Snapper: high-sensitive detection of methylation motifs based on Oxford Nanopore reads.

MOTIVATION: The Oxford Nanopore technology has a great potential for the analysis of methylated motifs in genomes, including whole-genome methylome profiling. However, we found that there are no methylation motifs detection algorithms, which would be sensitive enough and return deterministic results. Thus, the MEME suit does not extract all Helicobacter pylori methylation sites de novo even using the iterative approach implemented in the most up-to-date methylation analysis tool Nanodisco. RESULTS: We present Snapper, a new highly sensitive approach, to extract methylation motif sequences based on a greedy motif selection algorithm. Snapper does not require manual control during the enrichment process and has enrichment sensitivity higher than MEME coupled with Tombo or Nanodisco instruments that was demonstrated on H.pylori strain J99 studied earlier by the PacBio technology and on four external datasets representing different bacterial species. We used Snapper to characterize the total methylome of a new H.pylori strain A45. At least four methylation sites that have not been described for H.pylori earlier were revealed. We experimentally confirmed the presence of a new CCAG-specific methyltransferase and inferred a gene encoding a new CCAAK-specific methyltransferase. AVAILABILITY AND IMPLEMENTATION: Snapper is implemented using Python and is freely available as a pip package named "snapper-ont." Also, Snapper and the demo dataset are available in Zenodo (10.5281/zenodo.10117651).

FunFun: ITS-based functional annotator of fungal communities.

The study of individual fungi and their communities is of great interest to modern biology because they might be both producers of useful compounds, such as antibiotics and organic acids, and pathogens of various diseases. And certain features associated with the functional capabilities of fungi are determined by differences in gene content. Information about gene content is most often taken from the results of functional annotation of the whole genome. However, in practice, whole genome sequencing of fungi is rarely performed. At the same time, usually sequence amplicons of the ITS region to identify fungal taxonomy. But in the case of amplicon sequencing there is no way to perform a functional annotation. Here, we present FunFun, the instrument that allows to evaluate the gene content of an individual fungus or mycobiome from ITS sequencing data. FunFun algorithm based on a modified K-nearest neighbors algorithm. As input, the program can use ITS1, ITS2, or a full-size ITS cluster (ITS1-5.8S-ITS2). FunFun was realized as a pip-installed command line instrument and validated using a shuffle-split approach. The developed instrument can be very useful in the fungal community comparing and estimating functional capabilities of fungi under study. Also, the program can predict with high accuracy the most variable functions.

BioCAT: Search for biosynthetic gene clusters producing nonribosomal peptides with known structure.

Nonribosomal peptides are a class of secondary metabolites synthesized by multimodular enzymes named nonribosomal peptide synthetases and mainly produced by bacteria and fungi. NMR, LC-MS/MS and other analytical methods allow to determine a peptide structure precisely, but it is often not a trivial task to find natural producers of them. There are cases when potential producers should be found among hundreds of strains, for instance, when analyzing metagenomic data. We have developed BioCAT, a tool designed for finding biosynthetic gene clusters which may produce a given nonribosomal peptide when the structure of an interesting nonribosomal peptide has already been found. BioCAT unites the antiSMASH software and the rBAN retrosynthesis tool but some improvements were added to both gene cluster and peptide structure analysis. The main feature of the method is an implementation of a position-specific score matrix to store specificities of nonribosomal peptide synthetase modules, which has increased the alignment sensitivity in comparison with more strict approaches developed earlier. We tested the method on a manually curated nonribosomal peptide producers database and compared it with competing tools GARLIC and Nerpa. Finally, we showed the method's applicability on several external examples.

UniqPy: A tool for estimation of short-chain fatty acids composition by gas-chromatography/mass-spectrometry with headspace extraction.

Short-chain fatty acids are metabolites widely presented in many natural sources, including human feces and blood. Estimation of their composition is a common procedure, usually performed using nuclear magnetic resonance or gas chromatography with a flame ionization detector. However, the commonly used methods often depend on specific sample preparation, such as filtration and homogenization. The gas-chromatography/mass-spectrometry (GC/MS) method with headspace extraction allows sample preparation to be kept to a minimum regardless of the physical state of the sample, which can be potentially useful in metabolomics research of complex natural samples such as blood or feces. In this work, we have demonstrated the applicability of Headspace GC-MS for estimating short chain fatty acid (SCFA) composition. The main problem here is the complex, non-linear dependence between the composition of the compounds in the source phase and the relative pressures in the vapor phase, which are directly measured by this method. We have implemented a thermodynamic model that performs the reverse transformation of relative abundances in the vapor phase to relative concentrations in the liquid phase, and have tested it on some synthetic SCFA mixtures. The developed method is available as a pip package called UniqPy and can be used to describe liquid-vapor equilibrium for any multicomponent system if a sufficient amount of training data is provided. The gas chromatography method with headspace extraction in conjunction with the UniqPy data transformation showed satisfactory quantification accuracy for propionic acid, butyric acid, isobutyric acid, and valeric acid (R-squared > 0.96). The applicability of the method was additionally demonstrated on a series of fecal samples.