Biology subjects

Bernardes, J. S.

Publications and source records attributed to Bernardes, J. S..

4 recordsLinked to original sources

bioRxiv2025

Diversity and distribution of the subtelomeric Y' elements across Saccharomyces cerevisiae

The subtelomeric regions of eukaryotic chromosomes harbor repeated elements that contribute to genomic plasticity and adaptation. In Saccharomyces cerevisiae, the Y elements represent a major class of subtelomeric repeats, yet their diversity and evolutionary dynamics remain incompletely characterized. Here, we analyzed Y elements across 54 S. cerevisiae strains using high-quality telomere-to-telomere genome assemblies. We detected 893 high-confidence Y elements, which we classified into 12 major clusters, revealing a broader structural diversity than previously described, including canonical short ([~]5.2 kb) and long ([~]6.7 kb) elements, intermediate-size classes (mid1 and mid2), and a novel family containing CA-rich repeats. Sequence analyses showed that open reading frames (ORFs), including those encoding the putative Y-Help1 helicase, are highly conserved within clusters, suggesting selective maintenance of functional sequences. The distribution of Y elements varied widely across strains and chromosome extremities, with some strains lacking Y entirely and others, such as the clinical ADI isolate, carrying up to 149 copies. Interstitial telomeric sequences (ITS) were variably associated with Y elements and tandem Y repeats, potentially facilitating recombination and amplification. Analysis of telomere length data further revealed that the presence of long Y' elements, but not Y elements from other clusters, at the subtelomere is correlated with shorter telomeres at the same chromosome end. Our results provide the most comprehensive catalog of S. cerevisiae Y elements to date, uncovering unexpected structural and sequence diversity, and a potentially functional role in telomere length regulation.

genomics↗

bioRxiv2022

Reconstructing the evolutionary history of a BCR lineage with minimum spanning tree and clonotype abundances

B cell receptor (BCR) genes exposed to an antigen undergo somatic hypermutations and Darwinian antigen selection, generating a large BCR-antibody diversity. This process, known as B cell affinity maturation, increases antibody affinity, forming a specific B cell lineage that includes the unmutated ancestor and mutated variants. In a B cell lineage, cells with a higher antigen affinity will undergo clonal expansion, while those with a lower affinity will not proliferate and probably be eliminated. Therefore, cellular (genotype) abundance provides a valuable perspective on the ongoing evolutionary process. Phylogenetic tree inference is often used to reconstruct B cell lineage trees and represents the evolutionary dynamic of BCR affinity maturation. However, such methods should process B cell population data derived from experimental sampling that might contain different cellular abundances. There are a few phylogenetic methods for reconstructing the evolutionary history of B cell lineages; best-performing solutions are time-demanding and restricted to analyzing a reduced number of BCR IGH sequences, while time-efficient methods do not consider cellular abundances. We propose ClonalTree, a low-complexity and accurate approach to reconstruct B cell lineage trees that incorporates genotype abundances into minimum spanning tree (MST) algorithms. Using both simulated and experimental data, we demonstrated that ClonalTree outperforms MST-based algorithms and achieves a similar performance compared to a method that explores tree generating space exhaustively. However, ClonalTree has a lower running time, being more convenient for reconstructing phylogenetic lineage trees from high-throughput BCR sequencing data, mainly in biomedical applications, where a lower computational time is appreciable. It is hundreds to thousands of times faster than exhaustive approaches, enabling the analysis of a large set of sequences within minutes or seconds and without loss of accuracy. The source code is freely available at github.com/julibinho/ClonalTree.

bioinformatics↗

bioRxiv2021

DAVI: a tool for clustering and visualising protein domain architectures

The characterization of protein functions is one of the main challenges in bioinformatics. Proteins are often composed of individual units termed domains, motifs that can evolve independently. The domain architecture of a given protein is the particular order and the content of its numerous domains. Some computational approaches predict the most likely domain architecture for a set of proteins. However, a few numbers of visualization tools exist, and most of them are unavailable. Here we present DAVI, an efficient and user-friendly web server for protein domain architecture clustering and visualization. DAVI accepts the output of most used domain architecture prediction tools and also produces domain architectures for a set of protein sequences. It provides a rich visualization for comparing, analyzing, and visualizing domain architectures. Availabilityhttp://genome.lcqb.upmc.fr/Domain-Architecture-Viewer

bioinformatics↗

bioRxiv2020

Automatic generation of ground truth data for the evaluation of clonal grouping methods in B-cell populations

MotivationThe adaptive B-cell response is driven by the expansion, somatic hypermutation, and selection of B-cell clones. Their number, size and sequence diversity are essential characteristics of B-cell populations. Identifying clones in B-cell populations is central to several repertoire studies such as statistical analysis, repertoire comparisons, and clonal tracking. Several clonal grouping methods have been developed to group sequences from B-cell immune repertoires. Such methods have been principally evaluated on simulated benchmarks since experimental data containing clonally related sequences can be difficult to obtain. However, experimental data might contains multiple sources of sequence variability hampering their artificial reproduction. Therefore, the generation of high precision ground truth data that preserves real repertoire distributions is necessary to accurately evaluate clonal grouping methods. ResultsWe proposed a novel methodology to generate ground truth data sets from real repertoires. Our procedure requires V(D)J annotations to obtain the initial clones, and iteratively apply an optimisation step that moves sequences among clones to increase their cohesion and separation. We first showed that our method was able to identify clonally-related sequences in simulated repertoires with higher mutation rates, accurately. Next, we demonstrated how real benchmarks (generated by our method) constitute a challenge for clonal grouping methods, when comparing the performance of a widely used clonal grouping algorithm on several generated benchmarks. Our method can be used to generate a high number of benchmarks and contribute to construct more accurate clonal grouping tools. Availability and implementationThe source code and generated data sets are freely available at github.com/NikaAb/BCR_GTG

bioinformatics↗