Long-read sequencing technologies analyze long, continuous stretches of DNA. These methods have the potential to improve researchers' ability to detect complex genetic alterations in cancer genomes.

Despite advanced diagnostic tools, early detection of rare genetic conditions like Noonan syndrome (NS) remains challenging. We evaluated a deep learning model’s real-world performance in identifying ...

Systemic sclerosis (SSc) is a severe autoimmune disease with complex genetic causes. Some genetic contributors have been identified, but others remain unknown, which has impeded development of ...

Implicit sequence learning (SL) is crucial for language acquisition and has been studied in children with organic language deficits (e.g., specific language impairment). However, language delays are ...

Genomic testing based on chromosome microarray (CMA) and Next Generation Sequencing (NGS) revolutionized clinical genetics. That said, microarray, targeted panel, exome and generic whole genome ...

Genetic variants that cause rare disorders may remain elusive even after expansive testing, such as exome sequencing. The diagnostic yield of genome sequencing, particularly after a negative ...

The ability to synthesize and sequence vast numbers of DNA constructs is a cornerstone of modern high-throughput biological screening and discovery. As a result, the need for faster, easier, and more ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes means ...

Researchers at EMBL’s European Bioinformatics Institute (EMBL-EBI) have developed a new machine learning method called SAVANA that significantly reduces sequencing errors for cancer genomes. Long-read ...