No abstract

Antibiotic resistance is becoming a common problem in health care, veterinary medicine, agriculture or food industry. Multi-resistant bacterial strains occur in all regions of the world. One of the possible future solutions is the use of bacteriophages in therapy.Bacteriophages are the most abundant form of life in the biosphere, so it is highly likely that we can purify a specific phage against each target bacterium. A standard identification and consistent characterization of individual bacteriophages include host-specificity of viruses.Unfortunately, these routine methods are also considerably time consuming. With the advent of new modern sequencing methods, scientists are able to obtain multiple phage sequences from samples and identify more phages. However there appeared a problem with unknown host specificity of identified phages. The solution to this problem may be to use a bioinformatic approach in the form of prediction software capable to determine a bacterial host based on the phage whole-genome sequence. The result of our research is the machine learning algorithm based tool called PHERI. PHERI predicts suitable bacterial host genus for purification of individual viruses from different samples. In addition, the tool can identify and highlight protein sequences that are important for host selection.

Background The genomes of SARS-CoV-2 are classified into variants, some of which are monitored as variants of concern (e.g. the Delta variant B.1.617.2 or Omicron variant B.1.1.529). Proportions of these variants circulating in a human population are typically estimated by large-scale sequencing of individual patient samples. Sequencing a mixture of SARS-CoV-2 RNA molecules from wastewater provides a cost-effective alternative, but requires methods for estimating variant proportions in a mixed sample. Results We propose a new method based on a probabilistic model of sequencing reads, capturing sequence diversity present within individual variants, as well as sequencing errors. The algorithm is implemented in an open source Python program called VirPool. We evaluate the accuracy of VirPool on several simulated and real sequencing data sets from both Illumina and nanopore sequencing platforms, including wastewater samples from Austria and France monitoring the onset of the Alpha variant. Conclusions VirPool is a versatile tool for wastewater and other mixed-sample analysis that can handle both short- and long-read sequencing data. Our approach does not require pre-selection of characteristic mutations for variant profiles, it is able to use the entire length of reads instead of just the most informative positions, and can also capture haplotype dependencies within a single read.

Antibiotic resistance is becoming a common problem in medicine, food, and industry, with multidrug-resistant bacterial strains occurring in all regions. One of the possible future solutions is the use of bacteriophages. Phages are the most abundant form of life in the biosphere, so we can highly likely purify a specific phage against each target bacterium. The identification and consistent characterization of individual phages was a common form of phage work and included determining bacteriophages’ host-specificity. With the advent of new modern sequencing methods, there was a problem with the detailed characterization of phages in the environment identified by metagenome analysis. The solution to this problem may be to use a bioinformatic approach in the form of prediction software capable of determining a bacterial host based on the phage whole-genome sequence. The result of our research is the machine learning algorithm-based tool called PHERI. PHERI predicts the suitable bacterial host genus for the purification of individual viruses from different samples. In addition, it can identify and highlight protein sequences that are important for host selection.