Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models.

Characterization of the myostatin gene in the neotropical species Piaractus mesopotamicus and the possibility of its use in genetic improvement programs.

Background: The myostatin gene has played an important role in the genetic improvement of the main species of economic importance; however, it has not yet been described for some Neotropical fish essential for aquaculture. This study aimed to characterize the myostatin gene of pacu, Piaractus mesopotamicus, and investigate the association of a microsatellite marker in this gene with the weight of fish.

Methods And Results: The myostatin gene sequence was obtained after following a RACE-PCR strategy based on a partial mRNA sequence available in the GenBank database and the alignment of myostatin sequences from other fish species.

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the () gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models.