoraPost, EdwardBest, Myron G.Van Maerken, TomDe Wilde, BramVandesompele, JoDecock, Anneleen2023https://biblio.ugent.be/publication/01GST4A17DHB0WACV21RZF6NSAhttp://hdl.handle.net/1854/LU-01GST4A17DHB0WACV21RZF6NSAengMedicine and Health SciencesWhole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNAconferenceinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionHTSplotter : an end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening
https://biblio.ugent.be/publication/01HQ8GFWH81YM97YWYSY1ADJNQ
de Carvalho Nunes, CarolinaAnckaert, JasperDe Vloed, FannyDe Wyn, JolienDurinck, KaatVandesompele, JoSpeleman, FrankiVermeirssen, Vanessa2024In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.application/pdfhttps://biblio.ugent.be/publication/01HQ8GFWH81YM97YWYSY1ADJNQhttp://hdl.handle.net/1854/LU-01HQ8GFWH81YM97YWYSY1ADJNQhttp://doi.org/10.1371/journal.pone.0296322https://biblio.ugent.be/publication/01HQ8GFWH81YM97YWYSY1ADJNQ/file/01HQ8GG3C0PVQAE77J0MG5YHPJengCreative Commons Attribution 4.0 International Public License (CC-BY 4.0)info:eu-repo/semantics/openAccessPLOS ONEISSN: 1932-6203Medicine and Health SciencesBiology and Life SciencesChemistryHTSplotter : an end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screeningjournalArticleinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionLarge-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision
https://biblio.ugent.be/publication/01HQ8GXPTE6YC5X1XBRK9EWEFP
Vromman, MariekeAnckaert, Jasper Bortoluzzi, Stefania Buratin, Alessia Chen, Chia-Ying Chu, Qinjie Chuang, Trees-Juen Dehghannasiri, Roozbeh Dieterich, Christoph Dong, Xin Flicek, Paul Gaffo, Enrico Gu, Wanjun He, Chunjiang Hoffmann, Steve Izuogu, Osagie Jackson, Michael S. Jakobi, Tobias Lai, Eric C.Nuytens, Justine Salzman, Julia Santibanez-Koref, Mauro Stadler, Peter Thas, OlivierVanden Eynde, EvelineVerniers, Kimberly Wen, Guoxia Westholm, Jakub Yang, Li Ye, Chu-YuYigit, Nurten Yuan, Guo-Hua Zhang, Jinyang Zhao, FangqingVandesompele, JoVolders, Pieter-Jan2023The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.
This study describes benchmarking and validation of computational tools for detecting circRNAs, finding most to be highly precise with variations in sensitivity and total detection. The study also finds over 315,000 putative human circRNAs.application/pdfhttps://biblio.ugent.be/publication/01HQ8GXPTE6YC5X1XBRK9EWEFPhttp://hdl.handle.net/1854/LU-01HQ8GXPTE6YC5X1XBRK9EWEFPhttp://doi.org/10.1038/s41592-023-01944-6https://biblio.ugent.be/publication/01HQ8GXPTE6YC5X1XBRK9EWEFP/file/01HQ8GXYSYERT9BB51X44QTCWTengNo license (in copyright)info:eu-repo/semantics/restrictedAccessNATURE METHODSISSN: 1548-7091ISSN: 1548-7105Medicine and Health SciencesBiology and Life SciencesLarge-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precisionjournalArticleinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionDramatic impact of blood collection tube and RNA purification method on extracellular RNA transcriptomes
https://biblio.ugent.be/publication/01HNF7YNM70WR08YSTWVEX3SN5
Anckaert, JasperAvila Cobos, FranciscoDecock, AnneleenDecruyenaere, PhilippeDeleu, JillDe Preter, KatleenDe Wever, OlivierDe Wilde, JilkeDhondt, BertD'huyvetter, ThibautEveraert, CelineFierro, CarolinaHelsmoortel, HettyHendrix, AnHulstaert, EvaKoster, JanKuersten, ScottMercer, Tim RMestdagh, PieterMorlion, AnnelienNijs, NeleNuytens, JustinePhilippron, AnnouckPiofczyk, ThomasPoma Soto, Franco AlexanderSchoofs, KathleenSchroth, Gary P.Thas, OlivierVanden Eynde, EvelineVandesompele, JoVan Maerken, TomVan Paemel, RubenVerniers, KimberlyVerwilt, JasperYigit, Nurten2023Background
The use of blood-based extracellular RNA (cell-free RNA; exRNA) as clinical biomarker requires the implementation of a validated procedure for sample collection, processing, and profiling. So far, no study has systematically addressed the pre-analytical variables affecting transcriptome analysis of exRNAs. In the exRNAQC study, we evaluated ten blood collection tubes, three time intervals between blood draw and downstream processing, and eight RNA purification methods using the supplier-specified minimum and maximum biofluid input volumes.
Methods
The impact of these pre-analytics on deep transcriptome profiling of both small and messenger RNA from healthy donors’ plasma or serum was assessed for each pre-analytical variable separately and for interactions between a selected set of pre-analytics, resulting in 456 extracellular transcriptomes. Making use of 189 synthetic spike-in RNAs, the processing and analysis workflow was controlled.
Results
When comparing blood collection tubes, so-called preservation tubes do not stabilize exRNA well, and result in variable RNA concentration and sensitivity (i.e., the number of detected RNAs) over time, as well as compromised reproducibility. We also document large differences in RNA purification kit performance in terms of sensitivity, reproducibility, and observed transcriptome complexity, and demonstrate interactions between specific blood collection tubes, purification kits and time intervals. Our results are summarized in 11 performance metrics that enable an informed selection of the most optimal sample processing workflow for a given experiment.
Conclusions
In conclusion, we put forward robust quality control metrics for exRNA quantification methods with validated standard operating procedures (SOPs), representing paramount groundwork for future exRNA-based precision medicine applications.
Abstract submitted on behalf of the exRNAQC Consortium. Authors are listed in alphabetical order.application/pdfhttps://biblio.ugent.be/publication/01HNF7YNM70WR08YSTWVEX3SN5http://hdl.handle.net/1854/LU-01HNF7YNM70WR08YSTWVEX3SN5https://biblio.ugent.be/publication/01HNF7YNM70WR08YSTWVEX3SN5/file/01HR9VD422VEQATPB3GATMSJRYengNo license (in copyright)info:eu-repo/semantics/openAccess6th ACTC Advances in Circulating Tumor Cells 'Liquid Biopsy and Precision Oncology : Where do we stand now?' Book of AbstractsMedicine and Health SciencesBiology and Life SciencesDramatic impact of blood collection tube and RNA purification method on extracellular RNA transcriptomesconferenceinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionPerformance evaluation of RNA purification kits and blood collection tubes in the Extracellular RNA Quality Control (exRNAQC) study
https://biblio.ugent.be/publication/01HNFGN86Q47AYF1FAPYXC50HT
Decock, AnneleenDe Wever, OlivierEveraert, CelineHelsmoortel, HettyHendrix, AnMestdagh, PieterMorlion, AnnelienVandesompele, JoVan Paemel, RubenAvila Cobos, FranciscoKoster, JanPoma Soto, Franco AlexanderVerwilt, JasperAnckaert, JasperDe Wilde, JilkeFierro, CarolinaDecruyenaere, PhilippeDeleu, JillDhondt, BertHulstaert, EvaNijs, NeleNuytens, JustinePhilippron, AnnouckSchoofs, KathleenVanden Eynde, EvelineVerniers, KimberlyYigit, NurtenD'huyvetter, ThibautPiofczyk, ThomasThas, OlivierKuersten, ScottMercer, Tim RSchroth, Gary PDe Preter, KatleenVan Maerken, Tom2023https://biblio.ugent.be/publication/01HNFGN86Q47AYF1FAPYXC50HThttp://hdl.handle.net/1854/LU-01HNFGN86Q47AYF1FAPYXC50HTengPrIOMiC-OncoPoint Symposium 2023, AbstractsMedicine and Health SciencesPerformance evaluation of RNA purification kits and blood collection tubes in the Extracellular RNA Quality Control (exRNAQC) studyconferenceinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersion