Nachtigall, P. G. | Search bioRxiv

bioRxiv2025

First Genome-Wide Centromere Map of Trypanosoma cruzi Reveals Linear and 3D Compartment Boundaries and Spatial Clustering

Background: Trypanosoma cruzi, the etiological agent of Chagas disease, possesses a highly repetitive genome that has historically hindered high-quality assembly and structural characterization. Despite significant advances in assembling T. cruzi genomes, major gaps remain. Among these, the complete repertoire of centromeric sequences has remained elusive, representing a critical missing piece in our understanding of chromosome structure and inheritance. Results: Here, we generated high-coverage Hi-C (genome-wide chromosome conformation capture) data for the widely used T. cruzi Dm28c strain improving its genome assembly, reducing the number of scaffolds and producing a more contiguous and accurate genome. To investigate centromere organization, we performed ChIP-seq using the mNeonGreen-myc-tagged kinetochore proteins KKT2 and KKT3, resulting in the identification of 40 KKT-enriched peaks across 29 scaffolds. These peaks were located in regions enriched in retrotransposable elements, particularly L1Tc and VIPER, near strand switch regions, areas of high GC content, and at the boundaries between conserved genes and virulence-factor multigene families. Conclusion: Notably, Hi-C analysis revealed that centromeres may act as structural boundaries contributing to genome compartmentalization and frequently engage in 3D spatial clustering, suggesting a role in higher-order nuclear architecture. Overall, our study provides a high-quality reference genome for the Dm28c strain, presents the first genome-wide centromere map in T. cruzi, and offers novel insights into centromere-mediated 3D genome organization

genomics↗

bioRxiv2019

CodAn: predictive models for the characterization of mRNA transcripts in Eukaryotes

Characterization of the coding sequences (CDSs) is an essential step on transcriptome annotation. Incorrect characterization of CDSs can lead to the prediction of non-existent proteins that can eventually compromise knowledge if databases are populated with similar incorrect predictions made in different genomes. Even though some recent methods have succeeded in correctly prediction of the stop codon position in strand-specific sequences, prediction of the complete CDS is still far from a gold standard. More importantly, prediction in strand-blind sequences and in partial sequences is deficient, presenting very low accuracy. Here, we present CodAn, a new computational approach to predict CDS and UTR, that significantly pushes the boundaries of CDS prediction in strand-blind and in partial sequences, increases strand-specific full-CDS predictions and matches or surpasses gold-standard results in strand-specific stop codon predictions. CodAn is freely available for download at https://github.com/pedronachtigall/CodAn.

bioinformatics↗