Biomedical subjects

Samuel Sacco

Publications and source records attributed to Samuel Sacco.

5 recordsLinked to original sources

PubMed2026

Reference genome of the Californian trapdoor spider Aptostichus stephencolberti Bond 2008 (Araneae: Mygalomorphae: Euctenizidae).

We present a reference genome assembly for the trapdoor spider Aptostichus stephencolberti. This species, described in 2008, is endemic to the highly fragmented coastal dune habitats of Northern California from Monterey to the San Francisco Bay Area. Trapdoor spiders are ideal taxa for landscape scale genomic studies owing to their extreme site fidelity and limited dispersal capabilities; these same characteristics make them prone to extinction. Genomic studies of species like A. stephencolberti can reveal novel areas of endemism and high conservation value that may not be evident in species with wider ranges and greater dispersal capabilities. As part of the California Conservation Genomics Project, we constructed the A. stephencolberti reference genome from high quality long-read sequences, scaffolded with proximity ligation Omni-C data. The primary assembly comprises 551 scaffolds spanning 3.63 Gbp, a scaffold N50 of 62.2 Mbp and BUSCO completeness of 95.6%. We estimate 52 chromosomes yet find no (TTAGG)n telomer repeats. Expanding the telomeric repeat search finds an ancestral loss of the repeat from all spiders. Automated annotation using the NCBI refseq pipeline and RNAseq data from whole adults finds 14,067 genes with a BUSCO annotation completeness of 95.56%. Repeat annotation identified 77% of the genome to be interspersed repeats. This resource, the first for family Euctenizidae will facilitate future study and resulting conservation actions of A. stephencolberti and other Aptostichus sp. populations associated with the rapidly changing California coastal dune ecosystem.

Aptostichus stephencolberti↗

PubMed2025

Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic.

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing. We present DeepSomatic, a deep-learning method for detecting somatic small nucleotide variations and insertions and deletions from both short-read and long-read data. The method has modes for whole-genome and whole-exome sequencing and can run on tumor-normal, tumor-only and formalin-fixed paraffin-embedded samples. To train DeepSomatic and help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available the Cancer Standards Long-read Evaluation (CASTLE) dataset of six matched tumor-normal cell line pairs whole-genome sequenced with Illumina, PacBio HiFi and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples, both cell line and patient-derived, and across short-read and long-read sequencing technologies, DeepSomatic consistently outperforms existing callers.

Humans↗

PubMed2025

Recent Adaptation in a Threatened Salmonid Revealed by Museum Genomics.

Steelhead/rainbow trout (Oncorhynchus mykiss) is an imperilled salmonid with two main life history strategies: migrate to the ocean or remain in freshwater. Domesticated hatchery forms of this species have been stocked into almost all California waterways, possibly resulting in introgression into natural populations and altered population structure. We compared whole-genome sequence data from contemporary populations against a set of museum population samples of steelhead from the same locations that were collected prior to most hatchery stocking. We observed minimal introgression and few steelhead-hatchery trout hybrids despite a century of extensive stocking. Our historical data show signals of introgression with a sister species and indications of an early hatchery facility. Finally, we found that migration-associated haplotypes have become less frequent over time, a likely adaptation to decreased opportunities for migration. Since contemporary migration-associated haplotype frequencies have been used to guide species management, we consider this to be a rare example of shifting baseline syndrome that has been validated with historical data. We suggest cautious optimism that a century of hatchery stocking has had minimal impact on California steelhead population genetic structure, but we note that continued shifts in life history may lead to further declines in the ocean-going form of the species.

Animals↗

PubMed2025

Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing.

For the detection of somatic structural variation (SV) in cancer genomes, long-read sequencing is advantageous over short-read sequencing with respect to mappability and variant phasing. However, most current long-read SV detection methods are not developed for the analysis of tumor genomes characterized by complex rearrangements and heterogeneity. Here, we present Severus, a breakpoint graph-based algorithm for somatic SV calling from long-read cancer sequencing. Severus works with matching normal samples, supports unbalanced cancer karyotypes, can characterize complex multibreak SV patterns and produces haplotype-specific calls. On a comprehensive multitechnology cell line panel, Severus consistently outperforms other long-read and short-read methods in terms of SV detection F1 score (harmonic mean of the precision and recall). We also illustrate that compared to long-read methods, short-read sequencing systematically misses certain classes of somatic SVs, such as insertions or clustered rearrangements. We apply Severus to several clinical cases of pediatric leukemia/lymphoma, revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Humans↗

PubMed2024

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies.

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

Journal Article↗