Abstract Motivation Gene-damaging mutations are highly informative for studies seeking to discover genes underlying developmental disorders. Traditionally, these de novo variants are recognized by evaluating high-quality DNA sequence from affected offspring and parents. However, when parental sequence is unavailable, methods are required to infer de novo status and use this inference for association studies. Results We use data from autism spectrum disorder to illustrate and evaluate methods. Separating de novo from rare inherited variants is challenging because the latter are far more common. Using a classifier for unbalanced data and variants of known inheritance class, we build an inheritance model and then a de novo score for variants when parental data are missing. Next, we propose a new Random Draw (RD) model to use this score for gene discovery. Built into an existing inferential framework, RD produces a more powerful gene-based association test and controls the false discovery rate. Availability and implementation Codes are available at Github (https://github.com/HaeunM/TADA-RD) and Zenodo (DOI: https://doi.org/10.5281/zenodo.18531769).

UNLABELLED: AnnTools is a versatile bioinformatics application designed for comprehensive annotation of a full spectrum of human genome variation: novel and known single-nucleotide substitutions (SNP/SNV), short insertions/deletions (INDEL) and structural variants/copy number variation (SV/CNV). The variants are interpreted by interrogating data compiled from 15 constantly updated sources. In addition to detailed functional characterization of the coding variants, AnnTools searches for overlaps with regulatory elements, disease/trait associated loci, known segmental duplications and artifact prone regions, thereby offering an integrated and comprehensive analysis of genomic data. The tool conveniently accepts user-provided tracks for custom annotation and offers flexibility in input data formats. The output is generated in the universal Variant Call Format. High annotation speed makes AnnTools suitable for high-throughput sequencing facilities, while a low-memory footprint and modest CPU requirements allow it to operate on a personal computer. The application is freely available for public use; the package includes installation scripts and a set of helper tools. AVAILABILITY: http://anntools.sourceforge.net/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.