Taniguti, L. M. | bioRxiv Science

bioRxiv2025

Updated chromosome-level genome assembly of Sporisorium scitamineum with improved accuracy and completeness

In 2015, we published the complete genome assembly of Sporisorium scitamineum, the fungal pathogen responsible for sugarcane smut disease, generated from PacBio long-read sequencing and polished with Illumina short reads. Since then, the tools for genome assembly have improved considerably, including advances in sequencing technologies and bioinformatics workflows, motivating us to revisit our original assembly. Here, we present an updated genome version, using newly generated high-quality Illumina short-read data and DeepVariant-based polishing, resulting in corrections at approximately 5,500 genomic positions. BUSCO benchmarking in protein mode demonstrated increased genome completeness from the original 94.2% to 99.3%, indicating substantial improvements in both accuracy and gene annotation reliability. Additionally, comparison with publicly available assemblies reveals that most sequence polymorphisms occur in isolates belonging to the Asian lineage, consistent with earlier population-level studies. This enhanced genome assembly provides a significantly improved reference resource, enabling more robust genetic, evolutionary, and functional studies of this economically important sugarcane pathogen.

bioinformatics↗

bioRxiv2022

Developing best practices for genotyping-by-sequencing analysis using linkage maps as benchmarks

BackgroundGenotyping-by-Sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by PCR duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations. ResultsWe developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for SNP calling and updog, polyRAD, and SuperMASSA for genotype calling, and OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset-dependent) and others produce consistent advantageous results among them (dataset-independent). ConclusionsWe set as default in the Reads2Map workflows the approaches that showed to be dataset-independent for GBS datasets according to our results. This reduces the number required of tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.

bioinformatics↗