Microbiome studies are attracting increasing interest, especially in human health applications, where their use for disease prognostics, diagnostics and treatment can have immense effects on life quality. The settings in the microbiome data preprocessing stage can lead to the great variability of the generated operational taxonomic unit (OTU) tables, reflected in the size and sparseness of this data matrix. As there are still no solid guidelines on the best practices, it is valuable to assess which machine learning algorithms provide higher stability of results under variable preprocessing settings. In this study, we have generated OTU tables using data from the Moving pictures of human microbiome study using two different reference databases (Greengenes and Silva) and four levels of the similarity threshold (ranging from 90 to 99%), processed in the QIIME bioinformatics package. The results of the two best-performing classification and clustering algorithms are presented in detail: Random Forest classifier (RF) and Spectral clustering (SC). The random forest classifier has outperformed spectral clustering in terms of accuracy. As the rate of data generation increases, while the cost of labeling remains high, further improvement of clustering performance and ensemble approaches should be explored.

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

SUMMARY: BEsTRF (Best Estimated T-RF) provides a standalone environment for analyzing primers-enzymes-gene section combinations used in terminal-restriction fragment length polymorphism (T-RFLP) for its optimal resolution. User-defined sequence databases of several hundred thousand DNA sequences can be explored and the resolution of user-specified sets of primers and restriction endonucleases can be analyzed on either forward or reverse terminal fragments. Sequence quality, primer mismatches, insertions and deletions can be controlled and each primer pair-specific sequence collections can be exported for downstream analyses. The configuration for a novel T-RFLP population profiling using rpoB gene (DNA-directed RNA polymerase, beta subunit) on forward fluorescently labeled primer are presented. AVAILABILITY: BEsTRF is freely available at http://lie.fe.uni-lj.si/bestrf and can be downloaded from the same site. The online protocol, numerous primer and enzyme dictionaries, sequence collections and results generated during this work for various genes are available at our website http://lie.fe.uni-lj.si/bestrf.