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Rossen, J. W. A.

Publications and source records attributed to Rossen, J. W. A..

2 recordsLinked to original sources

Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry using a custom-made database, biomarker assignment or mathematical classifiers does not differentiate Shigella spp. and Escherichia coli

PurposeShigella spp. and E. coli are closely related and cannot be distinguished using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) with commercially available databases. Here, three alternative approaches using MALDI-TOF MS to identify and distinguish Shigella spp., E. coli, and its pathotype EIEC were explored. MethodsA custom-made database was developed, biomarkers were assigned, and classification models using machine learning were designed and evaluated using spectra of 456 Shigella spp., 42 E. coli, and 61 EIEC isolates, obtained by the direct smear method and the ethanol-formic acid extraction method. The isolates were identified using ipaH PCR and phenotypic and serological typing. ResultsIdentification with a custom-made database resulted in >94% Shigella identified at the genus level and >91% S. sonnei and S. flexneri at the species level, but distinction of S. dysenteriae, S. boydii, and E. coli was poor. Moreover, 10-15% of duplicates rendered discrepant results. With biomarker assignment, 98% S. sonnei isolates were correctly identified, although the S. sonnei biomarkers were not specific as other species, for instance S. boydii and E. coli were also identified as S. sonnei. Discriminating markers for S. dysenteriae, S. boydii, and E. coli were not assigned at all. Classifiers identified Shigella in 96% of isolates correctly, but most E. coli isolates were also assigned to Shigella. ConclusionNone of the proposed alternative approaches is suitable for use in clinical diagnostics for the identification of Shigella spp., E. coli, and EIEC, reflecting their relatedness and problematic taxonomical classification. We suggest the use of MALDI-TOF MS for the identification of the Shigella spp./E. coli complex, but other tests should be used for distinction.

microbiology

DEN-IM: Dengue Virus identification from shotgun and targeted metagenomics

Dengue virus (DENV) represents a public health and economic burden in affected countries. The availability of genomic data is key to understand viral evolution and dynamics, supporting improved control strategies. Currently, the use of High Throughput Sequencing (HTS) technologies, which can be applied both directly to patient samples (shotgun metagenomics) and to PCR amplified viral sequences (targeted metagenomics), is the most informative approach to monitor the viral dissemination and genetic diversity.\n\nDespite many advantages, these technologies require bioinformatics expertise and appropriate infrastructure for the analysis and interpretation of the resulting data. In addition, the many software solutions available can hamper reproducibility and comparison of results.\n\nHere we present DEN-IM, a one-stop, user-friendly, containerised and reproducible workflow for the analysis of DENV sequencing data, both from shotgun and targeted metagenomics approaches. It is able to infer DENV coding sequence (CDS), identify serotype and genotype, and generate a phylogenetic tree. It can easily be run on any UNIX-like system, from local machines to high-performance computing clusters, performing a comprehensive analysis without the requirement of extensive bioinformatics expertise.\n\nUsing DEN-IM, we successfully analysed two DENV datasets. The first comprised 25 shotgun metagenomic sequencing samples of varying serotype and genotype, including a spiked sample containing the existing four serotypes. The second dataset consisted of 106 targeted metagenomics samples of DENV 3 genotype III where DEN-IM allowed detection of the intra-genotype diversity.\n\nThe DEN-IM workflow, parameters and execution configuration files, and documentation are freely available at https://github.com/B-UMMI/DEN-IM.

bioinformatics