Biomedical subjects
Publications and source records attributed to Giulio Formenti.
We present a genome assembly from an individual male Gardnerycteris keenani (Chordata; Mammalia; Chiroptera; Phyllostomidae). The genome sequence is 2.2 Gb in span. The majority of the assembly is scaffolded into 32 chromosomal pseudomolecules, with the X and Y sex chromosomes assembled.
Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and structurally polymorphic regions of the genome unmapped. Consequently, existing variant benchmarks, generated by the same methods, fail to assess these complex regions. To address this limitation, we present a telomere-to-telomere genome benchmark that achieves near-perfect accuracy (i.e. no detectable errors) across 99.4% of the complete, diploid HG002 genome. This benchmark adds 701.4 Mb of autosomal sequence and both sex chromosomes (216.8 Mb), totaling 15.3% of the genome that was absent from prior benchmarks. We also provide a diploid annotation of genes, transposable elements, segmental duplications, and satellite repeats, including 39,144 protein-coding genes across both haplotypes. To facilitate application of the benchmark, we developed tools for measuring the accuracy of sequencing reads, phased variant call sets, and genome assemblies against a diploid reference. Genome-wide analyses show that state-of-the-art de novo assembly methods resolve 2-7% more sequence and outperform variant calling accuracy by an order of magnitude, yielding just one error per 100 kb across 99.9% of the benchmark regions. Adoption of genome-based benchmarking is expected to accelerate the development of cost-effective methods for complete genome sequencing, expanding the reach of genomic medicine to the entire genome and enabling a new era of personalized genomics.
Recent technological advances have facilitated the assembly of telomere-to-telomere (T2T) genomes. The current T2T CHM13 showcases the complete architecture of the human genome, yet its use in functional experiments is limited by discrepancies with the actual genome of the specific biological system under study. Access to reference assemblies for experimentally relevant cell lines is therefore essential in advancing sequencing-based analyses and precise manipulation, particularly in highly variable regions such as centromeres. Here, we present RPE1v1.1, the near-complete diploid genome assembly of the hTERT RPE-1 cell line, a non-cancerous human retinal epithelial model with a stable karyotype. Using high-coverage Pacific Biosciences and Oxford Nanopore Technologies long-read sequencing, we generate a high-quality de novo assembly, validate it through multiple methods, and phase it by integrating high-throughput chromosome conformation capture (Hi-C) data. Our assembly includes chromosome-level scaffolds that span centromeres for all chromosomes. Comparing both haplotypes with the CHM13 genome, we detect haplotype-specific genomic variations, including the translocation between chromosome 10 and chromosome X t(X;10)(Xq28;10q21.2) characteristic of RPE-1 cells, and divergence peaking at centromeres. Altogether, the RPE1v1.1 genome provides a reference-quality diploid assembly of a widely used cell line, supporting high-precision genetic and epigenetic studies in this model system.
MOTIVATION: Large sequencing datasets are being produced and deposited into public archives at unprecedented rates. The availability of tools that can reliably and efficiently generate and store sequencing read summary statistics has become critical. RESULTS: As part of the effort by the Vertebrate Genomes Project (VGP) to generate high-quality reference genomes at scale, we sought to address the community's need for efficient sequence data evaluation by developing rdeval, a standalone tool to quickly compute and interactively display sequencing read metrics. Rdeval can either run on the fly or store key sequence data metrics in tiny read 'snapshot' files. Statistics can then be efficiently recalled from snapshots for additional processing. Rdeval can convert fa*[.gz] files to and from other popular formats including BAM and CRAM for better compression. Overall, while CRAM achieves the best compression, the gain compared to BAM is marginal, and BAM achieves the best compromise between data compression and access speed. Rdeval also generates a detailed visual report with multiple data analytics that can be exported in various formats. We showcase rdeval's functionalities using long-read data from different sequencing platforms and species, including human. For PacBio long-read sequencing, our analysis shows dramatic improvements in both read length and quality over time, as well as the benefit of increased coverage for genome assembly, though the magnitude varies by taxa. AVAILABILITY AND IMPLEMENTATION: Rdeval is implemented in C++ for data processing and in R for data visualization. Precompiled releases (Linux, MacOS, Windows) and commented source code for rdeval are available under MIT license at https://github.com/vgl-hub/rdeval. Documentation is available on ReadTheDocs (https://rdeval-documentation.readthedocs.io). Rdeval is also available in Bioconda and in Galaxy (https://usegalaxy.org). An automated test workflow ensures the consistency of software updates.