HTSFinder: Powerful Pipeline of DNA Signature Discovery by Parallel and Distributed Computing

Mining Public Metagenomes for Environmental Surveillance of Parasites: A Proof of Principle

Parasites often have complex developmental cycles that account for their presence in a variety of difficult-to-analyze matrices, including feces, water, soil, and food. Detection of parasites in these matrices still involves laborious methods. Untargeted sequencing of nucleic acids extracted from those matrices in metagenomic projects may represent an attractive alternative method for unbiased detection of these pathogens. Here, we show how publicly available metagenomic datasets can be mined to detect parasite specific sequences, and generate data useful for environmental surveillance. We use the protozoan parasite Cryptosporidium parvum as a test organism, and show that detection is influenced by the reference sequence chosen. Indeed, the use of the whole genome yields high sensitivity but low specificity, whereas specificity is improved through the use of signature sequences. In conclusion, querying metagenomic datasets for parasites is feasible and relevant, but requires optimization and validation. Nevertheless, this approach provides access to the large, and rapidly increasing, number of datasets from metagenomic and meta-transcriptomic studies, allowing unlocking hitherto idle signals of parasites in our environments.

The emergence of new trends in clinical laboratory diagnosis

Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.