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Brejova, B.

Publications and source records attributed to Brejova, B..

14 recordsLinked to original sources

Noncanonical chromosomal-end-specific telomeric arrays in naturally telomerase-negative yeasts

In most eukaryotes, chromosomal DNA terminates with tandem repeats of a short G-rich motif, such as the canonical TTAGGG sequence. The arrays of telomeric repeats are maintained by telomerase or by alternative lengthening of telomeres (ALT). Here we report that nuclear chromosomes of several basidiomycetous yeasts classified into the order Microstromatales carry unusual telomeres. We demonstrate that instead of TTAGGG-like repeats these telomeres are composed of unique tandem arrays which are in most cases specific to a particular chromosomal end. In contrast to other basidiomycetes, the Microstromatales genomes lack orthologs coding for the telomerase catalytic subunit Est2 and a shelterin component Tpp1 indicating that noncanonical telomeric arrays are maintained by a telomerase-independent mechanism. We hypothesize that in a common ancestor of Microstromatales the loss of telomerase and Tpp1 was compensated by activation of an ALT mechanism, which promoted amplification of various motifs and formation of distinct telomeric arrays at most chromosomal ends.

molecular biology↗

Anyone can be the best: Impact of diverse methodologies on the evaluation of structural variant callers

Structural variants (SVs) are medium and large-scale genomic alterations that shape phenotypic diversity and disease risk. Numerous methods have been proposed for discovering SVs, however their benchmarking has been inconsistent across studies, often resulting in contradictory findings. One of the main sources of conflicting evaluation re-sults is the lack of consistency in the SV callsets used as ground truth, ranging from curated callsets released by consortia to more recent approaches that construct callsets from high-quality telomere-to-telomere de novo haplotype assemblies. The discrepancies between benchmarks are further compounded by the choice of the reference genome (GRCh37, GRCh38, and T2T-CHM13), where using T2T-CHM13reveals a different deletion/insertion profile, indicating reduced reference bias. We evaluated the performance of several state-of-the-art SV discovery methods from long-read whole-genome sequencing data and observed substantial variation in their performance and rankings, depending on the choice of ground truth, reference genome, and genomic regions used for evaluation. Counter-intuitively, the more complete reference genome T2T-CHM13does not inherently solve the problem of SV benchmarking; instead it reveals the limitations of each detection method in complex genomic regions. The substantial variation in detection accuracy across different genomic regions calls for additional caution in downstream analyses and in drawing conclusions based on predicted SVs. These findings underscore the complexity of evaluating SV detection methods and highlight the need for careful consideration and, ideally, field-standard best practices when reporting performance metrics.

bioinformatics↗

Pangebin: improving plasmid binning in bacterial isolates using pangenome-assembly graphs.

Short-read genome assemblies typically consist of many contigs of variable lengths and their putative connections represented as an assembly graph. Assembly graphs produced by different tools from the same data may differ significantly, posing a challenge to tools for downstream processing tasks. One such task is plasmid binning, that is identifying plasmids in sequenced bacterial isolates, which is crucial for monitoring the spread of antimicrobial resistance. When plasmid binning tools are applied to assembly graphs produced by different tools, they may exhibit different performance, and choosing the best results a priori can be difficult. To address the above issue, we propose the use of a pangenome graph, built from assembly graphs produced by assembling short reads of the same sample with different assemblers. The resulting pangenome-assembly graph highlights similarities between contigs from different assemblies while retaining information on contigs that appear only in one of the input assemblies. We then used the PlasBin-flow plasmid binning tool customized to take into account pangenome information to identify plasmid bins. The results for pangenome-assemblies built by Unicycler and Skesa show an increase in accuracy measures compared to the mean results obtained on single assemblies, leading to an overall more accurate prediction than a blind choice of assemblers. The source code of the pipeline is available at https://github.com/AlgoLab/pangebin along with the dataset used in this study.

bioinformatics↗

Pangenome graph augmentation from unassembled long reads

Pangenomes are becoming increasingly popular data structures for genomics analyses due to their ability to compactly represent the genetic diversity within populations. Constructing a pangenome graph, however, is still a time-consuming and expensive process. A promising approach for pangenome construction consists of progressively augmenting a pangenome graph with additional high-quality assemblies. Currently, there is no method for augmenting a pangenome graph with unassembled reads from newly sequenced samples without first aligning the reads to a reference genome and performing variant calling and genotyping on the new individuals. In this work, we present the first assembly-free and mapping-free approach for augmenting an existing pangenome graph using unassembled long reads from an individual not already present in the pangenome. Our approach consists of finding sample specific sequences in reads using efficient indexes, clustering reads corresponding to the same novel variant(s), and then building a consensus sequence to be added to the pangenome graph for each variant separately. Using simulated reads based on Human Pangenome Reference Consortium (HPRC) assemblies, we demonstrate the effectiveness of the proposed approach for progressively augmenting the pangenome with long reads, without the need for de novo assembly or predicting genetic variants of the new sample. The software is freely available at https://github.com/ldenti/palss.

bioinformatics↗

Efficient Analysis of Annotation Colocalization Accounting for Genomic Contexts

An annotation is a set of genomic intervals sharing a particular function or property. Examples include genes or their exons, evolutionarily conserved elements, and regions with a particular epigenetic state. A common task is to compare two annotations to determine if one is enriched or depleted in the regions covered by the other. We study the problem of assigning statistical significance to such a comparison based on a null model representing two random unrelated annotations. To incorporate more background information into such analyses, we propose a new null model based on a Markov chain which differentiates among several genomic contexts. These contexts can capture various confounding factors, such as GC content or assembly gaps. We then develop a new algorithm for estimating p-values by computing the exact expectation and variance of the test statistics and then estimating the p-value using a normal approximation. Compared to the previous algorithm by Gafurov et al., the new algorithm provides three advances: (1) the running time is improved from quadratic to linear or quasi-linear, (2) the algorithm can handle two different test statistics, and (3) the algorithm can handle both simple and context-dependent Markov chain null models. We demonstrate the efficiency and accuracy of our algorithm on synthetic and real data sets, including the recent human telomere-to-telomere assembly. In particular, our algorithm computed p-values for 450 pairs of human genome annotations using 24 threads in under three hours. Moreover, the use of genomic contexts to correct for GC bias resulted in the reversal of some previously published findings. AvailabilityThe software is freely available at https://github.com/fmfi-compbio/mcdp2 under the MIT licence. All data for reproducibility are available at https://github.com/fmfi-compbio/mcdp2-reproducibility

bioinformatics↗

Identifying Clusters in Graph Representations of Genomes

In many bioinformatics applications the task is to identify biologically significant locations in an individual genome. In our work, we are interested in finding high-density clusters of such biologically meaningful locations in a graph representation of a pangenome, which is a collection of related genomes. Different formulations of finding such clusters were previously studied for sequences. In this work, we study an extension of this problem for graphs, which we formalize as finding a set of vertex-disjoint paths with a maximum score in a weighted directed graph. We provide a linear-time algorithm for a special class of graphs corresponding to elastic-degenerate strings, one of pangenome representations. We also provide a fixed-parameter tractable algorithm for directed acyclic graphs with a special path decomposition of a limited width.

bioinformatics↗

Precise Nanopore Signal Modeling Improves Unsupervised Single-Molecule Methylation Detection

Base calling in nanopore sequencing is a difficult and computationally intensive problem, typically resulting in high error rates. In many applications of nanopore sequencing, analysis of raw signal is a viable alternative. Dynamic time warping (DTW) is an important building block for raw signal analysis. In this paper, we propose several improvements to DTW class of algorithms to better account for specifics of nanopore signal modeling. We have implemented these improvements in a new signal-to-reference alignment tool Nadavca. We demonstrate that Nadavca alignments improve unsupervised methylation detection over Tombo. We also demonstrate that by providing additional information about the discriminative power of positions in the signal, an otherwise unsupervised method can approach the accuracy of supervised models. Availability and implementationNadavca is available under MIT license at https://github.com/fmfi-compbio/nadavca. Nanopore sequencing data sets are available from ENA bioproject PRJEB64246. Jaminaea angkorensis reference genome assembly is available from Zenodo https://doi.org/10.5281/zenodo.8145315.

bioinformatics↗

Reduction of ribosomal expansion segments in yeast species of the Magnusiomyces/Saprochaete clade

Ribosomes are ribonucleoprotein complexes highly conserved across all domains of life. The size differences of ribosomal RNAs (rRNAs) can be mainly attributed to variable regions termed expansion segments (ESs) protruding out from the ribosomal surface. The ESs were found to be involved in a range of processes including ribosome biogenesis and maturation, translation, and co-translational protein modification. Here, we analyze the rRNAs of the yeasts from the Magnusiomyces/Saprochaete clade belonging to the basal lineages of the subphylum Saccharomycotina. We find that these yeasts are missing more than 400 nt from the 25S rRNA and 150 nt from the 18S rRNAs when compared to their canonical counterparts in Saccharomyces cerevisiae. The missing regions mostly map to ESs, thus representing a shift toward a minimal rRNA structure. Despite the structural changes in rRNAs, we did not identify dramatic alterations of the ribosomal protein inventories. We also show that the size-reduced rRNAs are not limited to the species of the Magnusiomyces/Saprochaete clade, indicating that the shortening of ESs happened independently in several other lineages of the subphylum Saccharomycotina. SignificanceExpansion segments are variable regions present in the ribosomal RNAs involved in the ribosome biogenesis and translation. Although some of them were shown to be essential, their functions and the evolutionary trajectories leading to their expansion and/or reduction are not fully understood. Here, we show that the yeasts from the Magnusiomyces/Saprochaete clade have truncated expansion segments, yet the protein inventories of their ribosomes do not radically differ from the species possessing canonical ribosomal RNAs. We also show that the loss of expansion segments occurred independently in several phylogenetic lineages of yeasts pointing out their dispensable nature. The differences identified in yeast ribosomal RNAs open a venue for further studies of these enigmatic elements of the eukaryotic ribosome.

genomics↗

WarpSTR: Determining tandem repeat lengths using raw nanopore signals

MotivationShort tandem repeats (STRs) are regions of a genome containing many consecutive copies of the same short motif, possibly with small variations. Analysis of STRs has many clinical uses, but is limited by technology mainly due to STRs surpassing the used read length. Nanopore sequencing, as one of long read sequencing technologies, produces very long reads, thus offering more possibilities to study and analyze STRs. Basecalling of nanopore reads is however particularly unreliable in repeating regions, and therefore direct analysis from raw nanopore data is required. ResultsHere we present WarpSTR, a novel method for characterizing both simple and complex tandem repeats directly from raw nanopore signals using a finite-state automaton and a search algorithm analogous to dynamic time warping. By applying this approach to determine the lengths of 241 STRs, we demonstrate that our approach decreases the mean absolute error of the STR length estimate compared to basecalling and STRique. AvailabilityWarpSTR is freely available at https://github.com/fmfi-compbio/warpstr Contactjozef.sitarcik@uniba.sk

bioinformatics↗

plASgraph - using graph neural networks to detect plasmid contigs from an assembly graph

Identification of plasmids from sequencing data is an important and challenging problem related to antimicrobial resistance spread and other One-Health issues. In our work, we provide a new architecture for identifying plasmid contigs in fragmented genome assemblies built from short-read data. Unlike previous machine-learning approaches for this problem, which classify individual contigs separately, we employ graph neural networks (GNNs) to include information from the assembly graph. Propagation of information from nearby nodes in the graph allows accurate classification of even short contigs that are difficult to classify based on sequence features or database searches alone. Our new species-agnostic software tool plASgraph outperforms recently developed PlasForest, which uses database searches to supplement sequence-based features. Since our tool does not rely on existing plasmid databases, it is more suitable for classification of contigs in novel species and discovery of previously unknown plasmid sequences. Our tool can also be trained on a specific species, and in that scenario it outperforms mlplasmids trained on the same species. On one hand, our work provides a new, accurate, and easy to use tool for plasmid classification; on the other hand, it serves as a motivation for more widespread use of GNNs in bioinformatics, such as in pangenome sequence analysis, where sequence graphs serve as a fundamental data structure. Availabilityhttps://github.com/cchauve/plASgraph

bioinformatics↗

Markov chains improve the significance computation of overlapping genome annotations

MotivationGenome annotations are a common way to represent genomic features such as genes, regulatory elements or epigenetic modifications. The amount of overlap between two annotations is often used to ascertain if there is an underlying biological connection between them. In order to distinguish between true biological association and overlap by pure chance, a robust measure of significance is required. One common way to do this is to determine if the number of intervals in the reference annotation that intersect the query annotation is statistically significant. However, currently employed statistical frameworks are often either inefficient or inaccurate when computing p-values on the scale of the whole human genome. ResultsWe show that finding the p-values under the typically used "gold" null hypothesis is[N][P] -hard. This motivates us to reformulate the null hypothesis using Markov chains. To be able to measure the fidelity of our Markovian null hypothesis, we develop a fast direct sampling algorithm to estimate the p-value under the gold null hypothesis. We then present an open-source software tool MCDP that computes the p-values under the Markovian null hypothesis in[O] (m2 + n) time and[O] (m) memory, where m and n are the numbers of intervals in the reference and query annotations, respectively. Notably, MCDP runtime and memory usage are independent from the genome length, allowing it to outperform previous approaches in runtime and memory usage by orders of magnitude on human genome annotations, while maintaining the same level of accuracy. AvailabilityThe software is available at https://github.com/fmfi-compbio/mc-overlaps. All data for reproducibility are available at https://github.com/fmfi-compbio/mc-overlaps-reproducibility

bioinformatics↗

Transcriptome and proteome profiling reveals complex adaptations of Candida parapsilosis cells assimilating hydroxyaromatic carbon sources

Many fungal species utilize hydroxyderivatives of benzene and benzoic acid as carbon sources. The yeast Candida parapsilosis metabolizes these compounds via the 3-oxoadipate and gentisate pathways, whose components are encoded by two metabolic gene clusters. In this study, we determine the chromosome level assembly of the C. parapsilosis strain CLIB214 and use it for transcriptomic and proteomic investigation of cells cultivated on hydroxyaromatic substrates. We demonstrate that the genes coding for enzymes and plasma membrane transporters involved in the 3-oxoadipate and gentisate pathways are highly upregulated and their expression is controlled in a substrate-specific manner. However, regulatory proteins involved in this process are not known. Using the knockout mutants, we show that putative transcriptional factors encoded by the genes OTF1 and GTF1 located within these gene clusters function as transcriptional activators of the 3-oxoadipate and gentisate pathway, respectively. We also show that the activation of both pathways is accompanied by upregulation of genes for the enzymes involved in {beta}-oxidation of fatty acids, glyoxylate cycle, amino acid metabolism, and peroxisome biogenesis. Transcriptome and proteome profiles of the cells grown on 4-hydroxybenzoate and 3-hydroxybenzoate, which are metabolized via the 3-oxoadipate and gentisate pathway, respectively, reflect their different connection to central metabolism. Yet we find that the expression profiles differ also in the cells assimilating 4-hydroxybenzoate and hydroquinone, which are both metabolized in the same pathway. This finding is consistent with the phenotype of the Otf1p-lacking mutant, which exhibits impaired growth on hydroxybenzoates, but still utilizes hydroxybenzenes, thus indicating that additional, yet unidentified transcription factor could be involved in the 3-oxoadipate pathway regulation. Moreover, we propose that bicarbonate ions resulting from decarboxylation of hydroxybenzoates also contribute to differences in the cell responses to hydroxybenzoates and hydroxybenzenes. Finally, our phylogenetic analysis highlights evolutionary paths leading to metabolic adaptations of yeast cells assimilating hydroxyaromatic substrates. Author summaryBenzene and its derivatives are simple aromatic compounds representing key substances for the chemical industry. While benzene itself is toxic and carcinogenic, benzoic acid is commonly used in the food industry and some of its derivatives are used in pharmacology (aspirin) or cosmetics (parabens). The benzene ring of aromatic molecules is relatively stable, but many microorganisms including yeasts break it enzymatically and, in a series of biochemical reactions, utilize resulting metabolites as carbon sources. Understanding the genetic basis of corresponding metabolic pathways and their regulation opens a venue for applications in biotechnology and bioremediation of polluted environments. Here we investigate the yeast Candida parapsilosis which assimilates various hydroxybenzenes and hydroxybenzoates via the 3-oxoadipate and gentisate pathways. We show that the genes coding for the substrate transporters and enzymes involved in both pathways are co-expressed and regulated by the transcriptional activators Otf1p and Gtf1p, respectively. Our results also reveal the connections of both pathways to central metabolism and organelle biogenesis and provide an insight into evolution of metabolism of hydroxyaromatic compounds.

genomics↗

Second Generation Antibodies Neutralize Emerging SARS-CoV-2 Variants of Concern

Recently emerged SARS-CoV-2 variants show resistance to some antibodies that were authorized for emergency use. We employed hybridoma technology combined with authentic virus assays to develop second-generation antibodies, which were specifically selected for their ability to neutralize new variants of SARS-CoV-2. AX290 and AX677, two monoclonal antibodies with non-overlapping epitopes, exhibit subnanomolar or nanomolar affinities to the receptor binding domain of the viral Spike protein carrying amino acid substitutions N501Y, N439K, E484K, K417N, and a combination N501Y/E484K/K417N found in the circulating virus variants. The antibodies showed excellent neutralization of an authentic SARS-CoV-2 virus representing strains circulating in Europe in spring 2020 and also the variants of concern B.1.1.7 and B.1.351. Finally, the combination of the two antibodies prevented the appearance of escape mutations of the authentic SARS-CoV-2 virus. The neutralizing properties were fully reproduced in chimeric mouse-human versions, which may represent a promising tool for COVID-19 therapy.

microbiology↗

DeepNano-blitz: A Fast Base Caller for MinION Nanopore Sequencers

MotivationOxford Nanopore MinION is a portable DNA sequencer that is marketed as a device that can be deployed anywhere. Current base callers, however, require a powerful GPU to analyze data produced by MinION in real time, which hampers field applications. ResultsWe have developed a fast base caller DeepNano-blitz that can analyze stream from up to two MinION runs in real time using a common laptop CPU (i7-7700HQ), with no GPU requirements. The base caller settings allow trading accuracy for speed and the results can be used for real time run monitoring (i.e. sample composition, barcode balance, species identification, etc.) or pre-filtering of results for more detailed analysis (i.e. filtering out human DNA from human-pathogen runs). Availability and ImplementationDeepNano-blitz has been developed and tested on Linux and is available under MIT license at https://github.com/fmfi-compbio/deepnano-blitz Contactvladimir.boza@fmph.uniba.sk

bioinformatics↗