Identification of highly variable sequence fragments in unmapped reads for rapid bacterial genotyping.

BACKGROUND: Bacterial genotyping is a crucial process in outbreak investigation and epidemiological studies. Several typing methods such as pulsed-field gel electrophoresis, multilocus sequence typing (MLST) and whole genome sequencing are currently used in routine clinical practice. However, these methods are costly, time-consuming and have high computational demands. An alternative to these methods is mini-MLST, a quick, cost-effective and robust method based on high-resolution melting analysis. Nevertheless, no standardized approach to identify markers suitable for mini-MLST exists. Here, we present a pipeline for variable fragment detection in unmapped reads based on a modified hybrid assembly approach using data from one sequencing platform. RESULTS: In routine assembly against the reference sequence, high variable reads are not aligned and remain unmapped. If de novo assembly of them is performed, variable genomic regions can be located in created scaffolds. Based on the variability rates calculation, it is possible to find a highly variable region with the same discriminatory power as seven housekeeping gene fragments used in MLST. In the work presented here, we show the capability of identifying one variable fragment in de novo assembled scaffolds of 21 Escherichia coli genomes and three variable regions in scaffolds of 31 Klebsiella pneumoniae genomes. For each identified fragment, the melting temperatures are calculated based on the nearest neighbor method to verify the mini-MLST's discriminatory power. CONCLUSIONS: A pipeline for a modified hybrid assembly approach consisting of reference-based mapping and de novo assembly of unmapped reads is presented. This approach can be employed for the identification of highly variable genomic fragments in unmapped reads. The identified variable regions can then be used in efficient laboratory methods for bacterial typing such as mini-MLST with high discriminatory power, fully replacing expensive methods such as MLST. The results can and will be delivered in a shorter time, which allows immediate and fast infection monitoring in clinical practice.

Using deep learning for gene detection and classification in raw nanopore signals.

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals-squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.

Rapid high-resolution melting genotyping scheme for Escherichia coli based on MLST derived single nucleotide polymorphisms.

Routinely used typing methods including MLST, rep-PCR and whole genome sequencing (WGS) are time-consuming, costly, and often low throughput. Here, we describe a novel mini-MLST scheme for Eschericha coli as an alternative method for rapid genotyping. Using the proposed mini-MLST scheme, 10,946 existing STs were converted into 1,038 Melting Types (MelTs). To validate the new mini-MLST scheme, in silico analysis was performed on 73,704 strains retrieved from EnteroBase resulting in discriminatory power D = 0.9465 (CI 95% 0.9726-0.9736) for mini-MLST and D = 0.9731 (CI 95% 0.9726-0.9736) for MLST. Moreover, validation on clinical isolates was conducted with a significant concordance between MLST, rep-PCR and WGS. To conclude, the great portability, efficient processing, cost-effectiveness, and high throughput of mini-MLST represents immense benefits, even when accompanied with a slightly lower discriminatory power than other typing methods. This study proved mini-MLST is an ideal method to screen and subgroup large sets of isolates and/or quick strain typing during outbreaks. In addition, our results clearly showed its suitability for prospective surveillance monitoring of emergent and high-risk E. coli clones'.

CNproScan: Hybrid CNV detection for bacterial genomes.

Discovering copy number variation (CNV) in bacteria is not in the spotlight compared to the attention focused on CNV detection in eukaryotes. However, challenges arising from bacterial drug resistance bring further interest to the topic of CNV and its role in drug resistance. General CNV detection methods do not consider bacteria's features and there is space to improve detection accuracy. Here, we present a CNV detection method called CNproScan focused on bacterial genomes. CNproScan implements a hybrid approach and other bacteria-focused features and depends only on NGS data. We benchmarked our method and compared it to the previously published methods and we can resolve to achieve a higher detection rate together with providing other beneficial features, such as CNV classification. Compared with other methods, CNproScan can detect much shorter CNV events.

Word Entropy-Based Approach to Detect Highly Variable Genetic Markers for Bacterial Genotyping.

Genotyping methods are used to distinguish bacterial strains from one species. Thus, distinguishing bacterial strains on a global scale, between countries or local districts in one country is possible. However, the highly selected bacterial populations (e.g., local populations in hospital) are typically closely related and low diversified. Therefore, currently used typing methods are not able to distinguish individual strains from each other. Here, we present a novel pipeline to detect highly variable genetic segments for genotyping a closely related bacterial population. The method is based on a degree of disorder in analyzed sequences that can be represented by sequence entropy. With the identified variable sequences, it is possible to find out transmission routes and sources of highly virulent and multiresistant strains. The proposed method can be used for any bacterial population, and due to its whole genome range, also non-coding regions are examined.

First Complete Genome of the Thermophilic Polyhydroxyalkanoates-Producing Bacterium Schlegelella thermodepolymerans DSM 15344.

Schlegelella thermodepolymerans is a moderately thermophilic bacterium capable of producing polyhydroxyalkanoates-biodegradable polymers representing an alternative to conventional plastics. Here, we present the first complete genome of the type strain S. thermodepolymerans DSM 15344 that was assembled by hybrid approach using both long (Oxford Nanopore) and short (Illumina) reads. The genome consists of a single 3,858,501-bp-long circular chromosome with GC content of 70.3%. Genome annotation identified 3,650 genes in total, whereas 3,598 open reading frames belonged to protein-coding genes. Functional annotation of the genome and division of genes into clusters of orthologous groups revealed a relatively high number of 1,013 genes with unknown function or unknown clusters of orthologous groups, which reflects the fact that only a little is known about thermophilic polyhydroxyalkanoates-producing bacteria on a genome level. On the other hand, 270 genes involved in energy conversion and production were detected. This group covers genes involved in catabolic processes, which suggests capability of S. thermodepolymerans DSM 15344 to utilize and biotechnologically convert various substrates such as lignocellulose-based saccharides, glycerol, or lipids. Based on the knowledge of its genome, it can be stated that S. thermodepolymerans DSM 15344 is a very interesting, metabolically versatile bacterium with great biotechnological potential.

A degeneration-reducing criterion for optimal digital mapping of genetic codes.

Bioinformatics may seem to be a scientific field processing primarily large string datasets, as nucleotides and amino acids are represented with dedicated characters. On the other hand, many computational tasks that bioinformatics challenges are mathematical problems understandable as operations with digits. In fact, many computational tasks are solved this way in the background. One of the most widely used digital representations is mapping of nucleotides and amino acids with integers 0-3 and 0-20, respectively. The limitation of this mapping occurs when the digital signal of nucleotides has to be translated into a digital signal of amino acids as the genetic code is degenerated. This causes non-monotonies in a mapping function. Although map for reducing this undesirable effect has already been proposed, it is defined theoretically and for standard genetic codes only. In this study, we derived a novel optimal criterion for reducing the influence of degeneration by utilizing a large dataset of real sequences with various genetic codes. As a result, we proposed a new robust global optimal map suitable for any genetic code as well as specialized optimal maps for particular genetic codes.

Advanced DNA fingerprint genotyping based on a model developed from real chip electrophoresis data.

Large-scale comparative studies of DNA fingerprints prefer automated chip capillary electrophoresis over conventional gel planar electrophoresis due to the higher precision of the digitalization process. However, the determination of band sizes is still limited by the device resolution and sizing accuracy. Band matching, therefore, remains the key step in DNA fingerprint analysis. Most current methods evaluate only the pairwise similarity of the samples, using heuristically determined constant thresholds to evaluate the maximum allowed band size deviation; unfortunately, that approach significantly reduces the ability to distinguish between closely related samples. This study presents a new approach based on global multiple alignments of bands of all samples, with an adaptive threshold derived from the detailed migration analysis of a large number of real samples. The proposed approach allows the accurate automated analysis of DNA fingerprint similarities for extensive epidemiological studies of bacterial strains, thereby helping to prevent the spread of dangerous microbial infections.

Rapid Bacterial Species Delineation Based on Parameters Derived From Genome Numerical Representations.

Species delineation based on bacterial genomes is an essential part of the research of prokaryotes. In silico genome-to-genome comparison methods are computationally demanding, but much less tedious and error prone than the wet-lab methods. In this paper, we present a novel method for the delineation of bacterial genomes based on genomic signal processing. The proposed method uses numerical representations of whole bacterial genomes, phase signal and cumulated phase signal, from which four parameters are derived for each genome. The parameters characterize a genome and their calculation is independent of the other genomes comprising a delineation dataset. The delineation itself is processed as a calculation of the parameters' average similarity. The method was statistically verified on 1826 bacterial genomes. A similarity threshold of 96% was set based on the receiver operating characteristic curve that featured sensitivity of 99.78% and specificity of 97.25%. Additionally, comparative analysis on another 33 bacterial genomes was conducted using standard delineation tools as these tools were not able to process the dataset of 1826 genomes using desktop computer. The proposed method achieved comparable or better delineation results in comparison with the standard tools. Besides the excellent delineation results, another great advantage of the method is its small computational demands, which enables the delineation of thousands of genomes on a desktop computer. The calculation of the parameters takes tens of minutes for thousands of genomes. Moreover, they can be calculated in advance by creating a database, meaning the delineation itself is then completed in a matter of seconds.

Bipartite Graphs for Visualization Analysis of Microbiome Data.

Visualization analysis plays an important role in metagenomics research. Proper and clear visualization can help researchers get their first insights into data and by selecting different features, also revealing and highlighting hidden relationships and drawing conclusions. To prevent the resulting presentations from becoming chaotic, visualization techniques have to properly tackle the high dimensionality of microbiome data. Although a number of different methods based on dimensionality reduction, correlations, Venn diagrams, and network representations have already been published, there is still room for further improvement, especially in the techniques that allow visual comparison of several environments or developmental stages in one environment. In this article, we represent microbiome data by bipartite graphs, where one partition stands for taxa and the other stands for samples. We demonstrated that community detection is independent of taxonomical level. Moreover, focusing on higher taxonomical levels and the appropriate merging of samples greatly helps improving graph organization and makes our presentations clearer than other graph and network visualizations. Capturing labels in the vertices also brings the possibility of clearly comparing two or more microbial communities by showing their common and unique parts.

Set of rules for genomic signal downsampling.

Comparison and classification of organisms based on molecular data is an important task of computational biology, since at least parts of DNA sequences for many organisms are available. Unfortunately, methods for comparison are computationally very demanding, suitable only for short sequences. In this paper, we focus on the redundancy of genetic information stored in DNA sequences. We proposed rules for downsampling of DNA signals of cumulated phase. According to the length of an original sequence, we are able to significantly reduce the amount of data with only slight loss of original information. Dyadic wavelet transform was chosen for fast downsampling with minimum influence on signal shape carrying the biological information. We proved the usability of such new short signals by measuring percentage deviation of pairs of original and downsampled signals while maintaining spectral power of signals. Minimal loss of biological information was proved by measuring the Robinson-Foulds distance between pairs of phylogenetic trees reconstructed from the original and downsampled signals. The preservation of inter-species and intra-species information makes these signals suitable for fast sequence identification as well as for more detailed phylogeny reconstruction.

Complete genome sequence of Clostridium pasteurianum NRRL B-598, a non-type strain producing butanol.

The strain Clostridium pasteurianum NRRL B-598 is non-type, oxygen tolerant, spore-forming, mesophilic and heterofermentative strain with high hydrogen production and ability of acetone-butanol fermentation (ethanol production being negligible). Here, we present the annotated complete genome sequence of this bacterium, replacing the previous draft genome assembly. The genome consisting of a single circular 6,186,879 bp chromosome with no plasmid was determined using PacBio RSII and Roche 454 sequencing.

Progressive alignment of genomic signals by multiple dynamic time warping.

This paper presents the utilization of progressive alignment principle for positional adjustment of a set of genomic signals with different lengths. The new method of multiple alignment of signals based on dynamic time warping is tested for the purpose of evaluating the similarity of different length genes in phylogenetic studies. Two sets of phylogenetic markers were used to demonstrate the effectiveness of the evaluation of intraspecies and interspecies genetic variability. The part of the proposed method is modification of pairwise alignment of two signals by dynamic time warping with using correlation in a sliding window. The correlation based dynamic time warping allows more accurate alignment dependent on local homologies in sequences without the need of scoring matrix or evolutionary models, because mutual similarities of residues are included in the numerical code of signals.

Evaluation of antioxidant activity, polyphenolic compounds, amino acids and mineral elements of representative genotypes of Lonicera edulis.

The aim of this study was to evaluate the bioactive substances in 19 berry cultivars of edible honeysuckle (Lonicera edulis). A statistical evaluation was used to determine the relationship between the content of selected bioactive substances and individual cultivars. Regarding mineral elements, the content of sodium was measured using potentiometry and spectrophotometry. The content of selected polyphenolic compounds with high antioxidant activity was determined by a HPLC-UV/ED method. The total amount of polyphenols was determined by the Folin-Ciocalteu method. The antioxidant activity was determined using five methods (DPPH, FRAP, ABTS, FR and DMPD) that differ in their principles. The content of 13 amino acids was determined by ion-exchange chromatography. The experimental results obtained for the different cultivars were evaluated and compared by statistical and bioinformatic methods. A unique feature of this study lies in the exhaustive analysis of the chosen parameters (amino acids, mineral elements, polyphenolic compounds and antioxidant activity) during one growing season.

Classification of genomic signals using dynamic time warping.

BACKGROUND: Classification methods of DNA most commonly use comparison of the differences in DNA symbolic records, which requires the global multiple sequence alignment. This solution is often inappropriate, causing a number of imprecisions and requires additional user intervention for exact alignment of the similar segments. The similar segments in DNA represented as a signal are characterized by a similar shape of the curve. The DNA alignment in genomic signals may adjust whole sections not only individual symbols. The dynamic time warping (DTW) is suitable for this purpose and can replace the multiple alignment of symbolic sequences in applications, such as phylogenetic analysis. METHODS: The proposed method is composed of three main parts. The first part represent conversion of symbolic representation of DNA sequences in the form of a string of A,C,G,T symbols to signal representation in the form of cumulated phase of complex components defined for each symbol. Next part represents signals size adjustment realized by standard signal preprocessing methods: median filtration, detrendization and resampling. The final part necessary for genomic signals comparison is position and length alignment of genomic signals by dynamic time warping (DTW). RESULTS: The application of the DTW on set of genomic signals was evaluated in dendrogram construction using cluster analysis. The resulting tree was compared with a classical phylogenetic tree reconstructed using multiple alignment. The classification of genomic signals using the DTW is evolutionary closer to phylogeny of organisms. This method is more resistant to errors in the sequences and less dependent on the number of input sequences. CONCLUSIONS: Classification of genomic signals using dynamic time warping is an adequate variant to phylogenetic analysis using the symbolic DNA sequences alignment; in addition, it is robust, quick and more precise technique.

Mammalian metallothioneins: properties and functions.

Metallothioneins (MT) are a family of ubiquitous proteins, whose role is still discussed in numerous papers, but their affinity to some metal ions is undisputable. These cysteine-rich proteins are connected with antioxidant activity and protective effects on biomolecules against free radicals, especially reactive oxygen species. In this review, the connection between zinc(II) ions, reactive oxygen species, heavy metal ions and metallothioneins is demonstrated with respect to effect of these proteins on cell proliferation and a possible negative role in resistance to heavy metal-based and non-heavy metal-based drugs.

Structural changes in metallothionein isoforms revealed by capillary electrophoresis and Brdicka reaction.

Metallothionein (MT) as a potential cancer marker is at the center of interest and its properties, functions and behavior under various conditions is intensively studied. In the present study, two major mammalian MT isoforms (MT-1 and MT-2) were separated using capillary electrophoresis (CE) coupled with UV detector in order to describe their basic behavior. Under the optimized conditions, the separation of both isoforms was enabled as well as estimation of detection limits as subunits and units of ng per µL for MT-2 and MT-1, respectively. Further, the effects of thermal treatment and the presence of denaturing agent such as urea on MT-1 and MT-2 isoforms were studied by CE-UV. Thermal treatment caused an increase in the signals of both isoforms. A new parameter called precipitation rate has been defined based on this finding. This parameter can be expressed as a slope of the linear regression of the time dependency curve recalculated on the MT concentration. The thermal precipitation rate for MT-1 and MT-2 was determined as 1.1 and 0.9 ng of MT/min, respectively. The chemical precipitation rate calculated from the linear regression for both isoforms provided the same value of 0.25 ng of MT/min. The results were confirmed by manual spectrometric measurements and by differential pulse voltammetry Brdicka reaction. Based on these results, a model of MT behavior under the conditions studied was suggested.

Mathematical evaluation of the amino acid and polyphenol content and antioxidant activities of fruits from different apricot cultivars.

Functional foods are of interest because of their significant effects on human health, which can be connected with the presence of some biologically important compounds. In this study, we carried out complex analysis of 239 apricot cultivars (Prunus armeniaca L.) cultivated in Lednice (climatic area T4), South Moravia, Czech Republic. Almost all previously published studies have focused only on analysis of certain parameters. However, we focused on detection both primary and secondary metabolites in a selection of apricot cultivars with respect to their biological activity. The contents of thirteen biogenic alpha-L-amino acids (arginine, asparagine, isoleucine, lysine, serine, threonine, valine, leucine, phenylalanine, tryptophan, tyrosine, proline and alanine) were determined using ion exchange chromatography with UV-Vis spectrometry detection. Profile of polyphenols, measured as content of ten polyphenols with significant antioxidant properties (gallic acid, procatechinic acid, p-aminobenzoic acid, chlorogenic acid, caffeic acid, vanillin, p-coumaric acid, rutin, ferrulic acid and quercetrin), was determined by high performance liquid chromatography with spectrometric/electrochemical detection. Moreover, content of total phenolics was determined spectrophotometrically using the Folin-Ciocalteu method. Antioxidant activity was determined using five independent spectrophotometric methods: DPPH assay, DMPD method, ABTS method, FRAP and Free Radicals methods. Considering the complexity of the obtained data, they were processed and correlated using bioinformatics techniques (cluster analysis, principal component analysis). The studied apricot cultivars were clustered according to their common biochemical properties, which has not been done before. The observed similarities and differences were discussed.

Paramagnetic antibody-modified microparticles coupled with voltammetry as a tool for isolation and detection of metallothionen as a bioindicator of metal pollution.

Low-molecular mass proteins rich in cysteines called metallothioneins (MT) can be considered as markers for the pollution of the environment by metals. Here, we report on suggestion for an automated procedure for the isolation of MT followed by voltammetric analysis. Primarily, we optimized the automated detection of MT using an electrochemical analyser. It was found that the most sensitive and repeatable analyses are obtained at a temperature of 4 °C for the supporting electrolyte. Further, we optimized experimental conditions for the isolation of MT by using antibody-linked paramagnetic microparticles. Under the optimal conditions (4 h long interaction between the microparticles and MT), the microparticles were tested on isolation of various amounts of MT. The lowest isolated amount of MT by antibody-linked paramagnetic microparticles was 5 µg ml(-1) of MT (50 ng). The automated procedure of MT isolation was further tested on isolation of MT from guppy fish (Poecilia reticulata) treated with silver(i) ions (50 µM AgNO(3)). The whole process lasted less than five hours and was fully automated. We attempted to correlate these results with the standard method for MT isolation. The correlation coefficient is 0.9901, which confirms that results are in good agreement. Moreover, the concentration of silver ions in tissues of fish treated with Ag(i) ions was determined by high performance liquid chromatography with electrochemical detection.

Effect of five different stages of ripening on chemical compounds in medlar (Mespilus germanica L.).

The study of changes of nutritional value of fruit during the ripening process can help estimate the optimal date for fruit harvesting to achieve the best quality for direct consumption and further utilization. The aim of this study was to monitor the changes of chemical composition of medlar fruit (Mespilus germanica L.) measured at five various ripening stages including 134, 144, 154, 164 and 174 days after full bloom (DAFB). Fruits were analyzed and ascorbic acid (AA) and total phenolic compound content with respect to the total antioxidant activity were determined. In addition, selected micronutrients and macronutrients were monitored. The results of our experiments demonstrate that ascorbic acid, total phenolic compound content and total antioxidant activity decreased significantly with increasing time of ripeness. The decreasing tendency in potassium, calcium and magnesium contents during the ripening stages was also determined. During the ripening period, the content of all micronutrients as well as phosphorus and sodium was balanced, with no statistically significant differences between the monitored ripening stages, which can be considered as a positive fact with respect to ideal consumption quality of fruit.