Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

Motivation The Flavivirus genus includes several important pathogens such as Zika, dengue and yellow fever virus. Flavivirus RNA genomes contain a number of functionally important structures in their 3' untranslated regions (3'UTRs). Due to the diversity of sequences and topologies of these structures, their identification is often difficult. On the other hand, predictions of such structures is important for understanding of flavivirus replication cycles and development of antiviral strategies. Results We have developed an algorithm for structured pattern search in RNA sequences, including secondary structures, pseudoknots and triple base interactions. Using the data on known conserved flavivirus 3'UTR structures, we constructed structural descriptors which covered the diversity of patterns in these motifs. The descriptors and the search algorithm were used for the construction of a database of flavivirus 3'UTR structures. Validating this approach, we identified a number of domains matching a general pattern of exoribonuclease Xrn1 - resistant RNAs in the growing group of insect-specific flaviviruses. Availability and implementation The Leiden Flavivirus RNA Structure Database is available at https://rna.liacs.nl. The search algorithm is available at https://github.com/LeidenRNA/SRHS.

To improve the effectiveness and efficiency of optical projection tomography (OPT) 3-D reconstruction, we present a fast post-processing pipeline, including cropping, background subtraction, center of rotation (COR) correction, and 3-D reconstruction. Regarding to the COR correction, a novel algorithm based on interest point detection of sinogram is proposed by considering the principle of OPT imaging. Instead of locating the COR on single sinogram, we select equally spaced sinograms in the detected full range of specimen to make the located COR more convincing. The presented post-processing pipeline is implemented in a parallel manner and the experiments show that the average runtime for each image of size 1036 ×1360 ×400 pixels is less than 1 min. To quantify and compare the reconstructed results of different COR correction approaches, the coefficient of variation instead of variance is employed. The results indicate that the proposed COR correction outperforms the three traditional COR alignment approaches in terms of effectiveness and computational complexity.

Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods DeepSVDD and R-graph performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that AE reconstruction error, Mahalanobis and PCA reconstruction error also performed well. Furthermore, we demonstrated the importance of a PCA transform prior to applying an outlier detection method since we observed that this increases the performance of several outlier detection methods. For only one of the three metabolomics datasets, we observed clinically satisfying performances for some outlier detection methods, where we were able to detect 90% of the IEM patient samples while detecting no false positives. These results suggest that outlier detection methods have the potential to aid the clinical investigator in routine screening for IEM using untargeted metabolomics data, but also show that further improvements are needed to ensure clinically satisfying performances.