Publications by authors named "J Hooyberghs"

69 Publications

Retrospective study of factors associated with bovine infectious abortion and perinatal mortality.

Prev Vet Med 2021 Apr 25;191:105366. Epub 2021 Apr 25.

Department of Reproduction, Obstetrics, and Herd Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820, Merelbeke, Belgium.

Abortion and perinatal mortality, leading causes of economic loss in cattle industry, are the consequence of both non-infectious and a wide range of infectious causes. However, the relative contribution of pathogens to bovine abortion and perinatal mortality is poorly documented, since available studies involved only a limited number of pathogens. Therefore, the objectives of the present monitoring study were to determine the prevalence of infectious agents associated with bovine abortion and perinatal mortality, and to identify differences in production type, gestation length, parity and seasonality by using mixed effect models (logistic regression). A pre-established sampling protocol based on the collection of the aborted fetus/calf and a corresponding maternal blood sample, involving diagnostic testing for 10 pathogens, was performed. At least one potential causal agent of the abortion or perinatal mortality was detected in 39 % of cases. In these diagnosed cases, Neospora caninum was the most detected pathogen, followed by Trueperella pyogenes, BVDv, Escherichia coli, and Aspergillus fumigatus. Neospora caninum [odds ratio (OR): 0.4; 95 % confidence interval (CI): 0.3-0.7] and Aspergillus fumigatus (OR: 0.1; 95 % CI: 0.1-0.3) were detected less in late versus early gestation. Aspergillus fumigatus was less common in dairy in comparison to beef abortion cases (OR: 0.2; 95 % CI: 0.1-0.6). Winter was associated with a lower positivity for Neospora caninum and BVDv in comparison to warmer seasons. Despite extensive diagnostic testing, an etiological diagnosis was not reached in 61 % of cases, highlighting the need for even more extensive (non-)infectious disease testing or more accurate tests.
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http://dx.doi.org/10.1016/j.prevetmed.2021.105366DOI Listing
April 2021

Constrained Standardization of Count Data from Massive Parallel Sequencing.

J Mol Biol 2021 May 29;433(11):166966. Epub 2021 Mar 29.

Universiteit Hasselt, Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Agoralaan, Diepenbeek BE 3590, Belgium; Universiteit Antwerpen, Centre for Proteomics, Groenenborgerlaan 171, Antwerpen BE 2020, Belgium. Electronic address:

In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets at well over 1 million count records per second whilst mitigating unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd's simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.
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http://dx.doi.org/10.1016/j.jmb.2021.166966DOI Listing
May 2021

CONSTANd: An Efficient Normalization Method for Relative Quantification in Small- and Large-Scale Omics Experiments in R BioConductor and Python.

J Proteome Res 2021 04 11;20(4):2151-2156. Epub 2021 Mar 11.

Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Universiteit Hasselt, Agoralaan, Diepenbeek 3590, Belgium.

For differential expression studies in all omics disciplines, data normalization is a crucial step that is often subject to a balance between speed and effectiveness. To keep up with the data produced by high-throughput instruments, researchers require fast and easy-to-use yet effective methods that fit into automated analysis pipelines. The CONSTANd normalization method meets these criteria, so we have made its source code available for R/BioConductor and Python. We briefly review the method and demonstrate how it can be used in different omics contexts for experiments of any scale. Widespread adoption across omics disciplines would ease data integration in multiomics experiments.
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http://dx.doi.org/10.1021/acs.jproteome.0c00977DOI Listing
April 2021

PRiSM: A prototype for exhaustive, restriction-free database searching for mass spectrometry-based identification.

Rapid Commun Mass Spectrom 2020 Oct 2:e8962. Epub 2020 Oct 2.

Universiteit Hasselt, Data Science Institute (DSI), Theoretical Physics, Diepenbeek, Belgium.

Rationale: The current methods for identifying peptides in mass spectral product ion data still struggle to do so for the majority of spectra. Based on the experimental setup and other assumptions, such methods restrict the search space to speed up computations, but at the cost of creating blind spots. The proteomics community would greatly benefit from a method that is capable of covering the entire search space without using any restrictions, thus establishing a baseline for identification.

Methods: We conceived the "mass pattern paradigm" (MPP) that enables the creation of such an identification method, and we implemented it into a prototype database search engine "PRiSM" (PRotein-Spectrum Matching). We then assessed its operational characteristics by applying it to publicly available high-precision mass spectra of low and high identification difficulty. We used those characteristics to gain theoretical insights into trade-offs between sensitivity and speed when trying to establish a baseline for identification.

Results: Of 100 low difficulty spectra, PRiSM and SEQUEST agree on 84 identifications (of which 75 are statistically significant). Of 15 of 100 spectra not identified in a previous study (using SEQUEST), 13 are considered reliable after visual inspection and represent 3 proteins (out of 9 in total) not detected previously.

Conclusions: Despite leaving noise intact, the simple PRiSM prototype can make statistically reliable identifications, while controlling the false discovery rate by fitting a null distribution. It also identifies some spectra previously unidentifiable in an "extremely open" SEQUEST search, paving the way to establishing a baseline for identification in proteomics.
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http://dx.doi.org/10.1002/rcm.8962DOI Listing
October 2020